Multi-channel Representation Research Articles

An RGB Multi-Channel Representation for Images on Quantum Computers

RGB multi channel representation is proposed for images on quantum computers (MCQI) that captures information about colors (RGB channels) and their corresponding positions in an image in a normalized quantum state. The proposed representation makes it possible to store the RGB information about an image simultaneously by using 2n+3 qubits for encoding 2n× 2npixel images, whereas pixel-wise processing is necessary in many other quantum image representations, e.g., qubit lattice, grid qubit, and quantum lattice. Simulation of storage and retrieval of MCQI images using human facial images demonstrated that 15 qubits are required for encoding 64 × 64 colored images, and encoded information is retrieved by measurement. Perspectives of designing quantum image operators are also discussed based onMCQI representation, e.g., channel of interest, channel swapping, and restrict version of color transformation.

Classification of multichannel EEG patterns using parallel hidden Markov models

In this paper, a parallel hidden-Markov-model (PHMM)-based approach is proposed for the problem of multichannel electroencephalogram (EEG) patterns classification. The approach is based on multi-channel representation of the EEG signals using a parallel combination of HMMs, where each model represents a particular channel. The performance of the proposed algorithm is studied using an artificial EEG database, and two real EEG databases: a database of two classes of EEGs elicited during a task of imagery of hand upward and downward movements of a computer screen cursor (db Ia), and a database of two classes of sensorimotor EEGs elicited during a feedback-regulated left-right motor imagery task (db III). The results show that the proposed algorithm outperforms other commonly used methods with classification rate improvement of 2 and 10% for db Ia and db III, respectively. In addition, the proposed method outperforms a support vector machine classifier with a linear kernel, when both classifiers utilize the same feature set. The results also show that a model architecture which includes a left-to-right scheme with no skips, five states and three Gaussians, outperforms the other tested architectures due to the fact that it allows a better modeling of the temporal sequencing of the EEG components.

Different rooms, different voices: double-breasting, multi-channel representation and the managerial agenda

Double-breasting has been identified as where companies run union voice and non-union voice mechanisms across different plants. While research has focused on the incidence of such arrangements, there is a dearth of evidence into the dynamics of it. This article seeks to complement existing research by examining the contours of double-breasting in a case study organisation. The findings suggest that more research is necessary into the dynamics of double-breasting in terms of how voice in sites affects each other and the extent to which running different regimes affects the managerial agenda.

The Innovations Approach to Single Frame Multichannel Blind Image Deconvolution

The linear mixture model (LMM) has recently been used for multi-channel representation of a blurred image. This enables use of multivariate data analysis methods such as independent component analysis (ICA) to solve blind image deconvolution as an instantaneous blind source separation (BSS) requiring no a priori knowledge about the size and origin of the blurring kernel. However, there remains a serious weakness of this approach: statistical dependence between hidden variables in the LMM. The contribution of this paper is an application of the ICA algorithms to the innovations of the LMM to learn the unknown basis matrix. The hidden source image is recovered by applying pseudo-inverse of the learnt basis matrix to the original LMM. The success of this approach is due to the property of the innovations of being more independent and more non-Gaussian than original processes. Our good, consistent simulation and experimental results demonstrate viability of the proposed concept.

Collaborative filtering based on multi-channel diffusion

In this paper, by applying a diffusion process, we propose a new index to quantify the similarity between two users in a user–object bipartite graph. To deal with the discrete ratings on objects, we use a multi-channel representation where each object is mapped to several channels with the number of channels being equal to the number of different ratings. Each channel represents a certain rating and a user having voted an object will be connected to the channel corresponding to the rating. Diffusion process taking place on such a user–channel bipartite graph gives a new similarity measure of user pairs, which is further demonstrated to be more accurate than the classical Pearson correlation coefficient under the standard collaborative filtering framework.

Multichannel Texture Segmentation Using Bamberger Pyramids

a multichannel texture segmentation algorithm is presented based on the image pyramids produced with the Bamberger directional filter bank. An extensive evaluation of Bamberger pyramids and their design parameters is presented. The impact on segmentation performance of factors like the number of pyramid levels, number of directional channels, redundancy and filter specifications is considered. The proposed system is shown to provide some of the best results reported to date when compared with other multichannel representations under similar evaluation conditions. It is further shown that segmentation results using the maximally decimated directional filter bank rival those of the undecimated case. To the knowledge of the authors, such performance has not been previously observed for decompositions with decimated channels.

