phd project in embedded system

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Electrical & Computer Engineering

Doctor of Philosophy (PhD), Secure Embedded Systems

Program description.

The Internet of Things (IoT) is an excellent opportunity for the progression of embedded systems. IoT mobile devices like smartphones, tablets, and wearables are already ubiquitous. As the IoT market is expanding, we expect that non-mobile IoT systems will outnumber the current IoT-enabled mobile devices that we know. With each passing day, embedded systems are getting smaller and smarter, enabling us to get more things done than before. As we embed more functionality in smaller device footprints, there is an upsurge in the security concerns as well. To address the security concerns, we present the Ph.D. in Secure Embedded Systems.

The Ph.D. program in Secure Embedded Systems targets highly motivated students. They have already obtained a bachelor’s or master’s degree and desire to pursue career opportunities in the following industries: academia, commercial industry, defense, government laboratories, federal agencies, consulting, military, or research.

Approved Master’s Degree Programs

List of approved Masters Degree programs for direct admission to the Ph.D. program:

• Masters in Electrical Engineering, Advanced Computing, Computer Science, or Computer Engineering
• Masters in Software Engineering
• Masters in Information Systems

Students with a master’s degree in the approved areas listed will be required to take a minimum of 18 credits of core, elective and/or research courses and 18 credits of Dissertation Research. Students who only have a bachelor’s degree or who do not have a master’s degree in the approved degree program listed in Table F will be required to take a minimum of 4 core courses totaling 12 credits, and a minimum of 18 credits of Dissertation research. Students in the Bachelors to Ph.D. track will receive an en passant, or “along-the-way,” masters degree, Master of Science in Secure Embedded Systems after completing 30 credits in the program.

The Program welcomes exceptional students with at least a 3.3 cumulative GPA (on a scale of 4.0) for all undergraduate and graduate work completed. Other requirements include a resume or curriculum vitae documenting current and previous professional activities, achievements, planned career goals, statement of research interest, and three letters of recommendation from professors or supervisors familiar with the applicant’s academic background. All application materials must be sent directly to the School of Graduate Studies through the application system for preliminary screening. Eligibility to be a student within the School of Graduate Studies is a prerequisite for admission into the Program.

Expected Student Learning Outcomes

Upon completion of the Program, students will have gained a broad technical, and interdisciplinary background enhances their ability to identify and tackle critical cybersecurity problems related to embedded system hardware and software. Specifically, upon completing the Program, students will be expected to:

• Demonstrate a breadth of knowledge in advanced cybersecurity, cryptography, networking, and reverse engineering; and exhibit deep expertise in any one or combination of the core breadth areas, such as lightweight cryptography for embedded systems, side-channel analysis, digital forensics;
• Apply mathematics, systems theory, principles of engineering, planning/or management in solving complex cybersecurity problems;
• Design independently and execute high-level research; and
• Communicate effectively both orally and in written form and function on an interdisciplinary team, particularly in a laboratory setting.

General Requirements

Students enrolled in the Program will be required to satisfy the following requirements:

• Form a doctoral advisory committee comprising four members, among whom at least three of them should be tenured or tenure-track faculty members. The chair of the committee must be a member of the graduate faculty and the ECE department or CAP faculty. A minimum of two ECE or CAP faculty must serve on the committee. The students form an advisory committee no later than the end of the first year. The committee approves the student’s program of study and guides the student’s research activities;
• Complete a minimum of 36 graduate credit hours (including 18 hours of dissertation-related research) of study beyond the Master’s degree or complete a minimum of 48 graduate credit hours (including 18 hours of dissertation-related research) of study beyond the bachelors’ degree.
• Pass a written qualifying exam within the first two years of study (one attempt within the first year), doctoral candidacy examinations (no sooner than a year of passing qualifying exam), administered by the dissertation committee, on the core subjects and declared concentration; DocuSign 
• Develop and defend a dissertation proposal within the first four years of admission; and
• Complete and successfully defend a dissertation based on timely and original research in a relevant area of Secure Embedded Systems within the six years of enrollment;
• The dissertation committee chair must determine the original contribution of the dissertation work.

To maintain good academic standing and remain in the Program, the student may not have course grades lower than B in any of the required core courses and must maintain a cumulative GPA of 3.5. Failure to meet these requirements will lead to academic probation for one academic year.

All candidates must satisfy eighteen credit hours of residency requirements in one of the following ways: enrolling in nine credit hours per semester for two consecutive semesters or part-time candidates must register for six credit hours per semester for three consecutive semesters. Upon completion of all the course requirements and examinations, the candidate must continue to register for Dissertation Research VI each semester until they successfully defend.

Program of Study

The required minimum coursework for the Ph.D. in Secure Embedded Systems is 60 graduate-credits beyond the Bachelor’s degree and 36 graduate-credits beyond the Master’s degree. Up to four courses (not to exceed 12 credits) from other accredited institutions may be accepted for transfer towards the Ph.D. degree, assuming that students do not use transfer courses to satisfy the academic requirements of the former program.

Pursuing PhD from Bachelor’s Degree (60 credits)

Core Courses 12 Credits

Elective Courses 12 Credits

Research Courses 18 Credits

Dissertation Research 18 Credits

Total 60 Credits

Core Courses (12 credits)

• EEGR 705 - Algorithm Foundations for Cybersecurity Applications 3 Credits
• EEGR 710 - Wireless Communications II 3 Credits
• EEGR 715 - Advanced Topics in Communications 3 Credits
• EEGR 720 - Advanced Topics in Signal Processing 3 Credits

Elective Courses (12 credits)

Students can choose electives courses from the following list of courses. Students can also have outside electives courses as approved by the Program Director.

• EEGR 725 - Advanced Topics in Control Theory 3 Credits
• EEGR 730 - Special Topics in Microwave Engineering 3 Credits
• EEGR 735 - Advanced Digital VLSI 3 Credits
• EEGR 740 - Special Topics in Solid State and Optical Electronics 3 Credits
• EEGR 745 - Advanced Secure Embedded Systems 3 Credits
• EEGR 750 - Trustworthy Machine Learning 3 Credits
• EEGR 755 - Advanced Software Assurance 3 Credits
• EEGR 760 - Special Topics in Computer Engineering 3 Credits
• EEGR 765 - Advanced Artificial Intelligence and Machine Learning 3 Credits

Research Courses (18 credits)

• EEGR 805 - Pre-Candidacy Research I 3 Credits
• EEGR 810 - Pre-Candidacy Research II 3 Credits
• EEGR 815 - Pre-Candidacy Research III 3 Credits
• EEGR 820 - Pre-Candidacy Research IV 3 Credits
• EEGR 825 - Pre-Candidacy Research V 3 Credits
• EEGR 830 - Pre-Candidacy Research VI 3 Credits

Dissertation Courses (18 credits)

Students finish the Ph.D. program with EEGR 930   or EEGR 997 .

• EEGR 905 - Dissertation Research I 3 Credits
• EEGR 910 - Dissertation Research II 3 Credits
• EEGR 915 - Dissertation Research III 3 Credits
• EEGR 920 - Dissertation Research IV 3 Credits
• EEGR 925 - Dissertation Research V 3 Credits
• EEGR 930 - Dissertation Research VI 3 Credits

Pursuing PhD from Masters Degree (36 credits)

Core courses or Elective courses or Research courses 18 Credits

Total 36 Credits

Core Courses

Elective courses, research courses, dissertation courses, graduate coordinator.

Dr. Cliston Cole, Ph.D., Assistant Professor

Schaefer Engineering Building (SEB), Room 344

[email protected]

(443) 885-4732

Deploying AI Algorithms on Embedded Systems with Limited Resources

Competition funded (UK/EU and international students)

Project code

SENE8720124

Start dates

Application deadline.

19 January 2024

Applications are invited for a fully-funded three year PhD to commence in April 2024 . 

The PhD will be based in the Schools of Energy and Electronic Engineering and Mechanical and Design Engineering, and will be supervised by Dr Hongjie Ma , Dr Edward Smart and Prof. Victor Beccera . 

Successful applicants will receive a bursary to cover tuition fees for three years and a stipend in line with the UKRI rate (£18,622 for 2023/24). Bursary recipients will also receive a £1,500 p.a. for project costs/consumables.

The work on this project could involve:

• Algorithm selection and optimisation: Choose algorithms suitable for low-power, limited computation scenarios, such as decision trees, support vector machines, or lightweight deep learning algorithms like MobileNet or LSTM.
• Model compression and pruning: Reduce model complexity by applying compression techniques (e.g., quantisation, weight sharing) and pruning methods to remove redundant parameters, lowering computation and memory requirements.
• Computation resource scheduling: Balance computational accuracy and speed through dynamic adjustment, selecting different algorithms or models based on task demands and hardware resources.
• System optimisation: Enhance code execution efficiency by using MCU/DSP-specific compilers, runtime libraries, and processor-specific code optimisations, including assembly instructions and hardware acceleration features.
• Low-power design: Implement low-power strategies, such as dynamic voltage frequency scaling (DVFS) and sleep/wake control, while maximising processor-built low-power features and peripherals, like low-power clock modes and disabling unnecessary devices.

Are you a passionate and talented UK/EU student eager to contribute to cutting-edge research in artificial intelligence (AI) and embedded systems? We are offering a fully funded PhD position to explore the development and deployment of AI algorithms on embedded systems with limited computational capacity and power consumption, with the aim of expanding their applications in industrial settings and daily life. You will be supervised by an experienced team led by Dr Hongjie Ma, with expertise in embedded system development and AI, Dr Edward Smart, a renowned expert in AI and industry-academia collaboration, and Prof Victor Becerra, a distinguished leader in energy and control systems with an extensive background in AI and rich PhD supervision experience.

This project presents exceptional opportunities for both academic and industrial collaboration. On the academic front, your research could facilitate the integration of AI into ongoing research projects involving embedded systems at our institution, such as audio signal processing, ultrasonic signal processing, and low-power consumption. In terms of industrial collaboration, our supervisory team is actively involved in projects with industrial companies, which all implement AI on embedded systems. Your PhD project will help strengthen existing partnerships and create new project opportunities. Your work will focus on algorithm selection and optimisation, model compression and pruning, computation resource scheduling etc.

Join us in pushing the boundaries of AI and embedded systems and become part of our thriving research community. Apply now for this exciting and fully funded PhD opportunity.

Entry requirements

General admissions.

You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

Specific candidate requirements

Foundational knowledge in artificial intelligence, particularly in algorithm selection and optimisation for resource-constrained environments. Familiarity with decision trees, support vector machines, and lightweight deep learning algorithms, such as MobileNet or LSTM, is essential.

Expertise in embedded system optimisation, particularly with respect to code execution efficiency on embedded systems. Experience with MCU/DSP-specific compilers, runtime libraries, and processor-specific code optimisations (including assembly instructions and hardware acceleration features) is required.

In addition to the technical skills outlined above, candidates should possess excellent analytical and problem-solving abilities, strong written and verbal communication skills, and a collaborative mindset. A bachelor's or master's degree in computer science, electrical engineering, or a closely related field is required.

How to apply

We’d encourage you to contact Dr Hongjie Ma ( [email protected] ) to discuss your interest before you apply, quoting the project code. When you are ready to apply, you can use our online application form . Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.  Our ‘ How to Apply ’ page offers further guidance on the PhD application process.

If you want to be considered for this funded PhD opportunity you must quote project code SENE8720124   w hen applying.

Embedded Systems

Today, there is computation in everything. Birthday cards can play songs, fireworks use microcontrollers rather than fuses for timing, homes and buildings are becoming "smart", and we wear many computers in our pockets and on our wrists. These systems are characterized by a tight coupling of hardware capabilities, software functionality, application requirements, and physical form factor. They raise many open questions in power management, networking, security, privacy, and administration. We look at new, fundamental ways to design these systems that will make them robust, secure, and long-lived.

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Postgraduate research project

• Optimizing machine learning for embedded systems

About the project

Artificial Intelligence has been hugely successful in solving complex tasks and a significant progress has been made in bringing the utility of deep neural networks on tiny resource-constrained embedded devices via optimizing inference. However, learning the deep learning models on the device still remains challenging due to the mismatch between the computation complexity of deep learning models against the resource availability on the device. In this project, we aim to tackle this challenge.

• explore the performance of different exisiting learning approaches to find their limitations
• investigate and design novel machine learning algorithms for learning, by solving the limitations, and propose system based optimisation techniques and design hardware accelerators
• work to bring the capability to perform the learning process of deep learning models directly on low-cost and low power tiny embedded devices

You'll work to bring the capability to perform the learning process of deep learning models directly on low-cost and low power tiny embedded devices. Learning paradigms include on-device training, on-device continual learning and on-device Bayesian learning.

Your work will have a potential to create socio-economic impact in many ways. Firstly, creating novel on-device efficient learning systems will allow masses to enjoy the advantages provided by deep learning for many useful and ubiquitous computing applications with full control over privacy. Further, low power solutions will result in reducing the carbon footprint of deep learning models contributing to sustainable computing. Lastly, your work can guide the development of the next generation of technologies and spark further research in applications on embedded devices leveraging AI.

This is a great opportunity to work with well-known external key partners - Stanford, OnSemi and Google- in a positive and vibrant university environment on cutting edge technology. University of Southampton is a top university (1% of world universities and in the top 10 of the UK) and the School of Electronics and Computer Science is one of the top CS departments in the UK. You will also be working with other researchers and PhD students in the team and will have a chance to publish and present your work at the top-tier conferences and journals in the area of Systems, ubiquitous computing, and machine learning. We have a large network of collaborations with academics at Imperial, Kings College London, Newcastle, Cambridge, Cornell, and industrial labs such as Samsung AI and Bell Labs in Cambridge. These will help you to foster further collaborations, strengthen your network and look out for internships.

