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This page shows you the most recent publications within this specialty of the MedWorm directory. This is page number 38.

Exact Reconstruction Conditions for Regularized Modified Basis Pursuit
In this work, we obtain sufficient conditions for exact recovery of regularized modified basis pursuit (reg-mod-BP) and discuss when the obtained conditions are weaker than those for modified compressive sensing or for basis pursuit (BP). The discussion is also supported by simulation comparisons. Reg-mod-BP provides a solution to the sparse recovery problem when both an erroneous estimate of the signal's support, denoted by $T$, and an erroneous estimate of the signal values on $T$ are available.
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Sparse Signal Reconstruction from Quantized Noisy Measurements via GEM Hard Thresholding
We develop a generalized expectation-maximization (GEM) algorithm for sparse signal reconstruction from quantized noisy measurements. The measurements follow an underdetermined linear model with sparse regression coefficients, corrupted by additive white Gaussian noise having unknown variance. These measurements are quantized into bins and only the bin indices are used for reconstruction. We treat the unquantized measurements as the missing data and propose a GEM iteration that aims at maximizing the likelihood function with respect to the unknown parameters. Under mild conditions, our GEM iteration yields a convergent mon...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Trellis-Search Based Soft-Input Soft-Output MIMO Detector: Algorithm and VLSI Architecture
In this paper, we propose a trellis-search based soft-input soft-output detection algorithm and its very large scale integration (VLSI) architecture for iterative multiple-input multiple-output (MIMO) receivers. We construct a trellis diagram to represent the search space of a transmitted MIMO signal. With the trellis model, we evenly distribute the workload of candidates searching among multiple trellis nodes for parallel processing. The search complexity is significantly reduced because the number of candidates is greatly limited at each trellis node. By leveraging the trellis structure, we develop an approximate Log-MAP...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Adaptive Emotional Information Retrieval From EEG Signals in the Time-Frequency Domain
This paper aims at developing adaptive methods for electroencephalogram (EEG) signal segmentation in the time-frequency domain, in order to effectively retrieve the emotion-related information within the EEG recordings. Using the multidimensional directed information analysis supported by the frontal brain asymmetry in the case of emotional reaction, a criterion, namely asymmetry index $(AsI)$ , is used to realize the proposed segmentation processes that take into account both the time and frequency (in the empirical mode decomposition domain) emotionally related EEG components. The efficiency of the $AsI$ -based “e...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Exact Sample Conditioned MSE Performance of the Bayesian MMSE Estimator for Classification Error—Part II: Consistency and Performance Analysis
In Part I of a two part study on the MSE performance of Bayesian error estimation, we have derived analytical expressions for MSE conditioned on the sample for Bayesian error estimators and arbitrary error estimators in two Bayesian models: discrete classification with Dirichlet priors and linear classification of Gaussian distributions with normal-inverse-Wishart priors. Here, in Part II, we examine the consistency of Bayesian error estimation and provide several simulation studies that illustrate the concept of conditional MSE and how it may be used in practice. A salient application is censored sampling, where sample po...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Exact Sample Conditioned MSE Performance of the Bayesian MMSE Estimator for Classification Error—Part I: Representation
In recent years, biomedicine has been faced with difficult high-throughput small-sample classification problems. In such settings, classifier error estimation becomes a critical issue because training and testing must be done on the same data. A recently proposed error estimator places the problem in a signal estimation framework in the presence of uncertainty, permitting a rigorous solution optimal in a minimum-mean-square error sense. The uncertainty in this model is relative to the parameters of the feature-label distributions, resulting in a Bayesian approach to error estimation. Closed form solutions are available for...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Cross-Layer Design of Wireless Multihop Random Access Networks
Joint design of flow control, multipath routing, and random access control is considered for wireless multihop networks. Based on a network utility maximization formulation, Aloha persistence probabilities are optimized together with multicommodity end-to-end rates and per-link flow rates. Although the joint optimization of Aloha and flow control was previously tackled using a convex reformulation, adding the routing component renders the problem inherently nonconvex. To cope with this challenge, a successive convex approximation approach is taken to obtain a locally optimal solution efficiently. A parallelized distributed...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Decentralized Delay Optimal Control for Interference Networks With Limited Renewable Energy Storage
In this paper, we consider delay minimization for interference networks with renewable energy source, where the transmission power of a node comes from both the conventional utility power (ac power) and the renewable energy source. We assume the transmission power of each node is a function of the local channel state, local data queue state and local energy queue state only. We consider two delay optimization formulations, namely the decentralized partially observable Markov decision process (DEC-POMDP) and noncooperative partially observable stochastic game (POSG). In DEC-POMDP formulation, we derive a decentralized onlin...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Channel Optimized Distributed Multiple Description Coding
In this paper, for robust and efficient transmission of multiple correlated sources over noisy channels with packet loss, a channel optimized distributed multiple description vector quantization (CDMD) scheme is presented. The proposed CDMD scheme enjoys low-complexity encoding and delay and a scalable CDMD decoder, which jointly reconstructs the symbols of an arbitrary number of correlated sources. This, for example, suits data-gathering applications in wireless sensor networks. The CDMD encoder is designed using a deterministic annealing approach based on a minimum mean squared error asymmetric CDMD. A CDMD decoder for a...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Distributed Spectrum Sensing With Sequential Ordered Transmissions to a Cognitive Fusion Center
The objective function is related directly to the primary and secondary throughputs with possible privilege for primary operation. Simulation results demonstrate the enhanced performance of the approaches proposed in this paper. We also investigate the case of fading/shadowing channels between the local sensors and the fusion center, and the situation in which the sensing cost is negligible.
