Presents the front cover for this issue of the publication.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Presents the introductory editorial for this issue of the publication.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

The articles in this special section focus on signal processing for neurorehabilitation and assistive technologies.

to accommodate the prosthesis![Special Reports]

Prostheses provide a means for individuals with amputations to regain some of the lost functions of their amputated limb. Human-machine interfaces (HMIs), used for controlling prosthetic devices, play a critical role in users' experiences with prostheses. This review article provides an overview of the HMIs commonly adopted for upper-limb prosthesis control and inspects collected signals and their

This article reviews technologies and algorithms for decoding volitional movement intent using bioelectrical signals recorded from the human body. Such signals include electromyograms, electroencephalograms, electrocorticograms, intracortical recordings, and electroneurograms. After reviewing signal features commonly used for interpreting movement intent, this article describes traditional movement

This tutorial reviews a series of reinforcement learning (RL) methods implemented in a reproducing kernel Hilbert space (RKHS) developed to address the challenges imposed on decoder design. RL-based decoders enable the user to learn the prosthesis control through interactions without desired signals and better represent the subject's goal to complete the task. The numerous actions in complex tasks

The human hand can perform many precise functions and is relied upon for countless aspects of daily life. When upperlimb amputation is necessitated, an affected individual's sense of independence is understandably impacted.

A rapidly aging population worldwide has spurred interest in developing new strategies to cope with neural declines and neurodegenerative disorders. Noninvasive brain stimulation (NIBS) is increasingly being used to explore functional mechanisms of the brain and induce the therapeutic modulation of behavior, cognition, and emotion. Galvanic vestibular stimulation (GVS), a safe and well-tolerated NIBS

Medical conditions and cases are growing at a rapid pace, and physical space is starting to be constrained. Hospitals and clinics no longer have the ability to accommodate large numbers of incoming patients. It is clear that the health industry needs to improve the current state of its valuable and limited resources. The evolution of Internet-of-Things (IoT) devices along with assistive technologies

An aging population is increasingly prevalent in both developed and developing countries, raising a series of social challenges and economic burdens. In particular, more elderly people are staying alone at home than are living with people who can take care of them. Therefore, assisted living (AL) and health-care monitoring (HM) can be critical issues in this era of human-centered artificial intelligence

People suffering from hearing impairment often have difficulties participating in conversations in so-called cocktail party scenarios where multiple individuals are simultaneously talking. Although advanced algorithms exist to suppress background noise in these situations, a hearing device also needs information about which speaker a user actually aims to attend to. The voice of the correct (attended)

Neural interfacing is essential for advancing our fundamental understanding of movement neurophysiology and for developing human-machine interaction systems. This can be achieved at different levels of the central nervous system (CNS) and peripheral nervous system (PNS); however, direct neural interfaces with brain and nerve tissues face important challenges and are currently limited to clinical cases

This article presents two networks that improve upon the behavior and performance of previously published sliding discrete Fourier transform (SDFT) algorithms. The proposed networks are structurally simple, computationally efficient, guaranteed stable networks used for real-time sliding spectrum analysis. The first real-time network computes one spectral output sample, equal to a singlebin output of

In the article “Teaching Digital Signal Processing by Partial Flipping, Active Learning, and Visualization” by Keshab K. Parhi [1], the URL provided in [10] in the “References” section was incorrect due to a production error. The correct URL for [10] is https:// www.youtube.com/playlist?list=PLT 1QAv48lhQLPwgL4Y--X Tz Ke QB ViAYKv.

Explains how to submit a quality article to the “Tips & Tricks” column.

Presents the SSCS calendar of upcoming events and meetings.

Presents the front cover for this issue of the publication.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents the introductory editorial for this issue of the publication.

Despite the impressive technological strides made over the years, human lives still depend very much on the natural environment. Fortunately, technology can now be used to help address critical environmental concerns in air quality, soil condition, and weather events. In all of these areas and many others, signal processing is supporting the ability to provide immediate and long-term observations and

Recounts the career and contributions of Peter Schultheiss.

