Presents the introductory editorial for this issue of the publication.

As a bio-inspired and emerging sensor, an event-based neuromorphic vision sensor has a different working principle compared to the standard frame-based cameras, which leads to promising properties of low energy consumption, low latency, high dynamic range (HDR), and high temporal resolution. It poses a paradigm shift to sense and perceive the environment by capturing local pixel-level light intensity

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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.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 President’s message for this issue of the publication.

Oceans cover approximately 71% of Earth's surface yet remain difficult to explore and monitor remotely. Communication challenges, including lengthy propagation delays, Doppler effects due to vehicle and water movement, and the highly dynamic multipath nature of the undersea environment, can result in significant errors and outliers in transmissions, received data measurements, and image analysis.

The articles in this special section were focused on the current state of the art as well as emerging trends in the design, development, and deployment of sensing and perception technologies for autonomous and automated driving. Such technologies include camera, ultrasound, Global Navigation Satellite System-, lidar-, and radar-based platforms integrat ing signa l processing components to process the

Autonomous vehicles (AVs) rely on accurate and robust sensor observations for safety-critical decision making in a variety of conditions. The fundamental building blocks of such systems are sensors and classifiers that process ultrasound, radar, GPS, lidar, and camera signals [1]. It is of primary importance that the resulting decisions are robust to perturbations, which can take the form of different

Automated vehicular safety functions that intervene in dangerous driving situations, e.g., by emergency braking, use sensor measurements for the interpretation of the driving situation. As a consequence, they are typically very vulnerable to sensor imperfections, and unavoidable measurement errors have a negative impact on both the safety and the satisfaction of the customer, which has to be taken

Autonomous vehicles rely on their perception systems to acquire information about their immediate surroundings. It is necessary to detect the presence of other vehicles, pedestrians, and other relevant entities. Safety concerns and the need for accurate estimations have led to the introduction of lidar systems to complement camera- or radar-based perception systems. This article presents a review of

The safety and success of autonomous vehicles (AVs) depend on their ability to accurately map and respond to their surroundings in real time. One of the most promising recent technologies for depth mapping is single-photon lidar (SPL), which measures the time of flight of individual photons. The long-range capabilities (kilometers), excellent depth resolution (centimeters), and use of low-power (eye-safe)

Autonomous driving relies on a variety of sensors, especially radars, which have unique robustness under heavy rain/fog/snow and poor light conditions. With the rapid increase of the amount of radars used on modern vehicles, where most radars operate in the same frequency band, the risk of radar interference becomes a compelling issue. This article analyzes automotive radar interference and proposes

Self-driving cars constantly assess their environment to choose routes, comply with traffic regulations, and avoid hazards. To that aim, such vehicles are equipped with wireless communications transceivers as well as multiple sensors, including automotive radars. The fact that autonomous vehicles implement both radar and communications motivates designing these functionalities in a joint manner. Such

Important requirements for automotive radar are high resolution, low hardware cost, and small size. Multiple-input, multiple-output (MIMO) radar technology has been receiving considerable attention from automotive radar manufacturers because it can achieve a high angular resolution with relatively small numbers of antennas. For that ability, it has been exploited in the current-generation automotive

Artificial neural networks (ANNs) have long been used as a mathematical modeling method and have recently found numerous applications in science and technology, including computer vision, signal processing, and machine learning [1], to name a few. Although notable function approximation results exist [2], theoretical explanations have yet to catch up with newer developments, particularly with regard

Recently, an observer-based algorithm was reviewed that is able to perform recursive sliding discrete Fourier transform (SDFT) [1]. DFT is the basic algorithm used for Fourier analysis [2], but it provides unbiased estimates only if the sampling is coherent, i.e., the ratio of the sampling frequency fs and the input signal frequency fin is a rational number. Coherent sampling of real-life periodic

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

Presents the introductory editorial for this issue of the publication.

Distributed learning has become a critical enabler of the massively connected world that many people envision. This article discusses four key elements of scalable distributed processing and real-time intelligence: problems, data, communication, and computation. Our aim is to provide a unique perspective of how these elements should work together in an effective and coherent manner. In particular,

There is a growing cross-disciplinary effort in the broad domain of optimization and learning with streams of data, applied to settings where traditional batch optimization techniques cannot produce solutions at time scales that match the interarrival times of the data points due to computational and/or communication bottlenecks. Special types of online algorithms can handle this situation, and this

Presents the front cover for this issue of the publication.

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 President�s message for this issue of the publication.

Researchers worldwide are continuing to advance photo and video technologies, developing faster high-resolution cameras and taking advantage of artificial intelligence (AI), machine learning (ML), and other cutting-edge tools. As a result, novel applications are appearing in an almost endless number of fields, including scientific research, sports, public safety, and personal security.

The articles in this special section focus on machine learning from distributed, streaming media. The field of machine learning has undergone radical transformations during the last decade. These transformations, which have been fueled by our ability to collect and generate tremendous volumes of training data and leverage massive amounts of lowcost computing power, have led to an explosion in research

The problem of simultaneously learning several related tasks has received considerable attention in several domains, especially in machine learning, with the so-called multitask learning (MTL) problem, or learning to learn problem [1], [2]. MTL is an approach to inductive transfer learning (using what is learned for one problem to assist with another problem), and it helps improve generalization performance

The past decade has witnessed great progress in automatic speech recognition (ASR) due to advances in deep learning. The improvements in performance can be attributed to both improved models and large-scale training data. The key to training such models is the employment of efficient distributed learning techniques. In this article, we provide an overview of distributed training techniques for deep

Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized. Training in heterogeneous and potentially massive networks introduces novel challenges that require a fundamental departure from standard approaches for large-scale machine learning, distributed optimization, and privacy-preserving data

Batch training of machine learning models based on neural networks is well established, whereas, to date, streaming methods are largely based on linear models. To go beyond linear in the online setting, nonparametric methods are of interest due to their universality and ability to stably incorporate new information via convexity or Bayes's rule. Unfortunately, when applied online, nonparametric methods

Game theory is a well-established tool for studying interactions among self-interested players. Under the assumption of complete information on the game composition at each player, the focal point of game-theoretic studies has been on the Nash equilibrium (NE) in analyzing game outcomes and predicting strategic behaviors of rational players.

