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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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

Medical imaging has progressed in impressive ways since the discovery of X-rays more than a century ago. Modern radiologists can now visualize body parts and organs to help physicians diagnose, treat, and monitor disease and/or injury in intricate detail, using techniques such as computed tomography (CT), positron emission tomography, and various other modalities.

Presents the SPS society 2021 Class of Distinguished Lecturers.

This special issue covering autonomous driving is presented in two parts: Part 1—Sensing and Perception was published in the July 2020 issue of IEEE Signal Processing Magazine and this issue, Part 2—Learning and Cognition. Learning and cognition models and, in particular, deep learning-based models are at the core of autonomous vehicles and automated driving. Autonomous driving and, more generally

The National Highway Traffic Safety Administration reported that more than 90% of in-road accidents in 2015 occurred purely because of drivers? errors and misjudgments, with such factors as fatigue and other sorts of distractions being the main cause of these accidents [1]. One promising solution for reducing (or even resolving) such human errors is via autonomous or computer-assisted driving systems

The interest in autonomous driving has continuously increased in the last two decades. However, to be adopted, such critical systems need to be safe. Concerning the perception of the ego-vehicle environment, the literature has investigated two different types of methods. On the one hand, traditional analytical methods generally rely on handcrafted designs and features while on the other hand, learning

Enabling autonomous driving (AD) can be considered one of the biggest challenges in today?s technology. AD is a complex task accomplished by several functionalities, with environment perception being one of its core functions. Environment perception is usually performed by combining the semantic information captured by several sensors, i.e., lidar or camera. The semantic information from the respective

Advanced automotive active safety systems, in general, and autonomous vehicles, in particular, rely heavily on visual data to classify and localize objects, such as pedestrians, traffic signs and lights, and nearby cars, to help the corresponding vehicles maneuver safely in their environments. However, the performance of object detection methods could degrade rather significantly in challenging weather

We present a review of 3D point cloud processing and learning for autonomous driving. As one of the most important sensors in autonomous vehicles (AVs), lidar sensors collect 3D point clouds that precisely record the external surfaces of objects and scenes. The tools for 3D point cloud processing and learning are critical to the map creation, localization, and perception modules in an AV. Although

Accurate behavior anticipation is essential for autonomous vehicles when navigating in close proximity to other vehicles, pedestrians, and cyclists. Thanks to the recent advances in deep learning and inverse reinforcement learning (IRL), we observe a tremendous opportunity to address this need, which was once believed impossible given the complex nature of human decision making. In this article, we

This article focuses on the trends, opportunities, and challenges of novel arithmetic for deep neural network (DNN) signal processing, with particular reference to assisted- and autonomous driving applications. Due to strict constraints in terms of the latency, dependability, and security of autonomous driving, machine perception (i.e., detection and decision tasks) based on DNNs cannot be implemented

The rapid evolution of autonomous vehicles (AVs) has exposed the need for fast-paced development and testing processes of a variety of perception, planning, and control algorithms. To expedite development, the AV industry and researchers leverage virtual vehicle environments to simulate a range of test scenarios that may otherwise be costly or difficult to conduct on a real test track. However, the

Maneuvering target tracking is an important technology in engineering applications [1]-[3]. The traditional methodologies for designing it can be divided into two categories: modelbased and model-free algorithms. Almost all tracking algorithms are model based. The main idea behind modelbased tracking algorithms is to choose a representation that fits the actual state trajectories of the target movement

Many of the systems in various signal processing applications are nonlinear due to, for example, hardware impairments, such as nonlinear amplifiers and finite-resolution quantization. The Bussgang decomposition is a popular tool used when analyzing the performance of systems that involve such nonlinear components. In a nutshell, the decomposition provides an exact probabilistic relationship between

Reports on the activities of the IEEE SPS society Applied Signal Processing Systems (ASPS) Technical Committee (TC), a brand-new member of the TC roster of the IEEE Signal Processing Society (SPS).

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.

The emerging field of graph signal processing (GSP) allows one to transpose classical signal processing operations (e.g., filtering) to signals on graphs. The GSP framework is generally built upon the graph Laplacian, which plays a crucial role in studying graph properties and measuring graph signal smoothness. Here, instead, we propose the graph modularity matrix as the centerpiece of GSP to incorporate

For the analysis of data defined over irregular domains, the recent area of graph signal processing extends classical signal processing techniques to the case of graphs. A prominent idea in this context is the notion of graph shift, which provides a foundation for distributed signal processing over networks. Despite its distributed nature, successive implementations of graph shift require a synchronization

Presents the departing editorial from the Editor-in-Chief.

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

Tech pundits claim that we are living in an increasingly visual world. That's probably true. Yet, while new imaging and video technologies grab the headlines, audio researchers are intently working on innovations that promise to improve public safety and security.

