Neural Network-Based Autonomous Search Model with Undulatory Locomotion Inspired by .

() exhibits sophisticated chemotaxis behavior with a unique locomotion pattern using a simple nervous system only and is, therefore, well suited to inspire simple, cost-effective robotic navigation schemes. Chemotaxis in involves two complementary strategies: klinokinesis, which allows reorientation by sharp turns when moving away from targets; and klinotaxis, which gradually adjusts the direction of motion toward the preferred side throughout the movement. In this study, we developed an autonomous search model with undulatory locomotion that combines these two chemotaxis strategies with its body undulatory locomotion.

Attention-Based Temporal-Frequency Aggregation for Speaker Verification.

Convolutional neural networks (CNNs) have significantly promoted the development of speaker verification (SV) systems because of their powerful deep feature learning capability. In CNN-based SV systems, utterance-level aggregation is an important component, and it compresses the frame-level features generated by the CNN frontend into an utterance-level representation. However, most of the existing aggregation methods aggregate the extracted features across time and cannot capture the speaker-dependent information contained in the frequency domain.

Deployment Optimization Method of Multistatic Radar for Constructing Circular Barrier Coverage.

To construct circular barrier coverage (CBC) with multistatic radars, a deployment optimization method based on equipartition strategy is proposed in this paper. In the method, the whole circular area is divided into several sub-circles with equal width, and each sub-circle is blanketed by a sub-CBC that is built based on the multistatic radar deployment patterns. To determine the optimal deployment patterns for each sub-CBC, the optimization conditions are firstly studied.

Minimum Eigenvector Collaborative Representation Discriminant Projection for Feature Extraction.

High-dimensional signals, such as image signals and audio signals, usually have a sparse or low-dimensional manifold structure, which can be projected into a low-dimensional subspace to improve the efficiency and effectiveness of data processing. In this paper, we propose a linear dimensionality reduction method-minimum eigenvector collaborative representation discriminant projection-to address high-dimensional feature extraction problems. On the one hand, unlike the existing collaborative representation method, we use the eigenvector corresponding to the smallest non-zero eigenvalue of the sample covariance matrix to reduce the error of collaborative representation.

A Robust Nonrigid Point Set Registration Method Based on Collaborative Correspondences.

The nonrigid point set registration is one of the bottlenecks and has the wide applications in computer vision, pattern recognition, image fusion, video processing, and so on. In a nonrigid point set registration problem, finding the point-to-point correspondences is challengeable because of the various image degradations. In this paper, a robust method is proposed to accurately determine the correspondences by fusing the two complementary structural features, including the spatial location of a point and the local structure around it.

High-precision synchronization detection method for bistatic radar.

To improve the accuracy of radar ranging and positioning under complex backgrounds, a high-precision synchronization detection method for bistatic radar is proposed based on different-frequency phase processing. First, the transmit signal and receive signal are converted to radio frequency pulses by frequency conversion. Then, the transmit signal is roughly measured with a field-programmable gate array.

A Fast Algorithm for 2D DOA Estimation Using an Omnidirectional Sensor Array.

The traditional 2D MUSIC algorithm fixes the azimuth or the elevation, and searches for the other without considering the directions of sources. A spectrum peak diffusion effect phenomenon is observed and may be utilized to detect the approximate directions of sources. Accordingly, a fast 2D MUSIC algorithm, which performs azimuth and elevation simultaneous searches (henceforth referred to as AESS) based on only three rounds of search is proposed.

PairMotifChIP: A Fast Algorithm for Discovery of Patterns Conserved in Large ChIP-seq Data Sets.

Identifying conserved patterns in DNA sequences, namely, motif discovery, is an important and challenging computational task. With hundreds or more sequences contained, the high-throughput sequencing data set is helpful to improve the identification accuracy of motif discovery but requires an even higher computing performance. To efficiently identify motifs in large DNA data sets, a new algorithm called PairMotifChIP is proposed by extracting and combining pairs of -mers in the input with relatively small Hamming distance.

RefSelect: a reference sequence selection algorithm for planted (l, d) motif search.

Background: The planted (l, d) motif search (PMS) is an important yet challenging problem in computational biology. Pattern-driven PMS algorithms usually use k out of t input sequences as reference sequences to generate candidate motifs, and they can find all the (l, d) motifs in the input sequences. However, most of them simply take the first k sequences in the input as reference sequences without elaborate selection processes, and thus they may exhibit sharp fluctuations in running time, especially for large alphabets.

High-Resolution Group Quantization Phase Processing Method in Radio Frequency Measurement Range.

Aiming at the more complex frequency translation, the longer response time and the limited measurement precision in the traditional phase processing, a high-resolution phase processing method by group quantization higher than 100 fs level is proposed in radio frequency measurement range. First, the phase quantization is used as a step value to quantize every phase difference in a group by using the fixed phase relationships between different frequencies signals. The group quantization is formed by the results of the quantized phase difference.

Adaptive quasi-Newton algorithm for source extraction via CCA approach.

This paper addresses the problem of adaptive source extraction via the canonical correlation analysis (CCA) approach. Based on Liu's analysis of CCA approach, we propose a new criterion for source extraction, which is proved to be equivalent to the CCA criterion. Then, a fast and efficient online algorithm using quasi-Newton iteration is developed.

Image registration algorithm using Mexican hat function-based operator and grouped feature matching strategy.

Feature detection and matching are crucial for robust and reliable image registration. Although many methods have been developed, they commonly focus on only one class of image features. The methods that combine two or more classes of features are still novel and significant.

[Research on the interlaced encoding pixels in Hadamard transform spectral imager based on DMD].

The key innovation in Hadamard transform spectral imager (HTSI) introduced recently is the use of digital micro-mirror device (DMD) to encode spectral information. However, it brings some new problems for us to solve synchronously. An interlaced encoding phenomenon caused by the application of DMD to our HTSI was investigated and analyzed.

Adaptive improved natural gradient algorithm for blind source separation.

We propose an adaptive improved natural gradient algorithm for blind separation of independent sources. First, inspired by the well-known backpropagation algorithm, we incorporate a momentum term into the natural gradient learning process to accelerate the convergence rate and improve the stability. Then an estimation function for the adaptation of the separation model is obtained to adaptively control a step-size parameter and a momentum factor.

Neural network learning algorithms for tracking minor subspace in high-dimensional data stream.

A novel random-gradient-based algorithm is developed for online tracking the minor component (MC) associated with the smallest eigenvalue of the autocorrelation matrix of the input vector sequence. The five available learning algorithms for tracking one MC are extended to those for tracking multiple MCs or the minor subspace (MS). In order to overcome the dynamical divergence properties of some available random-gradient-based algorithms, we propose a modification of the Oja-type algorithms, called OJAm, which can work satisfactorily.

A neural network learning for adaptively extracting cross-correlation features between two high-dimensional data streams.

This paper proposes a novel cross-correlation neural network (CNN) model for finding the principal singular subspace of a cross-correlation matrix between two high-dimensional data streams. We introduce a novel nonquadratic criterion (NQC) for searching the optimum weights of two linear neural networks (LNN). The NQC exhibits a single global minimum attained if and only if the weight matrices of the left and right neural networks span the left and right principal singular subspace of a cross-correlation matrix, respectively.