IEEE Signal Processing Magazine

Starting in 2017, the Women in Signal Processing (WISP) Subcommittee became a standing committee of the Membership Board, rather than a subcommittee of the Member Services Committee. This means that the WISP chair now holds a seat on the Membership Board.

As the size and speed of many conventional electronics technologies begin reaching their practical limits, a growing number of researchers is turning their attention to photonics and related optical-based approaches to continue the push toward smaller, faster, cheaper, and more innovative computer and communication devices.

By bringing research into the curriculum, this article explores new opportunities to refresh some classic signal processing courses. Since 2015, we in the Electrical and Electronic Engineering (EEE) Department of Imperial College London, United Kingdom, have explored the extent to which the level of student engagement and learning can be enhanced by inviting the students to perform signal processing exercises on their own physiological data.

Processing, storing, and communicating information that originates as an analog signal involves converting this information to bits. This conversion can be described by the combined effect of sampling and quantization, as shown in Figure 1. The digital representation is achieved by first sampling the analog signal to represent it by a set of discretetime samples and then quantizing these samples to a finite number of bits. 

Sparse representation can efficiently model signals in different applications to facilitate processing. In this article, we will discuss various applications of sparse representation in wireless communications, with a focus on the most recent compressive sensing (CS)-enabled approaches.

With the proliferation of Internet of Things (IoT) applications, billions of household appliances, phones, smart devices, security systems, environment sensors, vehicles, buildings, and other radio-connected devices will transmit data and communicate with each other or people, and it will be possible to constantly measure and track virtually everything.

Discrete-time rational transfer functions are often converted to parallel second-order sections due to better numerical performance compared to direct form infinite impulse response (IIR) implementations. This is usually done by performing partial fraction expansion over the original transfer function. 

This article describes the basic principles of ultrasound thermography (UST) and its real-time implementation using graphics processing unit (GPU)-enabled software architecture. In medicine, the term thermography is mostly associated with heat-sensing infrared cameras for recording surface temperature changes. In this article, we use this term to describe the qualitative noninvasive imaging of tissue temperature change using any imaging modality. Examples of these modalities include microwave radiometry, magnetic resonance imaging (MRI), US imaging, and photoacoustic tomography.