Ideas to Innovation Projects

The centerpiece of the ECE project-track MS is a yearlong immersive design experience that integrates an ambitious system design project with the development of students’ professional success skills. These end-to-end projects engage teams to work across the spectrum of materials and devices to circuits, systems, software, and reliability. An important part of your experience in the Project Track Master's Program is learning how to develop rough ideas for new projects into compelling cases for new product development. Developing a concrete plan for the project and successfully executing it are also important parts of the experience as is second stage planning for how this new technology demonstration could be turned into a new product line or start-up company. The conception, selling to “management,” project planning, and execution are the responsibility of the student teams. Plenty of advice and guidance will be provided. Student teams consist of members with different technical expertise. During the fall semester, each team identifies, proposes and initiates work. Most of the technical work takes place during spring semester and is completed during Summer Session.

The development of students’ success skills is an integral part of the design experience – tightly linked to the design project itself. Skills to be developed include written and oral communication to a variety of audiences, building successful teams, working in teams, leading teams, an appreciation of professional ethics, and the related business knowledge and understanding of intellectual property needed to develop technology demonstrations into start-up companies or new product lines in existing companies.

This yearlong experience is designed to prepare students to conceive compelling ideas for project, sell them to management, work in a team to successfully demonstrate proof-of-concept, and then to plan the stage two scale up to a successful small business or product line in a large company. The processes for project proposals, approvals to proceed, design reviews, and final reports are modeled on those of leading technology innovation companies.

Ideas for projects may come from the students themselves or from other sources. For example, several faculty research groups are eager to turn recent research advances into product demonstrations. Similarly, Purdue’s Office of Technology Commercialization is looking for teams to produce proof-of-concept demonstrations of intellectual property in Purdue’s portfolio.

From Concept to Completion

Some potential ideas for projects from Purdue ECE research groups are listed below.

Design of ultra-low power IoT platforms using emerging memory technologies 

Description: Forecasts project that, by 2020, there will be around 50 billion devices connected to the Internet of Things (IoT), helping to engineer new solutions to societal-scale problems such as healthcare, energy conservation, transportation, etc. A vast majority of the devices at the edge of the IoT will be battery-powered and hence, severely energy-constrained. In this project, student teams will work with graduate researchers in the Embedded Systems and IoT Lab to architect new IoT platforms that are highly energy-efficient and can be operated in an energy neutral manner using energy harvested from their operating environment, thus eliminating the need for battery replacement.

Advisor: Vijay Raghunathan

Design of secure and reliable implantable and wearable medical devices

Description: Implantable and wearable medical devices are used for monitoring, diagnosis, and treatment of an ever-increasing range of medical conditions, leading to an improved quality of life for patients. The addition of wireless connectivity to medical devices has enabled post-deployment tuning of therapy and access to device data virtually anytime and anywhere but, at the same time, has led to the emergence of security attacks as a critical concern. In this project, student teams will work with graduate researchers in the Embedded Systems and IoT Lab to design new hardware prototypes for wearable/implantable medical devices that are highly secure and reliable by design against a number of security attacks and threats. Hardware platforms will be designed, fabricated, and tested in the lab using a number of in-vitro experiments.

Advisors: Vijay Raghunathan and Anand Raghunathan

Design of low-cost, smart vents for retrofitting single-zone HVAC systems

Description: It is well-known that single-zone central heating and cooling systems are notoriously uneven (with some rooms invariably being too hot and some rooms too cold). Smart vents control airflow to rooms to help eliminate these temperature imbalances so that every room is the perfect temperature, every time. In this project, students will design, prototype, and demonstrate a low-cost smart vent that can serve as a drop-in replacement to a regular airflow vent. The vent will be controllable from a smartphone application as well as services such as Amazon Alexa, Google Home, etc. The student team will work with graduate students in the Embedded Systems and IoT Lab to design, build, and test these smart vents.

Advisor: Vijay Raghunathan

Optical Frequency Identification Devices - OFIDs

Description: A new class of sensing and identification tags entitled optical frequency identification devices (OFIDs) have been recently proposed. In OFIDs, solar cells are employed not only for energy harvesting, but also for the optical transmission and reception of information, through exploiting their luminescence emission properties. In this project, various OFID receivers and transmitters based on both board and chip design will be pursued.  Students who take this project will gain experience in the area of the design, implementation, packaging and measurements of analog, digital and optical circuits and systems.

Advisors: Walter Leon-Salas and Saeed Mohammadi

Developing wireless biosensors for point-of-care diagnostics

Description: With the aging population, health monitoring devices will be playing a significant role in healthcare. These devices will have to be miniaturized, operate at low power and equipped with wireless communication capability. In this project, a number of sensors and biosensors including pH, Oxygen and Glucose meters will be designed using a rapid prototyping techniques (e.g. laser-cutting and 3-D printing). Each sensor will be equipped with an analog and wireless interface circuit through a board or an ASIC chip design. Students who take this project will gain experience in the area of the design, implementation, packaging and measurements of biosensors and analog and RF circuits.

