Showing papers in "IEEE Signal Processing Magazine in 2016"

A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data: With applications in machine learning and signal processing

Abstract: This article presents a powerful algorithmic framework for big data optimization, called the block successive upper-bound minimization (BSUM). The BSUM includes as special cases many well-known methods for analyzing massive data sets, such as the block coordinate descent (BCD) method, the convex-concave procedure (CCCP) method, the block coordinate proximal gradient (BCPG) method, the nonnegative matrix factorization (NMF) method, the expectation maximization (EM) method, etc. In this article, various features and properties of the BSUM are discussed from the viewpoint of design flexibility, computational efficiency, parallel/distributed implementation, and the required communication overhead. Illustrative examples from networking, signal processing, and machine learning are presented to demonstrate the practical performance of the BSUM framework.

High-Accuracy Localization for Assisted Living: 5G systems will turn multipath channels from foe to friend

Abstract: Asisted living (AL) technologies, enabled by technical advances such as the advent of the Internet of Things, are increasingly gaining importance in our aging society. This article discusses the potential of future high-accuracy localization systems as a key component of AL applications. Accurate location information can be tremendously useful to realize, e.g., behavioral monitoring, fall detection, and real-time assistance. Such services are expected to provide older adults and people with disabilities with more independence and thus to reduce the cost of caretaking. Total cost of ownership and ease of installation are paramount to make sensor systems for AL viable. In case of a radio-based indoor localization system, this implies that a conventional solution is unlikely to gain widespread adoption because of its requirement to install multiple fixed nodes (anchors) in each room. This article therefore places its focus on 1) discussing radiolocalization methods that reduce the required infrastructure by exploiting information from reflected multipath components (MPCs) and 2) showing that knowledge about the propagation environment enables localization with high accuracy and robustness. It is demonstrated that new millimeter-wave (mm-wave) technology, under investigation for 5G communications systems, will be able to provide centimeter (cm)-accuracy indoor localization in a robust manner, ideally suited for AL.

Radar Signal Processing for Elderly Fall Detection: The future for in-home monitoring

Abstract: Radar is considered an important technology for health monitoring and fall detection in elderly assisted living due to a number of attributes not shared by other sensing modalities. In this article, we describe the signal processing algorithms and techniques involved in elderly fall detection using radar. A human?s radar signal returns differ in their Doppler characteristics, depending on the nature of the human gross motor activities. These signals are nonstationary in nature, inviting time-frequency analysis in both its linear and bilinear aspects, to play a fundamental role in motion identification, including fall features determination and classification. This article employs real fall data to demonstrate the success of existing detection algorithms as well as to report on some of the challenges facing technology developments for fall detection.

Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges

Abstract: Recent developments in sensing and communication technologies have led to an explosion in the use of mobile devices such as smartphones and tablets. With the increase in the use of mobile devices, users must constantly worry about security and privacy, as the loss of a mobile device could compromise personal information. To deal with this problem, continuous authentication systems (also known as active authentication systems) have been proposed, in which users are continuously monitored after initial access to the mobile device. In this article, we provide an overview of different continuous authentication methods on mobile devices. We discuss the merits and drawbacks of the available approaches and identify promising avenues of research in this rapidly evolving field.

Monitoring Activities of Daily Living in Smart Homes: Understanding human behavior

Abstract: Monitoring the activities of daily living (ADLs) and detection of deviations from previous patterns is crucial to assessing the ability of an elderly person to live independently in their community and in early detection of upcoming critical situations. ?Aging in place? for an elderly person is one key element in ambient assisted living (AAL) technologies.

Computational Snapshot Multispectral Cameras: Toward dynamic capture of the spectral world

Abstract: Multispectral cameras collect image data with a greater number of spectral channels than traditional trichromatic sensors, thus providing spectral information at a higher level of detail. Such data are useful in various fields, such as remote sensing, materials science, biophotonics, and environmental monitoring. The massive scale of multispectral data-at high resolutions in the spectral, spatial, and temporal dimensions-has long presented a major challenge in spectrometer design. With recent developments in sampling theory, this problem has become more manageable through use of undersampling and constrained reconstruction techniques. This article presents an overview of these state-of-the-art multispectral acquisition systems, with a particular focus on snapshot multispectral capture, from a signal processing perspective. We propose that undersampling-based multispectral cameras can be understood and compared by examining the efficiency of their sampling schemes, which we formulate as the spectral sensing coherence information between their sensing matrices and spectrum-specific bases learned from a large-scale multispectral image database. We analyze existing snapshot multispectral cameras in this manner, and additionally discuss their optical performance in terms of light throughput and system complexity.