Further, you will also be able to join International Centre for Spatial Computational Learning and ARM-ECS centre

Potential supervisors

Lead supervisor.

Dr Jagmohan Chauhan

Research interests.

• Digital healthcare
• Embedded AI

Supervisors

Professor Geoff Merrett PhD, BEng, PGCert, FHEA, SMIEEE, MIET

• Energy management of mobile/embedded systems
• Self-powered computing
• Internet of Things

Entry requirements

A 2:1 honours degree, or its international equivalent .

Fees and funding

For UK students, tuition fees and a stipend of £17,668 tax-free per year for up to 3.5 years.

You need to:

• choose programme type (Research), 2023/24, Faculty of Engineering and Physical Sciences
• choose “PhD Computer Science (Full time)”
• add the name of the supervisor (Jagmohan Chauhan)

Applications should include:

• a research proposal
• curriculum vitae
• 2 reference letters
• degree transcripts/certificates to date

Faculty of engineering and physical sciences

Email:  [email protected]

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Potential PhD projects

There are opportunities for talented researchers to join the School of Computer Science and Engineering, with projects in the following areas:

• Artificial Intelligence
• Biomedical Image Computing
• Data and knowledge research group

Embedded systems

• Networked systems
• Programming Languages & Compilers
• Service Orientated Computing
• Trustworthy Systems
• Theoretical Computer Science.

Artificial intelligence

Supervisory team : Professor Claude Sammut 

Project summary : Our rescue robot has sensors that can create 3D representations of its surroundings. In a rescue, it's helpful for the incident commander to have a graphical visualisation of the data so that they can reconstruct the disaster site. The School of Computer Science and Engineering and the Centre for Health Informatics have a display facility (VISLAB) that permits users to visualise data in three dimensions using stereo projection onto a large 'wedge' screen. 

This project can be approached in two stages. In the first stage, the data from the robot are collected off-line and programs are written to create a 3D reconstruction of the robot's surroundings to be viewed in the visualisation laboratory. In the second stage, we have the robot transmit its sensor data to the VISLAB computers for display in real-time. 

This project requires a good knowledge of computer graphics and will also require the student to learn about sensors such as stereo cameras, laser range finders and other 3D imaging devices. Some knowledge of networking and compression techniques will be useful for the second stage of the project. 

A scholarship/stipend may be available. 

For more information contact:  Prof. Claude Sammut

Supervisory team : Dr Raymond Louie

Project summary : Accurately predicting disease outcomes can have a significant impact on patient care, leading to early detection, personalized treatment plans, and improved clinical outcomes. Machine learning algorithms provide a powerful tool to achieve this goal by identifying novel biomarkers and drug targets for various diseases. By integrating machine learning algorithms with biological data, you will have the opportunity to push the boundaries of precision medicine and contribute to algorithms that can revolutionize the field.

We are looking for a highly motivated student who is passionate about applying computational skills to solve important health problems. Don’t worry, no specific biological knowledge is necessary, the important thing is you are enthusiastic and willing to learn. Please get in touch if you have any questions. 

For more information contact:  Dr. Raymond Louie

Supervisory team: Dr. Aditya Joshi

Project Summary: Discrimination and bias towards protected attributes have legal, social, and commercial implications for individuals and businesses. The project aims to improve the state-of-the-art in the detection of discrimination and bias in text. The project will involve creation of datasets, and development of new approaches using natural language processing models like Transformers. The datasets may include different text forms such as news articles, job advertisements, emails, or social media posts. Similarly, the proposed approaches may use techniques such as chain-of-thought prompting or instruction fine-tuning.

A scholarship/stipend may be available.

For more information, contact [email protected] .

Biomedical image computing

Supervisory team:  Dr Yang Song

Project summary:  Various types of microscopy images are widely used in biological research to aid our understanding of human biology. Cellular and molecular morphologies give lots of information about the underlying biological processes. The ability to identify and describe the morphological information quantitative, objectively and efficiently is critical. In this PhD project, we'll investigate various computer vision, machine learning (especially deep learning) and statistical analysis methodologies to develop automated morphology analysis methods for microscopy images.

More research topics in computer vision and biomedical imaging can be found  here .

For more information contact:  Dr Yang Song

Supervisor team:  Professor Erik Meijering and Dr John Lock

Project summary:  Biologists use multiparametric microscopy to study the effects of drugs on human cells. This generates multichannel image data sets that are too voluminous for humans to analyse by eye and require computer vision methods to automate the data interpretation. The goal of this PhD project is to develop, implement, and test advanced computer vision and deep learning methods for this purpose to help accelerate the challenging process of drug discovery for new cancer therapies. This project is in collaboration with the School of Medical Sciences (SoMS) and will utilise a new and world-leading cell image data set capturing the effects of 114,400 novel drugs on the biological responses (phenotypes) of >25 million single cells.

For more information contact:  [email protected] ,  [email protected]

Supervisor team:  Professor Erik Meijering and Professor Arcot Sowmya

Project summary:  Current commercial 3D ultrasound systems for medical imaging studies often do not provide the ability to record volumes large enough to visualise entire organs. The first goal of this PhD project is to develop novel computational methods for fast and accurate image registration to digitally reconstruct whole organs from multiple ultrasound volumes. The second goal is to develop computer vision and deep learning methods for automated volumetric image segmentation and downstream statistical analysis. This project will be in collaboration with researchers from the UNSW School of Women’s and Children’s Health to improve monitoring organ development during pregnancy to support clinical diagnostics.

For more information contact:  [email protected] ,  [email protected]

Supervisor team:  Prof. Arcot Sowmya, A/Prof. Lois Holloway (Ingham Medical Research Institute, Liverpool Hospital)

Project summary:  Decisions on the most appropriate treatment for diseases such as colorectal cancer and diverticulitis can be complex. Advanced imaging such as MRI and CT can provide information on the location of the disease compared to other anatomy and also functional information on the disease and surrounding organs. There is also the potential to gain additional information from these images using techniques such as radiomics. At Liverpool hospital, there is a database of previous patient histories, including outcome as well as imaging information which we can use in collaboration with medical specialists. This project will use machine learning and deep learning approaches to determine anatomical and disease boundaries and combine them with clinical and response data to model treatment response and develop treatment decision support tools. The incoming PhD student should ideally have a computer science qualification with research skills and an interest to develop deep learning and decision support techniques in the medical imaging field. Research in this area is subject to ethics approvals and institutional agreements.

For more information contact:  [email protected] ,  [email protected]

Supervisor team:  Prof. Arcot Sowmya and Dr Simone Reppermund

Depression and self-harm represent substantial public health burdens in the older population. Depression is ranked by the WHO as the single largest contributor to global disability and is a major contributor to suicide. This project will use large linked administrative health datasets to examine health profiles, service use patterns and risk factors for suicide in older people with depression. Given the vast amount of data included in linked datasets, new ways of analysing the data are necessary to capture all relevant data signals. This project will generate a sound epidemiological and service evidence base that informs our understanding of health profiles and service system pathways in older people with depression and risk factors for trajectories into suicide.

For more information contact:  [email protected] ,  [email protected]

Data & knowledge research group

Supervisory team:  Xuemin Lin, Wenjie Zhang 

Project summary:  Efficient processing of large scale multi-dimensional graphs.

This project aims to develop novel approaches to process large scale graphs such as social networks, road networks, financial networks, protein interaction networks, etc. The project will focus on the three most representative types of problems against graphs, namely cohesive subgraph computation, frequent subgraph mining and subgraph matching. The applications include anomaly detection, community search, fraud and crime detection.  

For more information contact:  [email protected]  or  [email protected]   

Supervisory team:  Wei Wang, Xin Cao 

Project summary:  The immense popularity of online social networks has resulted in a rich source of data useful for a wide range of applications such as marketing, advertisement, law enforcement, health and national security, to name a few. Ability to effectively and efficiently search required information from huge amounts of social network data is crucial for such applications. However, current search technology suffers from several limitations such as inability to provide geographically relevant results, inadequately handling uncertainty in data and failing to understand the data and queries, resulting in inferior search experience. This project aims to develop a next-generation search system for social network data by addressing all these issues. 

For more information contact:  [email protected]  or  [email protected]   

Supervisory team:  Sri Parameswaran 

Project summary:  Reliability is becoming an essential part in embedded processor design due to the fact that they are used in safety critical applications and they need to deal with sensitive information. The first phase in the design of reliable embedded systems involves the identification of faults that could be manipulated into a reliability problem. A technique that is widely used for this identification process is called fault injection and analysis. The aim of this project is to develop a fault injection and detection engine at the hardware level for an embedded processor. 

For more information contact:  [email protected]

Human-Centred Computing

Supervisory team:   Dr. Gelareh Mohammadi , Prof. Sowmya Arcot

Project summary:  We’ve seen stunning results from modern RL in arenas such as playing games, where the environment is constrained, predictable and it is possible to simulate a huge number of experiences. Major limiting factors are that current technology is not able to learn from a few experiences (few-shot learning), and to learn new tasks without forgetting old ones (continual learning). Neither has been well addressed and are usually investigated separately. In stark contrast, they are trivial for animals, and common in even simple real world scenarios. To advance the state-of-the-art continual and few-shot RL within a single architecture. The project is inspired by evidence that in our brains, the Hippocampus constitutes a short term memory and it replays to the frontal cortex directly. It is likely used for world-model building, as opposed to the mainstream view in cognitive science and ML - where 'experience replay' ultimately improves policy.

Supervisory team: Dr Gelareh Mohammadi ,  Prof. Wenjie Zhang

Project description: Previous studies have shown that cognitive training can effectively improve people's skillsets and emotional capabilities in cognitive deficits. Such training programs are known to enhance the participants' brain health and better prepare them for an independent life. However, the existing conventional technologies for such training are not scalable and lack personalized features to optimize the efficacy. In this project, we will develop a technology platform for automatically acquiring and processing multimodal training data. The project will be conducted in collaboration with Stronger Brains, a not-for-profit organization that provides cognitive training. We aim to develop a fully automated social and cognitive function assessment framework based on multimodal data. Such a framework is essential to establish a  system with less involvement of experts and increase its scalability. The project involves:

• Data collection.
• Developing multimodal predictive models for cognitive functions and affective states in cognitive deficits.
• Developing adaptation techniques to personalize the framework.

Supervisory team: Dr Gelareh Mohammadi , A/Prof. Nadine Marcus

Project description: The fields of Science, Technology, Engineering and Math, otherwise known as STEM, play a key role in the sustained growth and stability of any economy and are a critical component in shaping the future of our society. This project aims to develop new evidence-based guidelines for designing highly effective teaching simulations for a STEM subject that personalizes training to learner proficiency. In particular, we aim to design a novel AI-powered framework for dynamic adaptive learning in STEM educational technology to improve learning outcomes in an accessible and engaging environment. The potential contributions of the project involve:

• Developing a multimodal physio-behavioural AI for rapid assessment of proficiency level.
• Integration of affective state and cognitive load with proficiency level to form a comprehensive cognitive diagnosis and capture the interplay between affective and cognitive processes.
• Establishing dynamic adaptive learning in real-time based on the cognitive diagnosis that responds to the current individual needs of the learner.

Networked systems and security

Supervisory team:  Sanjay Jha, Salil Kanhere 

Project summary:  This project aims to develop scalable and efficient one-to-many communication, that is, broadcast and multicast, algorithms in the next generation of WMNs that have multi-rate multi-channel nodes. This is a significant leap compared with the current state of the art of routing in WMNs, which is characterised by unicast in a single-rate single-channel environment. 

For more information contact:  [email protected]

Supervisory team:  Mahbub Hanssan 

Project summary:  A major focuses of the Swimnet project will be to look at a QoS framework for multi-radio multi-channel wireless mesh networks. We also plan to develop traffic engineering methodologies for multi-radio multi-channel wireless mesh networks. Guarding against malicious users is of paramount significance in WMN. Some of the major threats include greedy behaviour exploiting the vulnerabilities of the MAC layer, location-based attacks and lack of cooperation between the nodes. The project plans to look at a number of such security concerns and design efficient protection mechanisms (Mesh Security Architecture). 

For more information contact:  [email protected]   

Supervisory team:  Wen Hu  

Project summary:  The mission of the SENSAR (Sensor Applications Research) group is to investigate the systems and networking challenges in realising sensor network applications. Wireless sensor networks are one of the first real-world examples of "pervasive computing", the notion that small, smart and cheap, sensing and computing devices will eventually permeate the environment. Though the technologies still in their early days, the range of potential applications is vast - track bush fires, microclimates and pests in vineyards, monitor the nesting habits of rare sea-birds, and control heating and ventilation systems, let businesses monitor and control their workspaces, etc. 

For more information contact:  [email protected]

Service orientated computing

Supervisory team:  Boualem Benatallah, Lina Yao, Fabio Casati

Project summary:  This project investigates the significant and challenging issues that underpin the effective integration of software-enabled services with cognitive and conversational interfaces. Our work builds upon advances in natural language processing, conversational AI and services composition.

We aim to advance the fundamental understanding of cognitive services engineering by developing new abstractions and techniques. We’re seeking to enable and semi-automate the augmentation of software and human services with crowdsourcing and generative model training methods, latent knowledge and interaction models. These models are essential for the mapping of potentially ambiguous natural language interactions between users and semi-structured artefacts (for example, emails, PDF files), structured information (for example, indexed data sets), apps and APIs.

