​​Undergraduate Projects & Research

As an undergraduate student, you can work side-by-side in the lab with some of the best faculty in engineering, medicine, and the sciences to solve problems, take entire courses focused on design, or create your own prototypes in our maker spaces and machine shop. You also can work with students from across WashU, including the medical school, through IDEA Labs, a student-run bioengineering design incubator that solves health care problems.


If you are looking for an opportunity for Undergraduate Research, please look at this list to see what projects are available. Do not hesitate to contact the professor listed below to continue working on these projects or if you have ideas of your own related to these projects. Typically, students will take ESE297 (Introduction to Undergraduate Research) first to get a broad, practical, hands-on introduction to signal processing implementations. Those students will take 2 additional semester of ESE497 where they work directly with a mentor on their research projects.

Current Undergraduate Research/Master’s Projects

Available Projects: Signal Processing

Available Projects: Systems Engineering

Available Projects: Applied Physics

Available Projects: Signal Processing

  • Cocktail Party Hearing Aid by Microphone Array (Professor: Arye Nehorai)

    Hearing in the presence of background noise is challenging enough for people with normal hearing. The problem is much worse for the hearing impaired. It is also a situation where traditional hearing aids don't perform well. In this project, the student will use a 64 element microphone array. We will place the array in the center of a table while several people sitting at the table carry on a normal conversation to produce the Cocktail Party Effect http://en.wikipedia.org/wiki/Cocktail_party_effect . Using data collected from the microphone array, the student will develop algorithms to remove the background noise and amplify the current speaker. The current speaker can be identified based on signal strength or from the pose of the listener identified with a camera. This filtered signal will then be transmitted to the smart phone of a hearing impaired person wearing one or two headphones.

  • Robotic Obstacle Avoidance using Kinect and UST (Professor: Arye Nehorai)

    This project was undertaken by Yifan Wang and Stephen Gower over the summer of 2015, in an attempt to implement avoidance using a paired sensor arrangement. The idea is simple: the Kinect has excellent mapping of it’s surroundings, but a large blind spot in its near-field, while the Ultrasonic Transducer(UST) does not have this same problem, yet lacks in its ability to gather detailed information about the surroundings especially at far-field. Using this knowledge, it would make sense then that the two sensors could be intelligently paired to mask each other’s failings while allowing for the strengths to shine for each sensor. The scope of this project is limited to getting a working model for a simple avoidance field. Neither I nor Yifan had extensive experience in robotic obstacle avoidance prior to undertaking this project and as such there was a lot of learning to be done. With this in mind, we are publishing the results of our project and the code with detailed explanation as to how we reached our result. In the case of our robot, we were satisfied with the relatively consistent performance and we managed to at least regularly avoid arrangements of at least three obstacles regularly.

  • Automated Sleep Stage Classification (Professor: Arye Nehorai)

    The objective of this project is to develop an efficient automated algorithm for sleep-wake staging. This project is motivated by recent research showing that Alzheimer's Disease (AD) is associated with sleep disruption and sleep disorders. AD is a progressive neurodegenerative disease that affect memory, cognition, and ability to carry out daily activities. Even in the very earliest stages of AD, there are abnormalities of sleeping such as excessive daytime napping and reduced sleep quality at night. Hence, a method of accurately detecting and scoring sleep in the community setting, consistent with American Academy of Sleep Medicine standards, is needed to identify individuals at high risk of having or developing AD. Ideal background for student candidates includes significant Matlab programming experience and prior coursework in signal processing.

  • Parkinson's Tremor Detection using Foot Mounted Inertial Movement Units (Professor: Arye Nehorai)

    A group of researchers from KTH (Royal Institute of Technology) in Sweden have developed a tracking system based on foot mounted inertial movement units (IMU)*. This project is to use this technology to detect tremors in Parkinson's patients. You will the typical tremor patterns in Parkinson’s patients. Then, attach the IMUs to your body and alternate between imitating the tremor and normal movement while streaming the IMU sensor data (Sampling Rate = 1 KSample/sec) and video to the computer over USB. Next, synchronize the video and the sensor data and develop signal processing algorithms to automatically detect the tremor. Then develop a real-time system that implements the algorithm. Another aspect of the project is that currently, the IMUs can stream data over USB at 1 KSample/sec but are limited to 100 Samples/sec over Bluetooth. However, this project can only be used in a clinical setting if it is completely wireless. The source code for this project is all open-source and readily available but will need to be modified to increase the sample rate over bluetooth to the 1 KSamples/sec.
    *2014 International Conference on Indoor Positioning and Indoor Navigation, 27th-30th October 2014, “Foot-mounted inertial navigation made easy”, John-Olof Nilsson, Amit K Gupta, and Peter Handel