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Robust Monotonic Optimization Framework for Multicell MISO Systems
The performance of multiuser systems is both difficult to measure fairly and to optimize. Most resource allocation problems are nonconvex and NP-hard, even under simplifying assumptions such as perfect channel knowledge, homogeneous channel properties among users, and simple power constraints. We establish a general optimization framework that systematically solves these problems to global optimality. The proposed branch-reduce-and-bound (BRB) algorithm handles general multicell downlink systems with single-antenna users, multiantenna transmitters, arbitrary quadratic power constraints, and robustness to channel uncertaint...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Power Allocation in Two-Hop Amplify-and-Forward MIMO Relay Systems With QoS Requirements
The problem of minimizing the total power consumption while satisfying different quality-of-service (QoS) requirements in a two-hop multiple-input multiple-output (MIMO) network with a single nonregenerative relay is considered. As shown by Y. Rong [“Multihop nonregenerative MIMO relays: QoS considerations,” IEEE Trans. Signal Process., vol. 59, no. 1, pp. 209–303, 2011] in [1], the optimal processing matrices for both linear and nonlinear transceiver architectures lead to the diagonalization of the source-relay-destination channel so that the power minimization problem reduces to properly allocating t...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Design and Analysis of Large MIMO Systems With Krylov Subspace Receivers
This paper studies large multiple-input multiple-output (MIMO) communication systems with linear precoding and reduced-rank Krylov subspace receivers. We design precoders and analyze their performance by exploiting large-dimensional random matrix theory. We first devise low-complexity precoding schemes that can improve performance of low-rank Krylov subspace receivers in the regime of high signal-to-noise ratio (SNR). We then introduce a potential theory-based method for analyzing the convergence behavior of the mean-squared error (MSE) for various transmission schemes. This method can be applied to a broader range of prob...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Interference Optimization for Transmit Power Reduction in Tomlinson-Harashima Precoded MIMO Downlinks
A novel strategy for reducing the power loss in Tomlinson-Harashima precoding (THP) is explored in this paper, based on optimizing the interference to be canceled. A multiple input multiple output (MIMO) downlink transmission is considered and the proposed strategy is motivated by the fact that both the desired and interfering signals originate from the base station (BS) of the downlink system itself. The resulting interference can therefore be influenced to reduce the transmission power required to cancel it, without altering the information content of the downlink message. This optimization aims at bringing the interfere...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Designing Low-Complexity Near-Optimal Multiple-Symbol Detectors for Impulse Radio UWB Systems
Multiple-symbol detection has recently caught attention in ultrawideband (UWB) communications because of its high performance without requiring explicit channel estimation. In this context, the generalized likelihood ratio test (GLRT) for multiple-symbol detection problem is developed to jointly detect multiple symbols, which exhibits considerable error performance improvement over transmitted reference transmissions methods. Unfortunately, the GLRT is a Boolean quadratic programming (BQP) problem, which is generally nondeterministic polynomial hard (NP-hard). In this paper, we propose two near-optimal detectors with polyn...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Power Efficient Resource Allocation for Downlink OFDMA Relay Cellular Networks
Resource allocation in orthogonal frequency division multiple access (OFDMA) relay cellular networks (RCN) has been investigated. We introduce an orthogonal frequency-and-time transmission (OFTT) protocol, in which orthogonal frequency and time resources are allocated to different communication modes and phases, respectively, and propose a simple algorithm for resource allocation. Communication modes (one- and two-hop modes), subchannels, and relay transmit power are sequentially allocated to enhance the power efficiency of the OFDMA RCN. We show an achievable quality-of-service tradeoff between one- and two-hop users. Fur...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Local CSI Based Selection Beamforming for Amplify-and-Forward MIMO Relay Networks
We investigate selection beamforming for a cooperative network that consists of a source, a destination, and two amplify-and-forward (AF) relays, which are all equipped with multiple antennas. The transmit and receive beamforming techniques are respectively applied at the source and destination, and the linear processing operator of the selected AF multiple-input multiple-output (MIMO) relay is optimized. We consider that the source has the instantaneous channel state information (CSI) of the channels from the source to relays but no information regarding the CSI of the channels from the relays to the destination. Partial ...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Sparsity Enhanced Decision Feedback Equalization
