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

Embedded Systems

Related courses.

Graduate-level ECE courses related to this area (click the ES column to see Major area courses)

ECE Grad Course List >

Embedded systems are special-purpose computers built into devices not generally considered to be computers. For example, the computers in vehicles, wireless sensors, medical devices, wearable fitness devices, and smartphones are embedded systems. The embedded systems market is growing 50% faster than that for general-purpose computing. 

Designing embedded systems is a huge challenge because they have so many requirements: they often need to be tiny, high-performance, inexpensive, reliable, and last a long time on poor power sources, all while sensing and influencing their surroundings. Faculty and students are applying their skills to the entire “stack,” from transistors and circuits to operating systems and applications.

ECE Faculty

Al-thaddeus avestruz, david blaauw, parag deotare, robert dick, james freudenberg, khalil najafi, euisik yoon, cse faculty, ronald dreslinski, scott mahlke, zhuoqing morley mao, alanson sample, affiliated faculty, cynthia chestek, yan long awarded predoctoral fellowship to support research impacting secure communications, jesse codling wins best presentation award for sensors that help protect these little piggies in their pens, prof. pei zhang solemnly swears that he’s up to some good, two members of ece will represent u-m at the 2019 rising stars in eecs workshop, more efficient machine vision technology modeled on human vision, prof. robert dick to apply cyber information to air quality management, eecs 461 (embedded control systems) and the freescale cup, xi chen and prof. robert dick receive date best paper award.

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.

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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.

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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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 ...

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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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Prospective: Robotics and Embedded Software

Program requirements.

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.

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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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500+ Embedded System Projects For Engineer, Diploma, MTech, PhD

Hello guys, welcome back to our blog. In this article, we will share the top 500+ embedded system projects for engineers, diplomas, MTech, and Ph.D. students, and we have created this list to provide the best-embedded system project ideas for engineers.

If you have any electrical, electronics, and computer science doubts, then  ask questions . You can also catch us on Instagram –  CS Electrical & Electronics .

Also, read:

• 200+ Electric Vehicle Projects For Engineers, MTech, Ph.D., Diploma
• 500+ Matlab Simulink Projects Ideas For Engineers, MTech, Diploma .
• Top 100 Capstone Project Ideas For Engineering Students In 2021 .