For more information contact:  [email protected]  or  [email protected]

Supervisory team:  Lina Yao and Defence Science & Technology Group

Project summary:  This research is supported by the Defence Science and Technology Group. It aims to develop intelligent methodologies to capture the environment in sufficient fidelity to evaluate (model and predict) what application/system changes need to occur to fulfil the requirements (goals) of the mission.

For more information contact:  [email protected]

Supervisory team:  Lina Yao, Boualem Benatallah and Quan Z. Sheng

Project summary:  The overall goal of this project is to develop novel machine learning and deep learning techniques that can accurately monitor and analyse human activities. These techniques will monitor and analyse daily living on a real-time basis and provide users with relevant, personalised recommendations, improving their lifestyle through relevant recommendations.

For more information contact:  [email protected]  or  [email protected]

Supervisory team:  Lina Yao

Project summary : This project is supported by Office of Naval Research Global (US Department of Navy). The aim of this project is to develop a software package for resilient context-aware human intent prediction for human-machine cooperation.

Supervisory team:  Lina Yao and Xiwei Xu

Project summary:  The research is supported by our collaborative research project with Data61. The aim is to develop an integrated end-to-end framework for fostering trust in Federated/Distributed AI systems.

For more information contact:  [email protected]  or  [email protected]

Supervisory team:  Helen Paik

Project summary:  Micro-transactions stored in blockchain create transparent and traceable data and events, providing burgeoning industry disruptors an instrument for trust-less collaborations. However, the blockchain data and its’ models are highly diverse. To fully utilise its potential, a new technique to efficiently retrieve and analyse the data at scale is necessary.

This project addresses a significant gap in current research, producing a new data-oriented system architecture and data analytics framework optimised for online/offline data analysis across blockchain and associated systems. The outcome will strongly underpin blockchain data analytics at scale, fostering wider and effective adoption of blockchain applications. A scholarship/stipend may be available.

For more information contact:  [email protected]

Supervisory team:  Fethi Rabhi and Boualem Benatallah

Project summary:  All modern organisations use some form of analytics tools. Configuring, using and maintaining these tools can be very costly for an organisation. Analytics tools require expertise from a range of specialties, including business insight, state-of-the-art modelling approaches and tools such as AI and machine learning as well as efficient data management practices. A knowledge engineering approach can deliver flexible and custom data analytics applications that align with organisational objectives and existing IT infrastructures. This model uses existing resources and knowledge within the organisation. The project uses semantic-web based knowledge modelling techniques to build a comprehensive view related to an organisation’s analytics objectives while leveraging open knowledge and open data to expand its scope and reduce costs.

We aim to help organisations utilise and reuse public and organisational knowledge efficiently when conducting data analytics. Our work also involves the rapid development and deployment of analytics applications that suit emerging analytics needs, plugging new data and software on-demand using new approaches such as APIs and cloud services. The proposed techniques have already been piloted in the areas of house price prediction in collaboration with the NSW Government and portfolio management in collaboration with Ignition Wealth.

For more information contact:  [email protected]  or  [email protected]

Theoretical computer science

Supervisory team:  Ron van der Meyden 

Project summary:  The technology of cryptocurrency and its concepts can be broadly applicable to range of applications including financial services, legal automation, health informatics and international trade. These underlying ideas and the emerging infrastructure for these applications is known as ‘Distributed Ledger Technology’. 

For more information contact:  [email protected]   

Trustworthy systems

Supervisory team:  Gernot Heiser, June Andronick 

Project summary:  seL4, the secure embedded L4 microkernel, is a key element of our research program. We developed seL4 to provide a reliable, secure, fast and verified foundation for building trustworthy systems. seL4 enforces security within componentised system architectures by ensuring isolation between trusted and untrusted system components and by carefully controlling software access to hardware devices in the system. 

For more information contact:  [email protected]  or  [email protected]

Supervisory team: Dr Arash Shaghaghi, Prof Sanjay Jha, Dr Raymond K. Zhao, Dr Nazatul Sultan

Project summary : A PhD scholarship is available for applicants with outstanding research potential and an interest in quantum-safe security measures for IoT deployments. The successful applicant will join a group of researchers from the School of Computer Science and Engineering (CSE) of UNSW Sydney, the UNSW Institute for Cyber Security (IFCYBER), and CSIRO. The research team brings a complementary track record and expertise aimed to tackle a project of significant potential for impact addressing an emerging topic of research.

We particularly encourage applications from those interested in practical system research (i.e., applied cryptography), noting that our goal is to enhance the resiliency of IoT deployments within intelligent transportation against quantum-based attacks. The project will develop a systematic approach and devise a testbed for evaluating quantum-based attacks against IoT deployments in critical infrastructure. The project’s findings will inform quantum-safe migrations in intelligent transport systems in Australia and internationally.

For more information contact: [email protected] or [email protected]

Projects with top up scholarship for domestic students

Supervisors:

• Dr Gelareh Mohammadi ( [email protected] )
• Prof. Wenjie Zhang ( [email protected] )

Project description:

Previous studies have shown that cognitive training can effectively improve people's skillsets and emotional capabilities in cognitive deficits. Such training programs are known to enhance the participants' brain health and better prepare them for an independent life. However, the existing conventional technologies for such training are not scalable and lack personalized features to optimize the efficacy. In this project, we will develop a technology platform for automatically acquiring and processing multimodal training data. The project will be conducted in collaboration with Stronger Brains, a not-for-profit organization that provides cognitive training. We aim to develop a fully automated social and cognitive function assessment framework based on multimodal data. Such a framework is essential to establish a  system with less involvement of experts and increase its scalability. The project involves:

• A/Prof. Nadine Marcus  ([email protected] )

The fields of Science, Technology, Engineering and Math, otherwise known as STEM, play a key role in the sustained growth and stability of any economy and are a critical component in shaping the future of our society. This project aims to develop new evidence-based guidelines for designing highly effective teaching simulations for a STEM subject that personalizes training to learner proficiency. In particular, we aim to design a novel AI-powered framework for dynamic adaptive learning in STEM educational technology to improve learning outcomes in an accessible and engaging environment. The potential contributions of the project involve:

Supervisor:  Dr Rahat Masood ( [email protected] )

Supervisory team:  Prof Salil Kanhere (CSE - UNSW), Suranga Seneviratne (USyd), Prof Aruna Seneviratne (EE&T – UNSW)

Children start using the Internet from a very early age for entertainment and educational purposes and continue to do so into their teen years and beyond. In addition to providing the required functionality, the online services also collect information about their users, track them, and provide content that may be inappropriate such as sexually explicit content; content that promotes hate and violence, and other content compromising users’ safety. Another major issue is that there is no established mechanism to detect the age of users on online platforms hence, leading children to sign up for services that are inappropriate for them. Through this research work, we aim to develop an age detection framework that can help detect children’s activities on online platforms using various behavioural biometrics such as swipes, keystrokes, and handwriting. The core of this project revolves around the ground-breaking idea that “User Touch Gestures” contain sufficient information to uniquely identify them, and the “Touch Behaviour” of a child is very different from that of an adult, hence leading to child detection on online platforms. The success of this project will enable online service providers to detect the presence of children on their platforms and offer age-appropriate content accordingly.

• Dr Rahat Masood ( [email protected] )
• Prof. Salil Kanhere (CSE - UNSW)

Users unintentionally leave digital traces of their personal information, interests and intents while using online services, revealing sensitive information about them to online service providers. Though, some online services offer configurable privacy controls that limit access to user data. However, not all users are aware of these settings and those who know might misconfigure these controls due to the complexity or lack of clear instructions. The lack of privacy awareness combined with privacy breaches on the web leads to distrust among the users in online services. Through this research study, we intend to improve the trust of users on the web and mobile services by designing and developing user-centric privacy-preserving solutions that involve aspects of user privacy settings, user reactions and feedbacks on privacy alerts, user behavioural actions and user psychology. The aforementioned factors will be first used in quantifying privacy risks and later used in designing privacy-preserving solutions. In essence, we aim to improve privacy in mobile and web platforms by investigating various human factors in: i) privacy risk quantification and assessment, and ii) privacy-preserving solutions.

• Dr Yang Song ( [email protected] ) - primary
• Prof. Maurice Pagnucco - joint

Deep learning techniques have shown great success in many applications, such as computer vision and natural language processing. However, in many cases, purely data-driven approaches would provide suboptimal results, especially when limited data are available for training the models. This dependency on large-scale training data is well understood as the main limitation of deep learning models. One way to mitigate this problem is to incorporate knowledge priors into the model, similarly to how humans reason with data; and there are various types of knowledge priors, such as data-specific relational information, knowledge graphs, logic rules and statistical modelling. In this PhD project, we will investigate novel methods that effectively integrate knowledge priors and commonsense reasoning with deep learning models. Such models can be developed for a wide range of application domains, such as computer vision, social networks, biological discovery and human-robot interaction.

Deep learning models are typically considered a black-box, and the lack of explainability has become a major obstacle to deploy deep learning models to critical applications such as medicine and finance. Explainable AI has thus become an important topic in research and industry, especially in the deep learning era. Various methods for explaining deep learning models have been developed, and we are especially interested in explainability in graph neural networks, which is a new topic that has emerged very recently. Graph neural networks are becoming increasingly popular due to their inherent capability of representing graph structured data, yet their explainability is more challenging to explore with the irregular and dynamic nature of graphs. In this PhD project, we will investigate novel ways of modelling explainability in graph neural networks, and apply this to various applications, such as computer vision, biological studies, recommender systems and social network analysis.

• Dr Dong Wen ( [email protected] ) - primary
• Prof. Wenjie Zhang ( [email protected] ) 

Due to the graph’s strong expressive power, a host of researchers are turning to graph modelling to support real-world data analysis. Given the prevalence of graph structures with temporal information in user activities, temporal and dynamic graph processing is an important and growing field of computer science. Driven by a wide spectrum of applications, such as recommendation and fraud detection in e-commerce, and malicious software detection in cybersecurity, this project aims to develop novel techniques for scalable and efficient temporal graph processing. The specific focus is to tame the challenges brought by the large volume, the high velocity, the complex structure of big temporal and dynamic graphs. The project will lay theoretical foundations and deliver substantial outcomes including computing frameworks, novel indexes and incremental and approximate algorithms to process large-scale graphs.

Supervision team

•  Dr Jiaojiao Jiao ( [email protected] ) – primary
•  Prof. Sanjay Jha ( [email protected] ) – joint
•  Stephen Doherty ( [email protected] ) - secondary

Most cyber threat intelligence platforms provide scores and metrics that are mainly derived from open-source and external sources. Organisations must then figure out if and how the output is relevant to them.

Research problems

• Dynamic threat risk/exposure score

Continuous monitoring and calculation of an organisation’s ‘Threat Risk’ posture score using a range of internal and external intelligence.

• Customised/targeted newsfeed

A curated cyber and threat newsfeed that is relevant to an organisation. The source of the newsfeed will leverage the internal and external analysis from the first question. The output will include information that helps users understand and digest their organisation’s threat posture in a non-technical manner.

Proposed approaches

We propose to develop dynamic GNN models for discovering dynamic cyber threat intelligence from blended sources. GNN has achieved state-of-the-art performance in many high-impact applications, such as fraud detection, information retrieval, and recommender systems, due to their powerful representation learning capabilities. We propose to develop new GNN models which can take blended intelligence sources into account in the threat intelligence prediction. Moreover, many GNN models are static that deal with fixed structures and parameters. Therefore, we propose to develop dynamic GNN models which can learn the evolution pattern or persistent pattern of dynamic graphs.

Research Topics in Embedded Systems for PhD

The amalgamation of software and hardware in a computer system is called an embedded system. The signal processors, microprocessors, and ...

• matlabprojects.org
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The amalgamation of software and hardware in a computer system is called an embedded system. The signal processors, microprocessors, and digital signal processors are functioning through the embedded systems. In addition, flexibility, scalability, energy, dependability, efficiency, and precision are some of the notable features of embedded systems. Let’s start this article with the significant research areas of embedded systems to derive the recent research topics in embedded systems for PhD.

Research Areas in Embedded Systems

• Intelligent and solar-operated robot
• Metal detector
• Pc-based bomb detector
• Heat controller
• Rain detector
• LCD thermometer
• Voice dialer for phone
• The speed limit of the vehicle
• Driverless car
• Gas leak detection
• Train accident alert
• Gesture control
• Anti-sleep alarm
• Auto door open
• Fridge door alarm
• Digital calendar
• Biomedical monitoring system
• Embedded-based auto lift
• Fingerprint car ignition system

At this moment, let us take a look at the list of research algorithms in this field of embedded systems. In addition to that, the research scholars have to go through and finalize the research fields before getting into the selection of research topics in embedded systems for PhD because the algorithms also have to place the research topic in a short note.

Algorithms in Embedded Systems

• The tasks in RMS are scheduled through the static priority which is determined using the duration. In addition, it guarantees time restraints capable of 70% CPU load
• In this process LLF, the tasks are scheduled through the order of laxity and it is not functioning in the soft real-time applications, it has to compete with the applications that are in higher priority
• It is denoted as the dynamic scheduling algorithm which is used in real-time operating systems. Along with that, it is accomplished to assign priority to the process which has the earliest deadline. The process of EDF is functioning over the tasks which are scheduled by the order of the deadline

For your quick reference, our technical experts in the embedded system have highlighted the list of substantial research ideas in the embedded system for your PhD topic selection. More than that, our research experts are ready to implement the scholar’s research ideas. So, discover the innovations with our better guidance.