For single-carrier systems with frequency domain equalization, decision feedback equalization (DFE) performs better than linear equalization and has much lower computational complexity than sequence maximum likelihood detection. The main challenge in DFE is the feedback symbol selection rule. In this paper, we give a theoretical insight on multiple symbol selection, based on sparsity and we present a simple algorithm for DFE. The algorithm converges fast and has a low computational cost. We show how the initial solution can be obtained via convex relaxation instead of linear equalization, and illustrate the impact that the...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Covariance Eigenvector Sparsity for Compression and Denoising
Sparsity in the eigenvectors of signal covariance matrices is exploited in this paper for compression and denoising. Dimensionality reduction (DR) and quantization modules present in many practical compression schemes such as transform codecs, are designed to capitalize on this form of sparsity and achieve improved reconstruction performance compared to existing sparsity-agnostic codecs. Using training data that may be noisy a novel sparsity-aware linear DR scheme is developed to fully exploit sparsity in the covariance eigenvectors and form noise-resilient estimates of the principal covariance eigenbasis. Sparsity is effe...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Hermitian Symmetric DFT Codes: A New Class of Complex DFT Codes
We introduce a new class of real number codes derived from DFT matrix, Hermitian symmetric DFT (HSDFT) codes. We propose a new decoding algorithm based on coding-theoretic as well as subspace based approach. Decoding of HSDFT codes requires only real arithmetic operations and smaller dimension matrices compared to the decoding of the state-of-art real BCH DFT (RBDFT) class of codes. HSDFT codes will also be shown to have more burst error correction capacity. For a Gauss–Markov source, on a binary symmetric channel at lower to moderate bit error rates (BERs), HSDFT codes show better performance than RBDFT codes, and ...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Dictionary Optimization for Block-Sparse Representations
Recent work has demonstrated that using a carefully designed dictionary instead of a predefined one, can improve the sparsity in jointly representing a class of signals. This has motivated the derivation of learning methods for designing a dictionary which leads to the sparsest representation for a given set of signals. In some applications, the signals of interest can have further structure, so that they can be well approximated by a union of a small number of subspaces (e.g., face recognition and motion segmentation). This implies the existence of a dictionary which enables block-sparse representations of the input signa...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Subcarrier Nulling Algorithms for Channel Shortening in Uplink OFDMA Systems
This paper considers a blind channel shortening approach based on subcarrier nulling criteria to mitigate the effect of interblock interference in uplink OFDMA systems. We derive a sufficient condition for a method known as carrier nulling algorithm (CNA) to shorten individual uplink user channels. Unlike several known methods, CNA can shorten channels in time domain even if there are many null subcarriers, and is hence suitable for uplink OFDMA in which individual user signals can be modeled as standard OFDM that includes multiple null subcarriers. In addition, we also develop a semiblind implementation of CNA by exploiti...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Topological Localization Via Signals of Opportunity
We consider problems of localization, disambiguation, and mapping in a domain filled with signals-of-opportunity generated by transmitters. One or more (static or mobile) receivers utilize these signals and from them characterize the domain, localize, disambiguate, etc. The tools we develop are topological in nature, and rely on interpreting the problem as one of embedding the domain into a sufficiently high-dimensional space of signals via a signal profile function. Varying kinds of signal processing (TOA, TDOA, DOA, etc.) and discretization are addressed. Finally, we describe experiments that demonstrate the feasibility ...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Moving Target Parameters Estimation in Noncoherent MIMO Radar Systems
The problem of estimating the parameters of a moving target in multiple-input multiple-output (MIMO) radar is considered and a new approach for estimating the moving target parameters by making use of the phase information associated with each transmit–receive path is introduced. It is required for this technique that different receive antennas have the same time reference, but no synchronization of initial phases of the receive antennas is needed and, therefore, the estimation process is noncoherent. We model the target motion within a certain processing interval as a polynomial of general order. The first three co...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Adaptive Strategies for Target Detection and Localization in Noisy Environments
This paper studies the problem of recovering a signal with a sparse representation in a given orthonormal basis using as few noisy observations as possible. Herein, observations are subject to the type of background clutter noise encountered in radar applications. Given this model, this paper proves for the first time that highly sparse signals contaminated with Gaussian background noise can be recovered by adaptive methods using fewer noisy linear measurements than required by any possible recovery method based on nonadaptive Gaussian measurement ensembles.