500+ Embedded System Projects

01. Password Based Door Lock System using 8051 Microcontroller

02. Human Detection Robot

03. GSM Controlled Robot using a Microcontroller

04. Password-based Circuit Breaker

05. Metal Detector Robot using 8051 Microcontroller

06. Fingerprint-based Biometric Attendance System

07. Bidirectional Visitor Counter using 8051 Microcontroller

08. Sun Tracking Solar Panel

09. Line Following Robot using Microcontroller

10. RFID-based Attendance System

11. Auto Intensity Control of Street Lights

12. Street Lights that Glow on Detecting Vehicle Movement

13. Digital Temperature Sensor

14. Bluetooth Controlled Electronic Home Appliances

15. Wireless Electronic Notice Board using Microcontroller and GSM

16. Digital Tachometer using 8051 Microcontroller

17. 8 Channel Quiz Buzzer Circuit using Microcontroller

18. 5-Channel IR Remote Control System using Microcontroller

19. Density Based Traffic Signal System using a Microcontroller

20. PWM-based DC Motor Speed Control using a Microcontroller

21. Water Level Controller using 8051 Microcontroller

22. Temperature Controlled DC Fan using a Microcontroller

23. Digital Voltmeter using 8051 Microcontroller

24. Ultrasonic Rangefinder using 8051 Microcontroller

25. Stepper Motor Interfacing with 8051 Microcontroller

26. Interfacing 7 Segment Display to 8051

27. LC Meter using 555 Timer

28. DC Motor Interfacing with 8051 Microcontroller

29. LED Interfacing with 8051

30. 2 Digit Up Down Counter

31. DTMF Based Home Automation System Circuit

32. Bipolar LED Driver Circuit

33. Celsius Scale Thermometer using AT89C51

34. Water Level Indicator

35. How to Interface Real-Time Clock with PIC18F

36. Automatic Railway Gate Controller with High-Speed Alerting System

37. Boolean Algebra Calculator

38. Interfacing GSM to 8051

39. Digital Clock using RTC DS12C887 and 8051 Microcontroller

40. Random Number Generator using 8051

41. Interfacing GPS with 8051 Microcontroller

42. Delay using 8051 Timers

43. Interfacing 16×2 LCD with 8051

44. Interfacing 16X2 LCD with PIC Microcontroller

45. Interfacing 16X2 LCD to AVR Microcontroller

46. A PIC Sonar (Ultrasonic) Range Finder Using a Seven Segment Display

47. Auto Metro Train to Shuttle between Stations

48. Auto Power Supply Control from 4 Different Sources: Solar, Mains, Generator & Inverter to Ensure No Break Power

49. Automated Town Water Management System Using PIC

50. Automation of Cars Using Embedded Systems Technology

51. Automatic Room light Controller with Visitor Counter (AT89S52)

52. Automatic Vehicle Speed Controller in Traffic Using RF Signal

53. A Long-Range Computational RFID Tag for Temperature and Acceleration Sensing Applications

54. Automated Irrigation System Using a Wireless Sensor Network and GPRS Module

55. Bank Locker Security System

56. Biomedical Monitoring System using AT89S52

57. Bluetooth Energy Meter

58. Bidirectional Visitor Counter Using IR sensors

59. Cell Phone Controlled Robotic Vehicle

60. Car parking monitoring system

61. Cell Phone Controlled Robot with Fire Detection Sensors

62. Dual Mode Robot: Obstacle Detector and RF Controlled

63. Density Based Traffic Signal System using 8051 Microcontroller

64. Embedded Web Tech in Traffic Monitoring System

65. Embedded System-Based Vehicle Speed Control System Using Wireless Technology

66. Embedded Automobile Engine Locking System Using GSM Technology

67. Embedded System-Based Air Pollution Detection in Vehicles

68. Embedded Surveillance System Using PIR Sensor

69. Four Quadrant DC Motor Speed Control with Microcontroller

70. Fingerprint-Based Electronic Voting Machine

71. GSM Based Smart Surveillance System using PIR sensors

72. Head Movement Controlled Human-Computer Interface

73. IC Tester Using 89S52 Microcontroller

74. Industrial Automation Using Speech Recognition with Wireless Monitoring