Matching Theory: Applications in wireless communications

Abstract: Matching theory is a powerful tool to study the formation of dynamic and mutually beneficial relations among different types of rational and selfish agents. It has been widely used to develop high performance, low complexity, and decentralized protocols. In this article, a comprehensive survey of matching theory, its variants, and their significant properties appropriate for the demands of wireless communications and network engineers is provided. Recent research progress in applying matching theory to wireless communications to address major technical opportunities and challenges is presented. A novel classification of matching models from the practical perspective is provided, and the properties and structure of each model are explained. This will enable a network designer to select an appropriate matching model for a specific application in wireless communications. Finally, the application of different matching models to various emerging wireless networks is discussed.

Principles of Light Field Imaging: Briefly revisiting 25 years of research

Abstract: Light field imaging offers powerful new capabilities through sophisticated digital processing techniques that are tightly merged with unconventional optical designs. This combination of imaging technology and computation necessitates a fundamentally different view of the optical properties of imaging systems and poses new challenges for the traditional signal and image processing domains. In this article, we aim to provide a comprehensive review of the considerations involved and the difficulties encountered in working with light field data.

Game Theory for Networks: A tutorial on game-theoretic tools for emerging signal processing applications

Abstract: The aim of this tutorial is to provide an overview, although necessarily incomplete, of game theory (GT) for signal processing (SP) in networks. One of the main features of this contribution is to gather in a single article some fundamental game-theoretic notions and tools that, over the past few years, have become widespread in the SP literature. In particular, both strategic-form and coalition-form games are described in detail, and the key connections and differences between them are outlined. Moreover, particular attention is also devoted to clarifying the connections between strategic-form games and distributed optimization and learning algorithms. Beyond an introduction to the basic concepts and main solution approaches, several carefully designed examples are provided to allow a better understanding of how to apply the described tools.

Compressive Covariance Sensing: Structure-based compressive sensing beyond sparsity

Abstract: Compressed sensing deals with the reconstruction of signals from sub-Nyquist samples by exploiting the sparsity of their projections onto known subspaces. In contrast, this article is concerned with the reconstruction of second-order statistics, such as covariance and power spectrum, even in the absence of sparsity priors. The framework described here leverages the statistical structure of random processes to enable signal compression and offers an alternative perspective at sparsity-agnostic inference. Capitalizing on parsimonious representations, we illustrate how compression and reconstruction tasks can be addressed in popular applications such as power-spectrum estimation, incoherent imaging, direction-of-arrival estimation, frequency estimation, and wideband spectrum sensing.

Recent Advances in Active Noise Control Inside Automobile Cabins: Toward quieter cars

Abstract: Minimization of interior cabin noise has been a key topic of research in the automobile industry for the last two decades. This problem was initially approached via passive noise cancelation methods, where physical treatments such as structural damping and acoustic absorption were used. However, with vehicle manufacturers striving for more economical and lightweight designs, the resulting car interiors invariably became noisier due to the increased structural vibrations. These noise fields are generally dominanted by low frequencies (i.e., 0-500 Hz) [1], [2], hence, the conventional passive noise cancelation approaches are less effective. In an attempt to resolve the aforementioned problem, active noise control (ANC) methods were developed where secondary sources were proposed to attenuate the noise inside the cabin. With modern in-car entertainment systems providing four to six built-in loudspeakers, the addition of an ANC system comes at a relatively low additional cost.

Combinations of Adaptive Filters: Performance and convergence properties

Abstract: Adaptive filters are at the core of many signal processing applications, ranging from acoustic noise supression to echo cancelation [1], array beamforming [2], channel equalization [3], to more recent sensor network applications in surveillance, target localization, and tracking. A trending approach in this direction is to recur to in-network distributed processing in which individual nodes implement adaptation rules and diffuse their estimation to the network [4], [5].

