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“Science without conscience is only ruin of the soul” said François Rabelais. This centuries-old quote still resonates, today maybe louder than ever. I began to write this editorial at the end of February when Russian tanks and soldiers invaded Ukraine and waves of bombers began dropping their bombs on Ukrainian cities, targeting civilian buildings, hospitals, and schools. This dramatic event was a

While I am writing this column, the Russia–Ukraine war is raging. As bombings, destruction, and human suffering flood the daily news, I deeply feel the pain of our Ukrainian colleagues, those who have friends and family in the affected areas, those who had to put their studies and careers on hold to fight for their survival. I also acknowledge the agony of those around the world who are watching the

Presents the recipients of SPS society awards.

Fire and water, two of nature’s basic forces, are each capable of sustaining or destroying life and property. Research projects in California and Hawaii are, respectively, helping displaced families cope with devasting wildfires, and investigating a way to increase water supply availability on isolated islands. Both projects are relying on signal processing to help them meet their goals.

The dramatic success of deep learning is largely due to the availability of data. Data samples are often acquired on edge devices, such as smartphones, vehicles, and sensors, and in some cases cannot be shared due to privacy considerations. Federated learning is an emerging machine learning paradigm for training models across multiple edge devices holding local data sets, without explicitly exchanging

Self-supervised representation learning (SSRL) methods aim to provide powerful, deep feature learning without the requirement of large annotated data sets, thus alleviating the annotation bottleneck—one of the main barriers to the practical deployment of deep learning today. These techniques have advanced rapidly in recent years, with their efficacy approaching and sometimes surpassing fully supervised

Augmented or mixed reality (AR/MR) is emerging as one of the key technologies in the future of computing. Audio cues are critical for maintaining a high degree of realism, social connection, and spatial awareness for various AR/MR applications, such as education and training, gaming, remote work, and virtual social gatherings to transport the user to an alternate world called the metaverse. Motivated

Timbre is the attribute of sound that makes, for example, two musical instruments playing the same note sound different. It is typically associated with the spectral (but also the temporal) envelope and assumed to be independent from the pitch (but also the loudness) of the sound [1] . This article shows how to design a simple but effective pitch-independent timbre feature, well adapted to musical

Massive multiple-input multiple-output (MIMO) and reconfigurable intelligent surface (RIS) are two promising technologies for 5G-and-beyond wireless networks, capable of providing large array gain and multiuser spatial multiplexing. Without requiring additional frequency bands, those technologies offer significant improvements in both spectral and energy efficiency by simultaneously serving many users

It is well known that the optimality of the Kalman filter relies on the Gaussian distribution of process and observation model errors, which in many situations is well justified [1] – [3] . However, this optimality is useless in applications where the distribution assumptions of the model errors do not hold in practice. Even minor deviations from the assumed (or nominal) distribution may cause the

Frequency and damping factor estimation, i.e., modal signal analysis, is a very important technical topic [1] – [11] . The Prony method [3] is widely used for frequency and damping estimation [4] , [5] and is quite popular, although it suffers from poor noise immunity. In this article, we present a simple-to-implement trick, without any additional computational load, that improves the noise immunity

Every person spends around one third of his/her life in bed. For an infant or a young toddler, this percentage can be much higher, and for bed-bound patients it can go up to 100% of their time. In-bed pose estimation is a critical step in many human behavior monitoring systems that are focused on prevention, prediction, and management of at-rest or sleep-related conditions in both adults and children

The annual IEEE 5-Minute Video Clip Contest (5-MICC) was launched in 2020 by the IEEE Signal Processing Society (SPS), and the selected topic for the competition at IEEE ICIP 2021 was “Automatic Medical Image Diagnosis.” The organizing committee selected three finalist videos and placed them online for public voting. The first one addresses a deep learning system that targets enhancement and disease

Presents the SPS society calendar of upcoming events and meetings.

For me and, probably, many readers, each issue of IEEE Signal Processing Magazine ( SPM ) is the opportunity and pleasure to learn something new in the area of signal and image processing. In addition to lecture notes, tips-and-tricks articles, special reports, and so on, which propose interesting and clever solutions to typical signal or image processing problems, the feature articles and special

The IEEE Signal Processing Society (SPS) is an international organization whose purpose is to advance and disseminate state-of-the-art scientific information and resources, educate the SP community, and provide a venue where people can interact and exchange ideas. To achieve its mission, the SPS relies heavily on volunteers working in the area of SP, governed by collaborative organizational practices