  • Predicting local renewable energy generation using machine learning (Professor: Arye Nehorai)

    Solar power has become a popular source of renewable energy for both commercial and presidential use. Unlike weather, solar power generation varies significantly across different regions, due to shade, surroundings, etc. Therefore, it is important to help solar power users accurately predict local generation amount. In this project, students will build real weather stations in multiple locations to measure local weather conditions (e.g., temperature, humidity, wind speed) and solar power generation, and predict local solar power generation based on weather forecasts. Machine learning techniques will be utilized to implement the prediction model, and measured data will be used to validate the designed prediction method.

  • Optimal and Distributed Demand Response Strategy Under Duopoly Competition (Professor: Arye Nehorai)

    In a duopoly market, electricity firms concern not only about minimizing the cost generated from the fluctuations of market demand, but also about optimizing their own payoffs under competition. In this project, we will investigate the decisions of choices of loads and production quantities made by firms under different assumptions of the market. We will also examine how the pricing schemes change under various situations. Possible models include Cournot’s model, Stackelberg’s model, and Bayesian game model with incomplete information. Students will learn both concepts and applications of game theory, and convex optimization methods. Knowledge of microeconomics is not required but can be helpful.

  • Face Recognition Algorithms (Professor: Arye Nehorai)

    Face recognition can be a very basic tool for smart phone applications and also home security system. In this project, we will investigate the face recognition problem with massive data of human face images. Robust face recognition methods will be built based on classification techniques. We will also focus on the sparsity and low-rank nature of the face images to improve the recognition accuracy. Several algorithms will be implemented in Matlab and results will presented in a technical paper and a presentation at the Undergraduate Research Symposium.

  • Car Security System: Detecting an intruder via a Smartphone (Professor: Arye Nehorai and Ravin Kodikara)

    The objective of the project is to design a security monitoring system for vehicles which can be controlled and monitored via a Smartphone. Two or more small video cameras with night vision capability will be places inside a vehicle. The user will use an application on the phone to activate the cameras from a distance and to monitor the surrounding before approaching the vehicle. A suggestion is to use Bluetooth signals (range enhanced) for the communication between detectors and the phone.

  • Identification and Modeling of Rhythms in Neural Recordings (Professor: ShiNung Ching)

    Brain activity often exhibits interesting dynamical patterns. We are interested in characterizing and modeling one such example of these dynamics: rhythms and oscillations. Our goal is to better understand their connection to neurological disease and basic brain function. We have several opportunities for using signal processing and dynamical systems theory to study rhythms and oscillations in recordings of human brain activity.

  • Development of Portable Visualization Suite for Low Cost EEG Systems (Professor: ShiNung Ching)

    Electrical activity in the brain is most commonly measured noninvasively using Electroencephalography (EEG). Low cost EEG systems have recently entered the marketplace making accessible consumer-level recording of neural activity. We are interesting in developing a visualization suite for portable devices that will provide real-time information from a low-cost EEG system.

  • Machine Learning Basics with Applications to Email Spam Detection (Professor: Arye Nehorai)

    Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data. For example, a machine learning system could be trained on email messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new email messages into spam and non-spam folders. In this project, students will be exposed to several important aspects of machine learning techniques and popular methods. This project is organized in the form of (i) discussion seminars and (ii) applications. The discussion seminars will cover topics such as dimensionality reduction: feature extraction and feature selection; supervised classification: nearest mean classifier/k-nearest neighbor, logistic regression, binary decision tree, support vector machine, naïve Bayesian; performance evaluation; and unsupervised classification. Several sets of homework related to the classification methods will be assigned to the students. The students will then implement an email spam detector using the machine learning techniques as the "application."


  • Learning the Power Load Profile (Professor: Arye Nehorai)

    Prediction of load is a major concern of utility companies. Prediction is important in determining the wholesale market price and lower the wasteful generation of energy. A good prediction of the load needed per day allows the utility companies to better plan their power generation in order to meet demand and lower the consumer price. In this project, students will compose the first dataset which will contain both the load history along with other factors such as temperature, humidity, price, weekday/weekend, etc. The students will use internet sites to acquire the information. The student will then use regression techniques in order to build a predictor for future load.

  • Remote Health Monitoring (Professor: Arye Nehorai)

    Remote health monitoring can provide useful physiological information in the home. This monitoring is useful for elderly or chronically ill patients who would like to avoid a long costly hospital stay. Wireless sensors are used to collect and transmit signals of interest and a processor is programmed to receive and automatically analyze the sensor signals. In this project, you are to choose appropriate sensors according to what you would like to detect and design algorithms to realize your detection. Examples are detection of a fall, monitoring cardiac signals to detect arrhythmias, brain signal monitoring (EEG), in-home ultrasound, etc.