Coordination of Cooperative Autonomous Vehicles: Toward safer and more efficient road transportation

Abstract: While intelligent transportation systems come in many shapes and sizes, arguably the most transformational realization will be the autonomous vehicle. As such vehicles become commercially available in the coming years, first on dedicated roads and under specific conditions, and later on all public roads at all times, a phase transition will occur. Once a sufficient number of autonomous vehicles is deployed, the opportunity for explicit coordination appears. This article treats this challenging network control problem, which lies at the intersection of control theory, signal processing, and wireless communication. We provide an overview of the state of the art, while at the same time highlighting key research directions for the coming decades.

Device-Free Radio Vision for Assisted Living: Leveraging wireless channel quality information for human sensing Wireless propagation is conventionally considered as the enabling tool for transporting information

Abstract: It?s not difficult. Every time I lift my arm, it distorts a small electromagnetic field that is maintained continuously across the room. Slightly different positions of my hand and fingers produce different distortions and my robots can interpret these distortions as orders. I only use it for simple orders: Come here! Bring tea! and so on.

Bayesian Machine Learning: EEG\/MEG signal processing measurements

Abstract: Electroencephalography (EEG) and magnetoencephalog?raphy (MEG) are the most common noninvasive brain-imaging techniques for monitoring electrical brain activity and inferring brain function. The central goal of EEG/MEG analysis is to extract informative brain spatiotemporal?spectral patterns or to infer functional connectivity between different brain areas, which is directly useful for neuroscience or clinical investigations. Due to its potentially complex nature [such as nonstationarity, high dimensionality, subject variability, and low signal-to-noise ratio (SNR)], EEG/MEG signal processing poses some great challenges for researchers. These challenges can be addressed in a principled manner via Bayesian machine learning (BML). BML is an emerging field that integrates Bayesian statistics, variational methods, and machine-learning techniques to solve various problems from regression, prediction, outlier detection, feature extraction, and classification. BML has recently gained increasing attention and widespread successes in signal processing and big-data analytics, such as in source reconstruction, compressed sensing, and information fusion. To review recent advances and to foster new research ideas, we provide a tutorial on several important emerging BML research topics in EEG/MEG signal processing and present representative examples in EEG/MEG applications.

Sensors in Assisted Living: A survey of signal and image processing methods

Abstract: Our society will face a notable demographic shift in the near future. According to a United Nations report, the ratio of the elderly population (aged 60 years or older) to the overall population increased from 9.2% in 1990 to 11.7% in 2013 and is expected to reach 21.1% by 2050 [1]. According to the same report, 40% of older people live independently in their own homes. This ratio is about 75% in the developed countries. These facts will result in many societal challenges as well as changes in the health-care system, such as an increase in diseases and health-care costs, a shortage of caregivers, and a rise in the number of individuals unable to live independently [2]. Thus, it is imperative to develop ambient intelligence-based assisted living (AL) tools that help elderly people live independently in their homes. The recent developments in sensor technology and decreasing sensor costs have made the deployment of various sensors in various combinations viable, including static setups as well as wearable sensors. This article presents a survey that concentrates on the signal processing methods employed with different types of sensors. The types of sensors covered are pyro-electric infrared (PIR) and vibration sensors, accelerometers, cameras, depth sensors, and microphones.

Lensless Imaging: A computational renaissance

Abstract: The basic design of a camera has remained unchanged for centuries. To acquire an image, light from the scene under view is focused onto a photosensitive surface using a lens. Over the years, the photosensitive surface has evolved from a photographic film to an array of digital sensors. However, lenses remain an integral part of modern imaging systems in a broad range of applications.