75. Image Processing Based Toll Automation Using ANPR

76. Integrated Mine Safety Monitoring and Alerting System Using ZigBee & Can Bus

77. Library Automation Using RFID

78. Microcontroller based Digital Over voltage Protection System for Home Use

79. Power Saver for Industries & Commercial Establishments

80. Pattern Recognition Using IR

81. PIC-Based Greenhouse Monitoring and Controlling System

82. Railway Track Security System

83. Rotary Automated Car Parking System

84. RFID-based Secured Access system

85. RFID and GPS Combination Approach Implementation in Fisher Boat Tracking System

86. Real-Time Patient Monitoring System Based On ECG Signals

87. Sound Localizing Camera

88. Smart Water Tank Pump Switcher

89. Smart Card for Banking with a Highly Enhanced Security System

90. Smart Metering and Home Automation Solutions for the Next Decade Using ZigBee Technology

91. Safety Timer for Home Appliances

92. Speed Control Unit Designed for a DC Motor

93. Solar-Powered Auto irrigation System

94. Solar Tracking System for Optimal Power Generation Embedded System Project

95. Speed Synchronization of Multiple Motors in Industries

96. Temperature Controller Using PIC Microcontroller

97. Touch Screen Based Remote Controlled Robotic Vehicle for Stores Management

98. The Design of the Scene of the Accident Alarm System Based on ARM and GPS

99. Tumor Recognition Using Matlab and PIC

100. Using TV Remote as a Cordless Mouse for the Computer

101. Underground Cable Fault Distance Locator

102. Ultra-Fast Acting Electronic Circuit Breaker

103. Utilization of ZigBee Transceiver in Agriculture and Structural Analysis

104. Wireless Home Automation System with Multiple Sensors

105. Wireless Control of Pick and Place Robotic Arm Using an Android Application

106. Wireless Gesture-Controlled Robot

107. Wireless Sensor Networks Based Monitoring and Controlling of Food Storage System using ZigBee & Bluetooth Modules

108. ZigBee-Based Remote Monitoring of Temperature and Relative Humidity Using PIC

109. ZigBee Technology For Home Automation and Security

110. Embedded System-Based Submersible Motor Control for Agricultural Irrigation Using GSM

111. FPGA-Based Embedded System for Industrial Power Plant Boiler Automation Using GSM Technology

112. Missile Detection by Ultrasonic and Auto Destroy System

113. Industry-Based Automatic Robotic Arm

114. Advanced Embedded Wireless Robot With Motion Detection System And Live Video / Audio Transmission

115. Gesture Recognition-Based Wireless control Using MEMS

116. Mobile Operated SCADA for Industries Embedded System Project

117. Transients Control for Home Appliances Project

118. Temperature-Controlled Fan Projects

119. Solar Tracking System for Optimal Power Generation Embedded System Projects

120. Development of Embedded Web Server on ARM9 Project

121. Design of Embedded Security Door Lock System

122. Embedded-Based Customized Wireless Message Circular System for College Industries

123. Wireless sensor-based health care system using genetic-based biometric security

124. Patient health monitoring system using wireless sensor networks using ZigBee wireless communication protocol

125. Home-based patient health monitoring system using wireless communication over web servers

126. The dangerous gas monitoring system in vehicles using wireless sensor networks

127. ZigBee based Body temperature, blood pressure, and sugar monitoring of patient using wireless communication

128. Patient health monitoring system using Accelerometer

129. Brain control wheelchair for disabled people using microcontroller and wireless communication

130. Intelligent wheelchair for disabled persons controlled with hand gestures

131. Automatic position digital photo frame based on accelerometer

132. controlled robot with a wireless video camera mounted on it using Micro Electro Mechanical Sensor (MEMS) Accelerometer

