Communications & Signal Processing
Overview
Communications is as essential to modern society as electricity, providing both connectivity (cell phone, WiFi, Internet) and storage (hard drive, flash memory). The field of communications spans signal processing and error control coding for transceiver design, information theory to identify fundamental performance benchmarks, and the design of network protocols. Signal processing, which refers to hardware and software to produce, transform and analyze signals (e.g., radio waves, sound, image, video, EEG, MRI), is a fundamental tool in every discipline in engineering and the sciences. Sophisticated digital signal processing algorithms, the cost of implementing which has been driven down leveraging Moore's law, are at the core of an ever increasing number of devices, including communications devices (cell phones, WiFi), digital music/video players, televisions, GPS receivers, radar and sonar systems, and medical imaging and monitoring.
The communications and signal processing faculty at UCSB engage in cutting-edge research in communication theory and networking (with current focus on next generation wireless communication and sensor networks), novel compression techniques (for audio, image, and video), pattern recognition (e.g., in images and sound signals), image reconstruction (e.g., for radar, sonar, microscopy, medical devices), and image informatics (extracting and organizing information from images for biological research, diagnosis, surveillance, production monitoring). In addition, research is also directed at advancing core knowledge in CSP in areas such as information theory, estimation theory and harmonic analysis. Much of the research in the CSP group involves interdisciplinary scientific and industrial collaborations, and results in software and hardware prototypes. Close connections with industry are maintained through faculty interactions and student internships, leading to an understanding of emerging trends and bottlenecks in technology.
For more detailed descriptions of faculty research and activities, please follow the links to the various research centers, labs, and groups indicated on this page.
Communications and Signal Processing Research Activities
Communications and signal processing research areas include but are not limited to:
- Signal processing for communication
- Wireless communication networks
- Wireless sensor networks
- Signal and image acquisition
- Signal storage, transmission, organization
- Signal processing (reconstruction, compression, enhancement, (de)coding)
- Image analysis (vision and bio-microscopy)
- Information theory
- Haptics, robotics, tactile sensors, signal and image acquisition (tactile), wearable computing, soft electronics
- Cyber-physical systems
- Smart cities
- High-dimensional signal and information processing
- Mathematical optimization
CSP Faculty Groups & Labs
- Center for Bioimage Informatics - Manjunath
- Center for Multimodal Big Data Science and Healthcare - Manjunath
- Imaging Systems Laboratory - Lee
- MIRAGE Lab - Sen
- Network and Information Sciences Lab - Pedarsani
- RE Touch Lab - Visell
- Sensor and Robotic Network Lab - Mostofi
- Signal Compression Lab - Rose
- Smart Infrastructure Systems Lab - Alizadeh
- Vision Research Lab - Manjunath
- ViVoNets (Video and Voice over Networks) Lab - Gibson
- Wireless Communication and Sensornets Lab - Madhow
- Big Data and Signal Processing Lab - Thrampoulidis
Affiliated Research Units
COE News – Alizadeh given NSF CAREER award
ECE Ass't. Prof. Mahnoosh Alizadeh hopes to increase the levels of wind and solar energy in the power grid
Alizadeh’s research project centers on the uncertainties involved with humans and their use of electricity.
“Renewable energy is produced randomly. It depends on when the wind blows or the sun shines,” explained Alizadeh. “Our goal is to design mechanisms that incentivize users to shift their electricity demand to times when there is more renewable energy being produced so that we can integrate higher levels of solar and wind energy into the power grid.”
COE News – "Two ECE Professors receive NSF Early CAREER Awards" (full article)
The UCSB Current – "Your Video Can ID You Through Walls"
Researchers in ECE Professor Yasamin Mostofi's lab have enabled, for the first time, determining whether the person behind a wall is the same individual who appears in given video footage, using only a pair of WiFi transceivers outside
This novel video-WiFi cross-modal gait-based person identification system, which they refer to as XModal-ID (pronounced Cross-Modal-ID), could have a variety of applications, from surveillance and security to smart homes. For instance, consider a scenario in which law enforcement has a video footage of a robbery. They suspect that the robber is hiding inside a house. Can a pair of WiFi transceivers outside the house determine if the person inside the house is the same as the one in the robbery video? Questions such as this have motivated this new technology.
“Our proposed approach makes it possible to determine if the person behind the wall is the same as the one in video footage, using only a pair of off-the-shelf WiFi transceivers outside,” said Mostofi. “This approach utilizes only received power measurements of a WiFi link. It does not need any prior WiFi or video training data of the person to be identified. It also does not need any knowledge of the operation area.”
This innovation builds on previous work in the Mostofi Lab, which has pioneered sensing with everyday radio frequency signals such as WiFi since 2009.
The UCSB Current – "Your Video Can ID You Through Walls" (full article)
Faculty Lab Spotlight
Big Data and Signal Processing Lab
Christos Thrampoulidis, Assistant Professor
Our lab does research at the intersection of high-dimensionalstatistics, mathematical optimization, signal-processing, and aspects of learning. Our work involves physical modeling, algorithm design, as well as building mathematical frameworks of analysis.
Some of our ongoing research projects are structured signal-recovery in high-dimensions; fundamental limits of non-smooth convex relaxation-based inference; and principles of lens-less cameras for imaging application.