Time-Varying Brain Connectivity in fMRI Data: Whole-brain data-driven approaches for capturing and characterizing dynamic states

Abstract: The study of whole-brain functional brain connectivity with functional magnetic resonance imaging (fMRI) has been based largely on the assumption that a given condition (e.g., at rest or during a task) can be evaluated by averaging over the entire experiment. In actuality, the data are much more dynamic, showing evidence of time-varying connectivity patterns, even within the same experimental condition. In this article, we review a family of blind-source separation (BSS) approaches that have proven useful for studying time-varying patterns of connectivity across the whole brain. Initial work in this direction focused on time-varying coupling among data-driven nodes, but more recently, timevarying nodes have also been considered. We also discuss extensions of these approaches, including transformations into the time-frequency domain. We provide a rich set of examples of various applications that yielded new information about healthy and diseased brains. Due in large part to developments in the field of signal processing, the fMRI community has seen major growth in the development of approaches that can capture whole-brain systemic connectivity information (connectomics) while also allowing this system to evolve over time as it naturally does (i.e., chronnectomics).

Joint Blind Source Separation for Neurophysiological Data Analysis: Multiset and multimodal methods

Abstract: Conventional blind source separation (BSS) methods have become widely adopted tools for neurophysiological data analysis. However, the increasing availability of multiset and multimodal neurophysiological data has posed new challenges for BSS methods originally designed to analyze one data set at a time. Concomitantly, there is growing recognition that joint analysis of neurophysiological data has the potential to substantially enhance our understanding of brain function by extracting information from complementary modalities and synergistically combining the results. Therefore, joint data analysis methods represent both a challenge and an opportunity for the neurophysiological signal processing community that attempts to enhance understanding of normal brain function and the pathophysiology of many brain diseases. Over the past decade, various joint blind source separation (JBSS) methods have been proposed to simultaneously accommodate multiple data sets. In this article, we provide an overview and taxonomy of representative JBSS methods. We show, through illustrative numerical simulations, that different statistical assumptions and tradeoffs underlie different JBSS methods, affecting which method should be ideally chosen for a given application. We then discuss several real-world neurophysiological applications from both multiset and multimodal perspectives, highlighting the benefits of the JBSS methods as effective and promising tools for neurophysiological data analysis. Finally, we discuss remaining challenges for future JBSS development.

Inertial Measurement Unit-Based Wearable Computers for Assisted Living Applications: A signal processing perspective

Abstract: There has been a very rapid growth in wearable computers over the past few years. Assisted living applications leveraging wearable computers will enable a healthier lifestyle and independence in a variety of target populations, including those suffering from neurological disorders, patients in need of rehabilitation after surgical procedures or injury, the elderly, individuals who might be at high risk of emotional stress, and those who are looking for a healthier lifestyle. Application paradigms for assisted living include activities of daily living (ADLs) monitoring, indoor localization, emergency and fall detection, and rehabilitation. All of these applications require monitoring of movements and physical activities for individuals. Wearable inertial measurement unit (IMU)-based sensors can offer low-cost and ubiquitous monitoring solutions for physical activities. Signal processing techniques with a focus on enhancing accuracy, lowering computational complexity, reducing power consumption, and improving the unobtrusiveness of the wearable computers are of interest in this article, which constitutes the first attempt made at reviewing the literature of wearable IMU-based signal processing techniques for assisted living applications. Various signal processing techniques with the aforementioned performance metrics in mind are reviewed here.

Signal Processing for Time-of-Flight Imaging Sensors: An introduction to inverse problems in computational 3-D imaging

Abstract: Time-of-flight (ToF) sensors offer a cost-effective and realtime solution to the problem of three-dimensional (3-D) imaging-a theme that has revolutionized our sceneunderstanding capabilities and is a topic of contemporary interest across many areas of science and engineering. The goal of this tutorial-style article is to provide a thorough understanding of ToF imaging systems from a signal processing perspective that is useful to all application areas. Starting with a brief history of the ToF principle, we describe the mathematical basics of the ToF image-formation process, for both time- and frequency-domain, present an overview of important results within the topic, and discuss contemporary challenges where this emerging area can benefit from the signal processing community. In particular, we examine case studies where inverse problems in ToF imaging are coupled with signal processing theory and methods, such as sampling theory, system identification, and spectral estimation, among others. Through this exposition, we hope to establish that ToF sensors are more than just depth sensors; depth information may be used to encode other forms of physical parameters, such as, the fluorescence lifetime of a biosample or the diffusion coefficient of turbid/scattering medium. The MATLAB scripts and ToF sensor data used for reproducing figures in this article are available via the author?s webpage: http://www.mit.edu/~ayush/Code.

