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

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 exciting new developments, cryo-EM was selected by Nature Methods as the "Method of the Year 2015," and the Nobel Prize in Chemistry 2017 was awarded to three pioneers in the cryo-EM field:

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

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Recounts the career and contributions of James Spilker, Jr.

In an increasingly networked world, signal processing is leading the way to innovations that promise to raise data throughput and capacity to levels scarcely dreamed of a decade ago. With the arrival of smart homes, cities, vehicles, industrial controls, and wearable devices, the race is on to create a new generation of groundbreaking network technologies.

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

The articles in this special section focus on computational magnetic resonance imaging (MRI) using compressed sensing applications. Presents recent developments in computational MRI. These developments are pushing the frontier of computational imaging beyond CS. Similar to CS, most of these algorithms rely on image representation in one form or another. However, the common recent thread is the departure

Since its inception in the early 1970s [1], magnetic resonance imaging (MRI) has revolutionized radiology and medicine. Apart from high-quality data acquisition, image reconstruction is an important step to guarantee high image quality in MRI. Although the very first MR images were obtained from data resembling radial projections of the imaged object by applying an iterative reconstruction algorithm

The development of compressed-sensing (CS) methods for magnetic resonance (MR) image reconstruction led to an explosion of research on models and optimization algorithms for MR imaging (MRI). Roughly 10 years after such methods first appeared in the MRI literature, the U.S. Food and Drug Administration (FDA) approved certain CS methods for commercial use, making CS a clinical success story for MRI

Magnetic resonance imaging (MRI) is widely used in clinical practice, but it has been traditionally limited by its slow data acquisition. Recent advances in compressed sensing (CS) techniques for MRI reduce acquisition time while maintaining high image quality. Whereas classical CS assumes the images are sparse in known analytical dictionaries or transform domains, methods using learned image models

In this article, we provide a detailed review of recent advances in the recovery of continuous-domain multidimensional signals from their few nonuniform (multichannel) measurements using structured low-rank (SLR) matrix completion formulation. This framework is centered on the fundamental duality between the compactness (e.g., sparsity) of the continuous signal and the rank of a structured matrix,

Magnetic resonance imaging (MRI) is a powerful and highly versatile imaging technique that has had a tremendous impact in both science and medicine. Unfortunately, MRI data acquisition is also time consuming and expensive, which has thus far prevented it from delivering on its full potential. As a result, the MRI field has always been interested in signal processing methods that can generate high-quality

Dynamic magnetic resonance imaging (MRI) can be used to scan a wide range of dynamic processes within the body, including the motion of internal organs, tissue-level nuclear magnetic resonance (NMR) relaxation, and dynamic contrast enhancement (DCE) of dye agents. The ability of MRI to safely provide unique soft-tissue contrast and comprehensive functional information has made dynamic MRI central to

Compressed sensing (CS) takes advantage of a low-dimensional signal structure to reduce sampling requirements to far below the Nyquist rate. In magnetic resonance imaging (MRI), this often takes the form of sparsity through wavelet transforms, finite differences, and low-rank extensions. Though powerful, these image priors are phenomenological in nature and do not account for the mechanism behind the

Magnetic resonance imaging (MRI) is a noninvasive diagnostic tool that provides excellent soft-tissue contrast without the use of ionizing radiation. Compared to other clinical imaging modalities (e.g., computed tomography or ultrasound), however, the data acquisition process for MRI is inherently slow, which motivates undersampling; thus, there is a need for accurate, efficient reconstruction methods

Compressed sensing (CS) reconstruction methods leverage sparse structure in underlying signals to recover high-resolution images from highly undersampled measurements. When applied to magnetic resonance imaging (MRI), CS has the potential to dramatically shorten MRI scan times, increase diagnostic value, and improve the overall patient experience. However, CS has several shortcomings that limit its

Following the success of deep learning in a wide range of applications, neural network-based machine-learning techniques have received interest as a means of accelerating magnetic resonance imaging (MRI). A number of ideas inspired by deeplearning techniques for computer vision and image processing have been successfully applied to nonlinear image reconstruction in the spirit of compressed sensing

Image reconstruction from undersampled k-space data has been playing an important role in fast magnetic resonance imaging (MRI). Recently, deep learning has demonstrated tremendous success in various fields and also shown potential in significantly accelerating MRI reconstruction with fewer measurements. This article provides an overview of deep-learning-based image reconstruction methods for MRI.

