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  • Articles  (4,410)
  • Institute of Electrical and Electronics Engineers (IEEE)  (4,410)
  • IEEE Transactions on Signal Processing  (2,516)
  • IEEE Transactions on Biomedical Engineering  (1,894)
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  • 1
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    Robust Acoustic Localization Via Time-Delay Compensation and Interaural Matching Filter (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-14
    Description: Acoustic localization is an essential technique in speech capturing, speech enhancement, video conferencing, and human–robot interaction. However, in practical situations, localization has to be performed in abominable environments, where the presence of reverberation and noise degrades the performance of available position estimates. Besides, the designed systems should be adaptive to locomotion of targets with low computational complexity. In the context, this paper introduces a robust hierarchical acoustic localization method via time-delay compensation (TDC) and interaural matching filter (IMF). Firstly, interaural time-delay (ITD) and interaural level difference (ILD), which are cues involved in first two layers, respectively, are yielded by TDC all at once. Then, a novel feature named IMF, which can eliminate the difference between binaural signals, is proposed in the third layer. The final decision making is based on a Bayesian rule. The relationships among the three layers are that the former layer provides candidate directions for later ones such that the searching space becomes gradually smaller to reduce matching time. Experiments using both a public database and a real scenario verify that TDC and IMF are robust for acoustic localization, and hierarchical system has less consumption time.
    Print ISSN: 1053-587X
    Electronic ISSN: 1941-0476
    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Signal Processing Society.
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  • 2
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    Distributed Canonical Correlation Analysis in Wireless Sensor Networks With Application to Distributed Blind Source Separation (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-14
    Description: Canonical correlation analysis (CCA) is a widely used data analysis tool that allows to assess the correlation between two distinct sets of signals. It computes optimal linear combinations of the signals in both sets such that the resulting signals are maximally correlated. The weight vectors defining these optimal linear combinations are referred to as “principal CCA directions”. In addition to this particular type of data analysis, CCA is also often used as a blind source separation (BSS) technique, i.e., under certain assumptions, the principal CCA directions have certain demixing properties. In this paper, we propose a distributed CCA (DCCA) algorithm that can operate in wireless sensor networks (WSNs) with a fully connected or a tree topology. The algorithm estimates the $Q$ principal CCA directions from the sensor signal observations collected by the different nodes in the WSN and extracts the corresponding sources. These network-wide principal CCA directions are estimated in a time-recursive fashion without explicitly constructing the corresponding network-wide correlation matrices, i.e., without the need for data centralization. Instead, each node locally computes smaller CCA problems and only transmits compressed sensor signal observations (of dimension $Q$ ), which significantly reduces the bit rate over the wireless links of the WSN. We prove convergence and optimality of the DCCA algorithm, and we demonstrate its performance by means of numerical simulations in a blind source separation scenario.
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    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Signal Processing Society.
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  • 3
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    Random Matrix Derived Shrinkage of Spectral Precision Matrices (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: There has been much research on shrinkage methods for real-valued covariance matrices and their inverses (precision matrices). In spectral analysis of $p$ -vector-valued time series, complex-valued spectral matrices and precision matrices arise, and good shrinkage methods are often required, most notably when the estimated complex-valued spectral matrix is singular. As an improvement on the Ledoit-Wolf (LW) type of spectral matrix estimator we use random matrix theory to derive a Rao-Blackwell estimator for a spectral matrix, its inverse being a Rao–Blackwellized estimator for the spectral precision matrix. A random matrix method has previously been proposed for complex-valued precision matrices. It was implemented by very costly simulations. We formulate a fast, completely analytic approach. Moreover, we derive a way of selecting an important parameter using predictive risk methodology. We show that both the Rao–Blackwell estimator and the random matrix estimator of the precision matrix can substantially outperform the inverse of the LW estimator in a time series setting. Our new methodology is applied to EEG-derived time series data where it is seen to work well and deliver substantial improvements for precision matrix estimation.
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    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Signal Processing Society.
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  • 4
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    Optimal Estimation for Discrete-Time Linear Systems in the Presence of Multiplicative and Time-Correlated Additive Measurement Noises (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: In this paper, the state estimation problem for discrete-time linear systems influenced by multiplicative and time-correlated additive measurement noises is considered where the multiplicative noises are zero-mean white noise sequences, and the time-correlated additive noise is described by a linear system model with white noise. An optimal linear estimator for the system under consideration is proposed, which does not require computing the inverse of state transition matrix. The proposed estimator has a recursive structure, and has time-independent computation and storage load. Computer simulations are carried out to demonstrate the performance of the proposed estimator. The simulation results show the superiority of the proposed estimator.
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    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Signal Processing Society.
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  • 5
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    Normalization of Linear Support Vector Machines (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-07
    Description: In this paper, we start with the standard support vector machine (SVM) formulation and extend it by considering a general SVM formulation with normalized margin. This results in a unified convex framework that allows many different variations in the formulation with very diverse numerical performance. The proposed unified framework can capture the existing methods, i.e., standard soft-margin SVM, $ell_{1}$ -SVM, and SVMs with standardization, feature selection, scaling, and many more SVMs, as special cases. Furthermore, our proposed framework can not only provide us with more insights on different SVMs from the “energy” and “penalty” point of views, which help us understand the connections and differences between them in a unified way, but also enable us to propose more SVMs that outperform the existing ones under some scenarios.
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    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Signal Processing Society.