133. The vibration monitoring system of industrial equipment using wireless communication

134. Wireless gadgets controlled with Wearable technology

135. Secure access control system with motion-based password using Wearable Technology

136. Controlled PC using Micro Electro Mechanical Sensor (MEMS) Accelerometer

137. Tilt operated touch free mobile phone using Micro Electro Mechanical Sensor (MEMS) Accelerometer

138. Automatic fall detection and monitoring for old persons using wireless communication

139. Voice-enabled wireless device switching for the disabled person using head movement

140. Heartbeat monitoring of multiple patients using a Wireless touchscreen

141 . Heartbeat Monitor with Display on pc using Microcontroller

142. Heartbeat monitoring system with the display on LCD

143. Wireless Heartbeat Monitoring and Alarming system

144. Card-based patient data monitoring system using microcontroller and data storage in SD card

145. Hand Gestures controlled Intelligent Wheelchair

146. Automatic ambulance rescue system

147. An Accelerometer-Based Digital Pen with a Trajectory Recognition Algorithm

148. MEMS Accelerometer-Based Nonspecific-User Hand Gesture Recognition

149. Human Postures Recognition Based on D-S Evidence Theory and Multi-sensor Data

150. A Low-Cost Hand Gesture Human-Computer Interaction System

151. Centralized Heart Rate Monitoring Telemetry System Using ZigBee Wireless Sensor

152. Ultrasonic Spectacles and Waist-belt for Visually Impaired and Blind Persons

153. An Intelligent Blind Rod and Navigation Platform Based on ZigBee Technology

154. MATLAB GUI-controlled Home/Industrial Automation

155. Heartbeat signal monitoring on PC using MATLAB

156. Implementation of PC-based Biological System

157. Biometric Voting Machine

158. Intruder Tracking Using Wireless Sensor Networks

159. User-independent voice commands based robot control with remote voice and image

160. Advanced and Intelligent wheelchair control system for leg amputees using Joystick

161. An Intelligent Blind Rod and Navigation Platform

162. Embedded-based Wireless Neuron-Muscular Stimulator with Intensity Control

163. PC controlling wirelessly using tongue motion

164. Real-time pantry information system using ZigBee

165. Design and construction of voice-operated mobile phone for people with no hands

166. Tongue-controlled speaking robot for paralyzed/physically handicapped persons

167. Home Security with Voice Alerts & Virtual Keypad Authentication

168. Stimulation of paralyzed muscle using IR with intensity-varying features

169. Pedestrian Navigation System with Fall Detection and Energy Expenditure

170. Remote patient monitoring system with GPRS and website-based communication

171. Advanced BMI Calculator with under and overweight alerts

172. Retrieval of Patient’s Records using Computing Technology and GUI

173. Activity monitoring system using Dynamic Time Warping for the elderly and disabled

174. Smart car parking by measuring the length of the car and providing space

175. Ethernet-based medical parameters monitoring system

176. Body movement recognition and alerting system based on Flex sensor

177. Wireless control of powered devices with tongue motion using Zigbee-based communication

178. Indoor wireless person tracking and voice-enabled announcement system using wireless sensor networks Zigbee

179. Blood pressure blood glucose level monitoring on LCD and using a gsm module

180. Heart rate monitoring and alerting system using wireless communication with WiFi-based Wireless

181. Intensive Care Unit (ICU) patients monitoring system with immediate action

182. Attender calling system using IR communication with the TV remote

183. Speech recognition based Wheel chair with elevated features

184. Patient data monitoring using RFID-based wireless communication

185. SMS Controlled Industrial Controller

186. Solar-Based Electromagnetic Braking System

187. Design of a Wireless Medical Monitoring System

188. RFID Warehouse Robot

189. Railway Security Monitoring System Using GSM Technology

190. Vehicle Information Communication Safety

191. Mobile Embedded Systems For Home Care Applications

192. Network-Based Robotic Controller

193. Remote Measurement And Control System For Greenhouse

194. Increasing Safety of Bomb Disposal Missions

195. Wireless Handheld Ordering Terminal

196. Handheld Multi-Parameter Monitoring

197. UPS Battery Management For Industries Using GSM

198. Multi-Functional Water Level Controller

199. Real-Time Atomization Of Agricultural Environment

200. SMS Based Patient Monitoring System

201. Modern Railway System

202. Intelligent Embedded Control Warning System For Car Reversing

203. Zigbee Wireless Vehicular Identification