Advisor: Saeed Mohammadi

Data Convertors for Wireless Sensing Applications

Description: Data converters are the core of mixed signal circuit design and have applications in sensor interface circuits. Among different data convertor topologies, a very popular design is the successive approximation analog to digital converter (SAR ADCs). SAR ADCs allow for high sample rates at very low powers, hence are suitable for low power biosensing applications such as neural recorders and other implantable medical devices. In addition to medical applications low power data converters are also necessary in such areas as IoT, RFIDs, long-range wireless sensing, etc. since operation without batteries is desired if not necessary. Students who take this project will gain experience in the area of the mixed signal circuit design, implementation, and measurements.

Advisor: Saeed Mohammadi

Creating a miniature Solar Farm

Description: The ever-increasing need for energy is satisfied in part by renewable energy sources, such as solar cells. Installing a large scale solar farm is very expensive and time consuming. In this project, students will design a scaled version of a solar farm that can replicate the performance of large scale solar farms at a fraction of the cost and time.  Students will instrument the system so that the information can be recorded and processed. The student team will select the solar cells, design the farm, integrate the sensors and quantify the relative advantages of different farm topologies, tracking algorithms.

Advisors: Muhammad Alam and Peter Bermel

Storage of Solar Energy  

Description: Storing solar energy when the sun is down and providing power whenever needed it is a key challenge that must be solved for further adoption of the PV technology. Depending on the solar irradiance, one must dynamically control the storage vs. load characteristics for optimum use of solar energy. In this project, the student team will use solar cells, batteries and/or solar thermal storage, power electronics, and control algorithms to create a highly efficient solar energy storage systems.

Advisors: Muhammad Alam and Peter Bermel

Shifting the cost curve with radiatively-cooled CPV

Description: Concentrating photovoltaics (CPV) take advantage of the natural increase in efficiency associated with focusing the sun onto a smaller region and reducing the number of PV cells needed. CPV is, however, not as widely used as one might expect, because these systems must operate either at high temperatures and/or with a large balance of systems. The former cuts down the lifetime significantly, while the latter renders the solution uneconomic. In this project, students will demonstrate the potential of radiative cooling to provide a lightweight, passive cooling solution for CPV cells and predict the impact on temperature, reliability, and costs.

Advisors: Peter Bermel and Muhammad Alam

Food, Water, Energy Miniature Testbed 

Description: The goal of the project is to create a replicate in miniature the ingredients of an eco-system that must dynamically allocate the solar spectrum for crop production, water purification, and energy generation. The student group will use implement a miniature system and optimize it through computation modeling.

Advisors: Peter Bermel, R. Agrawal, and Muhammad Alam

Semiconductor Manufacturing and Statistical Process Control

Description: Semiconductor manufacturing is a technology platform for micro/nanoelectronics, as well as micro-electro-mechanical systems (MEMS) and photonic-system components. In modern fabrication facilities (“fabs”) there are typically a few process families being run on many (thousands of) wafers every day.  For this large scale mass production, tight process control is paramount in maintaining high yields and quick-turn analysis of process problems, drifts, and assignable causes.  One analysis tool used extensively in wafer fabs is Statistical Process Control (SPC). In SPC, key parameters are logged for each batch of wafers that are run throughout every fab process, and focused statistical methods are used to monitor the process performance and identify assignable cause issues outside of standard random process variations. In this project, student teams will design and implement sets of experiments suitable for statistical process analysis during various phases of micro/nano-fabrication process development. Each team will address one or more unit process steps or a process integration challenge defined in collaboration with staff and users at the Birck Nanotechnology Center. The student team will implement and document fabrication steps and analysis techniques, including definition of process parameter space, design of test patterns for monitoring key material/performance parameters, characterization approaches and statistical analysis of both process parameters and electrical test results.

Advisors: David Janes and R. Reger

Bridging atomistic models and industrial TCAD tools

Description: State of the art nanodevices have features of a few nanometers only. This corresponds to a few tens of atoms. Designing such devices inevitably requires predictive computer models. Simulation tools that handle atomic features require huge numerical resources. In contrast, industrial TCAD tools avoid atomic resolution and can run on notebooks within minutes, but depend on the quality of their input parameters. In this project, students will design and implement tools that extract parameters from sophisticated atomistic simulations to enable fast TCAD simulations of devices with atomic features. Students will choose a device class (e.g. transistors, LEDs, memory devices, solar cells, etc.), define parameters to extract, and design the parameter tool so that it is most useful for device engineers. Depending on the chosen tools and devices to be modeled, sponsoring for licenses might be available. 
Advisor: Tillmann Kubis

Self-sustaining and self-calibrating devices for wearable and implantable smart electronics