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  • 6
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    Analysis and Design of Multiple-Antenna Cognitive Radios With Multiple Primary User Signals (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-21
    Description: We consider multiple-antenna signal detection of primary user transmission signals by a secondary user receiver in cognitive radio networks. The optimal detector is analyzed for the scenario where the number of primary user signals is no less than the number of receive antennas at the secondary user. We first derive exact expressions for the moments of the generalized likelihood ratio test (GLRT) statistic, yielding approximations for the false alarm and detection probabilities. We then show that the normalized GLRT statistic converges in distribution to a Gaussian random variable when the number of antennas and observations grow large at the same rate. Further, using results from large random matrix theory, we derive expressions to compute the detection probability without explicit knowledge of the channel, and then particularize these expressions for two scenarios of practical interest: 1) a single primary user sending spatially multiplexed signals, and 2) multiple spatially distributed primary users. Our analytical results are finally used to obtain simple design rules for the signal detection threshold.
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    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Signal Processing Society.
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  • 7
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    Electric Field Model of Transcranial Electric Stimulation in Nonhuman Primates: Correspondence to Individual Motor Threshold (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-21
    Description: Objective: To develop a pipeline for realistic head models of nonhuman primates (NHPs) for simulations of noninvasive brain stimulation, and use these models together with empirical threshold measurements to demonstrate that the models capture individual anatomical variability. Methods: Based on structural MRI data, we created models of the electric field (E-field) induced by right unilateral (RUL) electroconvulsive therapy (ECT) in four rhesus macaques. Individual motor threshold (MT) was measured with transcranial electric stimulation (TES) administered through the RUL electrodes in the same subjects. Results: The interindividual anatomical differences resulted in 57% variation in median E-field strength in the brain at fixed stimulus current amplitude. Individualization of the stimulus current by MT reduced the E-field variation in the target motor area by 27%. There was significant correlation between the measured MT and the ratio of simulated electrode current and E-field strength ( $r^{2} = 0.95$ , $p = 0.026$ ). Exploratory analysis revealed significant correlations of this ratio with anatomical parameters including of the superior electrode-to-cortex distance, vertex-to-cortex distance, and brain volume ( $r^{2} > 0.96$ , $p 〈 0.02$ ). The neural activation threshold was estimated to be $0.45 pm 0.07$ V/cm for 0.2-ms stimulus pulse width. Conclusion: These results suggest that our individual-specific NHP E-field models appropriately capture individual anatomical variability relevant to the dosing of TES/ECT. These findings are exploratory due to the small number of subjects. Sign- ficance: This study can contribute insight in NHP studies of ECT and other brain stimulation interventions, help link the results to clinical studies, and ultimately lead to more rational brain stimulation dosing paradigms.
    Print ISSN: 0018-9294
    Electronic ISSN: 1558-2531
    Topics: Medicine , Technology
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Engineering in Medicine and Biology Society.
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  • 8
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    IEEE Transactions on Biomedical Engineering publication information (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-21
    Print ISSN: 0018-9294
    Electronic ISSN: 1558-2531
    Topics: Medicine , Technology
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Engineering in Medicine and Biology Society.
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  • 9
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    Local Gradients in Electrotonic Loading Modulate the Local Effective Refractory Period: Implications for Arrhythmogenesis in the Infarct Border Zone (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-21
    Description: Ectopic electrical activity that originates in the peri-infarct region can give rise to potentially lethal re-entrant arrhythmias. The spatial variation in electrotonic loading that results from structural remodelling in the infarct border zone may increase the probability that focal activity will trigger electrical capture, but this has not previously been investigated systematically. This study uses in-silico experiments to examine the structural modulation of effective refractory period on ectopic beat capture. Informed by 3-D reconstructions of myocyte organization in the infarct border zone, a region of rapid tissue expansion is abstracted to an idealized representation. A novel metric is introduced that defines the local electrotonic loading as a function of passive tissue properties and boundary conditions. The effective refractory period correlates closely with local electrotonic loading, while the action potential duration, conduction, and upstroke velocity reduce in regions of increasing electrotonic load. In the presence of focal ectopic stimuli, spatial variation in effective refractory period can cause unidirectional conduction block providing a substrate for reentrant arrhythmias. Consequently, based on the observed results, a possible novel mechanism for arrhythmogenesis in the infarct border zone is proposed.
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    Topics: Medicine , Technology
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Engineering in Medicine and Biology Society.
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  • 10
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    Optimized Configurable Architectures for Scalable Soft-Input Soft-Output MIMO Detectors With 256-QAM (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2015-08-21
    Description: This paper presents an optimized low-complexity and high-throughput MIMO signal detector core for detecting spatially multiplexed data streams. The core architecture supports various layer configurations up to 4, while achieving near-optimal performance, and configurable modulation constellations up to 256-QAM on each layer. The core is capable of operating as a soft-input soft-output log-likelihood ratio (LLR) MIMO detector which can be used in the context of iterative detection and decoding. High area-efficiency is achieved via algorithmic and architectural optimizations performed at two levels. First, distance computations and slicing operations for an optimal 2-layer maximum a posteriori MIMO detector are optimized to eliminate use of multipliers and reduce the overhead of slicing in the presence of soft-input LLRs. We show that distances can be easily computed using elementary addition operations, while optimal slicing is done via efficient comparisons with soft decision boundaries, resulting in a simple feed-forward pipelined architecture. Second, to support more layers, an efficient channel decomposition scheme is presented that reduces the detection of multiple layers into multiple 2-layer detection subproblems, which map onto the 2-layer core with a slight modification using a distance accumulation stage and a post-LLR processing stage. Various architectures are accordingly developed to achieve a desired detection throughput and run-time reconfigurability by time-multiplexing of one or more component cores. The proposed core is applied also to design an optimal multiuser MIMO detector for LTE. The core occupies an area of 1.58 MGE and achieves a throughput of 733 Mbps for 256-QAM when synthesized in 90-nm CMOS.
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    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Signal Processing Society.
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