Tagged: Signal Processing
“Computable Performance Analysis of Sparsity Recovery”
Crime investigation TV shows, such as CSI, commonly feature a digital forensics laboratory capable of recognizing faces and vehicle license plates from extremely blurry shots. Photo and video evidence is displayed on a large projection screen while a recognition system attempts to identify the perpetrator’s identity.
This technology exists in research labs today thanks to advanced signal processing. Various developments in signal processing, particularly in sparsity-based image reconstruction, have recently emerged with the potential to dramatically improve system performance.
Prof. Arye Nehorai is a leader in this research area, and recently delivered a lecture on the “Computable Performance Analysis of Sparsity Recovery,” as part of the Department of Electrical and Computer Engineering Distinguished Lecture Series. Nehorai is the Eugene and Martha Lohman Professor and Chair of the Preston M. Green Department of Electrical and Systems Engineering at Washington University in St. Louis.
As part of his lecture, Prof. Nehorai discussed a movement within the signal processing community to update the classical framework based on the Nyquist-Shannon sampling theorem using a new approach known as compressive sensing. Compressive sensing makes it possible to acquire and represent signals using fewer samples than classical sampling methods, under the key assumption that the signal itself is sparse with respect to some basis. For instance, although a facial image is comprised of many, many pixels, it can still be accurately represented using just a few key features. Indeed, identification of a criminal based on a low-resolution, blurry image, while unthinkable a decade ago, is becoming increasingly viable in part due to modern image processing techniques based on compressed sensing. Other important applications include hyperspectral imaging and anomaly detection.
Prof. Nehorai’s recent work has focused on a challenging and important compressive sensing problem. In particular, while it is known that dramatic savings are possible via compressive sensing, it is often difficult to say exactly how many samples are required for a specific sampling scheme (or sensing matrix). Prof. Nehorai and his collaborators have developed a suite of efficient algorithms, based on convex programming that can rapidly ascertain the number of samples needed under a particular scheme. These algorithms can in turn be used to guide the development of better sensing schemes.
Nehorai’s talk was the first in the three-part Fall 2014 Distinguished Lecture Series. The next talk features Philippe Fauchet, Professor of Electrical Engineering, College of Engineering of Vanderbilt University. He will speak on the topic, “Nanoscale Silicon as an Optical Material.” His lecture will be held October 28, 2014 at 4 pm in PHO 210.
By Gabriella McNevin
By Paloma Parikh (COM’15)
Three ECE undergraduate students won grants from two programs affiliated with Boston University’s Undergraduate Research Opportunities Program. Annie Lane (ENG’16) and Maya Saint Germain (ENG’16) are recipients of the Clare Boothe Luce Award; and Dean Shi, (ENG’16) won the Hariri Award.
Annie Lane won the Clare Boothe Luce Award for her research project, “Data Center Power Regulation Modeling,” which she is working on with mentor Assistant Professor Ayse Coskun (ECE). The goal of the project is to minimize electricity costs for data centers. To do so, Lane is developing a power control policy based on a mathematical model. Additionally, she will evaluate alternative research models in the hopes of finding the most effective process. Lane believes the practicality of her project caught the attention of the judges. In an email correspondence, Lane mentioned that the project has potential for real-life application, “BU has partnered with other universities, the state, and companies to build and manage the Massachusetts Green High Power Computing Center (MGHPCC) in Holyoke, MA. The research results will help increase energy savings at MGHPCC.”
Maya Saint Germain, with mentor Professor and Associate Chair for Graduate Studies Hamid Nawab (ECE), won the Clare Boothe Luce Award to fund a project entitled “Human-in-Circuit Signal Processing.” Saint Germain explains Human-in-Circuit Signal Processing as, “a subfield of signal processing in which the signal that is being processed is produced by a human, and – after processing – will be perceived by a human.” Her goal is to improve how the signal is processed. Saint Germain feels proud that she won the award, “It means that my research is important and relevant.”
Dean Shi won the Hariri Award for his project, “Power Optimization and Development of Power Policies on Mobile Devices,” which he is working on with mentor Assistant Professor Ayse Coskun (ECE). Shi is working to lengthen battery life for cell phones. To do so, he is researching how cell phones use battery power through different functions, such as applications. With this understanding, he will be able to optimize power usage and make cell phone batteries last longer. Shi recalls, “All of my friends are always complaining, ‘Oh I just charged my phone this morning but it’s already at 10% battery.’” This award will help Shi achieve his goal of lengthening cell phone battery life.
The Undergraduate Research Opportunities Program (UROP) is a supportive resource for faculty-mentor research. It provides grants to students through various organizations such as the Clare Boothe Luce Program and the Rafik B. Hariri Institute for Computing and Computational Science & Engineering. The Clare Boothe Luce Program aims to support women in science, mathematics, and engineering. Recipients of the undergraduate research awards receive funding to conduct a research project with a faculty mentor. The Hariri Institute promotes innovation in the sciences of computing and engineering. With the Hariri award, they provide grants for collaborative research and training initiatives.