The articles in this special section focus on graph signal processing. Generically, the networks that sustain our societies can be understood as complex systems formed by multiple nodes, where global network behavior arises from local interactions between connected nodes. More succinctly, a network or a graph can be defined as a structure that encodes relationships between pairs of elements of a set

The study of sampling signals on graphs, with the goal of building an analog of sampling for standard signals in the time and spatial domains, has attracted considerable attention recently. Beyond adding to the growing theory on graph signal processing (GSP), sampling on graphs has various promising applications. In this article, we review the current progress on sampling over graphs, focusing on theory

With the surge in the volumes and dimensions of data defined in non-Euclidean spaces, graph signal processing (GSP) techniques are emerging as important tools in our understanding of these domains [1]. A fundamental problem for GSP is to determine which nodes play the most important role; so, graph signal sampling and recovery thus become essential [2]. In general, most of the current sampling methods

A major line of work in graph signal processing [2] during the past 10 years has been to design new transform methods that account for the underlying graph structure to identify and exploit structure in data residing on a connected, weighted, undirected graph. The most common approach is to construct a dictionary of atoms (building block signals) and represent the graph signal of interest as a linear

The notion of graph filters can be used to define generative models for graph data. In fact, the data obtained from many examples of network dynamics may be viewed as the output of a graph filter. With this interpretation, classical signal processing tools, such as frequency analysis, have been successfully applied with analogous interpretation to graph data, generating new insights for data science

A wide range of data science problems can be modeled in terms of a graph (or network), e.g., social, sensor, communication, infrastructure, and biological networks. The nodes in a graph/network represent the entities of interest, and the edges reflect relations between these entities, such as geographic proximity (e.g., wireless networks), social relations (e.g., Facebook), biological mechanisms (e

This article provides an overview of the current landscape of signal processing (SP) on directed graphs (digraphs). Directionality is inherent to many real-world (information, transportation, biological) networks, and it should play an integral role in processing and learning from network data. We thus lay out a comprehensive review of recent advances in SP on digraphs, offering insights through comparisons

The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains, such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing (GSP), a vibrant branch of signal processing models and algorithms that aims at handling data supported on graphs, opens new paths of research to

Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes of a graph that describes the underlying network topology. Successful learning from network data is built upon methods that effectively exploit this graph structure. In this article, we leverage graph signal processing (GSP) to characterize the representation space of graph neural networks (GNNs). We

Deep learning, particularly convolutional neural networks (CNNs), has yielded rapid, significant improvements in computer vision and related domains. But conventional deep learning architectures perform poorly when data have an underlying graph structure, as in social, biological, and many other domains. This article explores 1) how graph signal processing (GSP) can be used to extend CNN components

Graph signal processing (GSP) is an important methodology for studying data residing on irregular structures. Because acquired data are increasingly taking the form of multiway tensors, new signal processing tools are needed to maximally utilize the multiway structure within the data. In this article, we review modern signal processing frameworks that generalize GSP to multiway data, starting from

Uncovering hidden relations in complex data sets is a key step to making sense of the data, which is a hot topic in our era of data deluge. Graph-based representations are examples of tools able to encode relations in a mathematical structure enabling the uncovering of patterns like clusters and paths. However, graphs only capture pairwise relations encoded in the presence of edges, but there are many

An introductory course in signals requires a wide background of prerequisites covering complex numbers, series, calculus, and basic mathematical logic. Students often fail to engage with the material and find the course difficult because they do not recall the prerequisites. Time and curricular constraints usually limit in-class review. To address these issues, we present SigPrep, a set of open source

This lecture note briefly describes two examples in mathematics on how and why cross terms are disliked: orthogonal space-time block code (OSTBC) from orthogonal designs and free probability theory. The first example shows that the elimination of cross terms can significantly simplify the optimization that has many practical applications in, for instance, multiple antenna systems in wireless communications

In this lecture note, we derive and compare the asymptotic covariance matrices of two parametric estimators: the Gaussian maximum-likelihood estimator (MLE) and the optimally weighted leastsquares estimator (LSE). We assume a general model parameterization where the model's mean and variance are jointly parameterized and consider Gaussian and non-Gaussian data distributions.

Reports on new SPS society officers and committees.

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

Presents the calendar of SPS society events and meetings.

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

Linear time-invariant (LTI) systems find ubiquitous applications in digital signal processing [1], image processing [2], communication [3], and array processing [4], to name a few. An LTI system can be characterized by its impulse response, which is defined as the output response to an impulse input. For an arbitrary input signal, the output signal of an LTI system is obtained by the convolution of

Optimization lies at the heart of machine learning (ML) and signal processing (SP). Contemporary approaches based on the stochastic gradient (SG) method are nonadaptive in the sense that their implementation employs prescribed parameter values that need to be tuned for each application. This article summarizes recent research and motivates future work on adaptive stochastic optimization methods, which

During the past 20 years, low-rank tensor and matrix decomposition models (LRDMs) have become indispensable tools for signal processing, machine learning, and data science. LRDMs represent high-dimensional, multiaspect, and multimodal data using low-dimensional latent factors in a succinct and parsimonious way. LRDMs can serve a variety of purposes, e.g., data embedding (dimensionality reduction),

One of the major concerns for neural network training is that the nonconvexity of the associated loss functions may cause a bad landscape. The recent success of neural networks suggests that their loss landscape is not too bad, but what specific results do we know about the landscape? In this article, we review recent findings and results on the global landscape of neural networks.

Aimed at both casual spectators and active participants of optimization on manifolds alike, this introductory article presents a wide range of information I would have liked to have been told when I first entered the field. Several arguments are put forth: 1) it is not true that nonconvex implies difficult, 2) many optimization problems in signal processing are approached from the wrong perspective

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.