Morphological Diversity and Sparsity for Multichannel Data Restoration

Over the last decade, overcomplete dictionaries and the very sparse signal representations they make possible, have raised an intense interest from signal processing theory. In a wide range of signal processing problems, sparsity has been a crucial property leading to high performance. As multichannel data are of growing interest, it seems essential to devise sparsity-based tools accounting for such specific multichannel data. Sparsity has proved its efficiency in a wide range of inverse problems. Hereafter, we address some multichannel inverse problems issues such as multichannel morphological component separation and inpainting from the perspective of sparse representation. In this paper, we introduce a new sparsity-based multichannel analysis tool coined multichannel Morphological Component Analysis (mMCA). This new framework focuses on multichannel morphological diversity to better represent multichannel data. This paper presents conditions under which the mMCA converges and recovers the sparse multichannel representation. Several experiments are presented to demonstrate the applicability of our approach on a set of multichannel inverse problems such as morphological component decomposition and inpainting.

Single-Trial Decoding of Bistable Perception Based on Sparse Nonnegative Tensor Decomposition

The study of the neuronal correlates of the spontaneous alternation in perception elicited by bistable visual stimuli is promising for understanding the mechanism of neural information processing and the neural basis of visual perception and perceptual decision-making. In this paper, we develop a sparse nonnegative tensor factorization-(NTF)-based method to extract features from the local field potential (LFP), collected from the middle temporal (MT) visual cortex in a macaque monkey, for decoding its bistable structure-from-motion (SFM) perception. We apply the feature extraction approach to the multichannel time-frequency representation of the intracortical LFP data. The advantages of the sparse NTF-based feature extraction approach lies in its capability to yield components common across the space, time, and frequency domains yet discriminative across different conditions without prior knowledge of the discriminating frequency bands and temporal windows for a specific subject. We employ the support vector machines (SVMs) classifier based on the features of the NTF components for single-trial decoding the reported perception. Our results suggest that although other bands also have certain discriminability, the gamma band feature carries the most discriminative information for bistable perception, and that imposing the sparseness constraints on the nonnegative tensor factorization improves extraction of this feature.

Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization

The study proposes a method for supervised classification of multi-channel surface electromyographic signals with the aim of controlling myoelectric prostheses. The representation space is based on the discrete wavelet transform (DWT) of each recorded EMG signal using unconstrained parameterization of the mother wavelet. The classification is performed with a support vector machine (SVM) approach in a multi-channel representation space. The mother wavelet is optimized with the criterion of minimum classification error, as estimated from the learning signal set. The method was applied to the classification of six hand movements with recording of the surface EMG from eight locations over the forearm. Misclassification rate in six subjects using the eight channels was (mean ± S.D.) 4.7 ± 3.7% with the proposed approach while it was 11.1 ± 10.0% without wavelet optimization (Daubechies wavelet). The DWT and SVM can be implemented with fast algorithms, thus, the method is suitable for real-time implementation.

Beyond sparsity: Recovering structured representations by ${\ell}^1$ minimization and greedy algorithms

Finding a sparse approximation of a signal from an arbitrary dictionary is a very useful tool to solve many problems in signal processing. Several algorithms, such as Basis Pursuit (BP) and Matching Pursuits (MP, also known as greedy algorithms), have been introduced to compute sparse approximations of signals, but such algorithms a priori only provide sub-optimal solutions. In general, it is difficult to estimate how close a computed solution is from the optimal one. In a series of recent results, several authors have shown that both BP and MP can successfully recover a sparse representation of a signal provided that it is sparse enough, that is to say if its support (which indicates where are located the nonzero coefficients) is of sufficiently small size. In this paper we define identifiable structures that support signals that can be recovered exactly by L1 minimization (Basis Pursuit) and greedy algorithms. In other words, if the support of a representation belongs to an identifiable structure, then the representation will be recovered by BP and MP. In addition, we obtain that if the output of an arbitrary decomposition algorithm is supported on an identifiable structure, then one can be sure that the representation is optimal within the class of signals supported by the structure. As an application of the theoretical results, we give a detailed study of a family of multichannel dictionaries with a special structure (corresponding to the representation problem X = AS Phi^T ) often used in, e.g., under-determined source separation problems or in multichannel signal processing. An identifiable structure for such dictionaries is defined using a generalization of Tropp's Babel function which combines the coherence of the mixing matrix A with that of the time-domain dictionary Phi, and we obtain explicit structure conditions which ensure that both L1 minimization and a multichannel variant of Matching Pursuit can recover structured multichannel representations. The multichannel Matching Pursuit algorithm is described in detail and we conclude with a discussion of some implications of our results in terms of blind source separation based on sparse decompositions

