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Multivariate or multichannel data have become ubiquitous in many modern scientific and engineering applications, e.g., biomedical engineering, owing to recent advances in sensor and computing technology. Processing these data sets is challenging owing to their large size and multidimensional nature, thus requiring specialized algorithms and efficient hardware designs for online and real time processing. In this paper, we address this issue by proposing a fully FPGA based hardware architecture of a popular multi-scale and multivariate signal processing algorithm, termed as multivariate empirical mode decomposition (MEMD). MEMD is a data-driven method that extends the functionality of standard empirical mode decomposition (EMD) algorithm to multichannel or multivariate data sets. Since its inception in 2010, the algorithm has found wide spread applications spanning different engineering related fields. Yet, no parallel FPGA based hardware design of the algorithm is available for its online and real time processing. Our proposed architecture for MEMD uses fixed-point operations and employs cubic spline interpolation (CSI) within the sifting process. Finally, examples of decomposition of multivariate synthetic and real-world biological signals are provided. [ABSTRACT FROM AUTHOR]

Self-powered monitoring refers to a signal processing technique where the computational power is harvested directly from the signal being monitored. In this paper, we present the design and calibration of a CMOS event counter for long-term, self-powered mechanical usage monitoring. The counter exploits a log-linear response of the hot-electron injection process on a floating-gate transistor when biased in weak-inversion. By configuring an array of floating-gate injectors to respond to different amplitude levels of the input signal, a complete analog processor has been designed that implements a level counting algorithm, which is widely used in mechanical usage monitoring. Measured results from a fabricated prototype in a 0.5-μm CMOS process demonstrate that the processor can sense, store and compute over 105 usage cycles with an injection limit approaching one single electron per second and with a counting resolution of 5 bits. This paper also presents a calibration algorithm that is used for compensating the variations which arise due to device mismatch, power supply and temperature fluctuations. The maximum current rating of the fabricated analog processor has been measured to be less than 160 nA making it ideal for practical self-powered sensing applications. [ABSTRACT FROM AUTHOR]

Current transformer (CT) saturation can cause protective relay mal-operation or even prevent tripping. The wave shape of the secondary current is severely distorted as the CT is forced into deep saturation when the residual flux in the core adds to the flux change caused by faults. In this paper, a morphological lifting scheme is proposed to extract features contained in the waveform of the signal. The detection of the CT saturation is accurately achieved and the points of the inflection, where the saturation begins and ends, are found with the scheme used. This paper also presents a compensation algorithm, based upon the detection results, to reconstruct healthy secondary currents. The proposed morphological lifting scheme and compensation algorithm are demonstrated on a sample power system. The simulation results clearly indicate that they can successfully detect and compensate the distorted secondary current of a saturated CT with residual flux. [ABSTRACT FROM AUTHOR]

Provides instructions and guidelines to prospective authors who wish to submit manuscripts. [ABSTRACT FROM PUBLISHER]

This paper presents a patented type-based group- delay equalization technique to compensate for in-band group- delay distortion typically existing in analog/RF filter circuits. The underlining principle is that for a given modulation scheme and pulse-shaping function, the modulated signal has a unique statistical distribution, and that when the modulated signal passes through a circuit, any in-band group-delay distortion in the circuit distorts its output statistical distribution. The technique employs an equalization filter to minimize the in-band group-delay distortion, with the filter coefficients derived from a measure of the distortion in the output statistical distribution using a weighted nonlinear least square algorithm. It requires simple analog and digital hardware and firmware to implement, and its implementation is inherently adaptive, capable of tracking and compensating for any variation in the group-delay distortion characteristic due to component aging and temperature variation. Computer simulations have been performed to show that an accurate equalization filter can be obtained to effectively compensate for the group-delay distortion, achieving significant performance improvements. [ABSTRACT FROM PUBLISHER]

This paper is devoted to show the impact of non-white chopping on the offset compensation in time-interleaved analog-to-digital converters. We develop a theoretical framework allowing the selection of optimal chopping sequences. We show that, on the one hand, the adoption of these (generally nonwhite) sequences allows to achieve faster offset compensation (thus increasing the signal-to-noise ratio) and, on the other hand, a better spectral shaping (thus increasing the spurious-free dynamic range). As a byproduct of our analysis, we prove that the average offset estimation which is used in many ADC implementations is asymptotically the best available linear estimation of offset that, in turn, is the best estimation when the signal to be converted can be assumed to be a Gaussian process. [ABSTRACT FROM AUTHOR]