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

DoA and Polarization Estimation for Arbitrary Array Configurations
In this paper, algorithms for joint high-resolution direction-of-arrival (DoA) and polarization estimation using real-world arrays with imperfections are proposed. Both azimuth and elevation angles are considered. Partially correlated and coherent signals may be handled as well. Unlike most of the work available in the open literature, we consider the case when polarization sensitive antenna arrays, which may be disposed on a conformal surface, may have unknown (but fixed) geometries, be composed of elements with individual beampatterns and be subject to cross-polarization as well as mounting platform effects. Herein, rece...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Geolocation Performance With Biased Range Measurements
We study geolocation based on biased range estimates. Positive bias arises using time delay ranging methods in a multipath fading environment, when the line of sight direct path is severely attenuated. We model the range measurement as contaminated with Gaussian noise and an additive nonnegative bias term, and consider deterministic and random bias cases. We develop weighted least squares (WLS) and maximum likelihood (ML) geolocation estimators, and show that in general they yield biased geolocation estimates. A perturbation analysis technique is applied to find bias and mean square error (MSE) expressions for the WLS and ...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Classification of Multichannel Signals With Cumulant-Based Kernels
We consider the problem of training a discriminative classifier given a set of labelled multivariate time series (a.k.a. multichannel signals or vector processes). We propose a novel kernel function that exploits the spectral information of tensors of fourth-order cross-cumulants associated to each multichannel signal. Contrary to existing approaches the arising procedure does not require an (often nontrivial) blind identification step. Nonetheless, insightful connections with the dynamics of the generating systems can be drawn under specific modeling assumptions. The method is illustrated on both synthetic examples as wel...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Exploiting Statistical Dependencies in Sparse Representations for Signal Recovery
Signal modeling lies at the core of numerous signal and image processing applications. A recent approach that has drawn considerable attention is sparse representation modeling, in which the signal is assumed to be generated as a combination of a few atoms from a given dictionary. In this work we consider a Bayesian setting and go beyond the classic assumption of independence between the atoms. The main goal of this paper is to introduce a statistical model that takes such dependencies into account and show how this model can be used for sparse signal recovery. We follow the suggestion of two recent works and assume that t...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Sampled Signal Reconstruction With Causality Constraints—Part II: Theory
This paper provides the theoretic foundation for the design of ${L^2}$ optimal reconstructors (also known as interpolators/holds) with a prescribed degree of causality. A compact frequency-domain solution is derived that mimics known interpolation techniques for ordinary transfer functions. In parallel, an extensive state space solution is documented. It complements the frequency-domain solution in that it constructively proves the various claims, and it also makes the solution concrete. The state space solution requires the solution of one Riccati and one Lyapunov matrix equation.
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Sampled Signal Reconstruction With Causality Constraints–Part I: Setup and Solutions
We present a closed-form state-space solution of the problem, whose computational complexity does not depend on ${mbi l}$ and which can be efficiently calculated and implemented.
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Algebraic Signal Processing Theory: 1-D Nearest Neighbor Models
We present a signal processing framework for the analysis of discrete signals represented as linear combinations of orthogonal polynomials. We demonstrate that this representation implicitly changes the associated shift operation from the standard time shift to the nearest neighbor shift introduced in this paper. Using the algebraic signal processing theory, we construct signal models based on this shift and derive their corresponding signal processing concepts, including the proper notions of signal and filter spaces, $z$-transform, convolution, spectrum, and Fourier transform. The presented results extend the algebraic s...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

An Alternative Formulation for the Empirical Mode Decomposition
The Empirical Mode Decomposition (EMD) is a relatively new adaptive method for multicomponent signal representation which allows for analyzing nonlinear and nonstationary signals. In spite of its lack of mathematical foundations, very few papers are dedicated to defining new decompositions that would preserve the interesting properties of the EMD while improving the mathematical setting. The new decomposition based on direct constrained optimization we introduce in this article is an attempt in that direction.