A scalable spherical microphone array for spatial sound capture

With recent popular consumer acceptance of surround-sound audio, there is the need to develop new technologies that will allow for more accurate sound-field recording commensurate with or exceeding current multichannel audio playback systems. Real-world playback systems can have widely varying geometries and number of channels due to space and aesthetic requirements. Thus, there is a need to have a general recording scheme that allows for computationally simple modifications of the signals to optimize the playback for any geometry and number of playback speakers. One would also like to have a recording system that is scalable for future advances in spatial audio playback technology. A spherical microphone array has been proposed as a solution to these requirements. A spherical array has the desired property of enabling a beam pattern that is steering direction invariant with a relatively simple beamformer structure. The spherical array topology enables scalability and an elegant and mathematically straightforward beamformer design for efficient sampling and processing of the acoustic field. This talk will describe how the spherical array beamformer decomposes the sound field into compact multichannel representations that allow computationally efficient transformations to uniquely tailor the playback signals to any specific playback geometry.

Multichannel Frequency Representation of a Signal using Harmonic Spline Basis Functions

This paper presents a new technique for multichannel frequency representation of a signal using harmonic spline basis functions. Spectral properties of these harmonic spline basis functions are discussed. These basis functions are band-pass in nature and are suitable for the harmonic analysis of distorted signal. Experimentally it has been shown that band-pass filter outputs (by properly choosing the filter center frequency) are associated with harmonics and are useful to detect and analyse transients in a signal voltage disturbance.

Audio recording and reproduction in CARROUSO: Getting closer to perfection?

State-of-the-art systems for spatial audio reproduction utilize two to six discrete playback channels. A problem inherent to these systems is the relatively small area where the listener is able to experience a true 3-D sound sensation. This so-called ‘‘sweet spot’’ can be significantly enlarged by using loudspeaker arrays in combination with wave field synthesis (WFS) technology, initially developed at Delft University. By following this approach, actual sonic spaces can be reproduced in their entirety and not only discrete multichannel representations thereof. While loudspeaker arrays can be used to reproduce sound fields, microphone arrays can be used for sound field capture and analysis. Having high-quality audio reproduction in mind, microphone array designs are presented that need to fulfill stricter requirements than what has been traditionally considered for microphone array applications. Information on acoustic source position is essential for WFS-based rendering techniques. As will be shown, joint audio–video object tracking proves to be efficient for this task. Moreover, full-duplex applications based on WFS technology, like high-quality teleconferencing or remote music teaching, call for sophisticated multichannel acoustic echo cancellation algorithms. The European project ‘‘CARROUSO’’ aims at developing, integrating, and building a real-time system that embraces all previously described technologies in an MPEG-4 context.

Model-based multirate Kalman filtering approach for optimal two-dimensional signal reconstruction from noisy subband systems

Conventional synthesis filters in subband systems lose their optimality when additive noise (due, for example, to signal quantization) disturbs the subband components. The multichannel representation of subband signals is combined with the statistical model of input signal to derive the multirate state-space model for the filter bank system with additive subband noises. Thus the signal reconstruction problem in subband systems can be formulated as the process of optimal state estimation in the equivalent multirate state-space model. Incorporated with the vector dynamical model, a 2-D multirate state-space model suitable for 2-D Kalman filtering is developed. The performance of the proposed 2-D multirate Kalman filter can be further improved through adaptive segmentation of the object plane. The object plane is partitioned into disjoint regions based on their spatial activity, and different vector dynamical models are used to characterize the nonstationary object-plane distributions. Finally, computer simulations with the proposed 2-D multirate Kalman filter give favorable results.

Physiology and interpretation of the electromyogram.

The purpose of this review is to consider some issues in the interpretation of the electromyogram (EMG) and to discuss current areas of controversy regarding use of the EMG. We consider the underlying physiology and origin of the EMG signal and offer an abbreviated discussion of measurement issues and selected factors that affect the characteristics of the EMG signal. We discuss many of the problems affecting interpretation, including normalization, crosstalk, and issues specific to contraction. In the final section, we consider topics of current interest in electromyography, such as muscle fatigue, task specificity, multichannel representations, and muscle fiber conduction velocity. We present, in addition, alternative analysis techniques. This review should interest researchers and clinicians who seek to obtain the valuable information inherent in the EMG while respecting the potential sources of variance and misinterpretation.

Duality functions in a meson-baryon model

Duality functions, i.e. constraints on the integration variables in a multichannel representation of the Veneziano form are developed for the multiparticle amplitude describing the emission (or absorption) of n elementary mesons by a baryon with an isobaric spectrum. The sequence of graphs related by duality consists of all distinct permutations of the order of absorption of the mesons by the baryon, and the resulting amplitude is expressible in terms of the resonance poles associated with any one of them. This is the generalisation of a model proposed by Burnett and Schwarz. This model has also recently been generalized in a completely different direction by Mandelstam.