Jacket matrices motivated by the center weight Hadamard matrices have played important roles in signal processing, communication, image compression, cryptography, etc. In this paper we propose a notation called block Jacket matrix which substitutes elements of the matrix into common matrices or even block matrices. Employing the well-known Pauli matrices which are very important in many subjects, block Jacket matrices with any size are investigated in detail, and some recursive relations for fast construction of the block Jacket matrices are obtained. Based on the general recursive relations, several special block Jacket matrices are constructed. To decompose high order block Jacket matrices, a fast decomposition algorithm for the factorable block Jacket matrices is suggested. After that some properties of the block Jacket matrices are investigated. Finally, several remarks are presented. These remarks are associated with comparisons between the Clifford algebra and the block Jacket matrices, generations of orthogonal and quasi-orthogonal sequences, and relations of the block Jacket matrices to the orthogonal transforms for signal processing. Since the Pauli matrices are actually infinitesimal generators of SU(2) group, the proposed construction and decomposition algorithms for the block Jacket matrices are available in the signal processing, communication, quantum signal processing and information theory. [ABSTRACT FROM AUTHOR]

In this paper, we propose a fast algorithm for sign- extraction of a number given in the Residue Number System (2n - 1, 2n, 2n + 1). The algorithm can be implemented using three n-bit wide additions, two of which can be done in parallel. It can be used in a wide variety of problems, i.e., in algorithms for dividing numbers in the RNS, or in evaluating the sign of determinant in computational geometry, etc. [ABSTRACT FROM AUTHOR]

In some general state-space approaches to the multichannel blind deconvolution problem, e.g., the information backpropagation approach (Zhang and Cichocki 2000), an implicit assumption is usually involved therein, viz., the dimension of the state vector of the mixer is known a priori. In general, if the number of states in the state space is not known a priori, Zhang and Cichocki (2000) suggested using a maximum possible number of states; this procedure will introduce additional delays in the recovered source signals. In this paper, our aim is to relax this assumption. The objective is achieved by using balanced parameterization of the underlying discrete-time dynamical system. Since there are no known balanced parameterization algorithms for discrete-time systems, we need to go through a "circuitous" route, by first transforming the discrete-time system into a continuous-time system using a bilinear transformation, perform the balanced parameterization on the resulting continuous-time system, and then transform the resulting system back to discrete-time balanced parameterized system using an inverse bilinear transformation. The number of states can be determined by the number of significant singular values in the ensuing singular value decomposition step in the balanced parameterization. [ABSTRACT FROM AUTHOR]

This paper presents a constructive method for (sub)optimal finite-impulse response (FIR) approximation of infinite-impulse response (IIR) models. The method minimizes the Hankel norm of the approximation error by using an explicit solution to the norm-preserved dilation problem. It has advantages over the existing methods in that it is a unified method for both single-input single-output and multiple-input multiple-output systems which allows direct tradeoff between .the least-squares and Chebyshev error criteria by using a single tuning parameter, and that the approximation algorithm is constructive and only involves algebraic manipulations rather than iteration and convex optimization procedures. The lower and upper bounds on the l² and Chebyshev norms of the approximation error are derived and are related to the tuning parameter. The problem of approximating noncausal IIR models by causal FIR models is also formulated and solved. The effectiveness and properties of the proposed algorithms are demonstrated by examples. [ABSTRACT FROM AUTHOR]

The two-path algorithm is an adaptive filter algorithm based on a parallel filter structure, which has been found to be useful for line echo cancellation as well as for acoustic echo cancellation. It is well known that in finite precision arithmetic, the adaptation process of adaptive algorithms can be reduced or even halted due to finite precision effects. This paper proposes a variant of the two-path scheme where the effects of quantization are reduced, without any significant increase in complexity. The improvement is shown by simulations using bandlimited flat spectrum noise as well as real speech signals. [ABSTRACT FROM AUTHOR]

Blind source separation (BSS) aims at recovering statistically independent source signals from their linear mixtures without knowing the mixing coefficients. Besides independent component analysis, nonlinear principal component analysis (NPCA) is shown to be another useful tool for solving this problem, but it requires that the measured data be prewhitened. By taking into account the autocorrelation matrix of the measured data, we present in this paper a modified NPCA criterion, and develop a least-mean-square (LMS) algorithm and a recursive least-squares algorithm. They can perform the online BSS using directly the unwhitened observations. Since a natural gradient learning is applied and the prewhitening process is removed, the proposed algorithms work more efficiently than the existing NPCA algorithms, as verified by computer simulations on man-made sources as well as practical speech signals. [ABSTRACT FROM AUTHOR]