  • Seeking motivated and inquisitive BME undergraduates for a mentored research training experience (Professor: Arye Nehorai)

    My laboratory is pursuing studies on how anesthetics alter neuron networks in the human brain to disrupt cognitive processes. The student would serve as a research assistant in the acquisition and analysis of electrical, magnetic, and optical imaging data from human patients and volunteers. An exposure to clinical anesthesiology and research in the surgical environment are entailed but an interest in medical training is not required. Optimal background for candidates includes: significant Matlab programming experience and prior coursework in neuroscience and signal processing. Compensation and hours will be determined based on the project.

  • Modeling physiological correlates of the psychophysics of equal phon-loudness levels (Professor: Arye Nehorai)

    Fletcher-Munson Equal Loudness Contours define the sound pressure levels of pure tones as a function of frequency that are heard as equally loud as a 1000 Hz tone. The Phon-loudness in phon units is defined as numerically equal to the sound pressure level in dB SPL of the 1000 Hz matching tone. Phon data are valuable in audio engineering and have become an international standard that has been frequently revised with new measurements, as comprehensively reviewed by Suzuki and Takeshima (JASA 116, 918-932, 2004). The goal of the phon standard has been to provide a practical guide on relative loudness, in which many details of the original data are smoothed away. It has been discovered (Goldstein, 2011) that original unsmoothed loudness data provide information on physiological mechanisms that can be extracted with appropriate models. Parameters of physiological models generally have a distribution of values. An opportunity exists to study the distribution implicit in original loudness data that can be collected from articles cited by Suzuki and Takashima. The student will be provided with a block equation solver to estimate parameters of middle ear tuning and cochlear nonlinear sound compression.

  • Constructing Energy Harvesting Floor Tiles to Generate Electricity from Footsteps (Professor: Arye Nehorai)

    Join a team of engineering students that are researching tiles that transfer the kinetic energy of a footfall into usable electricity. The tiles will be designed to utilize piezoelectric materials (which produce electricity as a result of mechanical stress). The project’s initial phase consists of building a prototype system to test the limits of the technology. Efforts beyond this may include designing a more advanced system for use in applications. This project is done in cooperation with Washington University’s Engineers Without Borders (EWB) and IEEE student groups.

  • Eavesdrop With Neurobiological Surveillance System (Professor: Arye Nehorai)

    Do the terms electrode array or wireless electrosensory transmitter secretly excite you? Are you interested in pushing the limits of your engineering prowess through ingenuity and practical application in the lab and in the field? Interested in helping to solve a biological challenge with an engineering solution? Join a team of biologists and engineers to develop a back-pack device to be attached to fish with the capacity to listen and capture their electrical chatter for the purpose of understanding electrosensory-based communication and navigation. Another possible approach could include an array of field-deployable recording electrodes combined with video technology that allows for the automated analysis of electrical output to identify signaling fish and to track movement patterns of interacting groups of fish. This project is done in collaboration with Prof. Arye Nehorai in Electrical and Systems Engineering and Prof. Bruce Carlson in Biology.

  • Automatic Camera Tracking of a Speaker (Professor: Arye Nehorai)

    Design and develop a system that uses Ultrasound Sensors to track the position of the speaker. Then, use this position information to pan a camera automatically. The goal of this system is position the speaker in the middle of the image. You can verify that your system is working in the large auditorium in Green.

  • Autonomous Quadrocopter (Professor: Humberto Gonzalez)

    Working with quadrotor helicopters involves a mix of hardware integration, software development, and mathematical analysis. We have a small fleet of off-the-shelf helicopters capable of flying indoors, each carrying an Arduino processor, four motor controllers, three accelerometers, three gyroscopes, and a wireless radio for communication with the base station.

    There is a long list of problems to be solved and experiments to implement using our quadrotors. We are particularly interested in students with a background in programming (C, Python) or with a background in control and optimization.

  • Fall Detection for Elderly Patients (Professor: Arye Nehorai)

    Occasionally we hear stories in the news about elderly people who have fallen but have no way to notify someone. This is an opportunity to develop a solution to this serious problem. The goal of this project is to develop an algorithm which utilizes commercially available sensors to detect when an elderly patient has fallen. Initially, you determine which sensors you will uses and collect sensor data during normal activity and the falling condition. Then, using this data, you will develop the algorithm for detecting the fall. When the algorithm works well, you will implement it on the lab PCs. For the C programmers, there is also an option of implementing the alrothm on a wireless, embedded platform.