Robots are rapidly becoming an integral part of daily life. The mechanizing of routine tasks has been underway for decades, with development making particularly remarkable progress over the past several years. Now, with the development robots that can closely interact with humans, sensing users’ needs and often relieving people of dangerous tasks, robotic technology is entering a new phase of intimacy

A window function is a mathematical function that is zero valued outside some chosen interval [1] , [2] . For applications like filtering, detection, and estimation, the window functions take the form of limited time functions, which are in general real and even functions [3] , [4] , while for applications like beamforming and image processing, they are limited spatial functions. A spatial window can

Biological research on the fundamental structural and functional properties of life—from molecules to cells, tissues, organs, and complete organisms, including human life—relies heavily on advanced imaging systems and measurement devices generating data of ever-increasing quantity and complexity. Automated processing and analysis of these data through increasingly sophisticated computational methods

Recently, deep learning (DL) approaches have become the main research frontier for biological image reconstruction and enhancement problems thanks to their high performance and ultrafast inference times. However, due to the difficulty of obtaining matched reference data for supervised learning, there has been increasing interest in unsupervised learning approaches that do not need paired reference

Deep algorithm unrolling has emerged as a powerful, model-based approach to developing deep architectures that combine the interpretability of iterative algorithms with the performance gains of supervised deep learning, especially in cases of sparse optimization. This framework is well suited to applications in biological imaging, where physics-based models exist to describe the measurement process

Understanding how networks of neurons process information is one of the key challenges in modern neuroscience. A necessary step to achieving this goal is to be able to observe the dynamics of large populations of neurons over a large area of the brain. Light-field microscopy (LFM), which uses a type of scanless microscope, is a particularly attractive candidate for high-speed 3D imaging. It captures

The variety of bioimage data and their quality have dramatically increased over the last decade. In parallel, the number of proposed deep learning (DL) models for their analysis grows by the day. Yet, the adequate reuse of published tools by practitioners without DL expertise still raises many practical questions. In this article, we explore four categories of challenges faced by researchers when using

Deep learning (DL) has been extremely successful when applied to the analysis of natural images. By contrast, analyzing neuroimaging data presents some unique challenges, including higher dimensionality, smaller sample sizes, multiple heterogeneous modalities, and a limited ground truth. In this article, we discuss DL methods in the context of four diverse and important categories in the neuroimaging

Marked changes occur in the brain during people’s lives, and individual rates of aging have revealed pronounced differences, giving rise to subject-specific brainprints that are the signature of the brain. These are shaped by a great variety of factors, both endogenous and exogenous. Accurate predictions of brain age (BA) can be derived from neuroimaging endophenotypes by using machine and deep learning

Electroencephalogram (EEG) decoding aims to identify the perceptual, semantic, and cognitive content of neural processing based on noninvasively measured brain activity. Traditional EEG decoding methods have achieved moderate success when applied to data acquired in static, well-controlled lab environments. However, an open-world environment is a more realistic setting, where situations affecting EEG

Antenna array technology enables the directional transmission and reception of wireless signals for communication, localization, and sensing purposes. The signal processing algorithms that underpin it began to be developed several decades ago [1] , but it was with the deployment of 5G wireless mobile networks that the technology became mainstream [2] . The number of antenna elements in the arrays of

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Provides a listing of current staff, committee members and society officers.

First of all, I wish you and your relatives a very happy new year. I hope that 2022 will differ from the two previous years, in which the COVID-19 pandemic disrupted many of our lives, both personal and professional. Even if virtual events can have some advantages, I hope that the main conferences and workshops in 2022 will be held face to face or at least mixed, with both in-person and virtual interactions

The November 2021 IEEE Technical Activities Board meeting presentations articulated several warning signs and promising calls to action. A new, radical proposal to change the way IEEE elevates its Members to Fellow status may finally address the inclusion and equity issues that we discuss but have yet to address. The proposal is still in its infancy and was drafted by a committee chaired by our very

I am excited to start my service as the IEEE Signal Processing Society (SPS) president. I should note that I am the first SPS president directly elected by the SPS membership, due to the SPS Board of Governors (BOG) urging a stronger member voice in elections. This is a big honor for me and I would like to express my thanks to SPS members for their trust. I write this article to introduce myself, acknowledge

This year, all eligible IEEE Signal Processing Society (SPS) members were able to cast their votes for the next IEEE SPS president-elect. The president-elect will sit on the IEEE SPS Board of Governors (BoG) beginning 1 January 2022 and will serve until 31 December 2023, when she will be elevated to Society president. The Member-at-large results as well as the Regional director-at-large election results

The following volunteers have been named Awards Board chair and editor-in-chief of the IEEE Signal Processing Society: Sergios Theodoridis and Brendt Wohlberg.