Latest Interesting Research Ideas in Embedded Systems

• Communication propagation and antenna
• Embedded systems and secure applications
• Cognitive science
• Signal processing applications
• Water level controller
• PLC-based intruder information sharing
• Land rover robot
• Traffic signal auto stop
• Ultrasonic and voice-based walking for blind
• Multi-channel fire alarm
• GPRS-based industrial monitoring
• GSM-based ECG tele-alert system
• Data acquisition system
• Power failure indicator

Are you guys requiring the research topics in embedded systems for PhD with the discussion and to shape your research knowledge? Then you can approach our research experts at any time. The following is about the list of significant research topics that occurred in the contemporary research platform and it is used to select the project titles based on an embedded system.

Research Topics in Embedded Systems for PhD Scholars

• Representation of embedded system with the Petri synthesis property preservation
• Controlling the time nonlinear systems through the embedded systems
• Analysis, application, and modeling
• Nonvolatile data memory with a real-time embedded system
• PCP water injection system design
• Parallel heterogeneous system applications
• Deep neural networks and voice recognition
• Distinct stair recognition system and ultrasonic device frequency
• LPWAN is used while monitoring the coastal waves
• Power harvesting for smart sensor networks in monitoring water distribution system
• Accelerometer-based gesture recognition for wheelchair direction control
• Bedside patient monitoring with wireless sensor networks
• 4-GHz energy-efficient transmitter for wireless medical applications
• Automatic docking system for recharging home surveillance robots
• Hybrid RFID-GPS-based terminal system in vehicular communications
• RF fingerprinting physical objects for anti-counterfeiting applications
• Authenticated and access control system for the device using smart card technology
• Hazardous gas detecting method applied in coal mine detection robot
• Posture allocations and activities by a shoe-based wearable sensor
• Development of an ES for secure wireless data communication
• A microcontroller-based intelligent traffic controller system
• Monitoring and controlling of requirements for cultivation

The following is about the list of research applications based on the embedded system research field it is beneficial for research scholars to find the finest PhD research work through the provided list and these are highlighted by our experienced research professionals in embedded systems.

Applications in Embedded Systems

• Artificial intelligence and robotics
• Telephones, satellite, and radio communications
• Text interfaces
• Military control bases
• Firing of missiles
• Patient monitoring
• Heart treatments
• Radiation therapy
• Space station control
• Spaceship launch and monitoring
• Automobiles

We provide complete research assistance in the research field based on embedded systems for research scholars. On that note, the research scholars have to know about the components that are used in their selected research area. Thus, we have enlisted the components based on the embedded system for your quick reference to implement research topics in embedded systems for PhD.

Components in Embedded Systems

• The power supply is considered the significant component to provide power to the embedded system circuit. In addition, the embedded system requires a 5 V supply that ranges from 1.8 to 3.3. V
• 32-bit processor
• 16-bit processor
• 8-bit processor
• Several microcontrollers are utilized in the embedded system and the memory is the representation of the microcontroller itself. It includes two significant types as
• RAM is denoted as the volatile type of memory and it is used to store the data for a temporary period in the memory while switching off the system, the data will be lost from the memory
• It is denoted as the code memory and it is deployed to store the program when the system is switched on the embedded system fetch code from ROM memory
• The input is used in the embedded system where it is needed to interact with the system. The processors are used in the embedded system based on input and output. In addition, the proper configuration is required for using the input and output ports. In the embedded system there are fixed input and output ports to connect the devices only with the specified ports and P0, P1, P2, and more are examples of input and output ports
• The type of interface which is deployed to communicate with other types of embedded systems is called a communication port. When the small-scale application includes the embedded system and communication ports can be deployed in the form of a microcontroller and it includes the serial protocols used to send data from one system board to another system board
• The timer and counter are utilized in the embedded system and the programming is done in such a way that delay can be generating the embedded system. The delay period can be decided through the functions of the crystal oscillator and system frequency
• The application is used for embedded systems with the competition of hardware components. For instance, temperature sensor applications are requiring the temperature sensors to measure the temperature

Up to now, we have discussed some forceful research components that are used in an embedded system which every research scholar prerequisites to be aware of while selecting their research topics in an embedded system for PhD. Without any delay, let’s discuss research problems in the research field based on the embedded system in the following.

Research Challenges in Embedded Systems

• Performance
• DRM vs usability
• Creating the required latent process
• Moore’s law
• Globalization
• Future vs legacy
• Emerging behavior
• Heterogeneous vendors
• Heterogeneity

Below, our research professionals have enlisted the research questions that are asked by the research scholars to develop their research projects in the embedded system along with the appropriate answers.

People Asked Questions

What are the top research fields in embedded systems.

• Cyber security embedded system
• Embedded IoT application
• Embedded network design
• Embedded applications
• Embedded Linux system
• Microcontroller firmware

What are the different types of embedded systems?

• Mobile phones
• Digital camera
• Home security systems
• ATM machine
• Card swipe machine
• MP3 players
• Microwave ovens
• Traffic control system
• Military usage in the defense sector
• Medical usage in the health sector

What is a new technology in embedded systems?

• Cloud connectivity
• Embedded security
• Augmented reality and virtual reality
• Artificial intelligence
• Deep learning

What is the simulation tools used in embedded system?

What is the programming languages used in embedded system, what are the topics in embedded systems.

• Wireless meter for consumer utility
• An ultrasonic parking guidance system
• Iris-based door opening and closing
• Fault location in underground power networks
• Embedded-based calibration of the proximity sensor
• Accident prevention using eye blink
• Earthquake analyzer and reporter
• AC motor speed monitoring and control through telephone
• Bus information alert for the blind using Zigbee

What are the real-time examples in embedded systems?

• Medical Equipment
• Electronic Calculators
• Industrial machines
• Laser Printer
• Digital phones
• Televisions
• Washing Machine
• Digital watches

To this end, the research scholars can trust us for your PhD work and we shape your innovative research thoughts with proper research implementation by using the required simulation tools, protocols, algorithms, etc. Our research experts have years of experience in this research platform and also from research topics in embedded systems for PhD to paper publication too. We are strong in all the research fields in embedded systems and we are being learned through the fundamentals till the growth and now. Finally, the research scholars will acquire the finest result when you join hands with us. As well as, we teach you an easy way to acquire the finest research knowledge to shine in your research career.

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Academics   /   Graduate Study   /   MS Programs   /   Specializations Embedded Systems

Embedded systems are increasingly prevalent in nearly every aspect of human life. Innovations made possible by embedded systems are making our lives healthier, safer, cleaner, and more stimulating. Today, embedded systems are integral across a diverse range of application and industries, fueled by growth in smart devices, wearables, medical devices, automotive, manufacturing, security systems, communications, healthcare, aerospace and defense, and autonomous systems. These application-specific systems are typically designed for meeting real time constraints, such as speed, power, size, accuracy, reliability, and adaptability. However, as our world evolves with increasingly complex systems, engineers are often faced with critical challenges in designing, managing, and optimizing these systems for the rapidly changing requirements of tomorrow.

Embedded systems design stands at the intersection of hardware and software architecture. These engineers are experts in software application development with a profound understanding of the target hardware architecture. In this track, you will acquire deep knowledge about both domains, and the skills to combine them into a complete system that is optimized for performance, an important advantage that will dominate the future of engineering.

Program Details

Core courses.

Select at least six courses from the following list:

• CE 303 Advanced Digital Design
• CE 346 Microprocessor System Design
• CE 347-1 Microprocessor Systems Project I
• CE 347-2 Microprocessor Systems Project II
• CE 355 ASIC and FPGA Design
• CE 358 Intro to Parallel Computing
• CE 361 Computer Architecture I
• CE 364, 464 Cyber-Physical Systems Design and Application
• CE 366, 466 Embedded Systems
• CE 395, 495 Embedded Artificial Intelligence
• CE 452 Advanced Computer Architecture I
• EE 326 Electronic System Design I
• EE 327 Electronic System Design II
• EE 398 Electrical Engineering Design

Elective Courses

Select up to six courses from the following list:

• CE 362 Computer Architecture Project
• CE 365, 465 Internet-of-Things Sensors, Systems, and Applications
• CE 387 Real-Time Digital Design Systems Design and Verification with FPGAs
• CE 392 VLSI Systems Design Projects
• CE 393, 493 Advanced Low Power Digital and Mixed-signal Integrated Circuit Design
• CE 395, 495 Modeling and Synthesis of Cyber-Physical Systems
• CE 395, 495 Sustainable Internet of Things
• CE 395, 495 Wearable and Physical Computing
• CE 453 Parallel Architectures
• CE 456 Modern Topics in Computer Architecture
• CS 329 HCI Studio
• CS 330 Human Computer Interaction
• CS 343 Operating Systems
• CS 397, 497 Wireless and Mobile Health (mHealth)
• CS 397, 497 Wireless Protocols for the Internet of Things
• CS 446 Kernel and Other Low-level Software Development
• CS 497 Advanced and Database Systems
• EE 307 Communications Systems
• EE 359 Digital Signal Processing
• EE 360 Introduction to Feedback Systems
• EE 363 Digital Filtering
• EE 374 Intro to Digital Control
• EE 378 Digital Communications
• EE 380 Wireless Communications
• EE 395, 495 Bioelectric Systems Modeling & Analysis
• EE 395, 495 Biometrics
• EE 395, 495 Cardiovascular Instrumentation
• EE 395, 495 Geospatial Vision and Visualization
• EE 395, 495 Machine Learning for Medical Images and Signals
• EE 395, 495 Personal Health Systems
• EE 418 Advanced Digital Signal Processing
• ENTREP 475 NUvention: AI

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Top 10 PhD Topics in Embedded Systems

The term embedded system refers to the mechanism of a microcontroller & microprocessor in which software and hardware are performing their significant functionalities. As well as they are contributing their indispensable features in both software and hardware by their major elements. If you are searching for an article with PhD topics in embedded systems, then we are appreciating your navigation! You’ve enrooted to the exact handout!

At the end of this article, you would get all the relevant particulars about embedded systems with crystal clear explanations.   The main objective of bringing this article is to cut out the ambiguities of students in embedded systems. Initially, our academics of the institute have lighted up this handout with the introduction of embedded systems.

Introduction of Embedded Systems

An embedded system requires both a microcontroller & microprocessor in which they are performing. It is the mutual amalgamation of software & hardware. Peripheral hardware entities are the inclusion of memory, display, I/O interfaces, and user interfaces.

The complete embedded system has consisted of ports, timers, high-end processors & power supply.  On the other hand, it has unique features and it is mainly known for those.

• Operation Range
• Smaller Size
• Minimized Unit Costs
• Less Power Ingestion or Consumption

These are the major and significant features of embedded systems. There are so many concepts getting involved in the embedded systems . Among those, we would like to at least mention the main concepts of the same for the ease of your understanding.

• Robotics Models
• Decision oriented Controlling Mechanism
• BioSignature Evaluations
• Remote Control Devices
• Security Devices & Models

How Does Embedded Systems work actually?

Former embedded systems were supported with the microprocessors whereas newfangled embedded systems are supported by the microcontrollers.  For example, embedded radios are using microcontroller apps. We are using so many embedded devices in real-time and they are almost accessible at every user level.

Even smart homes are using embedded systems in the form of washing machines, micro ovens, and many other computerized devices. Besides, healthcare also makes use of the embedded system as health monitoring equipment such as pacemakers, scanners, digital surgery aiding monitors, and heartbeat & blood pressure checking mechanisms.

Embedded systems are often called irreplaceable technology. Doing research in these areas would abundantly yield marvelous outcomes . Now, we can have the section which is about major elements of embedded systems.

Major Elements of Embedded Systems

• It is comparing the final output of D-A converters with the actual output and warehouses the approved output
• D-A converts the processor’s digital data into the form of analog data
• A-D converts the sensor’s analog signal into the form of a digital signal
• Here, processors interpret the data to evaluate output and warehouse the same
• Reads peripheral quantity of an object and converts it into an electrical signal
• Further allows other devices to recognize the electrical signals emitted
• It is warehousing the measured quantity into the memory

The aforementioned are the 4 major elements comprised in the embedded systems or technology.  On the other hand, these elements are elevating the performance by accommodating the various application areas in real-time. Yes, we are going to let you know the different applications of embedded systems for making your understanding easy.

What are the Different Applications of Embedded System?

• Movement Control in Longitudinal
• Energy Control in Lambregts
• Thermal Anti-icing Models
• Turboprop Powered Mechanisms
• Chargeable E-vehicles
• Multi-agent Devices
• Substation Maintenance
• Innovated Modern Homes
• Demagnetization Errors
• Electrical Bow Thruster
• The converter of Energy Recovery
• Sensor-less BLDC
• E-Commerce in Mobile Phones
• Cellular Networking & Computing
• Wireless Communications
• Automation in assembly lines
• Multimedia Adaptation in Cars
• Motor Engine Safety
• Driving Control System

These are the different and dynamic application areas in which embedded systems are performing under various complexities. However, every technology has some barriers to means of execution. For example, every technology is a stack of security challenges. Likewise, embedded systems are also presented with some major challenges and they are mentioned in the following section to make your understanding better.

What are the Challenges of Embedded System?