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Performance Analysis of Norm Constraint Least Mean Square Algorithm
As one of the recently proposed algorithms for sparse system identification, $l_0$ norm constraint Least Mean Square ($l_0$ -LMS) algorithm modifies the cost function of the traditional method with a penalty of tap-weight sparsity. The performance of $l_0$-LMS is quite attractive compared with its various precursors. However, there has been no detailed study of its performance. This paper presents comprehensive theoretical performance analysis of $l_0$ -LMS for white Gaussian input data based on some reasonable assumptions, which are reasonable in a large range of parameter setting. Expressions for steady-state mean square...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Stochastic Behavior Analysis of the Gaussian Kernel Least-Mean-Square Algorithm
The kernel least-mean-square (KLMS) algorithm is a popular algorithm in nonlinear adaptive filtering due to its simplicity and robustness. In kernel adaptive filters, the statistics of the input to the linear filter depends on the parameters of the kernel employed. Moreover, practical implementations require a finite nonlinearity model order. A Gaussian KLMS has two design parameters, the step size and the Gaussian kernel bandwidth. Thus, its design requires analytical models for the algorithm behavior as a function of these two parameters. This paper studies the steady-state behavior and the transient behavior of the Gaus...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Greedy Sparse RLS
We present simulations showing that, for identifying sparse time-varying FIR channels, our algorithm is consistently better than previous sparse RLS methods based on the $ell_1$-norm regularization of the RLS criterion. We also use our sparse greedy RLS algorithm for computing linear predictions in a lossless audio coding scheme and obtain better compression than MPEG4 ALS using an RLS-LMS cascade.
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Computational Design of Sequences With Good Correlation Properties
In this paper, we introduce a computational framework based on an iterative twisted approximation (ITROX) and a set of associated algorithms for various sequence design problems. The proposed computational framework can be used to obtain sequences (or complementary sets of sequences) possessing good periodic or aperiodic correlation properties and, in an extended form, to construct zero (or low) correlation zone sequences. Furthermore, as constrained (e.g., finite) alphabets are of interest in many applications, we introduce a modified version of our general framework that can be useful in these cases. Several applications...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Sampling Uniformly From the Set of Positive Definite Matrices With Trace Constraint
We derive a parameterization of positive definite matrices using the Cholesky decomposition in combination with hyperspherical coordinates. Based on the parameterization we develop a simple and efficient method to randomly generate positive definite matrices with constant or bounded trace according to a uniform distribution. Further, we present an efficient implementation using the inversion method and either rejection sampling or transforming a beta distribution. The matrix parameterization might be of independent interest, whereas the random sampling algorithm finds applications in Monte Carlo simulations, testing of alg...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

A General Class of Outage Error Probability Lower Bounds in Bayesian Parameter Estimation
In this paper, a new class of lower bounds on the outage error probability in Bayesian parameter estimation is proposed. The outage error probability is an important criterion in parameter estimation that provides meaningful information even in the presence of large errors and is useful for prediction of the system operation region. Computation of the minimum outage error probability is usually not tractable and thus, lower bounds on this probability can be very useful for performance analysis. The proposed class of lower bounds on the outage error probability is derived using Hölder's inequality. Several bounds in ...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Bernoulli Particle/Box-Particle Filters for Detection and Tracking in the Presence of Triple Measurement Uncertainty
This work presents sequential Bayesian detection and estimation methods for nonlinear dynamic stochastic systems using measurements affected by three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. Following Mahler's framework for information fusion, the paper develops the optimal Bayes filter for this problem in the form of the Bernoulli filter for interval measurements. Two numerical implementations of the optimal filter are developed. The first is the Bernoulli particle filter (PF), which turns out to require a large number of particles in order to achieve a satisfactory performance. ...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Partially Linear Estimation With Application to Sparse Signal Recovery From Measurement Pairs
We address the problem of estimating a random vector $X$ from two sets of measurements $Y$ and $Z$ , such that the estimator is linear in $Y$. We show that the partially linear minimum mean-square error (PLMMSE) estimator does not require knowing the joint distribution of $X$ and $Y$ in full, but rather only its second-order moments. This renders it of potential interest in various applications. We further show that the PLMMSE method is minimax-optimal among all estimators that solely depend on the second-order statistics of $X$ and $Y$ . We demonstrate our approach in the context of recovering a signal, which is sparse in...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Direction Estimation by Minimum Squared Arc Length
Circular statistics has found substantial application in science and engineering. One of the fundamental problems in circular statistics is that of estimating the mean direction of a circular random variable from a number of observations. The standard approach in the literature is called the sample circular mean and its asymptotic properties are well known. It can also be computed efficiently in a number of arithmetic operations that is linear in the number of observations. In this paper we consider an alternative estimator called the sample intrinsic mean that is based on minimizing squared arc length. We show how this es...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Bias Reduction for an Explicit Solution of Source Localization Using TDOA
This paper proposes two methods to reduce the bias of the well-known algebraic explicit solution (Chan and Ho, “A simple and efficient estimator for hyperbolic location,” IEEE Trans. Signal Process., vol. 42, pp. 1905–1915, Aug. 1994) for source localization using TDOA. Bias of a source location estimate is significant when the measurement noise is large and the geolocation geometry is poor. Bias also dominates performance when multiple times of independent measurements are available such as in UWB localization or in target tracking. The paper starts by deriving the bias of the source location estimate...