This paper aims at applying H.264 in medical video compression applications and improving the H.264 rate control algorithm with better perceptual quality. First, H.264 is briefly reviewed and introduced to the area of medical video compression. Second, a new motion complexity (MC) measure is defined to express the complexity of motion contents in a video frame, and a new H.264 rate control scheme with the MC measure and perceptual bit allocation is proposed for medical video compression. Third, two sets of experiments are conducted: the comparison between MPEG-4 and H.264, and the comparison between JVT-H014 [1], which is the H.264 adopted rate control algorithm, and our proposed rate control scheme. The first set of experiments shows that compared with MPEG-4, H.264 can achieve a significant average peak signal-to-noise ratio (PSNR) gain of up to 4.35 dB for the test medical video sequences, and thus is much more effective when applied in medical video compression. The second set of experiments shows that compared with H014, the proposed rate control scheme can achieve better perceptual video quality, with an average PSNR gain of up to 0.19 dB for the test medical video sequences. [ABSTRACT FROM AUTHOR]

In this paper, new recursive structures for computing radix-r two-dimensional (2-D) distrete cosine transfonn (DCT) and 2-D inverse DCT (IDCT) are proposed. The 2-D DCT/IDCT are first decomposed into cosine-cosine and sine-sine transforms. Based on indexes of transform bases, the regular pre-addition preprocess is established and the recursive structures for 2-P DCT/IDCT, which can be realized in a second-order infinite-impulse response (IIR) filter, are derived without involving any transposition procedure. For computation of 2-D DCT/IDCT, the recursive loops proposed structures are less than that of one-dimensional DCT/IDCT recursive structures, which require data transposition to achieve the so-called row-column approach. With advantages of fewer recursive loops and no transposition, the proposed recursive structures achieve more accurate results and less power consumption than the existed methods. The regular and modular properties are suitable for very large-scale integration (VLSI) implementation. By using similar procedures, the recursive structures for 2-D DST and 2-D IDST are also proposed. [ABSTRACT FROM AUTHOR]

A method for distributed information processing in grid sensor networks using the Fornasini-Marchesini (FM) local state space model has been proposed in the literature recently. The method can be used to implement linear systems in grid sensor networks. It can be shown that, the system matrices of the FM state space model have to satisfy particular conditions for the system to be implementable in real time in a sensor network. This constraint limits the type of systems implementable in real time on sensor networks. A necessary and sufficient condition for a causal transfer matrix to be realizable in the constrained FM model is established. A realization algorithm to derive an FM model that satisfies the desired condition, given an admissible causal transfer matrix, is also derived. The corresponding problem for the realization of noncausal transfer matrices is also addressed. [ABSTRACT FROM PUBLISHER]

Six preprocessing algorithms for the detection of firearm gunshots are statistically evaluated, using the receiver operating characteristic method as a previous feasibility metric for their implementation on a low-power VLSI circuit. Circuits are intended to serve as the input detection sensors of a low-power environmental surveillance network. Some possible VLSI implementations for the evaluated algorithms are also evaluated. Results indicate that the use of wavelet bank filters, either discrete or continuous, might be the best choice in terms of the compromise between detection efficiency and the power requirements of the intended application. [ABSTRACT FROM PUBLISHER]

Identifying relevant signals within high-dimensional observations is an important preprocessing step for efficient data analysis. However, many classical dimension reduction techniques such as principal component analysis do not take the often rich statistics of real-world data into account, and thereby fail if for example the signal space is of low power but meaningful in terms of some other statistics. With "colored subspace analysis," we propose a method for linear dimension reduction that evaluates the time structure of the multivariate observations. We differentiate the signal subspace from noise by searching for a subspace of nontrivially autocorrelated data. We prove that the resulting signal subspace is uniquely determined by the data, given that all white components have been removed. Algorithmically we propose three efficient methods to perform this search, based on joint diagonalization, using a component clustering scheme, and via joint low-rank approximation. In contrast to temporal mixture approaches from blind signal processing we do not need a generative model, i.e., we do not require the existence of sources, so the model is applicable to any wide-sense stationary time series without restrictions. Moreover, since the method is based on second-order time structure, it can be efficiently implemented and applied even in large dimensions. Numerical examples together with an application to dimension reduction of functional MRI recordings demonstrate the usefulness of the proposed method. The implementation is publicly available as a Matlab package at http://cmb.helmholtzmuenchen. de/CSA. [ABSTRACT FROM AUTHOR]