• Testing Complexities
• Increased Power Indulgence
• Rigidity in Application Running
• Lack of Performance Management
• Small Scale Factors
• Reduced Energy Levels
• Low-Security Levels in IoT
• SDLC High Requirements
• Testing Constraints
• Quality Compromises
• Unstructured Behaviors
• Lack of Uniformity in Behaviors

For many more decades, the challenge of designing software is being existed till now. Many end-users of embedded systems aspire to have high-performance processors and battery life for a long time. So the system fails here. As security is one of the major issues in embedded systems, developers are focused to enhance the same by utilizing various technical experiments.

SDLC (Software Development Life Cycle) highly requires the exact utilities to perform.  Every embedded system is not compatible with unnatural system behaviors. On the other hand, the user of this system requires constant behaviors even under various conditions or environments. Now, we can have general solutions to some of the challenges.

Solutions for Embedded System Challenges

FPGA is one of the interesting logic used and integrated into the embedded technology. It is also known as the substitute for ASIC fabrics.  FPGA logic is widely aided with custom logic which is modeled after the manufacturing processes.

Many developers are treating this approach as the best tactic to customize the software components.  Researchers are making use of many more toolkits and toolboxes to evaluate FPGA systems’ performance and their components in a multiprocessor. For instance, sensor networks are the greatest examples of FPGA-based embedded systems.

In short, embedded systems’ challenges can be raided by FPGA logic. Embedded systems are classified into 2 different categories based on their functional requirement & performance. Come, let’s have the taxonomies section.

Taxonomies of Embedded System

• Refined or Sophisticated Scale
• Medium or Average Scale
• Small Scale
• Mobile Networks
• Standalone Devices
• Real-time Applications

This is how the embedded system is categorized according to the 2 different parameters. Do you know something, embedded systems are widely supported in every phase of technology by their indispensable operations? Some of the popular operational areas of embedded systems are discussed in the immediate section for ease of your understanding.

Operational Areas of Embedded Systems

Thermal-Aware & Low Power Consumption

The advancement in the nano-CMOS scaling technology caused the power consumption rates to be higher.  So the reason behind elevating power chip temperatures is because of this high power consumption.

Scaling technology is crossing the determined limits hence it is resulting in scaling discontinuation. TDP is one of the conventional thermal power budgets. Further, developers have shown their effort in the former budgets to bring out the novel budget called TSP (Thermal Safe Power).

TSP is providing the power limit values safely according to the major operations. Systems performance is being examined under various conditions . One is temperature limitations such as dark silicon which is stimulated the researchers to execute the newfangled resource managing techniques by exploring the system properties.

Cybersecurity in Embedded Systems

In this process, the system is focused on shielding the confidentiality levels in reconfigurable systems which are embedded. Along with this, they are also concentrated on preventing the embedded systems from data losses.

As many cyber crooks are trying to steal data, it is very important to prevent the network with cyber security techniques. Cyber attackers are very much intellectual in stealing data and they are interpreting the network with a various number of attacks.

A side-channel attack is one of the most vulnerable attacks which can make the data leak. The data transmitted from one tunnel to another tunnel may be sensitive or non-sensitive and even neither encrypted nor unencrypted.

Resource-constrained Machine Learning

According to the modernization of IoT, automated devices such as supercomputers highly have the chance to interact with other possible devices. On the other hand, they are limited to storage, power & energy consumption.

However, machine learning-based algorithms are allowing the system to progress with high-end inputs like sensors, videos & images. In this regard, we would also like to enlighten the advantages of embedded systems for your better understanding.

• Enhanced Process Quality
• Operationally Fast & Smart Portability
• Low Power Consumption
• High Reliability
• Effective Mass Fabrication

These are the few advantages of having embedded systems in general. Apart from this, there are uncountable merits are exist. If you do want further details about embedded systems, feel free to approach our researchers at any time. We are very delighted to serve utilizing technology. Further, it is the right time to know about the research areas in embedded systems.

Research Areas in Embedded Systems

• Robotics & Automation
• GPS based Location Tracking
• Intrusion Prevention System (IPS) & Intrusion Detection System (IDS)
• Raspberry Pi
• Internet of Things (IoT)
• Video Communication Software
• Wireless Communication Networks
• Fiber Optical Sensor Mechanisms
• Photonic Elements
• Photonic Methods & Networks

The foregoing passage has conveyed to you several possible research areas based on an embedded system. Additionally, you can begin to work on the above-listed research areas. As this article is mainly focused on giving PhD topics in embedded systems, here we are going to state to you the top 10 topics for your valuable consideration.

Top 10 PhD Research Topics in Embedded Systems

1.      Particle Swarm Optimization & Historical Archives based Distributed Virtual Network

2.      Integrated Edge Computing & Pedestrian Detection Systems

3.      Real-Time Embedded System Learning & Virtual Engineering

4.      Multi-Core Embedded Systems with Guaranteed Inter or Intra-Core Communication Techniques

5.      Embedded Systems using Laboratory Course Designs

6.      Combination of FPGA & RASA Approaches in Risky Environs

7.      Generation of Embedded Software Evaluation Construction

8.      Standardized Embedded Systems by Neuro-Fuzzy Approaches

9.      Airborne Embedded System’s Performance Estimation by GPU (Workload-Aware)

10.  Multiprocessor Embedded Systems Simulation & Testing on Sync Issues

In the foregoing section, we have used some acronyms and we felt that mentioning the abbreviation for that will help the beginners. Come; let us also have the quick insights on it.

·        RASA – Reliability Aware Scheduling Approach

• FPGA – F ield P rogrammable G ateway A rray

As of now, we have brainstormed in the major areas of embedded systems ranging from overview to top 10 PhD topics in embedded systems with clear explanations. As many of the students from all over the world is hitting our digital platforms to put an article in the areas of embedded system with their recent trends, we are here going to let you know the same.

Recent Trends in Embedded Systems

• Deep Learning
• Mesh Networking
• Cloud Systems
• Embedded Security Devices
• Reduced Energy Consumptions
• IoT(s)-Internet of Things
• Machine Learning
• Augmented & Virtual Reality
• Artificial Intelligence

Machine learning and artificial intelligence are the major technologies involved in the significant progress of embedded technology. As artificial intelligence is widely used in every stage of digital technology, it is also pillaring the embedded systems indispensably.

Many of the average end-users also expect the technologies’ gigantic growth. This could only be possible by accommodating different kinds of algorithms according to the necessities. Yes, you people guess right! We are exactly going to tell you the various algorithms used in the embedded system for the ease of your understanding.

Different Algorithms used in Embedded Systems

• FPGA & ISFET – PH Level Meter
• CMOS – SEU Latch
• MOSFET – Double-Gate based Cylindrical Surrounding
• ADC & CMOS – Hybrid Data Converters
• Pin Diodes – Buck Converters
• Piezoelectric Transducer – Energy Harvesting Circuit
• CMOS – Sensor aided Streaming or Floating Gate
• DC To DC Converter – Dynamic Bias PFM
• nMOSFET & pMOSFET – Body Bias Optimization

These are some of the protocols being involved in the process of embedded systems. Without these algorithms, we are not supposed to yield the best results. On the other hand, we need to consider the protocols practiced for communication in embedded technology .

Communication channels among the different components are established by intra protocols inside the electric circuit boards. Besides, it is increasing the possibility to add other components that are connected to microcontrollers. Intra-system communication protocols are often called device communication protocols.

The embedding system is giving much importance to the communication protocols as it is the only medium to transmit data packets from one end to another end. Now, we can have further discussions about the other protocols in embedded systems with clear explanations.

What are the Protocols in Embedded Systems?

• RS-485 Protocol

RS-485 is the major protocol that can connect the external devices and microcontrollers effectively . This protocol is permitting the device to utilize identical user interfaces for exchanging data. It has consisted of 4 major pins called,

• Universal Serial Bus (USB) Protocol

USB protocol is widely used in every phase of technology to exchange data. In other words, it is the improved protocol for digital communication.  Whenever a device connects to the network or host, the host or network will assign an IP address to that device utilizing performing communication. They are also using 4 major pins as mentioned below,

• D-  – Data Receiving in Differential Line Pin
• D+ – Data Transferring in Differential Line Pin
• GND – Circuit Closing Pin
• VCC – Power Supply Pin
• Control Area Network (CAN) Protocol

This protocol is introduced to reduce the complexities in cabling and connectivity between the interfaces. Primarily, it is used in the automobile industry to reduce the ethernet cables in the devices such as engines, motors, AC, and so on.

Data transmitted over the CAN protocols are making the data available in every node connected to that bus topography. There are 2 types of CAN protocols that are used Extended CAN & Standard CAN. In addition, this protocol is utilizing the 2 main ethernet cables to communicate among other CAN peripheral devices like,

• Universal Synchronous / Asynchronous Receiver / Transmitter (USART) Protocol

Communication modes such as half-duplex, full-duplex, and simplex are supported by the USART protocols which are serial protocols. Every microcontroller is presented with USART protocol and they are using the 2 pins called,

• TX – Transmits outgoing data (sender side)
• RX – Receives incoming data (receiver side)
• Inter-Integrated Circuit (I2C) Protocol

I2C is the twisted wire-based communication protocol and is exactly interconnected with 2 cables which are widely integrated with the low processing devices such as input or output devices, digital converters, analog converters, and microcontrollers.

They are compatible with the data speed ranging from 100 Kbit/s to 400 Kbit/s. This is based on the type and mode of operation held. On the other hand, I2C protocols are using the 2 major pins for establishing communication,

• Serial Clock Line (SCL)
• Serial Data Line (SDA)
• Serial Peripheral Interface (SPI) Protocol

SPI is the only duplex protocol that makes use of the slave configuration for establishing a communication channel between the sender and receiver. Besides, it is widely utilized in the areas of microcontroller interfaces such as LCD & EEPROM displays. Further, it is mainly comprised of 4 major pins called,

• Slave Select (SS) – Master Selects Slaves
• Master Input Slave Output (MISO) – Slave Output Data
• Master Output Slave Input (MOSI) – Master Output Data
• Serial Clock (SCLK) – Data Transmission by Clock Source

These are the various protocols used to establish communication among various users of the network.  If you are a person with aspirations in embedded systems, then you can begin to investigate the protocols and other entities used in the same system.

Besides, you can also avail our assistance to make the explorations better. Now, our researchers of the institute have listed you some of the simulation modules which are highly beneficial to the embedded systems in general. Shall we get into that? Come on!!!

Simulation Modules of Embedded Systems

• Error Correction & Speech Encoding Design
• E-Vehicles & allied Transport Systems
• Satellite-based Power Systems
• 3 Phase Electric Mechanisms
• Automation in Controlling System
• Transmission using Relay
• MEMS-based Accelerometer
• Distribution Generation Structure
• Grid Models & Battery Maintenance
• Conversion of Power Controls
• Motor Management

The foregoing area has significantly enumerated to you some of the interesting simulation modules of embedded technology. It is also important to make note of the different software well-suited for the embedded system. Don’t you know about that? Don’t worry!! We are also going to highlight the same for your fine considerations.

Which Software is best for Embedded systems?

• Visual Studio

The above listed are the various software used to develop embedded systems in real-time. In that, ARM Keil is offering a wide range of environ for generating embedded systems in ARM-oriented peripheral devices. Verilog HDL is the hardware-based description language as well as C is widely used as the programming language in embedded systems.

In addition to these sections, ultimately we need to consider the performance of embedded systems. For this, there are several metrics are predefined by top world-class engineers. Without knowing the efficacy of systems performance , we cannot run a proper embedded system. Hence, it is very essential to consider the performance metrics. Some of them are listed in the immediate section.

List of Embedded System’s Performance Metrics

• Response Time – It is the time taken for responding to events
• Start Time – This is the time, in which a response is given & event starts
• Finish Time – It is the end closure of the event

Itemized above are the major performance metrics taken into account while evaluating the system’s performance. As this technology is moving to rapid growth, there is a huge opportunity even in the embedded technologies.  Many scopes are nuzzled with the embedded systems. Yes, we are also going to have a look at future directions of embedded systems.

Future Research Directions of Embedded Systems

• Artificial Intelligence based Software & Hardware
• Energy-Constrained Embedded Systems
• Enhanced Security Levels in Embedded Systems
• Edge Computing in Embedded High-end Processes

Embedded systems are the only source of artificial intelligence as it has the weightage in every stage of technology. The combination of hardware and software of embedded systems is focused to give the best solutions to some of the challenges that arouse such as system consistency, safety, and security measures.

As well as they are effectively handling the sensitive data exposed in human-controlled environments.  They are also known for their critical situation tolerance capacities and they are performing self-driven testing & validations.   In short, it is aimed to offer better scalability compared to the current state.

So far, we had gone through the various areas of embedded technology with newfangled fine facts. As the technology is exposing more opportunities to research, we are appreciating your effort in reading this article along with this we are cordially wishing you, people, to grab the success fruits in embedded technology. If you require any assistance in embedded system research work and phd topics in electronics and communication , we are always there to help you.

“Let’s start to begin your research voyage with the emerging technologies by our incredible assistances”

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All EECS MEng students should expect to complete four (4) technical courses within the EECS department at the graduate level, the Fung Institute’s engineering leadership curriculum, as well as a capstone project that will be hosted by the EECS department. You must select a project from the list below.

Capstone Projects

The two-semester capstone experience will challenge you to integrate your technical and leadership skills to innovate in a dynamic, results-driven environment. Working with a team of fellow students, you will engineer solutions using cutting-edge technology and methods to address crucial industry, market, or societal needs. Capstone teams consist of three to five students. The size of each team is curated to optimize team dynamics and provide rich opportunity for developing effective leadership and teaming skills. There are two major project types: faculty projects and partner projects. Capstone matching and assignment takes place in the start of Fall semester. Projects vary from year to year, and there is no guaranteed placement until students have gone through the matching process.