The articles in this special section focus on ways to deliver both innovative educational content and the core signal processing curriculum to current electrical engineering and computer science degrees in order to provide high-quality and hands on multidisciplinary skills, experience, and inspiration for students at all levels.

Education thrives on innovation. As technology continues to advance, new opportunities continue to present themselves for innovation in the classroom. Discusses what these opportunities are and examines the impact to engineers in the signal processing industry.

The effectiveness of teaching digital signal processing (DSP) can be enhanced by reducing lecture time devoted to theory and increasing emphasis on applications, programming aspects, visualization, and intuitive understanding. An integrated approach to teaching requires instructors to simultaneously teach theory and its applications in storage and processing of audio, speech, and biomedical signals

Signal processing is an engineering discipline known to involve abstract and complex concepts. Curriculum development should be informed by an understanding of the most critical and challenging learning in the field. Threshold concept theory and threshold capability theory provide a framework describing the features of the most critical and challenging learning in any discipline. The framework describes

The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses his/her weaknesses to ultimately meet his/her desired goal. This concept emerged several years ago and is being adopted by a rapidly growing number of educational institutions around the globe. In recent years, the rise of artificial intelligence (AI) and machine

Digital signal processing (DSP) education has traditionally employed more demanding mathematics than most topics found among courses in electrical/electronic/computer engineering. In some cases, the technical challenges posed by some courses have made it difficult for students to complete those courses successfully. Here, we advocate for creativity to be nurtured in the first place, after which the

Due to its open-ended nature, project-based learning (PBL) is a suitable methodology to achieve student motivation and enrich creativity skills. This article describes the design of PBL activities in two undergraduate courses on digital signal processing techniques. The academic context is a bachelor's degree in telecommunication engineering at the Universidad de Extremadura in Spain. Full specifications

In this artaicle, we illustrate how music may serve as a vehicle to support education in signal processing. Using Fourier analysis as a concrete example, we demonstrate how the music domain provides motivating and tangible applications that make learning signal processing an interactive pursuit. Furthermore, we indicate how software tools, originally developed for music analysis, provide students multiple

As today's practitioners and educators, we bear responsibility for training our students to achieve the deep conceptual understanding that they require to be the next generation of signal processing innovators. This article describes our work using think-aloud problem-solving sessions to reveal student misconceptions and to design improved conceptual problems for use in active learning classrooms.

Generally, the curriculum design for undergraduate students enrolled in digital signal processing (DSP)-related engineering programs covers hard topics from specific disciplines, namely, mathematics, digital electronics, or programming. Typically, these topics are very demanding from the point of view of both students and teachers due to the inherent complexity of the mathematical formulations. However

One exciting challenge for future scientists and engineers will be to contribute to the development and introduction of self-driving cars. Under the objective of making young talent attending engineering-focused secondary schools and universities enthusiastic about this highly innovative topic, we have developed an educational platform for advanced driver assistance systems (ADASs) and autonomous driving

In June 2005, three State University of New York (SUNY) campuses, namely Stony Brook University, the University at Buffalo, and Binghamton University, joined forces to provide a completely online electrical engineering program that would lead to a bachelor of science in electrical engineering (BSEE) degree. The program was put together to serve a nontraditional student body that is otherwise unable

Modern signal processing (SP) classes should provide a balance between theory and application as well as use active learning exercises to engage students and facilitate learning. A new sensor processing course, Sensor Processing for Autonomous Vehicles (SPAV), was designed with two specific objectives: 1) to successfully engage students using active and collaborative learning and 2) integrate a state-of-the-art

In this article, we describe and discuss the design-based approach for signal processing education at the undergraduate level at the University of New South Wales (UNSW) Sydney. The electrical engineering (EE) undergraduate curriculum at UNSW Sydney includes three dedicated signal processing courses as well as a design course that involves a major signal processing task. Design- and project-based teaching

We describe an ecosystem for teaching data science (DS) to engineers that blends theory, methods, and applications, developed at the Faculty of Physical and Mathematical Sciences (FCFM is its Spanish acronym), Universidad de Chile, over the last three years. This initiative has been motivated by the increasing demand for DS qualifications both from academic and professional environments.