204. Vehicle Tax Pay And Access System

205. Remote Controlled Robot

206. Voice Aided System

207. Realtime Pantry Information System Using Zigbee

208. Solar Operated Robot

209. Vehicle Superintending System With Voice Feedback

210. Monitoring and Controlling Of Requirements for Cultivation

211. Motion Operated Scrolling Display For LED Panel

212. Switchgear Contact Temperature Online Monitoring

213. Green House Monitoring

214. Solar Cell Tester

215. Solar Mobile Charger

216. Moving Person Detection System Using Ultrasonic Sensor

217. Microcontroller-Based Spy Robot

218. Cellphone-Based Speed Control of the Motor

219. Modern Traffic System with Camera

220. Body Motion Information Collection

221. Solar-Based Mobile Charger

222. RFID-Based Asset Management

223. Intelligent Robot

224. MC-Based Multipurpose Student Smart Card

225. Tongue Motion Controlled Wheel Chair

226. Metal Detector

227. Microcontroller Based Intelligent Traffic Controller System

228. Surveillance System for Railway

229. Programmable On-Off Timer

230. Line Sensing Robotic Car

231. Energy Management With Zigbee

232. Wireless For Monitoring Mine Safety System

233. Mobile Based Robot

234. Development Of Antirigging Voting System Using FingerPrint

235. Finger Print Based Voting Machine using IoT

236. Voice Controlled Robot with Fire Extinguisher

237. GPS-Based Target Sensing System

238. Prepaid Energy Meter

239. Wireless Petroleum Level Indicator

240. Autonomous Vehicle with Obstacle Detection

241. Wireless Temperature Analyzer and Controller

242. Heartbeat Classification Using Feature Selection

243. Robotic Wheelchair To Follow A Caregiver

244. Wireless Electric Power Transmission

245. Micro Controller-Based Mobile Jammer

246. RTC-Based Timer For Motor Control

247. Intelligent Transport System

248. Child Monitoring System

249. Bank Security with Automatic Voice Announcement System

250. Self-Paced Brain-Controlled Wheelchair

251. GPRS and GPS-Based Intelligent Patient Monitoring

252. Development of an ES for Secure Wireless Data Communication

253. Wireless Printer And Keyboard

254. Inbuilt UPS for PC

255. Battery Charger with Auto Cutoff

256. Dam Controller

257. RFID-Based Book Tracking System For Libraries

258. Mobile-Based Earth Quake Detection

259. Mobile Tracking System Using GPS

260. Laser-Based Bank Security System

261. PC Based on Bomb Detector

262. Posture Allocations And Activities By A Shoe-Based Wearable Sensor

263. Remote Sleep Monitoring And Medical Alarm System

264. Computer-Based PLC with Application

265. Moving Message Display

266. Watcher Security System

267. Intruder Information Sharing

268. Smart Home Monitoring System For the Elderly

269. Internet-based Speed Monitoring

270. Automatic Car Dash Board

271. DC Motor Controller Using PWM

272. Communication Satellite

273. Three-Phase To Single-Phase Power-Conversion System

274. Soldier Tracking System

275. Pedestrian Collision Avoidance

276. Cellphone-Based AC Motor Speed Controller

277. Voice Interactive System for Schools/Colleges

278. Microcontroller Based Mini Inverter

279. Power Failure Indicator

280. E-Assortment Validation Check For Pharmaceuticals

281. Car Security System with Remote Lock

282. Data Acquisition System

283. Coin-Based Mineral Water System

284. Intel Processor-Based Device Control

285. E -Cash Pay Off For Fuel Station

286. Decoding Infra Red Remote Control

287. Power Harvesting For Smart Sensor Networks In Monitoring Water Distribution Systems

288. Water Level Controller

289. LDR Based on Automatic Lamp Illumination

290. Intelligent Traffic Light Controller

291. Laser Torch-Based Voice Transmitter and Receiver

292. GSM Based on Automatic Emergency Care

293. Wireless Sensor Network for Tsunami Prediction System

294. Wireless Voting Machine

295. Car Overspeed Detector

296. Mobile-Based Theft Tracking System

297. Heat Controller

298. Mobile Theft Tracking System

299. On-Board Diagnostic (OBD) System

300. Intelligent Robotic Car

301. Electronic Watchman for Smart Home Automation

302. GSM Based Device Controller

303. Hazardous Gas Detecting Method Applied In Coal Mine Detection Robot

304. Wireless Transmission Line Fault Identification

305. Authenticated And Access Control System For Devices Using Smart Card Technology

306. MPPT Based Stand-Alone Water Pumping System

307. SMS-Based Electronics Notice Board

308. GSM-Based ECG Tele-Alert System

309. RF Fingerprinting Physical Objects For Anti-counterfeiting Applications

310. Wireless Mobile Monitoring of Heartbeat

311. Accelerometer-Based Gesture Recognition For Wheel Chair Direction Control

312. Microcontroller Based Electronic Queue Control Systems

