Communications and Signal Processing

The research area of Communications and Signal Processing is dedicated to addressing the challenges associated with the efficient processing and transmission of data. This research encompasses various sources of data, such as sound, images, and sensor output signals. Signal processing algorithms play a crucial role in efficiently transforming these signals into digital data streams.

Within this field, communications research focuses on finding effective ways to transmit data streams from one location to another. A key objective is to investigate techniques that enable the transmission of ever-increasing data rates while accommodating multiple users, all while consuming less radio frequency spectrum and transmitted signal power. This involves exploring innovative modulation, coding, and multiplexing schemes, as well as advanced channel equalization and interference mitigation techniques.

Researchers in the Communications and Signal Processing area work on developing robust and efficient communication systems. This includes studying various aspects of wireless and wired communication systems, such as channel modeling, error control coding, resource allocation, multiple access techniques, and network protocols. The ultimate goal is to enable reliable, high-speed, and energy-efficient data transmission in a wide range of applications, including wireless networks, satellite communications, internet of things (IoT), and multimedia streaming.

In summary, the Communications and Signal Processing research area focuses on tackling the challenges of processing and transmitting data efficiently. Through advancements in signal processing algorithms and communication techniques, researchers aim to enable high-speed, reliable, and energy-efficient data transmission while optimizing the utilization of radio frequency spectrum and transmitted signal power.

Associated Labs/Centers

  • Aerial Experimentation Research Platform for Advanced Wireless (AERPAW)
  • IBM Quantum Hub (IBM Q)
  • Center for Advanced Electronics through Machine Learning (CAEML)
  • Wireless Systems Innovations Lab (WSIL)
  • Wireless Ad-Hoc and Local Area Networks Research Lab (WIRELESS)
  • Networking of Wireless Information Systems (NetWIS)
  • Active Robotics Sensing (AROS)

Research Showcase

Helping Electric Cars Get Where They’re Going

Professor Mo-Yuen Chow has developed new software that estimates how much farther electric vehicles can drive before needing to recharge. The new technique uses big data techniques to estimate how far the vehicle can go before recharging.

Digital Communications

The subject of digital communications (DCOMM) involves the transmission of information in digital form from a source that generates the information to one or more destinations. Of particular importance in the analysis and design of communication systems are the characteristics of the physical channels though which the information is transmitted.

In DCOMM, the characteristics of a channel greatly affect the design of the basic building blocks of the communication system. As such, digital communications research strives to come to a understanding about all physical elements of a communication system and its functions.

Digital Communications makes use of functions and methods such as statistical channel modeling, modulation and demodulation techniques, optimal receiver design, performance analysis techniques, source coding, quantization, and fundamentals of information theory.

Digital Signal Processing

Digital signal processing (DSP) is the study of signals in a digital representation and the processing methods of these signals. DSP and analog signal processing are subfields of signal processing. DSP has at least three major subfields: audio signal processing, digital image processing and speech processing.

Since the goal of DSP is usually to measure or filter continuous real-world analog signals, the first step is usually to convert the signal from an analog to a digital form, by using an analog to digital converter. Often, the required output signal is another analog output signal, which requires a digital to analog converter.

The algorithms required for DSP are sometimes performed using specialized computers, which make use of specialized microprocessors called digital signal processors (also abbreviated DSP). These process signals in real time and are generally purpose-designed ASICs.

Image Analysis and Computer Vision

Computer image analysis largely contains the fields of computer or machine vision, and medical imaging, and makes heavy use of pattern recognition, digital geometry, and signal processing. This field of computer science developed in the 1950s at academic institutions such as the MIT A.I. Lab, originally as a branch of artificial intelligence and robotics.

Computers are indispensable for the analysis of large amounts of data, for tasks that require complex computation, or for the extraction of quantitative information. On the other hand, the human visual cortex is an excellent image analysis apparatus, especially for extracting higher-level information, and for many applications – including medicine, security, and remote sensing – human analysts still cannot be replaced by computers. For this reason, many important image analysis tools such as edge detectors and neural networks are inspired by human visual perception models.

Computer vision is the science and technology of machines that see. As a scientific discipline, computer vision is concerned with the theory and technology for building artificial systems that obtain information from images or multi-dimensional data. Information is that which enables a decision. Since perception can be seen as the extraction of information from sensory signals, computer vision can be seen as the scientific investigation of artificial systems for perception from images or multi-dimensional data.

Computer vision can also be described as a complement (but not necessarily the opposite) of biological vision. In biological vision, the visual perception of humans and various animals are studied, resulting in models of how these systems operate in terms of physiological processes. Computer vision, on the other hand, studies and describes artificial vision systems that are implemented in software and/or hardware. Interdisciplinary exchange between biological and computer vision has proven increasingly fruitful for both fields. Applications of computer vision systems include robots and autonomous vehicles, detection, organizing information, and modeling objects.