The Connected Brain: Causality, models, and intrinsic dynamics

Abstract: Recently, there have been several concerted international efforts-the BRAIN Initiative, the European Human Brain Project, and the Human Connectome Project, to name a few-that hope to revolutionize our understanding of the connected brain. During the past two decades, functional neuroimaging has emerged as the predominant technique in systems neuroscience. This is foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. In this article, we summarize pedagogically the (deep) history of brain mapping. We highlight the theoretical advances made in the (dynamic) causal modeling of brain function, which may have escaped the wider audience of this article, and provide a brief overview of recent developments and interesting clinical applications. We hope that this article engages the signal processing community by showcasing the inherently multidisciplinary nature of this important topic and the intriguing questions that are being addressed.

Acoustic Microphone Geometry Calibration: An overview and experimental evaluation of state-of-the-art algorithms

Abstract: Today, we are often surrounded by devices with one or more microphones, such as smartphones, laptops, and wireless microphones. If they are part of an acoustic sensor network, their distribution in the environment can be beneficially exploited for various speech processing tasks. However, applications like speaker localization, speaker tracking, and speech enhancement by beamforming avail themselves of the geometrical configuration of the sensors. Therefore, acoustic microphone geometry calibration has recently become a very active field of research. This article provides an application-oriented, comprehensive survey of existing methods for microphone position self-calibration, which will be categorized by the measurements they use and the scenarios they can calibrate. Selected methods will be evaluated comparatively with real-world recordings.

The Race to Improve Radar Imagery: An overview of recent progress in statistical sparsity-based techniques

Abstract: The exploitation of sparsity has significantly advanced the field of radar imaging over the last few decades, leading to substantial improvements in the resolution and quality of the processed images. More recent developments in compressed sensing (CS) suggest that statistical sparsity can lead to further performance benefits by imposing sparsity as a statistical prior on the considered signal. In this article, a comprehensive survey is made of recent progress on statistical sparsitybased techniques for various radar imagery applications.

Driver-Behavior Modeling Using On-Road Driving Data: A new application for behavior signal processing

Abstract: This article reviews data-centric approaches for statistical modeling of driver behavior. Modeling driver behavior is challenging due to its stochastic nature and the high degree of inter- and intradriver variability. One way to deal with the highly variable nature of driving behavior is to employ a data-centric approach that models driver behavior using large amounts of driving data collected from numerous drivers in a variety of traffic conditions. To obtain large amounts of realistic driving data, several projects have collected real-world driving data. Statistical machine-learning techniques, such as hidden Markov models (HMMs) and deep learning, have been successfully applied to model driver behavior using large amounts of driving data. We have also collected on-road data recording hundreds of drivers over more than 15 years. We have applied statistical signal processing and machine-learning techniques to this data to model various aspects of driver behavior, e.g., driver pedal-operation, car-following, and lane-change behaviors for predicting driver behavior and detecting risky driver behavior and driver frustration. By reviewing related studies and providing concrete examples of our own research, this article is intended to illustrate the usefulness of such data-centric approaches for statistical driver-behavior modeling.

Why Time Reversal for Future 5G Wireless? [Perspectives]

Abstract: As the demand for wireless voice and data services has continued to grow dramatically, operators struggle to satisfy this demand with acceptable quality of service. The main approach until now was to increase the system bandwidth and spectral efficiency. For instance, there was an almost tenfold increase for each new generation of cellular technology [the first generation (1G) technology can support up to 30 kHz, second generation (2G) around 200 kHz, third generation (3G) around 1.25?5 MHz, and fourth generation (4G) up to 20 MHz]. Meanwhile, technologists have begun seeking more innovative and efficient communication technologies to meet the ever-increasing demand for data traffic with advanced signal processing capabilities for the 5G wireless communication systems. It is expected that 95% of data traffic will come from indoor locations in a few years [1]. Compared to outdoor propagation, wireless medium in an indoor environment often exhibits richer multipath propagation mostly without a strong line-of-sight (LOS) component, which makes the design of 5G indoor communication systems even more challenging.

