This paper addresses the sensor selection problem in time-difference-of-arrival (TDOA) localization system, where a subset of K sensors is chosen from a total of N sensors such that the trace of the Cramér-Rao lower bound (CRLB) is minimized. We present a simulated annealing (SA) based method to solve the resulting minimization problem. Simulation results demonstrate the superior performance of the proposed method compared with the previous semidefinite relaxation (SDR) based method.

A general and efficient method for scaling window sidelobe magnitude has been reported by Lim et al. Although the method always converges in practice, a rigorous proof of convergence is unavailable. In this paper, we introduce a new technique with guaranteed convergence for window sidelobe magnitude reduction. Without further modifications, the convergence speed of this new algorithm is quite the same as that of the previous one. Modifications aimed at speeding up convergence while maintaining the guaranteed convergence property are also presented.

A rectangular-window sidelobe magnitude reduction method by widening the main lobe width was proposed by Saramäki. A technique for trading off main lobe width against sidelobe magnitude for any arbitrary window was reported by Lim et al.; a fast convergence algorithm for its implementation was proposed by the same authors in another article, where the derivatives of the window function are expressed in Chebyshev polynomials which have high arithmetic complexity. All the coefficients of a rectangular-window are equal; this special property is exploited, in this paper, for deriving the window function’s derivatives without the use of Chebyshev polynomials resulting in a great reduction in the arithmetic complexity.

A technique for trading off the main lobe width against sidelobe magnitude for any arbitrary window was reported in Lim et al. and subsequently, a fast convergence method for its implementation was proposed by the same authors. These methods require the computation of derivatives involving the evaluation of trigonometric and hyperbolic functions. In this paper, we show that the derivatives can be computed without evaluating trigonometric and hyperbolic functions if the window function is a sum of exponentials such as a Fourier series.

In the context of multi-source direction of arrival (DOA) estimation in an enclosed environment, the challenges include reverberation and overlapping of multiple simultaneous active sources. To address these interferences, the identification of time-frequency (TF) bins dominated by the sources signals is essential. In this work, we propose an intensity vector (IV) based TF bins selection technique for DOA estimation using a single acoustic vector sensor (AVS). The proposed technique involves multi-level inliers selection and outliers removal (MLISOR), which is implemented in three steps. In the first step, we derive the distribution of IVs and then select IVs using a norm metric. In the second step, the regions with the highest local IV density in each time frame are identified. In the third step, we cluster the IVs according to their directions and remove the outliers based on the member-to-centroid angle metric. Simulation results show that both the accuracy and the robustness of the proposed technique outperform the existing techniques. The indoor experimental results also verify that the proposed technique is effective and robust in practical situations.

Training neural networks with backpropagation (BP) requires a sequential passing of activations and gradients. This has been recognized as the lockings (i.e., the forward, backward, and update lockings) among modules (each module contains a stack of layers) inherited from the BP. In this brief, we propose a fully decoupled training scheme using delayed gradients (FDG) to break all these lockings. The FDG splits a neural network into multiple modules and trains them independently and asynchronously using different workers (e.g., GPUs). We also introduce a gradient shrinking process to reduce the stale gradient effect caused by the delayed gradients. Our theoretical proofs show that the FDG can converge to critical points under certain conditions. Experiments are conducted by training deep convolutional neural networks to perform classification tasks on several benchmark data sets. These experiments show comparable or better results of our approach compared with the state-of-the-art methods in terms of generalization and acceleration. We also show that the FDG is able to train various networks, including extremely deep ones (e.g., ResNet-1202), in a decoupled fashion.

Many types of windows have been designed in the past decades for various applications. Each window type has its own specific characteristics. In this letter, we present a general technique for trading off main lobe width against sidelobe magnitude for any arbitrary window while keeping the number of sidelobe peaks and their relative magnitudes unchanged although their exact locations and magnitudes are changed.

Windows such as Dolph-Chebyshev window and Kaiser window are adjustable, whereas windows such as Hamming window and Blackman window are traditionally not adjustable. In [1], a technique for trading off main lobe width against sidelobe magnitude for any arbitrary window, including the traditionally non-adjustable windows, was presented. However, the method in [1] requires a large number of iterations if the specification is very tight. Developed based on a new perspective on the window adjustment principle, a new method to achieve the same adjustment capability as in [1], but at a very much fewer iterations is presented in this letter. Our new method is particular useful if the specification is very tight.

A very efficient technique for significantly reducing the number of multipliers and adders in implementing narrow transition band linear-phase finite-impulse response (FIR) digital filters is to use the frequency-response masking (FRM) approach. This paper studies the construction of the masking filter pair in the FRM technique using a common part for further reducing the arithmetic complexity. Extensive simulations are included showing that the use of the common part always considerably decreases the arithmetic complexity at the expense of a slight increase in the overall filter order.

This letter presents a class of infinite impulse response (IIR) filters synthesized using a system of IIR subfilters based on the frequency-response masking (FRM) technique. In the proposed class of filters, the bandedge shaping filters are a parallel connection of two allpass filters, and the masking filters are approximately linear phase IIR filters composed of a parallel connection of a delay line and an allpass filter. A design method which jointly optimizes the IIR subfilters is introduced. The proposed class of IIR filters has lower coefficient sensitivity and roundoff noise compared with conventional IIR filters.

IIR frequency-response masking (FRM) filter where the prototype filter is an IIR filter and the masking filters are FIR filters is effective in reducing the group delay of FRM filters. In this paper, we present a constrained optimization method for the design of near linear phase IIR FRM filters, where the phase error and magnitude error are independently controlled. A design example is presented to demonstrate the performance of the method.

People today rely more and more on global positioning system (GPS) for navigation when driving in unfamiliar environments. While GPS navigation is indispensable in an intelligent vehicle and provides convenience for road direction, concerns are also raised if the use of GPS may distract drivers to increase unnecessary stress. In this paper, we explore the effects of using GPS navigation on driver stress utilizing electrocardiogram (ECG) signals. In particular, the effects of higher or lower density of GPS instructions are studied. To analyze the driver stress, eight heart rate variability (HRV) features, which were commonly utilized in human stress related studies, were computed from ECG signals. Statistical significance tests were then performed to each HRV feature, so that those effective features for detecting driver stress may be localized. Our studies, based on road driving experiments with ten healthy subjects, showed that MeanRR, SDNN and HRVTri are the top three effective features to detect driver stress, while frequency domain features in general are not sensitive to driver stress. Based on the effective features, our analysis further showed that basically, driving with higher density of GPS instructions has no significant driver stress difference from driving with lower density of GPS instructions.