Faculty Projects

Students working on faculty projects are advised by PhD candidates, post-doctoral researchers, and faculty from the UC Berkeley College of Engineering.

Partner Projects

Students working on partner projects are advised by a technical expert from a partnering organization.

Deliverables

Each Capstone team will have three types of deliverables:

• Technical deliverables are set in consultation with the advising team. These vary from project to project and may include: prototypes, experiments, data analyses, etc.
• Project management and teaming deliverables are shared by the MEng cohort. These include: project charter, project plan, stakeholder engagement strategy, etc.
• Reporting deliverables are shared by the MEng cohort. These include: final report, final presentation slidedeck, project brief, Capstone showcase, etc.

Technical Courses

At least THREE of your four technical courses should be chosen from the list below. The remaining technical courses should be chosen from your own or another MEng area of concentration within the EECS Department.

Typical Fall Semester Course Offerings —

• EECS 206A, Introduction to Robotics
• EE 213A, Power Electronics
• EE C220A, Advanced Control Systems
• EE C220B, Experiential Advanced Control Design
• EE 221A, Linear System Theory
• EE C249A, Introduction to Embedded Systems
• CS 289A, Introduction to Machine Learning

Typical Spring Semester Course Offerings —

• CS 280, Computer Vision
• CS 287H, Algorithmic Human-Robot Interaction
• EECS 206B, Robotic Manipulation and Interaction
• EE C220C, Experiential Advanced Control Design
• EE C222, Nonlinear Systems – Analysis, Stability, and Control
• EE 223, Stochastic Systems: Estimation and Control
• EECS 227AT, Optimization Models in Engineering
• EE C227C, Convex Optimization and Approximiation
• EE C249B, Design of Embedded Systems: Models, Validation, Synthesis

Note: The courses listed here are not guaranteed to be offered, and the course schedule may change without notice. Refer to the UC Berkeley Course Schedule for further enrollment information.

Prospective M.Eng. Student Projects and Courses by Area

• Prospective: Data Science
• Prospective: Physical Electronics and Integrated Circuits
• Prospective: Signal Processing and Communications
• Prospective: Visual Computing and Computer Graphics
• Prospective: Computer Systems

embedded systems Recently Published Documents

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A Modular End-to-End Framework for Secure Firmware Updates on Embedded Systems

Firmware refers to device read-only resident code which includes microcode and macro-instruction-level routines. For Internet-of-Things (IoT) devices without an operating system, firmware includes all the necessary instructions on how such embedded systems operate and communicate. Thus, firmware updates are essential parts of device functionality. They provide the ability to patch vulnerabilities, address operational issues, and improve device reliability and performance during the lifetime of the system. This process, however, is often exploited by attackers in order to inject malicious firmware code into the embedded device. In this article, we present a framework for secure firmware updates on embedded systems. This approach is based on hardware primitives and cryptographic modules, and it can be deployed in environments where communication channels might be insecure. The implementation of the framework is flexible, as it can be adapted in regards to the IoT device’s available hardware resources and constraints. Our security analysis shows that our framework is resilient to a variety of attack vectors. The experimental setup demonstrates the feasibility of the approach. By implementing a variety of test cases on FPGA, we demonstrate the adaptability and performance of the framework. Experiments indicate that the update procedure for a 1183-kB firmware image could be achieved, in a secure manner, under 1.73 seconds.

Computer development based embedded systems in precision agriculture: tools and application

Low-power on-chip implementation of enhanced svm algorithm for sensors fusion-based activity classification in lightweighted edge devices.

Smart homes assist users by providing convenient services from activity classification with the help of machine learning (ML) technology. However, most of the conventional high-performance ML algorithms require relatively high power consumption and memory usage due to their complex structure. Moreover, previous studies on lightweight ML/DL models for human activity classification still require relatively high resources for extremely resource-limited embedded systems; thus, they are inapplicable for smart homes’ embedded system environments. Therefore, in this study, we propose a low-power, memory-efficient, high-speed ML algorithm for smart home activity data classification suitable for an extremely resource-constrained environment. We propose a method for comprehending smart home activity data as image data, hence using the MNIST dataset as a substitute for real-world activity data. The proposed ML algorithm consists of three parts: data preprocessing, training, and classification. In data preprocessing, training data of the same label are grouped into further detailed clusters. The training process generates hyperplanes by accumulating and thresholding from each cluster of preprocessed data. Finally, the classification process classifies input data by calculating the similarity between the input data and each hyperplane using the bitwise-operation-based error function. We verified our algorithm on `Raspberry Pi 3’ and `STM32 Discovery board’ embedded systems by loading trained hyperplanes and performing classification on 1000 training data. Compared to a linear support vector machine implemented from Tensorflow Lite, the proposed algorithm improved memory usage to 15.41%, power consumption to 41.7%, performance up to 50.4%, and power per accuracy to 39.2%. Moreover, compared to a convolutional neural network model, the proposed model improved memory usage to 15.41%, power consumption to 61.17%, performance to 57.6%, and power per accuracy to 55.4%.

Getting Started with Secure Embedded Systems

Design principles for embedded systems, cyense: cyclic energy-aware scheduling for energy-harvested embedded systems, embedded systems software development, esqumo an embedded software quality model.

Embedded systems are increasingly used in our daily life due to their importance. They are computer platforms consisting of hardware and software. They run specific tasks to realize functional and non functional requirements. Several specific quality attributes were identified as relevant to the embedded system domain. However, the existent general quality models do not address clearly these specific quality attributes. Hence, the proposition of quality models which address the relevant quality attributes of embedded systems needs more attention and investigation. The major goal of this paper is to propose a new quality model (called ESQuMo for Embedded Software Quality Model) which provides a better understanding of quality in the context of embedded software. Besides, it focuses the light on the relevant attributes of the embedded software and addresses clearly the importance of these attributes. In fact, ESQuMo is based on the well-established ISO/IEC 25010 standard quality model.

Embedded Systems and Architectures

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Embedded Systems Project Topics With Abstracts and Base Papers 2024

Embark on a transformative journey into the world of embedded systems with our meticulously curated selection of M.Tech project topics for 2024, thoughtfully complemented by trending IEEE base papers. These projects encapsulate the forefront of innovation and challenges in embedded systems, offering an indispensable resource for  M.Tech students seeking to delve into the dynamic landscape of intelligent and interconnected devices .Our comprehensive compilation covers a diverse range of Embedded Systems project topics, each intricately paired with an associated base paper and a succinct abstract. From IoT applications and real-time systems to edge computing and hardware-software co-design, these projects mirror the current trends in embedded systems development. Stay ahead of the curve by exploring projects that align with the latest IEEE standards and technological breakthroughs. Whether you’re a student, researcher, or industry professional, our collection serves as a gateway to the cutting edge of embedded systems advancements. The project titles are strategically chosen to incorporate keywords that resonate with the latest trends in embedded systems , ensuring relevance and alignment with the evolving technological landscape. Delve into the abstracts to quickly grasp the scope, methodologies, and potential impacts of each project.

M.Tech Projects Topics List In Embedded Systems

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Latest Projects Based on Embedded Systems

The following projects are based on embedded systems. This list shows the latest innovative projects which can be built by students to develop hands-on experience in areas related to/ using embedded systems.

1. Night vision SPYBOT

Night vision spy bot is a unique type of robotic system which can be used for spying on enemy territories. This type of robots can be used to collect information and monitor suspicious activities, even we can track location of terrorist organizations. These type of robots can be used for surveillance of any disaster affected area or a place which is difficult to reach. Its night vision camera makes it active and effective even in darkness using infrared lighting.

2. Human Detection Robot using IR sensors

This project involves building a robot that uses PIR (passive infra-red) sensors to detect the human presence. It makes use of the PIR sensor application of sensing the infra-red rays that emits when heat is generated from the human body. The application of this project varies from rescue operations to finding out the capacity of humans at a place.

3. Interfacing GPS with 8051 Microcontroller

GPS – Global Positioning System is generally used to determine the location of a vehicle or a person from a remote location. These GPS modules are generally used to provide the location, position and navigation services to the users from anywhere on earth. There are about 24 to 32 satellites which facilitates the seamless working of GPS.

The main application for the GPS involves mapping, tracking and surveillance. The GPS module calculates the position of any user, by recognizing the signals that are transmitted by the satellites. GPS receiver calculates the distance to each satellite and the raw data is converted to latitude, longitude, altitude, speed and time.

4. GPS Ambulance Tracker

Ambulance is a concept that is made to help the suffering people instantaneously to reach the medical facilities on time. This vehicle is used to move patients quickly to the critical care in case of emergency situations. They are also used for other non-critical purposes like transfers between the hospitals, organ or blood transfer etc. but nonetheless it involves a significant amount of importance on comparison with the other conventional vehicles.

SLNOTE Ambulances will be equipped with critical tools and will have well trained responders who can come to the site quickly and help the person in need with the lifesaving actions. For such kind of vehicles safety is the foremost thing that anyone should provide. For this the proposed system is GPS tracking systems which can be implemented onto the ambulances and track their movement.

5. GPS and GSM based School bus tracking system

The proposed project provides an effective solution to the most widely addressed concern, about the safety of school students. It is one of the most worrying concern among the individuals where a lot of students are being kidnapped, inability to track the bus real-time, monitoring the location of the bus when there are only less number of students in the bus etc. Even it will effectively provide solution to find out the best possible route to reach all the pickup/drop points efficiently.

This project solves exactly this problem where the system continuously monitors the longitude/latitude of the system real-time and sends a text message to all the users for monitoring purposes. Upon receiving the sms, the user can use the google map to track the bus movements.

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6. GPS tracker for Blind people using GSM technology

The proposed project integrates both GPS and GSM technology to provide an efficient solution to track the disabled persons when they go missing or lost. There have been a lot of buzz lately about the reports that we are receiving about the number of persons that had gone missing. And a significant number of them are blind people who can’t tell the whereabouts when they gone missing.

This project ease this off by letting their families know about their location by sending the latitude/longitude as a sms. Also a special keypad will be embedded onto the system which can be used to send some specific sms to their families at times of emergency. The proposed system not only assures that their location is monitored but also assists at times of emergency to pass some sensitive information.

7. Accident detection system with GPS and GSM

With the urbanisation on the rise, the number of accidents that are happening every day is on the rise. And the major issue here is that non availability of medical help at the right time. Where the medical institutions are not getting the information at the right time and they end up reacting to the situation very late that it may even cost a life of a person.

To provide an effective solution to this, here we are proposing a system that will be fitted inside the front and back bonnet of the vehicle. It works on the simple principle in which two metallic plates will be placed close together with a minimum gap between them and whenever there is an impact the plates will come in contact with each other. When they come in contact with each other, conductance will happen and this signal will be then sent to the microcontroller.

8. Smart Shopping Trolley

Nowadays in Metro cities, People face a lot of problem in supermarkets to buy things using trolleys. Every time we need to push the trolley to the bring the trolley to the place where the object required by us is present. Imagine that there is a trolley which will get all the groceries on its own. The only thing you need to do is just send the list of groceries through simple SMS. Through this project, you are going to build a Smart Shopping Trolley which will collect the groceries you need on its own.

9. Gesture Based Wheel chair

Mostly Physically Handy-capped people always face trouble in moving from one place to another place. Through this project, you are going to create a unique wheelchair which can be controlled with the Wrist movements using an accelerometer.

10. Finger Controlled Wheel chair

Generally Old and Physically Handy-capped people face trouble in moving from one place to another place. Though they use supporting stick or wheelchair, they need to give some force to move the chair or they need someone to move the chair. Through this project, you are going to create a unique wheelchair which can be controlled with the finger movements.

11. GSM Controlled Level Crossing

12. gsm based door locking and unlocking system.

Today the most important aspect all of concerned very much is Security Systems. Consider Home security. Generally, we have door locks. Imagine that you had locked your house and went outside. Suppose you had lost your key, What will you do? Obviously, we need to break the look or order new key. Both will take certain and money. Through this project, you are going to build a unique Security system where you can open/close the lock with your mobile.

13. Voice Based Speed Control of DC Motor

Motor plays an important role in many engineering applications. Engineers always search for an efficient and easy way to control these motors. Some many techniques are available in both AC and DC. Pulse width modulation is used often to control DC motor.

14. Solar Panel Belt Conveyor

As the conventional energy sources are depleting day by day, it is very essential to search for alternative energy sources. Nowadays belt conveyor is playing a key role in material handling application, they are used to carry materials from one end to another end. Based on the size, nature of the material, different conveyors are being used.

15. Land Mine Detector Robot

Every country is preparing for a strong army to prevent them from terrorists, the soldier undergo a particular period of training in the camp and finally they will be shifted to the most dangerous place of where terrorist area camp, even to protect the borderline between the two countries.

16. Vehicle Accident Prevention System with Eye Blink Sensor

Automobile has great importance in our daily life. We utilize it to go to our workplace, travel all over the place, deliver goods. Speed is one of the important to consider while driving and the driver stress of the over-shift it also leads to the sudden accident. The Speed of the vehicle can be measured with help of speed limiter so the speed is controlled and also if the driver is tired or not can be checked.

17. Vehicle based Intensity controlled Street lighting System

One of the biggest problem the world facing now is Wastage of Power. Generally, in almost all the countries, you can see that whether there is vehicle present or not, the street will glow continuously for the entire night. Now imagine that there is a system where whenever the vehicle comes near the street light that street glows with full intensity and as soon as the vehicle move from that area the streetlight will be glow with very less intensity. Through this project, you are going to build a Power Efficient Street Lighting system.