This article provides an overview of distributed gradient methods for solving convex machine learning problems of the form minx ∈ Rn (1/m) ∑i = 1m fi(x) in a system consisting of m agents that are embedded in a communication network. Each agent i has a collection of data captured by its privately known objective function fi(x). The distributed algorithms considered here obey two simple rules: privately

Decentralized methods to solve finite-sum minimization problems are important in many signal processing and machine learning tasks where the data samples are distributed across a network of nodes, and raw data sharing is not permitted due to privacy and/or resource constraints. In this article, we review decentralized stochastic first-order methods and provide a unified algorithmic framework that combines

We provide a discussion of several recent results which, in certain scenarios, are able to overcome a barrier in distributed stochastic optimization for machine learning (ML). Our focus is the so-called asymptotic network independence property, which is achieved whenever a distributed method executed over a network of n nodes asymptotically converges to the optimal solution at a comparable rate to

Fueled by recent advances in deep neural networks, reinforcement learning (RL) has been in the limelight because of many recent breakthroughs in artificial intelligence, including defeating humans in games (e.g., chess, Go, StarCraft), self-driving cars, smart-home automation, and service robots, among many others. Despite these remarkable achievements, many basic tasks can still elude a single RL

The current big data era routinely requires the processing of large-scale data on massive distributed computing clusters. In these applications, data sets are often so large that they cannot be housed in the memory and/or the disk of any one computer. Thus, the data and the processing are typically distributed across multiple nodes. Distributed computation is thus a necessity rather than a luxury.

Statistical inference and machine-learning algorithms have traditionally been developed for data available at a single location. Unlike this centralized setting, modern data sets are increasingly being distributed across multiple physical entities (sensors, devices, machines, data centers, and so on) for a multitude of reasons that range from storage, memory, and computational constraints to privacy

Since the 1970s, various image and video coding techniques have been explored, and some of them have been included in the video coding standards issued by the International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC) Motion Pictures Expert Group (MPEG) and International Telecommunication Union-Telecommunication Standardization Sector (ITU-T) Video Coding Experts

Presents highlights from the IEEE Video and Image Processing Cup 2019 Student Competition.

Presents the SPS society caledar of upcoming events and meetings.

Reports on the activities of the SPS society Information Forensics and Security Technical Committee (IFS-TC).

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.

Reports on the role and responsibilities of the Editor-in-Chief of the IEEE Signaling Processing Magazine.

Presents information on SPS society award recipients and Fellows.

Presents a listing of SPS members who were elevated to the status of IEEE Fellow.

A new generation of exponentially more intelligent and capable robots is on the way, helped along by talented and imaginative engineers and heavy doses of signal processing. In fields spanning almost every aspect of human professional and personal life, robots are ready to perform tasks faster, better, and more efficiently than their human counterparts. Even major sports organizations are now looking

Presents a list of articles published by the IEEE Signal Processing Society (SPS) that ranked among the top 100 most downloaded IEEE Xplore articles.

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

Cache-aided communications have shown potential for substantial improvement in network performance, which goes far beyond that of traditional caching. Traditional caching (i.e., the bringing and storing of data closer to the end users) is only efficient when a significant portion of the popular files can be locally stored. In cache-aided communications, however, information stored at one user is useful

At the core of many sensing and imaging applications, the signal of interest can be modeled as a linear superposition of translated or modulated versions of some template [e.g., a point spread function (PSF) or a Green's function] and the fundamental problem is to estimate the translation or modulation parameters (e.g., delays, locations, or Dopplers) from noisy measurements. This problem is centrally

In recent years, an abundance of new molecular structures have been elucidated using cryo-electron microscopy (cryo-EM), largely due to advances in hardware technology and data processing techniques. Owing to these new exciting developments, cryo-EM was selected by Nature Methods as Method of the Year 2015, and the Nobel Prize in Chemistry 2017 was awarded to three pioneers in the field. The main goal

Linear time-invariant (LTI) systems play a fundamental role in signal processing. Continuity is an important property of LTI systems, without which many conclusions about LTI systems, such as convolution formula and commutative law, are not true in general. However, this concept does not receive as much attention as it should in the literature of signal processing. In this "Lecture Notes" article,

Delivering products, services, and applications to the masses is a problem that more companies face every day in a globalized economy. Imagine what the problem was like 30 years ago when the delivery of media content was a reality but had been achieved by providing solutions that applied to specific media types, delivery methods, countries, regions, industries, and companies. Telecommunication companies

The Moving Picture Experts Group (MPEG) is an International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) working group that develops media coding standards. These standards include a set of ontologies for the codification of intellectual property rights (IPR) information related to media. The Media Value Chain Ontology (MVCO) facilitates rights tracking for fair

Recounts the career and contributions of Augustine Heard “Steen” Gray.