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

IEEE Transactions on Signal Processing publication information
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Table of Contents
Source: IEEE Transactions on Signal Processing - April 16, 2012 Category: Biomedical Engineering Source Type: research

Noninvasive induction implant heating: An approach for contactless altering of mechanical properties of shape memory implants
This article shows an approach to change the properties of an orthopaedic shape memory implant within biological tissue, using contactless induction heating. Due to inducing the one way-memory effect, triggered by the rise of temperature within the implant, the geometry and hence the mechanical properties of the implant itself, are altered. The power uptake of the implant, depending on the induction parameters as well as on its position within the induction coil, is shown. Thermographic measurements are carried out in order to determine the surface temperature distribution of the implant. In order to simulate biological ti...
Source: Medical Engineering and Physics - April 16, 2012 Category: Biomedical Engineering Authors: Ronny Pfeifer, Michael Hustedt, Volker Wesling, Christoph Hurschler, Gavin Olender, Martin Mach, Thomas Gösling, Christian W. Müller Tags: Papers Source Type: research

Independent effects of adding weight and inertia on balance during quiet standing
Conclusions: Adding inertia and adding weight had different effects on balance. Adding inertia by itself had no effect on balance. Adding weight by itself had a negative effect on balance. When adding inertia and weight at the same time, the added inertia appeared to lessen (but did not eliminate) the negative effect of adding weight on balance. These results improve our fundamental understanding of how added mass influences human balance.
Source: BioMedical Engineering OnLine - April 16, 2012 Category: Biomedical Engineering Authors: Kerry CostelloSara MatrangolaMichael Madigan Source Type: research

Editorial Board
Source: Medical Engineering and Physics - April 13, 2012 Category: Biomedical Engineering Source Type: research

Axon Myelination and Electrical Stimulation in a Microfluidic, Compartmentalized Cell Culture Platform
Abstract Axon demyelination contributes to the loss of sensory and motor function following injury or disease in the central nervous system. Numerous reports have demonstrated that myelination can be achieved in neuron/oligodendrocyte co-cultures. However, the ability to selectively treat neuron or oligodendrocyte (OL) cell bodies in co-cultures improves the value of these systems when designing mechanism-based therapeutics. We have developed a microfluidic-based compartmentalized culture system to achieve segregation of neuron and OL cell bodies while simultaneously allowing the formation of myelin sheaths....
Source: NeuroMolecular Medicine - April 13, 2012 Category: Neurology Tags: NeuroMolecular Medicine Source Type: research

Non-invasive Estimation of Cerebrospinal Fluid Pressure Waveforms by Means of Retinal Venous Pulsatility and Central Aortic Blood Pressure.
In conclusion, the study demonstrated a good correlation between two different methods of estimating CSFp non-invasively and may provide a novel method to estimate CSF waveforms non-invasively. PMID: 22527007 [PubMed - as supplied by publisher]
Source: Annals of Biomedical Engineering - April 13, 2012 Category: Biomedical Engineering Authors: Golzan SM, Kim MO, Seddighi AS, Avolio A, Graham SL Tags: Ann Biomed Eng Source Type: research