Rapid progress in the development of technological and computational tools has motivated substantial changes in the educational approach to the different disciplines of signal, image, and video processing. Moreover, the parallel evolution of sensor systems, data acquisition methods, and computational intelligence has emphasized the importance of signal processing and information engineering, particularly

Modern engineering education is increasingly assuming an interdisciplinary character, where developments in one area almost invariably affect other areas. A prominent example is that of signal processing, which has undergone significant changes with the emergence of machine learning (ML) and deep learning (DL) in recent years. While the impact of ML/DL is clearly visible from the viewpoint of research

Crises, especially the recent COVID-19 pandemic, have significantly impacted traditional teaching pedagogy, which often relies on face-to-face interactions. It is crucial that various stakeholders in education, including administrators, staff members, teachers, parents, learners, government officials, and so on, adapt to abrupt changes and disruptive transformations caused by emergency situations.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Dirac delta functions are introduced to students of signal processing in their sophomore year. Quite understandably, Dirac delta functions, which should be more aptly called generalized functions or distributions, cannot be comprehensively given to a young audience at the beginning of their engineering education. Instead, a simplified and abridged definition is presented, and the implications of the

Alternative data paths for the cascaded integrator-comb (CIC) decimator are presented that can be derived upon applying one or two modifications. In the first modification, the integrator-comb pair that surrounds the downsampling register is replaced by a simpler Integrate-and-Dump (ID) block that performs the same decimation task. The resulting data path is referred to as CIC-ID. In the second modification

Promoting Diversity in Signal Processing (PROGRESS) is a new initiative of the IEEE Signal Processing Society (SPS), aiming to motivate and support women and underrepresented minorities to pursue academic careers. Reports on the goals and mission of SPS PROGRESS.

Presents the SPS society calendar of upcoming events and meetings.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

In this lecture note, we discuss a fundamental concept, referred to as the characteristic rank, that suggests a general framework for characterizing the basic properties of various low-dimensional models used in signal processing. We illustrate this framework through two examples-matrix and three-way tensor completion problems-and consider basic properties, including the identifiability of matrices

The old adage "you are what you wear" is taking on an entirely new meaning as smart watches, fitness trackers, and a rapidly expanding array of other wearable devices flood onto the market, enabling users to monitor their exercise progress, retrieve critical health data, and accomplish a wide range of other useful and informative tasks. Researchers are now looking to take wearable technology to even

Deep neural networks provide unprecedented performance gains in many real-world problems in signal and image processing. Despite these gains, the future development and practical deployment of deep networks are hindered by their black-box nature, i.e., a lack of interpretability and the need for very large training sets. An emerging technique called algorithm unrolling, or unfolding, offers promise

Many problems in signal processing [e.g., filter bank design, independent component analysis (ICA), beamforming design, and neural network training] can be formulated as optimization over groups of transformations that depend continuously on real parameters (Lie groups). Such problems are usually tackled in two ways: using a constrained optimization procedure or using some parameterization to transform

Capturing high-dimensional (HD) data is a long-term challenge in signal processing and related fields. Snapshot compressive imaging (SCI) uses a 2D detector to capture HD (≥3D) data in a snapshot measurement. Via novel optical designs, the 2D detector samples the HD data in a compressive manner; following this, algorithms are employed to reconstruct the desired HD data cube. SCI has been used in hyperspectral

Seismic inversion is a fundamental tool in geophysical analysis, providing a window into Earth. In particular, it enables the reconstruction of large-scale subsurface Earth models for hydrocarbon exploration, mining, earthquake analysis, shallow hazard assessment, and other geophysical tasks.

The goal of this lecture note is to review the problem of free energy minimization as a unified framework underlying the definition of maximum entropy modeling, generalized Bayesian inference, learning with latent variables, the statistical learning analysis of generalization, and local optimization. Free energy minimization is first introduced, here and historically, as a thermodynamic principle.

A digital signal processing (DSP) architecture is presented to compute single bins of the discrete Fourier transform (DFT) with low complexity. This architecture consists of a cascade of simple accumulators working at the input sampling rate and a polyphase finite impulse response (FIR) filter with complex coefficients working at a lower rate. The advantage of this structure is that, for several cases