IEEE Signal Processing Magazine (IEEE SIGNAL PROC MAG )

Publisher: Institute of Electrical and Electronics Engineers; IEEE Signal Processing Society, Institute of Electrical and Electronics Engineers


The practical applications aspects of acoustics, speech, and signal processing.

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    IEEE signal processing magazine, Institute of Electrical and Electronics Engineers signal processing magazine, Signal processing magazine, I.E.E.E. signal processing magazine, IEEE SP magazine
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    Periodical, Internet resource
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    Journal / Magazine / Newspaper, Internet Resource

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Institute of Electrical and Electronics Engineers

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Publications in this journal

  • [show abstract] [hide abstract]
    ABSTRACT: We present the fundamentals of multiple-input, multiple-output (MIMO) signal processing for mode-division multiplexing (MDM) in multimode fiber (MMF). As an introduction, we review current long-haul optical transmission systems and how continued traffic growth motivates study of new methods to increase transmission capacity per fiber. We describe the key characteristics of MIMO channels in MMF, contrasting these with wireless MIMO channels. We review MMF channel models, the statistics derived from them, and their implications for MDM system performance and complexity. We show that optimizing performance and complexity requires management of channel parameters?particularly group delay (GD) spread and mode-dependent loss and gain?by design of transmission fibers and optical amplifiers, and by control of mode coupling along the link. We describe a family of fibers optimized for low GD spread, which decreases with an increasing number of modes. We compare the performance and complexity of candidate MIMO signal processing architectures in a representative long-haul system design, and show that programmable frequency-domain equalization (FDE) of chromatic dispersion (CD) and adaptive FDE of modal dispersion (MD) is an attractive combination. We review two major algorithms for adaptive FDE of MD?least mean squares (LMS) and recursive least squares (RLS)?and analyze their complexity, throughput efficiency, and convergence time. We demonstrate that, with careful physical link design and judicious choice of signal processing architectures, it is possible to overcome MIMO signal processing challenges in MDM systems.
    IEEE Signal Processing Magazine 01/2014; 31(2):25-34.
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    ABSTRACT: Starting with a simple generative model and the assumption of statistical independence of the underlying components, independent component analysis (ICA) decomposes a given set of observations by making use of the diversity in the data, typically in terms of statistical properties of the signal. Most of the ICA algorithms introduced to date have considered one of the two types of diversity: non-Gaussianity?i.e., higher-order statistics (HOS)?or, sample dependence. A recent generalization of ICA, independent vector analysis (IVA), generalizes ICA to multiple data sets and adds the use of one more diversity, dependence across multiple data sets for achieving an independent decomposition, jointly across multiple data sets. Finally, both ICA and IVA, when implemented in the complex domain, enjoy the addition of yet another type of diversity, noncircularity of the sources?underlying components. Mutual information rate provides a unifying framework such that all these statistical properties?types of diversity?can be jointly taken into account for achieving the independent decomposition. Most of the ICA methods developed to date can be cast as special cases under this umbrella, as well as the more recently developed IVA methods. In addition, this formulation allows us to make use of maximum likelihood theory to study large sample properties of the estimator, derive the Cram?r?Rao lower bound (CRLB) and determine the conditions for the identifiability of the ICA and IVA models. In this overview article, we first present ICA, and then its generalization to multiple data sets, IVA, both using mutual information rate, present conditions for the identifiability of the given linear mixing model and derive the performance bounds. We address how various methods fall under this umbrella and give examples of performance for a few sample algorithms compared with the performance bound. We then discuss the importance of approaching the performance bound depending on the goal, and use - edical image analysis as the motivating example.
    IEEE Signal Processing Magazine 01/2014; 31(3):18-33.
  • IEEE Signal Processing Magazine 01/2014;
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    ABSTRACT: Advances in hyperspectral sensing provide new capability for characterizing spectral signatures in a wide range of physical and biological systems, while inspiring new methods for extracting information from these data. HSI data often lie on sparse, nonlinear manifolds whose geometric and topological structures can be exploited via manifold-learning techniques. In this article, we focused on demonstrating the opportunities provided by manifold learning for classification of remotely sensed data. However, limitations and opportunities remain both for research and applications. Although these methods have been demonstrated to mitigate the impact of physical effects that affect electromagnetic energy traversing the atmosphere and reflecting from a target, nonlinearities are not always exhibited in the data, particularly at lower spatial resolutions, so users should always evaluate the inherent nonlinearity in the data. Manifold learning is data driven, and as such, results are strongly dependent on the characteristics of the data, and one method will not consistently provide the best results. Nonlinear manifold-learning methods require