The existence and uniqueness conditions are a prerequisite to ensure the reliable reconstruction of sparse signals from reduced sets of measurements within the compressive sensing (CS) paradigm. However, despite their underpinning role in practical applications, the existing uniqueness relations are either computationally prohibitive to implement [the restricted isometry property (RIP)] or involve

Presents informationon on the 27th European Signal Processing Conference in A Coruna, Spain: Looking Into the Future of Signal Processing [Conference Highlights]

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Spiking neural networks (SNNs) are nature's versatile solution to fault-tolerant, energy-efficient signal processing. To translate these benefits into hardware, a growing number of neuromorphic spiking NN processors have attempted to emulate biological NNs. These developments have created an imminent need for methods and tools that enable such systems to solve real-world signal processing problems

Spiking neural networks (SNNs) are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse synaptic communications. The sparsity of the synaptic spiking inputs and the corresponding event-driven nature of neural processing can be leveraged by energy-efficient hardware implementations, which can offer significant energy

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In my September editorial [1], I outlined the important components of good feature articles in response to feedback from our recent IEEE Periodicals Review and Advisory Committee (PRAC) meeting. In this issue's editorial, I discuss the process of organizing a special issue (SI) for IEEE Signal Processing Magazine (SPM). The purpose is twofold. First, I want to encourage readers to become involved in

Presents the President’s message for this issue of the publication.

This is an age of mobility. Phones, tablets, notebook computers, smart watches, and various other devices now supply people around the world with instant communication capabilities that were only dreamed of a generation ago. Mobility technologies are also transforming medicine, helping to improve the quality of care for people at all stages of life, giving both patients and healthcare providers deeper

Artificial neural networks (ANNs) and computational neuroscience models have made tremendous progress, enabling us to achieve impressive results in artificial intelligence applications, such as image recognition, natural language processing, and autonomous driving. Despite this, biological neural systems consume orders of magnitude less energy than today's ANNs and are much more flexible and robust

Event sensors implement circuits that capture partial functionality of biological sensors, such as the retina and cochlea. As with their biological counterparts, event sensors are drivers of their own output. That is, they produce dynamically sampled binary events to dynamically changing stimuli. Algorithms and networks that process this form of output representation are still in their infancy, but

Neurobiological systems operate at power levels that are unattainable by modern electronic systems while exhibiting broader information processing capabilities for a number of important tasks. A variety of engineered systems designed for energy efficiency or hardware simplicity use time-based signal representations, which share similar mathematical principles with those that arise naturally in biology

A class of brain-inspired recurrent algorithms known as reservoir computing (RC) networks reduces the computational complexity and cost of training machine-learning models by using random, fixed synaptic strengths. This article offers insights about a spiking reservoir network, the liquid state machine (LSM), the inner workings of the algorithm, the design metrics, and neuromorphic designs. The discussion

Inventors of the original artificial neural networks (ANNs) derived their inspiration from biology [1]. However, today, most ANNs, such as backpropagation-based convolutional deeplearning networks, resemble natural NNs only superficially. Given that, on some tasks, such ANNs achieve human or even superhuman performance, why should one care about such dissimilarity with natural NNs? The algorithms of

Machine learning has emerged as the dominant tool for implementing complex cognitive tasks that require supervised, unsupervised, and reinforcement learning. While the resulting machines have demonstrated in some cases even superhuman performance, their energy consumption has often proved to be prohibitive in the absence of costly supercomputers. Most state-of-the-art machine-learning solutions are

Once a popular theme of futuristic science fiction or far-fetched technology forecasts, digital home assistants with a spoken language interface have become a ubiquitous commodity today. This success has been made possible by major advancements in signal processing and machine learning for so-called far-field speech recognition, where the commands are spoken at a distance from the sound-capturing device

A volcano is a rupture in Earth's crust that unleashes lava, volcanic ash, and gases from a cavity beneath the surface. Monitoring volcanoes is crucial for getting an insight into the dynamics of the different events taking place, modeling their behavior, and establishing mechanisms to efficiently deal with eruptions. The seismic signals coming from volcanoes are a great source of information about

Graphs are irregular structures that naturally represent the multifaceted data attributes; however, traditional approaches have been established outside signal processing and largely focus on analyzing the underlying graphs rather than signals on graphs. Given the rapidly increasing availability of multisensor and multinode measurements, likely recorded on irregular or ad hoc grids, it would be extremely

Sequential Monte Carlo methods, also known as particle filtering, have seen an explosion of development both in theory and applications. The publication of [1] sparked huge interest in the area of sequential signal processing, particularly in sequential filtering. Ever since, the number of publications in which particle filtering plays a prominent role has continued to grow. An early reference of development

One of the most important properties of linear time-invariant (LTI) systems is the superposition principle, which states that the overall response of an LTI system to a weighted sum of signals is simply the same weighted sum of the responses to each of the individual signals. In this "Lecture Notes" column, we will provide a deeper insight into this principle. In what follows, if the superposition

The Gaussian function (GF) is widely used to explain the behavior or statistical distribution of many natural phenomena as well as industrial processes in different disciplines of engineering and applied science. For example, the GF can be used to model an approximation of the Airy disk in image processing, a laser heat source in laser transmission welding [1], practical microscopic applications [2]

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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.