18. Occupancy based Street lighting System

Right now the entire world has taken steps to reduce the wastage of power. You may have observed that during night time whether anyone present on the road or not, the street lights will glow continuously for the entire night. Now imagine that there is a system where whenever someone is walking through the street, immediately Street lights are turned on and as soon as the person left the street, the street lights will be turned off. Through this project, you are going to build an autonomous street Lighting system based on occupancy.

19. Wrist Controlled Robotic Arm

As there is rapid development in the industries, manual operations for the repeated works has been replaced completely by automation through Robotic Arms. The introduction of these Robotic Arm is a major change which entirely changed the look of an industry. Generally, these Robotic Arm will be pre-programmed for the tasks to which they are going to assign. Now Imagine there is a robotic arm which can be controlled by the wrist (whenever you move your fingers, accordingly the Robotic arm will move). Isn’t it great?? Definitely. Through this project, you are going to create a unique Robotic Arm which can be controlled by your own wrist.

20. Automated Unmanned Railway Crossing Level Using Arduino

In Railway System, India is the largest second place to have big railway network. People almost use railway system to travel everyday, revenue generated from railways also plays a major role to our government. Everyday people are crossing the railway track and near the area at times because of track keeper fault or unavailability of track keeper or obstruction of vehicles in the railway lines causes major accidents near the railway gates.

21. Bidirectional Count using 8051 Microcontroller

The IR Sensor finds its application in many application fields, in industries the mass production count of the product is done by human beings. And other is meeting hall or public meeting where the visitor count is essential and difficult to count. So IR sensor is implemented to count the production in industries and also the visitor count who enters and exit.

22. Ultrasonic Distance Finder Using 8051

Today's world technology ultrasonic response is more needed like in robotics, automation. Here ultrasonic is interfacing with 8051 Microcontroller to find the range of objects. HC-SR04 is an ultrasonic ranging module designed for embedded projects like this.

23. Under Ground parking System using RFID

Today one of the most important issue which everyone has to consider is the parking management in highly populated cities in order to avoid public disturbance, traffic jams and unnecessary fines. Imagine there is a system where you can park vehicle underground. Once you had parked your vehicle, whenever you need your vehicle you can get it back by simply scanning your RFID card. The parking slot with your vehicle will come back to you. Isn’t this good? Through this project you are going to create a unique Under Ground Parking System using RFID.

24. Sign Language Translator

Sign languages are languages which are used to convey messages using manual communication like simultaneous hand gestures, movement, and orientation of fingers, arm or body movements, and facial expressions. These languages are mainly used by deaf and dumb community. There are many types of sign languages, but the universal sign language is used all over the world.

25. Gesture based Robotic Arm

Robotic arms are used in various fields like Industries, Medical and many more. In industries the robots are used in the manufacturing process, packaging, in the production line, these robotic arms are used in the place where human intervention is impossible. In the medical field the robotic arms are used in operating the patients in a surgery. The robotic arms can be controlled by many means, in gesture based control of a robotic arm the user can control it precisely by his hand gestures.

26. Smart mirror

A Mirror is a part of every person’s life, everybody looks in the mirror every day and how would it be if you can display the weather details, the calendar, time and date, reminders, news and anything you need to see before you leave somewhere.

27. Build a GPS Based Location Tracker Using Raspberry Pi

It is reported that for every 14 minutes there is a vehicle stolen in India, and there is 44% rise in the theft and attempt to theft last year and only few of the vehicles reported are recovered. Theft has to be prevented, even after prevention some of the vehicles will be stolen. The vehicle’s position can be known and tracked, by this the thief can be caught and the vehicle will be recovered. The Packages which are sent are not recieved at the destination, or may be delayed and using this project you can track your package.

28. Digilock using Raspberry pi

The security is very much needed everywhere, it can be in the home, office or anywhere. In this project, you will build a digital lock, this locking system opens only when you give the exact passkey.

29. Wordpress server using Raspberry Pi

Wordpress server is a PHP and MySQL based free and open-source blogging platform and a content management system. You can create your own website or a personal blog. This Wordpress is a tool for creating and personalizing a website or a blog where there are many free themes and design plan.

30. Weight sensing automatic gate

Automation is everywhere now, from industries to home, the automation has lead to a drastic change in this world. There is Automation or Automatic Control everywhere now, from machines, the process in factories, to microwave ovens, switching in telephone, steering, and many more. In this project, you can automate your gate or door and open it whenever a person's or a vehicle's weight is detected.

31. Raspberry Pi Clusters

Supercomputing clusters are multiple computers which are connected and interlinked with each other, these computers are set to function for a specific task where each computer contributes to the function or the specific task.

32. Motion capture camera

Time lapse footages are made up of images of the same scene captured over a brief period of time. These footage’s usually consists of cities, landscapes, sky views, constructions, city traffic, sea shores, ocean photography etc. These sceneries usually change over a brief period of time, so capturing images at timer short intervals without any considerable scenery change leads to wastage of storage space.

33. Drink and drive detection with Ignition lock

Drink & drive is a leading cause of road accidents. Detecting drunk driving requires stopping vehicles and manually scanning drivers by using breath analyzers. Well, here this is a system which allows detecting drunk driving in the vehicle itself. The system uses an alcohol sensor with raspberry pi along with a GSM modem for SMS notification.

34. Image Processing based fire detection

The main advantage of Image Processing Based Fire Detection System is the early warning benefit. This system can be installed just about anywhere in a commercial building, malls and at many more public places for fire detection. This system uses the camera for detecting fires. So we do not need any other sensors to detect fire. The system processes the camera input and then processor processes it to detect fires.

35. Collision Detector

As per a survey, nearly 1.2 million people die in road accidents every year. In this project you will use piezoelectric sensor to detect collision. Then, system will send an SMS to any nearby hospital. The SMS will contain the location of the victim and personal details. As soon as they receive the SMS, they will send the AMBULANCE.

36. Smart Building using IoT and PIR

Smart building refers to the place where the system can keep a track of people and maintains a database. The building will be used to get information about the number of people, usage of resources, etc. Such type of buildings is known as Smart buildings. To run that type of building we will need a system which can track people with the help of sensors and then display the results visually. Let’s have a brief look at the smart building using IoT project.

37. How to Build a Fire Fighting Drone?

In this project, you will learn how to build a fire fighting drone, which can able to detect and put off the fire both autonomously and manually. Making a final year project on drones will definitely help you to build a nice career. 

In recent years drones gained more popularity because of its wide range of applications and the day to day advancements in their features. Fire fighting is one of the difficult tasks where firefighters risk their lives to save the victims. To make the rescuing process easy and safe, drones are implemented to extinguish the fire.

38. Bluetooth controlled RC car

Design your own Bluetooth controlled RC car. You can do it as a mini project in your academics. In this project, you will learn the types of components used to have input and output, usage of Bluetooth connectivity and interfacing them to other electronic devices like Arduino, raspberry pi.

39. Bluetooth controlled Fan using Arduino

How do we use a fan, we usually turn on the fan manually. But If the control is in your hands the speed control would be easy. Because there is no need to go near switchboard and vary fan speed. Easy process right? Yes, you heard that right! Control in your hands. This mini-project will make that process easy. We use the Bluetooth connectivity to control the speed of fan remotely. We use the combination of microcontroller and Bluetooth connectivity to achieve the purpose to control the speed of the fan.

40. Bluetooth controlled LCD Display

Imagine you want to display your name on LCD display in front of your door or wherever you want. You want to change it again and again. In this Bluetooth controlled LCD project you will learn to display information on the LCD display and to change it via Bluetooth. We can change it over and over during necessity arises. We are using Bluetooth connectivity for this purpose. So, the command can be given only over the range of area of Bluetooth.

41. Arduino based smart irrigation monitoring and controller system using ESP8266

Farmers normally operate on broad portions of the field to develop numerous crop varieties. It's not always easy for a single person to maintain control of the entire agricultural land all the time. Often a certain area of the land may receive more water which may lead to sludge or it may receive very little water that dries the soil. The crops may get affected in any of the situations, and farmers may suffer damage.

42. Street light monitoring using Arduino based on vehicle movement

The primary aim of Automobile Movement Street Light project is to save energy by turning on the street lights when the system senses the traffic activity by using Arduino Uno. The mechanism turns the street light ahead of the car and also turns off the trailing lights.

The motion of automobiles is sensed through sensors. The mechanism immediately switches on the lights ahead of the identified car, and the following lights are turned off as long as the vehicle passes forward.

43. Baby Pram monitoring system using IoT

Introduction - The baby pram monitoring system is a monitoring technique by which you can keep an eye over your child when you are physically unavailable by the use of sensors and feedback systems. This system can be used in varied situations like when you are in another room doing your work, in office and your child is with a baby sitter, etc.

44. Develop Communication device for disabled using Arduino

We have often seen that Physically challenged are struggling to communicate. The people who can’t able to move and struggling to communicate with others, someone has to look after them. If they ask for water, they have struggled a lot. People who are caring for them face difficulty to understand their requirements and give the things they wanted. For the care of those people, we had a project that will assist their caring. This Arduino project helps those people to say their common requirement for people who care for them. Thus, they can communicate with others. They meet their requirements through these types of communication. 

45. Develop an Edge Detection Robot using Arduino

Robots are similar to humans but they are robust and more efficient than human beings. Similar to humans they can also sense the environment and react with the help of sensors. Because of its various applications, it is widely used in fields such as military, space exploration, manufacturing industries, etc.

In this robotics project, you will use Arduino to develop an autonomous robot which can able to detect and avoid the edges with the help of IR sensors.

46. PCB(Printed Circuit Board) Design

Printed Circuit Board (PCB) acts as a skeleton for the electronic components placed on it. It is found in almost all electronic devices. It is used to support mechanically and electrically connect electronic components with the help of conductive paths made of copper. All the components such as IC’s, sensors, resistors, capacitors are soldered to the PCB to form a PCB assembly.

In this electronics project, we will fabricate a PCB prototype to power an LED when a sound is produced. When you make a sound the microphone will detect the sound and convert it into an electrical signal. The electrical signal is then processed by the IC and it turns ON the LED.

47. Persistence of Vision (PoV) based display using Arduino

Persistence of Vision is a kind of optical illusion that happens in our brain. The human brain cannot process more than 10-12 images per second when it exceeds this number of images optical illusion is created. The same principle is used to make animated movies. 

In this electronics project, we are going to fabricate a PoV display using Arduino, motor and LED. We will synchronize the LED flashing with the motor speed to make a Pov display with the help of Arduino programming techniques. 

48. Rugged elevation four legged robot using Arduino

Espionage robots are not best suited for surveillance on rugged terrain because of its wheeled systems. Due to the inability to operate on rugged terrain, robotics and drones get jammed.

Here the model presents a rugged terrain beetle robot that can quickly move across jungles, hilly and rocky areas with minimal effort. Its compact size helps it to travel across rugged terrain like a little animal with little noise moving through the forest. To perform this function the robot uses a crawling method.

49. Terrain Following Drone

Drones are getting advanced day by day and their usage also increased in various fields. With the help of drones, you can able to accomplish various types of missions by spending less. In this drone project, we will discuss how to build a drone with terrain following mode using the Mission Planner software.

50. DSP Implementation of Moving object detection and tracking based on MPEG-4

Moving object detection is widely used in fields like aviation, marine, road monitoring for video surveillance and safety purposes. In this Digital signal processing project, we are going to discuss MPEG-4 based moving object detection and tracking system.

MPEG - 4 Moving Picture Experts Group is an organization who is responsible for video encoding standard. When compared to MPEG-1 and MPEG-2, MPEG - 4 encodes the video file in a smaller size which is why it is used widely in online streaming platforms and media file transfer. MPEG 2 is used in DVD’s and it has good video quality compared to MPEG 4.

51. Crowd Control using Anti-riot Drone

Crowd control and monitoring is a difficult task for the police forces if the strength of the crowd is huge. Usually, the police will use tear gases to disperse the crowds in uncontrolled situations but during this process, the police might be injured to avoid such situations we can use the drone to control the crowds.

With the advancement in technologies, the usage of drones increased a lot. In this drone project, we will propose a solution to this problem.

52. Amphibious Fixed-wing drone

The use of Unmanned Aerial Vehicles (UAV) and Unmanned Underwater Vehicles (UUV) in both military and commercial sectors have expanded greatly in recent years. The advancement in technologies paved a way for developing a single vehicle which is capable of working on both Air and underwater environment.

In this drone project, you are going to develop a fixed-wing drone that can able to fly like an aircraft and swim like a fish.

53. Design and Implementation of a Real-time Traffic Light Control

Traffic lights help people to move properly in the junctions by stopping the route for one side and allowing the other. But most of the traffic lights have fixed time controller which makes the vehicles to stop for a long time during peak hours. Because of this, traffic congestion is increased during peak hours. Sometimes traffic police placed in the congestion areas to manage the traffic this shows the ineffectiveness of the system. While for smaller roads sensors are used to control the traffic autonomously.

In this VLSI design project, we will design an FPGA based traffic light controller system which reduces the waiting time of the drivers during peak hours. VHDL is used to design FPGA because with VHDL you can simulate the operation of digital circuits from an easy one to complex gates.

54. Modular Drone

Drones are becoming more popular than ever due to their unimaginable applications and fascinating modifications. Along with their applications, the manufacturing cost of the drones also will increase. To overcome the above problem in this project idea, we are going to propose a solution. The best way to reduce the cost is by reducing the components used.