  • Decoding EEG for Prosthetics and Brain Computer Interfaces (BCI) (Professor: Arye Nehorai)

    Electroencephalography (EEG) provides the least invasive and most readily accessible technique for recording signals from the human brain, which makes EEG the current leading tool for assisting patients with severe speech or motor impairments due to stroke or traumatic brain injury. However, EEG signals tend to be far weaker as well as more temporally and spatially diffuse than more invasive recording techniques such as recording from single neurons. Therefore, we must develop algorithms and signal processing techniques which better predict the cognitive processes underlying an EEG signal, which will allow for the development of novel devices that are controlled directly from brain signals. The aims of this project include:

    1. Evaluating existing spatial filtering techniques and selecting the optimal technique for recording EEG signals
    2. Developing statistical algorithms and protocols that detect signal features associated with cognitive events without a priori knowledge of the signal structure
    3. Implementing and optimizing spectral estimation techniques for better spectral resolution and detection of significant signal features
    4. Adapting EEG signals to control mobile robotic platform
    5: Designing and implementing artifact detection and removal algorithms for improving real world control

    Interested students will develop a use a 14 electrode array from Emotiv to collect EEG signals and develop a system around these sensors that uses EEG to control a motor.

  • Using Kinect Xbox Camera on E-Car (Professor: Arye Nehorai)

    The FALCON (Freely Autonomous Lightweight Cognitive Operations Navigator) project was started by 2 EE undergraduates in 2010 and is currently under development. This is a toy car that is outfitted with a laptop, a camera and a data acquisition system. The goal of the project is to create an autonomous navigation system for use on campus. An interesting addition to this project is to add obstacle avoidance. This can be done inexpensively thanks to the $150 Kinect Camera for the Xbox from Microsoft(http://www.xbox.com/en-US/Kinect/healthyfun). Interested students will develop the software interface to the Open Source driver for the Kinect and implement an obstacle avoidance algorithm that runs on the car.

  • Infrared Sensors for Intruder Detection (Professor: Arye Nehorai)

    Pyroelectric Sensors are sensitive to Infrared Radiation. When a person passes in front of the detector, the heat emitted from their body can be detected. This sensor is placed behind a Fresnel lens so that the optical viewing area is segments so you get a pulse when the body moves from one segment to another. If you have a pair of detectors, the time of arrival can be used to detect both speed and direction. Interested students will use these sensor as part of the Robotic Sensing project for target tracking.

  • Interactive LED Dance Floor (Professor: Arye Nehorai)

    Students are designing and building an interactive LED dance floor. The research project was started by students in Washington University's IEEE student branch and the finished dance floor will be used at the Engineering Student Council's Vertigo dance party in the spring. The dance floor research project involves a large group of students and teamwork is highly emphasized. FPGA hardware design, microcontroller software design, power distribution, and mechanical CAD drawings are among the various aspects of the research being conducted by a team of students from programs including Electrical Engineering, and Systems Science and Engineering, Mechanical Engineering, Computer Engineering, and Computer Science students.

  • Autonomous E-Car (Professor: Arye Nehorai)

    Design and develop a system that uses Ultrasound Sensors to track the position of the speaker. Then, use this position information to pan a camera automatically. The goal of this system is position the speaker in the middle of the image. You can verify that your system is working in the large auditorium in Green.

  • Sniper Locator Using Acoustic Vector Sensors (Professor: Arye Nehorai)

    In this project, we aim to implement algorithms using acoustic vector sensors to classify the acoustic profiles of the measured sound sources in the environment. We aim to build a robust system that will locate the position of a sniper rifle using this setup. Typical methods for direction of arrival estimation like time-difference of arrival will not be suitable for sniper detection because the signal is weakened by the muffler. This task is often required for civilian security and defense applications.

  • Sensing Using iPhone (Professor: Arye Nehorai)

    In this project, we aim to use an iPhone as a platform to develop applications capable of remote monitoring of certain physiological processes such as heartbeat in patients. We will develop the required hardware and create an application that will facilitate the communication. We will also build a sensor integrated on to the iPhone for monitoring several local phenomena like temperature of the surrounding environment, etc.

  • Biologically Inspired Chemical Sensing (Professor: Arye Nehorai)

    In this project, we will build an experimental setup for acquiring and processing signals emitted from various chemical sources. We plan to mimic the mechanism sharks use to find prey and classify the sources into several odor groups. We will then mount the sensing system on a robot. The robot will sense the odors, classify them, find their locations, and move towards the source of interest.