parameter tuning, although experimental results are typically stable over a range of values, and have higher computational overhead than linear methods, which is particularly relevant for large-scale remote sensing data sets. Opportunities for advancing manifold learning also exist for analysis of hyperspectral and multisource remotely sensed data. Manifolds are assumed to be inherently smooth, an assumption that some data sets may violate, and data often contain classes whose spectra are distinctly different, resulting in multiple manifolds or submanifolds that cannot be readily integrated with a single manifold representation. Developing appropriate characterizations that exploit the unique characteristics of these submanifolds for a particular data set is an open research problem for which hierarchical manifold structures appear to h- ve merit. To date, most work in manifold learning has focused on feature extraction from single images, assuming stationarity across the scene. Research is also needed in joint exploitation of global and local embedding methods in dynamic, multitemporal environments and integration with semisupervised and active learning.
    IEEE Signal Processing Magazine 01/2014; 31(1):55-66.
  • IEEE Signal Processing Magazine 01/2014; 31(1):6-6.
  • IEEE Signal Processing Magazine 01/2014; 31(3):16-17.
  • [show abstract] [hide abstract]
    ABSTRACT: The separation of speech signals measured at multiple microphones in noisy and reverberant environments using only the audio modality has limitations because there is generally insufficient information to fully discriminate the different sound sources. Humans mitigate this problem by exploiting the visual modality, which is insensitive to background noise and can provide contextual information about the audio scene. This advantage has inspired the creation of the new field of audiovisual (AV) speech source separation that targets exploiting visual modality alongside the microphone measurements in a machine. Success in this emerging field will expand the application of voice-based machine interfaces, such as Siri, the intelligent personal assistant on the iPhone and iPad, to much more realistic settings and thereby provide more natural human?machine interfaces.
    IEEE Signal Processing Magazine 01/2014; 31(3):125-134.
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    ABSTRACT: In recent years, source separation has been a central research topic in music signal processing, with applications in stereo-to-surround up-mixing, remixing tools for disc jockeys or producers, instrument-wise equalizing, karaoke systems, and preprocessing in music analysis tasks. Musical sound sources, however, are often strongly correlated in time and frequency, and without additional knowledge about the sources, a decomposition of a musical recording is often infeasible. To simplify this complex task, various methods have recently been proposed that exploit the availability of a musical score. The additional instrumentation and note information provided by the score guides the separation process, leading to significant improvements in terms of separation quality and robustness. A major challenge in utilizing this rich source of information is to bridge the gap between high-level musical events specified by the score and their corresponding acoustic realizations in an audio recording. In this article, we review recent developments in score-informed source separation and discuss various strategies for integrating the prior knowledge encoded by the score.
    IEEE Signal Processing Magazine 01/2014; 31(3):116-124.
  • [show abstract] [hide abstract]
    ABSTRACT: Professor of electrical and computer engineering, University of Minnesota.
    IEEE Signal Processing Magazine 01/2014; 31(1):157-159.
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    ABSTRACT: Cycle slip (CS) compensation is a critical technique for nondifferential coded coherent optical transmission. By monitoring the sparse, asymmetric polarization block-coded symbol mapped signal phases of two orthogonal polarizations, the CS can be estimated from a relatively short stretch of symbols (unit). Simulation shows that the polarization block coding-based method compensates CS and improves the Q-factor by 1 dB or more compared to differential coding.
    IEEE Signal Processing Magazine 01/2014; 31(2):57-69.
  • [show abstract] [hide abstract]
    ABSTRACT: Image inpainting refers to the process of restoring missing or damaged areas in an image. This field of research has been very active over recent years, boosted by numerous applications: restoring images from scratches or text overlays, loss concealment in a context of impaired image transmission, object removal in a context of editing, or disocclusion in image-based rendering (IBR) of viewpoints different from those captured by the cameras. Although earlier work dealing with disocclusion has been published in [1], the term inpainting first appeared in [2] by analogy with a process used in art restoration.
    IEEE Signal Processing Magazine 01/2014; 31(1):127-144.
  • [show abstract] [hide abstract]
    ABSTRACT: The articles in this special section aims to highlight diverse recent advances in digital signal processing (DSP)and coding, enabling TbE and multi-TbE optical transport while addressing bandwidth and energy constraints. The addressed topics range from sophisticated modulation and coding schemes to advanced detection schemes. The multi-Tb/s optical transports over either singlemode fibers or few-mode/few-core fibers are also topics covered in this special issue. The main technologies covered by this special issue comprise multiple-input, multiple-output (MIMO) signal processing, advanced multilevel and multidimensional modulation schemes, advanced multiplexing schemes, signal processing for superchannel transmission, advanced DSP for signal detection and equalization, and advanced coding, all aiming at achieving multi-Tb/s optical transports.
    IEEE Signal Processing Magazine 01/2014; 31(2):15-142.