313. Online Real Time Vehicle Tracking

314. Heart Beat Monitoring and Reporting System using IoT

315. Controlling Human Attention through Robot’s Gaze Behaviors

316. Multi-Track Digital Audio Amplifier

317. Wireless Robotic Control with Web Cam

318. PLC Based on Intruder Information Sharing

319. Hand-Glove Controlled Wheel Chair

320. Mobile User Info Tracking System

321. Traffic Priority Control for Ambulance

322. Railway Accident Avoiding System

323. Train Distance Indicator for Unguarded Level Crossing

324. On-Line Recognition Of Driving Road Condition

325. Land Rover Robot

326. Microcontroller Based Overload Protection System

327. GPRS Based Industrial Monitoring

328. Multi-Sensor Integrated Navigation System For Land Vehicle

329. GSM-Based Library Token System

330. Wireless Temperature Analyzer

331. Wireless Prepaid Energy Meter

332. RFID Technology in Parking Lot Access System

333. Safety Driving Using Alcohol Sensor

334. Talking Robot

335. Multi-Channel Fire Alarm

336. Attendance Management Using Face Recognition System

337. Rain Detector

338. LCD Thermometer

339. Voice-Based Security System

340. Fall Detection And Activity Monitoring For Oldsters

341. Ultrasonic & Voice-Based Walking For Blind

342. Gas Leak Detection

343. Traffic Signal Auto Stop

344. Computer Controlled Car

345. Driverless Car

346. Multi-Function Solar Tracking System

347. Mobile Robots in Mine Rescue and Recovery

348. Speed Limit of Vehicle

349. Voice Dialer for Phone

350. Submersible Pump Controller

351. Wireless Water Level Controller

352. Wind Mill Controlling System

353. First Aid Android In Defense

354. Switchless Calling Bell

355. Pollution less Mobile Horn System

356. Pyroelectric Infrared Sensor Based Security System

357. Wheel Chair Control using a mobile application

358. Indoor Activity Monitoring System

359. Temperature Humidity Monitoring System Of Soil

360. Bomb Detecting Robot

361. Voice-Controlled Pick and Place Robot

362. Train Accident Alert

363. Bio Metric Authentication System For Foyer Accessing

364. Voice-Controlled Welding Robot

365. Intelligent Lighting System For Exhibition Applications

366. FP Based on Physical Access Control Vehicle Immobilizer

367. Remote Fan Regulator

368. Telephone Line Vigilant

369. Automatic Light Dim Dip Controller

370. XP Operating System in Mobile

371. Automatic Auditorium Controller

372. Voice Controlled Stepper Motor

373. Automatic AC Control System For Cars

374. Vehicle Security System

375. Weather Forecasting System

376. GPRS Based Motor Speed Control

377. Fiscal Access Control System Using Smartcard

378. ATM Terminal Design Based On Fingerprint Recognition

379. Automatic Load Controller and Sharing System

380. Bluetooth Based Stepper Motor Controller

381. Accident Avoidance Systems in Blind Carvel

382. Gesture Control

383. Automatic Room Light Controller

384. Electronic Voting Machine

385. Brake Failure Indicator For Four Wheelers

386. Accident Alert in Modern Traffic System with Camera

387. Embedded Real-Time Damage Detection

388. Automatic Side Stand For Two Wheeler

389. Automatic Plant Irrigation System

390. Automated Urban Drinking Water Supply Control

391. EB Theft Finder and Analyzer Using Embedded Systems

392. Accident Alert and Auto Dialer in Modern Traffic

393. Door Bell For The Deaf

394. AVR-Based Smart Electricity Meter

395. Bedside Patient Monitoring With Wireless Sensor Networks

396. Embedded-Based Accident Alert

397. Bluetooth Car Lock And Ignition Control

398. Automatic Street Light Control

399. Electric Power from Wind Mill

400. Environment Monitoring System

401. Finger Print Based Security System

402. Airport Security System Using RFID Technology

403. Embedded Based Multi-Channel Data Transceiver

404. Dynamic Vehicle Routing For Robotic Systems

405. AC Motor Speed Monitoring & Controlling Through Telephone

406. Extensible Embedded Web Server Architecture

407. Automatic Gate Controller

408. Automatic Changeover for Multi Generators

409. Enhancing Public Transportation

410. EB Load Monitoring Through Telephone

411. Digital Remote Electronic Starter

412. Earth Quake Analyzer and Reporter

413. Advanced FM Intercom

414. Automatic Health Alert and Reporting Through Telephone

415. Electricity Theft Identification System

416. Accident Alert in Mist

417. Bus Information Alert for Blind Using Zigbee

418. Embedded Intelligent Security System

419. Automatic Speed Controller in Schools And Colleges

420. Emission Monitoring System

421. Bluetooth Based Robotic Controller

422. 4-GHz Energy-Efficient Transmitter For Wireless Medical Applications

423. Door Control Using Finger Print Scanner

424. Energy Meter Rate Display

425. Door Control Using RFID