  • Home Security Using Infrared and Ultrasound sensors (Professor: Arye Nehorai)

    In this project, students will build security systems to detect the motion in the surrounding environment by measuring the radiation emitted by the target bodies and changes in the frequencies of the reflected signals due to the motion of the bodies. They will use a combination of infrared and ultrasound sensors. The infrared sensors measure the radiation emitted from the bodies and the ultrasound Doppler sensors measure the apparent change in frequency by measuring the Doppler.

  • Infrared Sensing for Environment Mapping (Professor: Arye Nehorai)

    We build a range estimator using an IR sensor, to find the distances to near-by reflecting surfaces. We mount the sensor on top of an sbRio starter kit (National Instruments). We program this kit to move within a region to generate an indoor map of the scene. The students learn about sensors, imagers and additional signal processing required to mitigate the noise in the acquired measurements. They also work with the sbRio starter kits and program them to navigate within the region of interest.

  • Eye Tracker to Help ALS Patients with Writing (Professor: Arye Nehorai)

    Develop an algorithm that tracks the movement of the pupils and design the hardware necessary to implement the algorithm. This system could then be used in order to allow sufferers of ALS write using their eyes.

  • Robotic Sensing Description (Professor: Arye Nehorai)

    Robotic Sensing is a new multi-team undergraduate project. Students will take leadership roles in multi-semester projects to implement sensor systems for mobile robots that make autonomous decisions based on the sensed environment. These systems include acoustic, chemical, RF electromagnetic, infra-red, and visual sensors. The project is multidisciplinary, involving hardware, signal processing, imaging, control, communications, and computer interfaces.

  • Electronic Nose: Classification of Smells Using Chemical Sensors (Professor: Arye Nehorai)

    Sensors are available today that can be used to detect smell. Here is a chance to work with these sensors and apply signal processing algorithms to classify the smells. Or, use the smell information to direct a robot to locate the source. See the additional information link for more details.

  • Mobile Robot Equipped with Ultrasonic Transducer Array (Professor: Arye Nehorai)

    Design a mobile robot equipped with a USB wireless hub and a USB datat acquisition module and an array of ultrasonic transducers. Use the Digital to Analog Converter to pulse the transducer and the Analog to Digital Converter to collect the reflection. This information to steer the mobile robot in a variety of experiments.

    Link to possible transducers:
    Typical Application:

  • Body-surface and inverse electrocardiography for assessing risk of VT and electrical consequences of diabetes (Professor: Martin Arthur)

    Body-surface electrical maps (BSM) acquire comprehensive measurement of cardiac electrical activity, potentially providing improved characterization of electrical dysfunction. In conjunction with BSM, computerized surface models of patient-specific torso and heart (constructed via spatially localized echocardiographic images) allow mathematical inverse solutions to estimate potentials on or near the heart surface, further improving characterization of electrical function. Previously, these methods were used to assess risk for ventricular tachycardia in patients with prior myocardial infarction. Currently, we are investigating cardiac electrical consequences of diabetes and metabolic disorders.

    R.M. Arthur and J.W. Trobaugh, Department of Medicine.

  • Temperature imaging using change in backscattered ultrasound energy (Professor: Martin Arthur)

    In hyperthermia treatment of cancer, tumors are elevated to cytotoxic temperatures (41- 45o C) in order to aid in their control. Noninvasive temperature imaging would enhance the ability to heat tumors uniformly at therapeutic levels while minimizing damage to adjacent normal tissue. Our long-term goal is to produce three-dimensional temperature maps in soft tissue, noninvasively, conveniently, and at low cost, with 0.5o C accuracy and 1 cm3 resolution. Our approach to temperature imaging exploits the change in ultrasonic backscattered energy (CBE) from tissue inhomogeneities, which we have confirmed in multiple experiments, including several tissue types in vitro and preliminary small animal tests in vivo. In addition to improving hyperthermia treatment, temperature imaging with CBE may benefit other thermal therapies and ultrasonic diagnostic techniques.

    R.M. Arthur, J.W. Trobaugh, Department of Medicine, and W.L. Straube, Department of Radiation Oncology

  • Design Assistive Technology for People with Cognitive Disabilities (Professor: Arye Nehorai)

    Design and build a prototype of a new tool that enables people with cognitive disabilities to accomplish activities of daily living more effectively and independently. For example,

    Memory aids
    Automated monitoring and coaching
    Cognitive accessibility for the Web
    Aids for using public transportation
    Prompting systems for work or daily life tasks
    Sensor systems
    Communication devices
    Educational games usable by a person with a cognitive disability.