In an age when signal processing lies at the core of so many different technologies, nothing is more important than its contribution to health care. From improved cardiac patient support to enhanced magnetic resonance imaging (MRI) and advanced diagnostics, signal processing is helping physicians work more safely, efficiently, and accurately. Here is a look at three important research projects that

The development of new materials has been a hallmark of technological advancement since at least the Bronze Age, when copper and tin were alloyed to create tools that had properties favorable to those made from stone. For thousands of years, until the development of microscope imaging, new materials continued to be developed “blindly,” without any understanding of the effects of the structure at the

The Markov random field (MRF) is one of the most widely used models in image processing, constituting a prior model for addressing problems such as image segmentation, object detection, and reconstruction. What is not often appreciated is that the MRF owes its origin to the physics of solids, making it an ideal prior model for processing microscopic observations of materials. While both fields know

The arrival of direct electron detectors (DEDs) with high frame rates in the field of scanning transmission electron microscopy (TEM) has enabled many experimental techniques that require collection of a full diffraction pattern at each scan position, a field which is subsumed under the name four-dimensional scanning transmission electron microscopy ( 4D-STEM ). DED frame rates approaching 100 kHz

Multiscale 3D characterization is widely used by materials scientists to further their understanding of the relationships between microscopic structure and macroscopic function. Scientific computed tomography (SCT) instruments are one of the most popular choices for 3D nondestructive characterization of materials at length scales ranging from the angstrom scale to the micron scale. These instruments

Lensless X-ray nanoimaging provides 3D views of a wide range of materials with a spatial resolution better than 10 nm. These advances are enabled in part by dramatic gains in coherent X-ray flux, but they also rely on advances in signal processing to obtain images from coherent diffraction data. We outline the scientific problems that can be addressed by X-ray nanoimaging, the various imaging approaches

Thermographic imaging is a fast and contactless way of inspecting material parts. Usually, with model-driven evaluation procedures, lateral heat flow is ignored, and, thus, 1D reconstruction is applied to detect defects. However, to correctly size defects, the lateral heat flow must be considered, which requires a full 3D reconstruction. The 3D thermal defect imaging is a major challenge because heat

We employ physics-informed neural networks (PINNs) to quantify the microstructure of polycrystalline nickel by computing the spatial variation of compliance coefficients (compressibility, stiffness, and rigidity) of the material. The PINNs are supervised with realistic ultrasonic surface acoustic wavefield data acquired at an ultrasonic frequency of 5 MHz for the polycrystalline material. The ultrasonic

Deep neural networks and deep learning have achieved great success in many signal and image processing applications, especially those with large-scale annotated training data for supervised learning. Although in principle deep-learning methods can be applied to boost the performance of processing materials-science images, i.e., microscopic images that capture important microstructures of various material

Transmission electron microscopy (TEM) is a powerful tool for imaging material structure and characterizing material chemistry. Recent advances in data collection technology for TEM have enabled high-volume and high-resolution data collection at a microsecond frame rate. Taking advantage of these advances in data collection rates requires the development and application of data processing tools, including

Materials researchers are generating data in ever-increasing volumes, and these data are, in turn, fueling the discovery and design of new materials. These changes are enabled by advances in data capture via laboratory information management systems (LIMS), high-throughput experiment and computation, improved data collection capabilities, automated experiment selection and execution, and new analytical

Deep fakes have recently become popular. The term refers to doctored media content where one’s face is swapped with someone else’s face or performs someone else’s face movements. In the last couple of years, numerous video clips, often involving celebrities and politicians, have gone viral on social media platforms. This has been enabled by easy-to-use apps capable of processing user-generated content

Several current ultrawide band applications, such as millimeter-wave radar and communication systems [1] – [3] , require high sampling rates and therefore expensive and energy-hungry analog-to-digital converters (ADCs). In applications where cost and power constraints exist, the use of high-precision ADCs is not feasible, and the designer must resort to ADCs with coarse quantization. Consequently,

The shape of a surface determines how it interacts with wireless radio-frequency signals. Taking a homogenous metal plate as an example, we can bend and rotate it in different ways to make the incident wireless signal become diffusely or specularly reflected in the desired manner. The same effect can be electronically achieved by using an intelligent reflecting surface (IRS), which is a 2D array of

Provides a notice of upcoming events of interest to practitioners and researchers.

Describes the above-named upcoming conference event. May include topics to be covered or calls for papers. The 29th IEEE International Conference on Image Processing (ICIP 2022) will be held in Bordeaux, France, on October 16- 19, 2022. ICIP is the world’s largest and most comprehensive technical conference focused on image and video processing and computer vision.

This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name

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