JPEG XT: A Compression Standard for HDR and WCG Images [Standards in a Nutshell]

Abstract: High bit depth data acquisition and manipulation have been largely studied at the academic level over the last 15 years and are rapidly attracting interest at the industrial level. An example of the increasing interest for high-dynamic range (HDR) imaging is the use of 32-bit floating point data for video and image acquisition and manipulation that allows a variety of visual effects that closely mimic the real-world visual experience of the end user [1] (see Figure 1). At the industrial level, we are witnessing increasing traction toward supporting HDR and wide color gamut (WCG). WCG leverages HDR for each color channel to display a wider range of colors. Consumer cameras are currently available with a 14- or 16-bit analog-to-digital converter. Rendering devices are also appearing with the capability to display HDR images and video with a peak brightness of up to 4,000 nits and to support WCG (ITU-R Rec. BT.2020 [2]) rather than the historical ITU-R Rec. BT.709 [3]. This trend calls for a widely accepted standard for higher bit depth support that can be seamlessly integrated into existing products and applications.

Conversational In-Vehicle Dialog Systems: The past, present, and future

Abstract: Automotive technology rapidly advances with increasing connectivity and automation. These advancements aim to assist safe driving and improve user travel experience. Before the realization of a full automation, in-vehicle dialog systems may reduce the driver distraction from many services available through connectivity. Even when a full automation is realized, in-vehicle dialog systems still play a special role in assisting vehicle occupants to perform various tasks. On the other hand, in-vehicle use cases need to address very different user conditions, environments, and industry requirements than other uses. This makes the development of effective and efficient in-vehicle dialog systems challenging; it requires multidisciplinary expertise in automatic speech recognition, spoken language understanding, dialog management (DM), natural language generation, and application management, as well as field system and safety testing. In this article, we review research and development (RaD) activities for in-vehicle dialog systems from both academic and industrial perspectives, examine findings, discuss key challenges, and share our visions for voice-enabled interaction and intelligent assistance for smart vehicles over the next decade.

Smart Driver Monitoring: When Signal Processing Meets Human Factors: In the driver's seat

Abstract: This article provides an interdisciplinary perspective on driver monitoring systems by discussing state-of-the-art signal processing solutions in the context of road safety issues identified in human factors research. Recently, the human factors community has made significant progress in understanding driver behaviors and assessed the efficacy of various interventions for unsafe driving practices. In parallel, the signal processing community has had significant advancements in developing signal acquisition and processing methods for driver monitoring systems. This article aims to bridge these efforts and help initiate new collaborations across the two fields. Toward this end, we discuss how vehicle measures, facial/body expressions, and physiological signals can assist in improving driving safety through adaptive interactions with the driver, based on the driver's state and driving environment. Moreover, by highlighting the current human factors research in road safety, we provide insights for building feedback and mitigation technologies, which can act both in real time and postdrive. We provide insights into areas with great potential to improve driver monitoring systems, which have not yet been extensively studied in the literature, such as affect recognition and data fusion. Finally, a high-level discussion is given on the challenges and possible future directions for driver monitoring systems.

Indoor Global Positioning System with Centimeter Accuracy Using Wi-Fi [Applications Corner]

Abstract: The global positioning system (GPS) is a space-based navigation system that can provide location and time information whenever there is an unobstructed line of sight (LOS) to four or more GPS satellites [1]. Such a system provides critical capabilities to military, civil, and commercial applications around the world. On the other hand, considering the fact that people today spend more than 80% of their time in indoor environments, accurate indoor localization is highly desirable and has a great potential impact on many applications. Unfortunately, the use of GPS satellites to enable indoor localization is a nonstarter due to a variety of reasons including poor signal strength, multipath effect, and limited on-device computation and communication power [2]. Therefore, over the past two decades, the research community has been urgently seeking new technologies that can enable high-accuracy indoor localization. However, the results are still mostly unsatisfactory. Microsoft hosted Indoor Localization Competitions in recent years and concluded that "the indoor location problem is not solved" [3].