  • Automated Music Generation for Sight Reading (Professor: Arye Nehorai)
  • Electronics Projects for the University NanoSat Satellite project (Professor: Arye Nehorai)

    Design, build and test various electronic sub-systems for the exciting University NanoSat Project in collaboration with the Aerospace Systems Lab in the Mechanical Engineering Department. Projects include Zigbee RF communications, miniaturization of power modules used to control heaters, fans and boosters, temperature control for animal habitats in space, robotic arm control to encourage animal movements in the habitats, image processing and many others. Opportunities for undergraduate research credit, senior design credit and summer internships. This is an excellent opportunity for networking in the small satellite industry and to acquire resume quality work experience.

  • Sensor Signal Processing on Mobile Robots (Professor: Arye Nehorai)

    We are interested in applying statistical signal processing algorithms for adaptive source detection using an array of sensors mounted on a mobile robot with communication capability for transmitting and receiving data to and from a host computer. The ultimate goal is find the location of the source.

    Adaptive source detection is the simultaneous process of sensing the environment (actively or passively) and modifying the sensing device in order to improve the performance of the target position estimator given a certain criteria. We would like to study scenarios based on stationary targets as well as moving targets with and without interference.

    In particular, we would like to implement sensor arrays on a robotic device, such as the Lego Mindstorm. This robotic device is simple to work with and it can be controlled remotely via USB or Bluetooth. Moreover, it has several sensors that could be used for robot navigation (i.e. path planning) through the particular scenario. We would use commercially available USB data acquisition devices connected via a battery powered wireless USB hub. However, in the first stage, it is sufficient to use a wired USB hub or a direct connection to any computer USB port. Finally, a unit for signal conditioning should be considered based on the sensing modality.

    Among the passive sensing modalities, we would like to use acoustic, magnetic, and thermal sensors. Also, we would like to apply source detection algorithms using forward models and measurement models based on simple scenarios for each sensing modality, respectively. This approach will allow for comparing the experimental results with theoretical results in each case.

    For example, one performance criterion to be considered could be the Cramer-Rao lower bound (CRLB) on the mean-squared error of an unbiased estimator of target position. The CRLB, given a particular scenario and sensing modality, depends typically on the number of sensors, number of samples, array geometry and signal-to-noise ratio. We could modify the sensing device such that the CRLB of an unbiased target position estimator is lowered. This can be done by changing the array geometry configuration, changing its position with respect to the target, and changing the transmitting waveform in the case of active sensing devices, such as radar. Another performance criterion to be considered could be the probability of target detection which, based on the scenario, might depends on same parameters as the CRLB. For example, for energy constrained scenarios we could keep one sensor active until the presence of a source is detected, then, the additional sensors can be activated up to guarantee the desired performance.

    Note: Sensing systems which measure energy that is naturally available, for example, generated by the target of interest, are called passive sensors. Sensing systems which provide their own energy source for target illumination, are called active sensors. Active sensors emit energy which is directed toward the target to be investigated. The energy reflected from the target is then measured by the sensor.

Available Projects: Systems Engineering

  • Smart Scheduling Algorithms for Charging Plug-In Hybrid Electric Vehicles (Professor: Arye Nehorai)

    With the growing public awareness of clean energy and efforts towards a sustainable society, the penetration of plug-in hybrid electric vehicles (PHEVs) has been the trend in recent years. The PHEVs utilizes rechargeable batteries or other energy storage devices, and are much more energy-efficient than traditional gasoline or diesel vehicles. With the increasing electrification of passenger vehicles, the charging of large amount of PHEVs could raise problems to the power grid – increased demand for electricity and unwanted peak loads due to simultaneous charging. An optimal scheduling strategy is required to coordinate the charging of PHEVs, taking into account cost efficiency, user demands, and load distribution. Students will research the leading PHEV technologies, analyze the economic and environmental impacts of PHEVs, and propose a scheduling algorithm for PHEV charging.

  • Combinatorial Optimization (Professor: Arye Nehorai)

    The weapon to target assignment problem is a classic NP-Complete problem. Due to its complexity, heuristic approaches to solve this problem have been widely proposed. We will delve into the realm of combinatorial optimization while specifically addressing the WTA problem. The students will develop a heuristic, or metaheuristic algorithm to solve the combinatorial optimization problem and will use existing algorithms as a benchmark for the solutions. In the past students have implemented Genetic Algorithms and Ant Colony Optimization Algorithms. Other algorithms that may be explored include, but are not limited to, Intelligent Waterdroplet Algorithms, Ensemble Algorithms and other nature inspired algorithms.