426. Accident Prevention Using Eye Blink

427. Embedded-Based Auto Escalator

428. Bluetooth-Based Sensor Network for Industrial Monitoring

429. GPRS Based Visitor Counter

430. Bluetooth-Based Temperature Management

431. GPRS Based Wireless Home Security System

432. Door Control Using E_Card

433. Device Control with Password Protection

434. EB Data Reading Through IR

435. Embedded-Based Automatic College Bell System

436. Anti Sleep Alarm

437. Ambulance Route Search Based On The Internet

438. Attendance Manager Using Electronic Card

439. Car Accident Identification with Auto Dialer

440. Face Recognition and Door Control using IoT

441. Electronic Letter Box

442. Diamond Security System

443. Electronic Auto Dipper

444. Electronic Code Lock

445. Electronic Passport

446. Intel Processor-Based Motor Speed Control

447. Biometrics ATM System

448. Brain-Actuated Humanoid Robot Navigation Control

449. Digital Calendar

450. Auto Door Open

451. Four-Stage FM Transmitter

452. Drop Coin and Get Power

453. Embedded-Based Calibration of Proximity Sensor

454. Railway Track Crack and Obstacles

455. Finger Print Based Car Starting System

456. Detecting Video Camera

457. Fridge Door Alarm

458. Embedded System-Based JCB Operation

459. Digital Tachometer

460. Fault Location In Underground Power Networks

461. Embedded-Based Auto Lift

462. Automatic Three Phase Changer

463. Automatic Docking System For Recharging Home Surveillance Robots

464. Digital Frequency Meter

465. Bank Token Number Display with Voice

466. Biomedical Monitoring System

467. Electronic Circuit Breaker

468. Digital Fan Regulator

469. Emotion Monitoring System

470. Fire Fighting Robot

471. Dual Functional Reconfigurable Mobile Robot

472. Design Of Landslide Warning System

473. Fingerprint Car Ignition System for car

474. Iris-Based Door Opening and Closing

475. Hybrid RFID-GPS-Based Terminal System In Vehicular Communications

476. Palpitation Panel Based Automation

477. Wireless Endorsed Operated Robot

478. Innovative Congestion Control System

479. Remote Monitoring Patients System

480. Smart Paroxysm Prediction And Life Saver System

481. Ultrasonic Parking Guidance System

482. Patient Vital Signs Monitoring

483. Solar Based Traffic Light Control System

484. PC Regimented Defence Android Using Zigbee

485. Palpitation Screen-Based Driving System

486. Controlling Of Transformer from Substation

487. Wireless Meter For Consumer Utility

488. Embedded System for Detecting Rash Driving on Highways

489. Application of Embedded System for Street Light Control

490. Embedded System for Traffic Signal Control System

491. Application of Embedded System for Vehicle Tracking

492. Embedded System for Auto-Intensity Control

493. Application of Embedded System for Home Automation System

494. Embedded System for Industrial Temperature Control

495. Application of Embedded System for War Field Spying Robot

496. Speed Control Unit Designed for a DC Motor

497. Thyristor Power Control with IR Remote

498. Three Phase Solid State Relay with ZVS

499. Auto Power Supply Control from Sources: Solar & Generator to ensure No Break Power

500. Thyristor Controlled Power for Induction Motor

501. Lamp Life Extender by ZVS (Zero Voltage Switching)

502. Industrial Power Control by Integral Cycle Switching without Generating Harmonics

503. Industrial Battery Charger by Thyristor Firing Angle Control

504. Object Counter with 7-Segment Display

505. Ultra Fast Acting Electronic Circuit Breaker

506. Automatic Plant Irrigation System on Sensing Soil Moisture Content

507. Precise Illumination Control of Lamp

508. Automatic Wireless Health Monitoring System in Hospitals for Patients

509. Speed Synchronization of Multiple Motors in Industries

510. Applications of embedded systems in robotics

511. PC Controlled Scrolling Message Display for Notice Board

512. Touch Screen-Based Industrial Load Switching

513. RF Controlled Robotic Vehicle with Laser Beam Arrangement

514. Power Saver for Industries & Commercial Establishments

515. Auto Metro Train to Shuttle between Stations

516. Automatic Bell System for Institutions

517. CellPhone Operated Robotic Vehicle

518. Density-based Traffic Signal System using PIC Microcontroller

519. Energy Meter Billing with Load Control over GSM with User Programmable Number Features by PIC Microcontroller

520. Heart-rate analyzer on color GLCD using Pic microcontroller

521. Active cell balancing using a fly-back transformer

522. Battery management system

These are the 500+ Embedded System Projects . I hope this article “ 500+ Embedded System Projects ” may help you all a lot. Thank you for reading.

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