  • IEEE Signal Processing Magazine 01/2014; 31(1):18-21.
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    ABSTRACT: Hyperspectral imaging applications are many and span civil, environmental, and military needs. Typical examples include the detection of specific terrain features and vegetation, mineral, or soil types for resource management; detecting and characterizing materials, surfaces, or paints; the detection of man-made materials in natural backgrounds for the purpose of search and rescue; the detection of specific plant species for the purposes of counter narcotics; and the detection of military vehicles for the purpose of defense and intelligence. The objective of this article is to provide a tutorial overview of detection algorithms used in current hyperspectral imaging systems that operate in the reflective part of the spectrum (0.4 - 24 μm.) The same algorithms might be used in the long-wave infrared spectrum; however, the phenomenology is quite different. The covered topics and the presentation style have been chosen to illustrate the strong couplings among the underlying phenomenology, the theoretical framework for algorithm development and analysis, and the requirements of practical applications.
    IEEE Signal Processing Magazine 01/2014; 31(1):24-33.
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    ABSTRACT: The analysis, processing, and parameters estimation of signals whose spectral content changes in time are of crucial interest in many applications, including radar, acoustics, biomedicine, communications, multimedia, seismic, and the car industry [1]? [11]. Various signal representations have been introduced to deal with this kind of signals within the area known as time-frequency (TF) signal analysis. The oldest analysis tool in this area is the short-time Fourier transform (STFT), as a direct extension of the classical Fourier analysis. The other key tool is the Wigner distribution (WD), introduced in signal analysis from quantum mechanics. The aim of this lecture note is to present and relate these two of the most important tools in the TF signal analysis, the STFT and the WD (introduced by two Nobel prize winners, D. Gabor and E. Wigner, respectively). This relation is a basis for the S-method (SM), an efficient and simple TF signal analysis tool providing a gradual transition between these two representations.
    IEEE Signal Processing Magazine 01/2014; 31(3):163-174.
  • IEEE Signal Processing Magazine 01/2014; 31(3):4-4.
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    ABSTRACT: Source separation models that make use of nonnegativity in their parameters have been gaining increasing popularity in the last few years, spawning a significant number of publications on the topic. Although these techniques are conceptually similar to other matrix decompositions, they are surprisingly more effective in extracting perceptually meaningful sources from complex mixtures. In this article, we will examine the various methodologies and extensions that make up this family of approaches and present them under a unified framework. We will begin with a short description of the basic concepts and in the subsequent sections we will delve in more details and explore some of the latest extensions.
    IEEE Signal Processing Magazine 01/2014; 31(3):66-75.
  • [show abstract] [hide abstract]
    ABSTRACT: It is argued that the current peer-review system has some drawbacks - this does not only apply to conference papers but also to journal submissions. The major challenge is to find experienced reviewers who would be able to complete reviews in the short time frame provided. There is no doubt that the shorter the review time allocated to experts is, the better it is for the authors and the journals in general; every one of us would like to see his or her fresh, original ideas published as soon as possible, and every editor wishes a minimum turnaround time of paper reviews. The challenge for editors is therefore to secure comprehensive reviews so as to make an expert decision, acceptable to the authors, and in the shortest amount of time. A central question has always been a topic of discussion at many editorial board meetings this Editor has attended over the years: "How can one secure high-quality reviews by experienced researchers?" It is concluded that we will continue to struggle to find three reviewers for each single submitted paper and, in a situation like ICASSP 2014, with a number of over 3,500 submissions, simple mathematics will show that this is an overwhelming task for all involved. The Editor takes this opportunity to wholeheartedly thank all of the colleagues and friends who are always willing to help review articles for IEEE Signal Processing Magazine.
    IEEE Signal Processing Magazine 01/2014; 31(2):4-4.
  • IEEE Signal Processing Magazine 01/2014; 31(1):4-4.
  • [show abstract] [hide abstract]
    ABSTRACT: In active sensing, transmitters emit probing waveforms into the environment. The probing waveforms interact with scatters that reflect distorted copies of the waveforms. Receivers then measure the distorted copies to infer information about the environment. The choice of the probing waveform is important because it affects slant range resolution, Doppler tolerance, clutter, and electronic countermeasures. A traditional performance metric for the probing waveform is the ambiguity function, which describes the correlation between the waveform and a delayed and (narrowband) Doppler shifted copy of the same waveform [1]. The direct synthesis of a waveform given a desired ambiguity function is exceedingly difficult [2]. Often designers focus on optimizing only the waveform?s autocorrelation function (which is the zero Doppler cut of the ambiguity function). Any method that optimizes the autocorrelation function is implicitly performing spectral shaping by trying to flatten the passband of the waveform?s spectrum [1], [2].
    IEEE Signal Processing Magazine 01/2014; 31(3):157-162.

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