  • Multi-Agent Robotics (Professor: Arye Nehorai)

    We will investigate the problem where there are several robots all searching together in order to track down specific targets. We will investigate the case where the robots have no way to communicate. We will also investigate the case where an the robots will dynamically adjust to obstacles placed in the field. Applying game theory and particle swarm optimization, we will develop new algorithms to solve multi-agent robotics problems. Several algorithms will be implemented and results will presented in a paper and presentation at the Undergraduate Research Symposium.

  • Network Science (Professor: Arye Nehorai)

    Network Science deals with how connections are made in networks. We will explore various topics of Network Science including Graphical Modelling, Diffusion of Information, Network Learning and Strategic Modelling of Networks. We will apply the basic foundations of Network Science to model the social network at Wash U.

  • Robotics Path Planning (Professor: Arye Nehorai)

    Optimal path planning is a versatile problem that can be extended to problems from many different fields. Applying this problem to the field of robotics, we will formulate an optimization problem based on various objectives and real world constraints. We will then extend the problem to include multi-objective functions and observe the case in which the knowledge of obstacles is only partially known. Some game theory may be introduced in which multiple robots are competing to finish a task and/or multiple robots are directly competing to achieve an objective.

  • Traffic Light Control (Professor: Arye Nehorai)

    Controlling traffic lights can be a very useful tool for society. Imagine having to rush to an event, only to sit at a traffic light with no cars going in the other directions. Smart algorithms for traffic lights can decongest traffic, and can also save a lot of time (and fuel) for drivers. We will examine different intelligent traffic light control algorithms, and develop our own algorithm. Several algorithms will be implemented and results will presented in a paper and presentation at the Undergraduate Research Symposium.

  • Train Pathing (Professor: Arye Nehorai)

    Train Planning Optimization or Train Pathing is a complex real world problem which deals with assigning Trains to links and tracks to optimize a train network. We will graphically model a train network system and then develop algorithms in order to find solutions in an efficient manner to the train routing problem. Several algorithms will be implemented and results will presented in a paper and presentation at the Undergraduate Research Symposium.

  • Robotics 3D Bin-Picking Optimization (Professor: Dennis Mell)

    The objective of this research is to provide pre-target information to a robotic manipulator in order to facilitate a fast and accurate part pick from a bin of randomly placed parts. This project will require the use of 3D imaging systems to optimize each pick by identifying (in real-time) the next target with the highest probability of success. Data from the imaging system will also be utilized to aid in the path ingress and egress planning. Additionally, the student will need to consider the problem of obstacle avoidance as the sidewalls of the bin become more pronounced when reaching deeper into the bin.

  • Robotic Bin Picking using 2 Kinect Cameras Simultaneously (Professor: Arye Nehorai)

    This project is a variation on the Robotics 3D Bin-Picking Optimization which adds a 2nd Kinect camera in order to improve accuracy. Initially, the work will be done using video files (RGB and depth) saved using the single Kinect application with the camera in 2 different vantage points. The goal is to ultimately incorporate the 2nd camera into the real-time bin picking application.

  • Research Opportunity in Engineering (Professor: Hiro Mukai)

    What we need to design: (1) An input device which facilitates the entering of necessary defects data into the existing defect data base (there are 3 possible ideas: GPS, image recognition, 3-D model; probably a device like an I pad or Asus Eee Pad Transformer Prime); (2) Visualization interfaces which facilitate the analysis of defects themselves individually and their trends collectively; (3) Statistical or other analytical tools to discern defects patterns and trends. Matlab required. If you are interested, please contact Professor Mukai at mukai@ese.wustl.edu.

  • Optimal Robot Path Planning (Professor: Arye Nehorai)

    Optimal path planning is a versatile problem that can be extended to problems from many different fields. Applying this problem to the field of robotics, we will formulate an optimization problem based on various objectives and real world constraints. We will then extend the problem to include multi-objective functions and observe the case in which the knowledge of obstacles is only partially known. Some game theory may be introduced in which multiple robots are competing to finish a task and/or multiple robots are directly competing to achieve an objective.

  • Feedback Control of Climate Dynamics (Professor: Jr-Shin Li)

    We are interested in studying complex climate models with carbon-cycle feedbacks using control theoretic techniques. Global warming is a serious issue facing the world in this century. Current global climate change literature has reported that not only does the carbon in the atmosphere lead to a warming trend, but that increased temperatures lead to reduced absorption by carbon reservoirs – further increasing the amount of carbon in the atmosphere. This circular dependence creates a feedback loop in the complete carbon/climate model. The complexity of the integrated model and presence of feedback loops will benefit greatly from a control systems approach.