Description: A transistor amplifies, a solar cell transforms sunlight to electricity, a sensor senses analytes, and a battery stores energy.  We are told that the devices are developed with  a specific function is mind and our job as engineers  is tailor core functionality of the device for the specific role it needs to play in a circuit or system.   This rigid single-device single-function paradigm is beginning to change.  After all, the traditional model  is akin to saying that simply because   a painter paints, a novelist write, and a professor teaches, they cannot or do not do anything else! A solar cell can use it own output as the brightness sensor of the sky to orient itself to maximize output, a fuel-cell glucose sensor can obviate the battery  by using the energy of the reaction to drive itself, the wires carrying the current to the transistors can serve as its   temperature-sensor,  a single-transistor can serve as multi-color photo-detectors,  and a camera  can charge  its own battery by working part-time as a solar cell part-time. In this project, we will build a toy model for the specific dual-use system the students are interested in and analyze the results to define the the trade-off among efficiency, detection-limit, and sampling rate of these multi-use autonomous systems. Through this project, there is an opportunity to define the design principles of self-sustaining and self-calibrating electronic devices needed for smart-healthcare, smart-agriculture, and other systems.

Advisors: Muhammad Alam, Babak Ziaie, Rahim Rahimi

Autonomous Racing

Description: In this project, our goal is to optimize a high-speed autonomous racing algorithm for a simulated environment. We are currently competing in the Indy Autonomous Challenge ( Our team is also partnering with the United States Military Academy, West Point, and a professional racing team, whose expertise will provide valuable guidance. 

Familiarity with the Robot Operating System (ROS), Simultaneous Localization and Mapping (SLAM) algorithms, and/or machine learning algorithms (especially deep learning) is a plus.

Advisor: Aly El Gamal

Machine Learning for Wireless Communications

Description: The goal of this project is to propose and analyze a framework for autonomous and collaborative co-existence in wireless unlicensed bands. The proposed framework should be for autonomous communication strategies, and exploit state of the art data-driven machine learning techniques for source identification as well as behavioral and contextual understanding. Further, collaborative spectrum sharing could be incentivized through monetary mechanisms and coordination protocols whose feasibility are justified by the rising success of the blockchain technology.

Familiarity with wireless communication principles and/or deep learning programming environments is a plus.

Advisor: Aly El Gamal

Motor Health Condition Monitoring in Electric Vehicles – Wireless sensing, Data-based Learning, and Diagnosis

Description: The global electric vehicle (EV) market is projected to reach $803 billion by 2027. Performance, safety, and reliability are the key design parameters of EV. Monitoring, diagnosing, and even predicting the health condition of an electric motor, one of the most crucial components of EV, is important for safety and reliability. The goal of the project is to develop a motor health condition monitoring system utilizing a high-precision rotor position sensor and its data. The students will be involved in the development of a non-invasive high-speed high-precision rotor position sensor, real-time sensor data processing algorithm embedded in a microprocessor, online database, and data-based machine learning platform. The students will be working with the grad students in Wireless Sensing Lab to build a prototype for concept verification.

Advisor: Byunghoo Jung

Enabling Mixed Reality on Commodity Mobile Devices

Description: Mixed Reality (MR) blends the physical and digital worlds into a programmed experience that allows users to visualize and interact with digital information such as 3D overlays and real-time data. MR has found many important societal applications including simulation-based learning, remote working, military training, and health care such as surgeries. CNET predicts MR applications will be downloaded 10 billions times by 2024. Current MR platforms however require expensive custom hardware devices such as Microsoft HoloLens or Magic Leap, and as a result faces the “content-adoption” dilemma: slow market penetration of MR devices leads to slow content creation, and vise versa. In this project, student teams will develop key technologies and develop prototypes that enable high-resolution MR on commodity high-end smartphones such as Pixel 2 supplemented with a dummy headset such as the Google Daydream view. The technologies will help the MR industry to overcome the above content-adoption dilemma and pave the way for wider adoption of MR.

Advisor: Y. Charlie Hu

High-resolution 360-Degree Video Streaming over the Internet

Description: Video traffic has been increasing at an unprecedented rate due to the popularity of video sharing websites and social media channels such as Youtube and Facebook. A most recent advance in video technology is 360-degree video delivery, which gives an immersive viewing experience to the viewers. Streaming high-resolution 360-degree videos over the Internet while providing the user with high QoE is far more challenging compared to streaming regular videos due to the much higher bitrates. In this project, student teams will start with an open-source basic streaming system, explore the technical and engineering challenges in designing a high-performance end-to-end 360-degree video streaming system and ways of tackling the challenges with traditional techniques such as adaptive bitrate selection (ABR) and Viewport prediction as well as recent neural-network based techniques such as Super Resolution, and develop and deploy a state-of-the-art 360-degree video streaming prototype serving a controlled viewer population. 

Advisor: Y. Charlie Hu