A class of second-order complex domain blind source extraction algorithms is introduced to cater for signals with noncircular probability distributions, which is a typical case in real-world scenarios. This is achieved by employing the so-called augmented complex statistics and based on the temporal structures of the sources, thus permitting widely linear (WL) predictability to be the extraction criterion. For rigor, the analysis of the existence and uniqueness of the solution is provided based on both the covariance and the pseudocovariance and for both noise-free and noisy cases, and serves as a platform for the derivation of the algorithms. Both direct solutions and those requiring prewhitening are provided based on a WL predictor, thus making the methodology suitable for the generality of complex signals (both circular and noncircular). Simulations on synthetic noncircular sources support the uniqueness and convergence study, followed by a real-world example of electrooculogram artifact removal from electroencephalogram recordings in real time. [ABSTRACT FROM AUTHOR]

An extension of blind source extraction (BSE) of one or a group of sources to the case of ill-conditioned and post-non-linear (PNL) mixing is introduced. This is achieved by a "mixed objective" type of cost function which jointly maximizes the kurtosis of a recovered source and estimates a measure of nonlinearity within the mixing system. This helps to circumvent problems with existing BSE methods, which are limited to noiseless and linear mixing models. Simulations illustrate the performance of the proposed algorithm and its usefulness, especially in the presence of very ill-conditioned mixing systems. [ABSTRACT FROM AUTHOR]

A novel algorithm for designing low-power and hardware-efficient linear-phase finite-impulse response (FIR) filters is presented. The algorithm finds filter coefficients with reduced number of signed-power-of-two (SPT) terms given the filter frequency response characteristics. The algorithm is a branch-and-bound-based algorithm that fixes a coefficient to a certain value. The value is determined by finding the boundary values of the coefficient using linear programming. Although the worst case run time of the algorithm is exponential, its capability to find appreciably good solutions in a reasonable amount of time makes it a desirable CAD tool for designing low-power and hardware-efficient filters. The superiority of the algorithm on existing methods in terms of SPT term count, design time, hardware complexity, and power performance is shown with several design examples. Up to 30% reduction in the number of SPT terms is achieved over unoptimized Remez coefficients, which is 20% better than compared optimization methods. The average power saving is 20% over unoptimized coefficients, which is up to 14% better than optimized coefficients obtained with existing methods. [ABSTRACT FROM AUTHOR]

Most of the previous signal processing identification results have been achieved using either time-domain or frequency-domain algorithms. In this study, the two methods were combined to create a novel identification algorithm called the empirical frequency-domain optimal parameter (EFOP) estimate and the recursive EFOP algorithm for common linear stochastic systems disturbed with noise. A general prediction error criterion was introduced in the time-domain estimate. By minimizing the frequency-domain estimate, some general prediction error criteria were constructed for Black-box models. Then, the parameter estimation was obtained by minimizing the general prediction error criterion. This method theoretically provides the globally optimum frequency-domain estimate of the model. It has advantages in anti-disturbance performance, and can precisely identify a model with fewer sample numbers. Lastly, some simulations were carried out to demonstrate the validity of the new method. [ABSTRACT FROM AUTHOR]

We present a computation reduction technique called computation sharing differential coefficient (CSDC) method, which can be used to obtain low-complexity multiplierless implementation of finite-impulse response (FIR) filters. It is also applicable to digital signal processing tasks involving multiplications with a set of constants. The main component of our proposed CSDC method is to combine the strength of the augmented differential coefficient approach and subexpression sharing. Exploring computation reuse through algorithmic equivalence, the augmented differential coefficient approach greatly expands the design space by employing both differences and sums of filter coefficients. The expanded design space is represented by an undirected and complete graph. The problem of minimizing the adder cost (the number of additions/subtractions) for a given filter is transformed into a problem of searching for an appropriate subexpression set that leads to a minimal adder cost. A heuristic search algorithm based on genetic algorithm is developed to search for low-complexity solutions over the expanded design space in conjunction with exploring subexpression sharing. It is shown that up to 70.1 % reduction in the adder cost can be obtained over the conventional multiplierless implementation. Comparison with several existing techniques based on the available data shows that our method yields comparable results for multiplierless FIR filter implementation. [ABSTRACT FROM AUTHOR]