  • Development of Control and Optimization Methods for NMR and MRI Systems (Professor: Jr-Shin Li)

    Every application in Nuclear Magnetic Resonance (NMR) spectroscopy and imaging (MRI) is enabled by single or multiple pulse sequences which are developed for the purpose of simultaneously manipulating a large number of nuclear spins. The development of optimal pulses requires concepts and techniques from optimal control and optimization. We are interested in developing new analytical and numerical methods for such design problems.

Available Projects: Applied Physics

  • Electromagnetic analysis of metamaterials (Professor: Barry Spielman)

    Undergraduate research projects in electromagnetic analysis of metamaterials for scattering, radiation, and filtering. For example, beam forming of scattered signals for radar and biomedical applications or computational modeling of signals in two-dimensional structures.

  • Novel Photonic Structure to Enhance Solar Energy Conversion for Electricity and Hydrogen Production (Professor: Lan Yang)

    Solar energy is a promising clean, sustainable and renewable energy alternative for significant reduction in greenhouse gas emissions. As a band-gap semiconductor material, titanium dioxide has found an exciting solar-energy application to produce hydrogen by a photosplitting reaction of water, which could provide an environmentally clean and sustainable source of hydrogen fuel for the energy industry. By absorbing photons in the range of UV, free carriers are generated in the nano-structured TiO2 films, which are subsequently used to decompose water to generate hydrogen. The characteristics of nanostructured TiO2, such as morphology and feature size, will affect its photocatalytic performance. It is therefore important to (1) develop a reliable method to synthesize the TiO2 films with desired characteristics; (2) increase the photoactivity of the materials and (3) design a new architecture in the films to trap photons inside the photovoltaic materials in order to make full use of the light absorbed by the materials. To fulfill these goals, we create this collaborative and multidisciplinary project that integrates materials preparation with device fabrication.

  • A Novel Photonic Tool for Sensing (Professor: Lan Yang)

    Microsystems for detection of biomolecules can play important roles in biomedical research, clinical diagnosis, food safety, homeland security and pharmaceutical testing. In this project we will develop silicon chip-based microlasers that approximate the size of eukaryotic cells for ultra-sensitive label-free biosensing of proteins, protein modifications and protein-protein interactions. The basis for the technology is that physical associations and interactions of biomolecules on a microlaser surface alter the residence time of photons in a way that can be measured and quantified. This project will include fabrication of ultra-high-quality microlasers on silicon chips, evaluation of the microlasers in solution, surface functionalization of the microlaser in order to bind antibodies and/or proteins of interest, and testing the ability to detect antibody-target protein or protein-protein interactions. The microlaser system has the potential to revolutionize and replace the conventional enzyme-linked immunosorbent assays (ELISA).

  • Independent Learning Group on Sustainable Energy [An ESE 100 opportunity] (Professor: William Pickard)

    What do you really know about the coming Energy Crunch?
    What should you know about the coming Energy Crunch?
    For that matter, what does the Instructor know about the coming Energy Crunch? Probably, in all three cases, not nearly as much as befits a well-informed citizen of our democracy, which is facing one. A fundamental prerequisite for continued existence of our technically rich civilization is Sustainable Energy, energy that will be available even when our dowry of fossil fuels and fissionable elements is only a memory. The purpose of this Group is to attain understanding of the daunting problems which have to be worked through to achieve that Sustainability. Topics will include: Ragone charts; nuclear transformations and gravity as primary energy sources; sunshine, tides, and geothermal heat as secondary energy sources; fossil fuels, biomass, elevated bodies of water, wind, etc. as tertiary energy sources; the electric power grid; load levelling using quaternary energy sources; also, the slowly approaching Mineral Resource Crunch.

    This Group will meet an average of one and one-half hours a week for provocative discussion of the texts, which are required reading. Serious students will wish to submit a short essay late in the course.

    CREDIT: 0 or 1 units: Pass-Fail only.
    TIME: Tu 8:30-10; makeups in case of cancelled Tuesday meetings will be Th 8:30-10:00.
    PLACE: ESE Conference Room, Bryan 220B.
    PREREQUISITES: Freshman standing, plus high school physics and chemistry.
    TEXTBOOKS: Goodstein, DL. 2004. Out of Gas. Norton: New York, NY, USA . Cassedy, ES. 2000. Prospects for Sustainable Energy. Cambridge: Cambridge, UK.
    ENROLLMENT LIMITS: No fewer than eight nor more than sixteen.


of Engineering undergraduate students engage in research and independent projects with faculty