Modular drones are aerial vehicles which can be modified on the go. A single drone can be modified and used for various purposes. For example, you want the drone to capture high-quality videos - use an HD camera mod or you want to use the drone to convey information - fix the speaker mod or you want the drone to display something - attach the screen mod.

55. Airborne wind energy generation using Aerostat

As we are advancing in technology it is necessary to find a newer method of energy generation with minimal damage to the environment. The wind is renewable source energy which will not pollute the environment. The energy generated from the wind almost fulfils the energy requirement of most of the households. But wind turbines have drawbacks like high implementation cost and difficulty in the transportation of components.

Airborne wind energy generation is the only solution to solve the problem. Here the turbine is placed airborne and the power generated is transmitted to the ground with the help of power cables. This saves a lot of cost in the implementation and transportation compared to the conventional wind turbine system.

In this aeronautical project, we are going to develop an airborne wind energy system using lighter than air aircraft.

56. Airborne wind energy generation using kites

Generating power from winds is the purest form of energy generation method when compared to other energy generation methods like thermal powerplant, nuclear power plant, etc. It also doesn’t affect the atmosphere by releasing harmful gases like Carbon dioxide or Carbon monoxide. But for generating electricity from the wind needs a huge setup such as blades, pillars, generators, etc. It also requires a lot of maintenance after the implementation. The cost of setting up the wind turbine is also very high.

In this aeronautical project idea, we will propose an innovative solution to generate electricity from the wind. We are going to develop an Airborne wind energy system to replace the traditional windmills.

Drones are the major innovation of this century which is widely used for various applications. They already proved their ability in various fields. Here we are going to use tethered drones to generate power from the wind. Here the drones are launched in the air and positioned at an altitude of 600-1000 feet. At this altitude, the turbulence of the wind will be less compared to the altitude where the conventional wind turbine is positioned.

57. Fuzzy based PID controller using VHDL for Transportation Application

Nowadays, accidents in highways are increased due to the increase in the number of vehicles. To avoid collisions between vehicles the speed of the vehicle is reduced or the driver is alerted when it nears the preceding vehicle. In this VLSI design project, we will design a PID controller based on fuzzy logic using Very Highspeed Integration Circuit Hardware language for automobile’s cruising system. 

58. Design and VLSI implementation of anti-collision robot processor using RFID technology

Nowadays, robots are used for various applications. From home to big industries robots are implemented to perform repetitive and difficult jobs. Robots are preferred over human workers because robots are machines which can able to work 24x7 without getting tired. Because of its wide range of applications some industries use multiple robots in the same place. In such a case, there might be a chance of collision between robots. To solve this problem we are going to propose a solution using RFID tags.

In this VLSI design project, we are going to develop an anti-collision robot processor which is combined with a smart algorithm to avoid crashes with other robots and physical objects using RFID. The algorithm is implemented in VHDL (VHSIC - HDL Very Highspeed Integrated Circuit - Hardware Description Language) and simulated using Xilinx simulation software.

59. Sensor Guided Robotics

You can build this project at home. You can build the project using online tutorials developed by experts. 1-1 support in case of any doubts. 100% output guaranteed. Get certificate on completing.

60. Home Automation System

61. 4 smart energy projects, 62. animatronic hand, 63. automated railway crossing, 64. maze solver robot, 65. embedded systems (career building course), 66. smart traffic lighting system, 67. robotics training & internship, 68. embedded systems training & internship, 69. summer training in embedded systems (aug 15), 70. summer training in embedded systems (july 15), 71. summer training in embedded systems (june 15), 72. summer training in embedded systems (may 15), 73. solar & smart energy systems, latest projects based on embedded systems, any questions.

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Study programme

Embedded Systems Design Master of Science

Embedded systems what does this mean.

From washing machines to satellites - there is hardly any product or machine on the market that is not equipped with integrated digital technology or software determining its function and performance. Embedded systems are omnipresent in technical installations and utilities and, as „hidden“ systems, form a key technology in terms of product innovations in mechanical and plant engineering as well as in medical and scientific devices and instruments. The University of Applied Sciences Bremerhaven supports and furthers such development with the Master Programme „Embedded Systems Design“. An embedded system includes mechanical or mechatronic systems which assume its essential function through open or closed loop control using electronic components as well as the required software. Modules such as Digital Systems/VHDL, System-on-Chip Design, mechatronic and discrete control systems enable students to develop and implement innovative embedded systems.

Why choose this programme

Developer for embedded systems (in automation engineering, mechatronics engineering, computer sience), research and development, production, operation, service, technical sales and distribution.

With their technical background and specialist knowledge, the graduates are in demand on the job market as pros who can initiate and implement decisive product improvements. Since products without embedded system components will no longer be competitive in the future, the need for specially trained personnel continues to grow. The increasing digitalization of industrial production, vehicles, systems and products is leading to stable growth rates in the embedded systems sector. Potential employers include automobile manufacturers and suppliers, medical technology (diagnostics, healthcare), the aerospace industry, industrial automation, robotics, machine tool manufacturers, measurement technology, testing technology, as well as manufacturers of systems for audio or image processing. The programme graduates are mainly employed in these companies as development and research engineers.

Facts about the study programme

Degree of studies, term of admission, main course language, standard period of study, classes and laboratories.

The students learn to develop embedded systems in industrial drive and control technology, in scientific systems and in technical medical devices. With their general technical background and specialist knowledge, they can develop applications and processes that lead to the implementation of new systems. The potential of the technology currently available should be made accessible to the students by acquiring knowledge in the fields of mechatronics, discrete (control) systems, real-time software, digital technology/VHDL and system-on-chip design. Practical training, e.g. through laboratory instruction and projects, as well as the imparting of interdisciplinary skills, creates the prerequisites for both development tasks and research activities. Through interdisciplinary qualifications in the areas of project planning, scientific documentation and presentation as well as teamwork, the course also contributes to the students’ personal development.

Programme overview

In the first semester of the ESD Master‘s programme, a sound foundation is laid for the application-oriented courses in the second semester. In modules such as Safety and Reliability, Mechatronics or Digital Systems, the participants mainly first learn about process-independent theory. Based on the basic knowledge of the Bachelor‘s programme, the existing knowledge is deepened and completed in the first semester in order to master the relevant subjects at the Master‘s level. In the second semester, the applica tion modules from the areas of industrial systems, scientific systems and medical devices follow, focusing on three labour market points. Cross-departmental qualifications in the form of project planning and management, teamwork, technical documentation and presentation are part of ‘Requirements Engineering‘ and the Embedded Systems Project. The Master’s thesis is written in the third semester.

International developments are of great significance to the field of embedded systems. Due to the orientation of internationally active companies on the labour market, a good command of English is vital. The courses are therefore held exclusively in English. This makes the study programme more easily accessible for international students and learners enjoy a lively exchange with students from other nations (in addition to Germany and other EU countries, especially Asia and Africa). This dialogue also contributes to the personal development of the students and their ability to engage socially. A stay abroad is not part of the curriculum, but is generally possible within the framework of the Master‘s programme and is an option in the second semester.

Admission and application

Prospective students should be enthusiastic about control engineering, mechanics, digital technology and software development and be able to prove basic knowledge that was acquired by attending corresponding courses. The study programme begins in the summer semester. German students may read the formal admission requirements from the link "Zulassungsordnung Embedded Systems Design" on the German web page. The formal admission requirements in English language for students (from abroad) are listed here: - Completion of a bachelor's degree in an engineering study program with 210 credit points (ECTS) - CGPA equivalent German grade of 2.3 or better (The required German grade may also be enhanded by 0.3 for individuals who can provide proof of at least 2 years of relevant professional experience after obtaining their bachelor’s degree.) - Proof of English language skills at level B2 of the Common European Framework of Reference for Languages (or IELTS 6.0 or TOEFL IBT 72 or Bachelor’s program with English as the medium of instruction MOI) - Proof of German language skills at level A1 from Goethe Institut (or DSH or Test DaF or TELC or DAAD or UNIcert). Certificates from other organisations will probably not be accepted! - 15 credit points (ECTS) in mathematics/physics (e.g. linear algebra, calculus, laplace transforms, differential equations, statistics) - 10 credit points (ECTS) in electrical engineering (e.g. DC and AC circuits, passive electrical elements, resistors, coils, capacitors, active electrical elements, diodes, transistors, MOSFETs, ICs) - 5 credit points (ECTS) in mechanical engineering (e.g. civil engineering, machines, drives) - 5 credit points (ECTS) in programming languages (e.g. C, C#, Java, VHDL) - 5 credit points (ECTS) in control engineering (e.g. feedback controls in frequency domain, control systems, linear control) Further information about special admissions can be obtained from the Admissions Office of the University of Applied Sciences Bremerhaven.

The Embedded Systems Design (ESD) study programme admits students in the summer semester every year. The application deadline is 15 February. Please apply online through our ecampus application portal where you can upload all the necessary documents. The Bremerhaven University of Applied Sciences is a member of UNI-ASSIST (Application Services for International Students). Prospective students who have gained their university entrance qualification or bachelor’s degree outside the European Union (EU) can therefore submit their application for the summer semester directly to UNI-ASSIST by 31 December. Following a successful review, this is forwarded on to the Bremerhaven University of Applied Sciences. UNI-ASSIST charges applicants a fee to process the application. You can find further information on the application procedure on the Registration and Examination Office’s website.

Learn more about the study programme

Embedded Systems Design

Come to the next info event

If you have any questions we will be happy to help you student advisery service and head of programme, student advisory service, karsten peter, professor für automatisierungstechnik und mechatronik.

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Mitchell Leads Multi-Institutional Effort with NASEM Grant Researching Safer Offshore Energy Systems

March 25, 2024

Drs. Mitchell and Son serve as co-principal investigators on research aimed at strengthening Louisiana's offshore safety culture

BATON ROUGE, LA - The LSU School of Leadership & Human Resource Development (SHLRD) proudly announced Tyree Mitchell, PhD, was a member of one of five research teams awarded a prestigious grant from the Gulf Research Program (GRP) of the National Academies of Sciences, Engineering, and Medicine (NASEM). The grant aims to support projects focused on enhancing safety culture and safety management systems (SMS) in the offshore energy industry.

Tyree Mitchell, PhD, Associate Professor at SHLRD, and Changwon Son, PhD, CSP, Assistant Professor of Industrial, Manufacturing & Systems Engineering at Texas Tech University, will serve as co-principal investigators on a grant totaling $855,760 to conduct groundbreaking research titled "Empowering and Enhancing Offshore Workers’ SWA, UWA, Reporting Unsafe Conditions, and Employee Participation." The project seeks to address challenges faced by offshore workers in implementing critical safety protocols by investigating the effects of leadership and organizational factors that contribute to offshore energy workers’ perceptions that they are empowered to and safe to stop work when they notice conditions are unsafe.

The primary objective of Mitchell and Son's project is to improve the implementation of Stop work authority, Ultimate work authority, Reporting unsafe conditions, and Employee participation (SURE) programs within the Bureau of Safety and Environmental Enforcement’s Safety and Environmental Management System. These programs, introduced in 2013, have encountered obstacles due to poor safety culture and insufficient hazard awareness skills among offshore workers.

To overcome these challenges, Mitchell and Son will develop and implement the "SURE Toolbox," a collection of worker-centered safety tools designed to enhance safety culture and hazard awareness skills among offshore workers. The toolbox will include educational materials, operating procedures, observation and reporting cards, end-user manuals, team safety meeting guidelines, and data analytics of SURE activities.

"I am thrilled to collaborate with Dr. Son on a project that not only advances our understanding of offshore safety but also directly impacts the communities and environment of Louisiana," said Mitchell. "This grant represents an exciting opportunity to contribute to the Scholarship First agenda and elevate LSU's role as a leader in addressing pressing societal challenges."

Insights learned from this grant may be useful in disaster prevention (e.g., BP oil spill or Deepwater Horizon oil spill), as climate change and other factors make Deepwater drilling more complex for offshore energy workers. The research findings and products generated by this endeavor will be made widely accessible through various channels, including a publicly accessible digital repository, a mobile app, webinars, workshops, online videos, and presentations at professional and academic conferences.

About SHLRD

About SLHRD The LSU School of Leadership & Human Resource Development (SLHRD) offers programs dedicated to producing world-class practitioners, leaders, and instructors in human resource and leadership development. The BS, MS, and PhD are designed to develop the leadership, planning, analytical, problem solving, and change management capabilities that today's globalized organizations need to be successful. Additionally, SLHRD also offers 100% online programs: BS in Leadership and HR Development, BS in Learning Experience and Instructional Design, MS in Leadership and HR Development and MS in LHRD with a concentration in Workforce Development. The School is housed within the College of Human Sciences & Education. Visit the School of Leadership & Human Resource Development website.

The College of Human Sciences & Education (CHSE) is a nationally accredited division of Louisiana State University. The college is comprised of the School of Education, the School of Information Studies, the School of Kinesiology the School of Leadership & Human Resource Development, the School of Social Work, and the University Laboratory School. These combined schools offer 8 undergraduate degree programs, 18 graduate programs, and 7 online graduate degree and/or certificate programs, enrolling more than 1,900 undergraduate and 1,120 graduate students. The College is committed to achieving the highest standards in teaching, research, and service and is committed to improving quality of life across the lifespan. Visit the College of Human Sciences & Education website.

Abbi Rocha Laymoun

LSU Media Relations

Shane Portier

School of Leadership & Human Resource Development

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