Jiuchao Feng

Impact of Topology on Performance and Energy Efficiency in Wireless Sensor Networks for Source Extraction

In this paper, the problem of source extraction in bandwidth-constrained wireless sensor networks is considered. The sensor observations are assumed to be instantaneous linear mixtures of sources in a sensing field, possibly corrupted by noise. Because of the bandwidth constraint, the observations are quantized before being transmitted. Three kinds of sensor network topologies (cluster-based sensor network, sensor network with a fusion center, and concatenated sensor network) are considered in order to show the impact of topology on the performance and energy efficiency. The mixtures are reconstructed based on the received quantized data, and source extraction is performed by using a fast fixed-point algorithm with prewhitening. This algorithm was proposed by Hyvarinen and Oja, published in Neural Computation, and has the advantage of simplicity and fast convergence. The case of source extraction where the sensed data are undistorted is used as the performance benchmark. The results show that in all these sensor networks, the performance can be close to that of the benchmarking case. The energy efficiency and lifetime of these sensor networks are compared when the same source extraction task is executed. The effects of sensor failure and link failure on the performance are also discussed.

A neural-network-based channel-equalization strategy for chaos-based communication systems

This work addresses the channel-distortion problem and proposes a technique for channel equalization in chaos-based communication systems. The proposed equalization is realized by a modified recurrent neural network incorporating a specific training (equalizing) algorithm.

Spherical Simplex-Radial Cubature Kalman Filter

In this letter, a new class of cubature Kalman filters (CKF) based on a spherical simplex-radial rule is proposed to further improve accuracy and efficiency of the traditional CKF. The transformation group of the regular simplex and the moment matching method are utilized to compute the spherical and radial integrals, respectively. In addition, with the increase of the state dimension, the new CKF of the fifth degree uses fewer quadrature points than the traditional CKF with the same degree. Simulations on moments calculation and nonlinear state estimation show that the proposed CKFs can achieve higher accuracy and better efficiency than the traditional CKFs.

Minimizing effective energy consumption in multi-cluster sensor networks for source extraction

This paper studies a multi-cluster sensor network which is applied for source extraction in a sensing field. Both the performance of source extraction and the total energy consumption in the sensor network are functions of the number of clusters. In this paper, we aim at finding the optimal number of clusters by minimizing the effective energy consumption which is defined as the ratio of the performance of source extraction to the total energy consumption in the sensor network. A particle swarm optimization (PSO) algorithm is employed to form the clusters which enables every cluster to perform source extraction. The existence and the uniqueness of the optimum number of clusters is proven theoretically and shown by numerical simulations. The relationship between the optimum number of clusters and the various system parameters are investigated. A tradeoff between the performance and the total energy consumption is illustrated. The results show that the performance is greatly improved by adopting the multi-cluster structure of the sensor network.

A Class of Stable Square-Root Nonlinear Information Filters

Information filters can process nonlinear systems with uncertain prior knowledge, and the particular square-root form of adaptive filters can improve numerical stability. Based on a square-root decomposition of information matrix and an extra positive definite matrix, the unscented transform and the cubature rule are applied to the information filtering architecture for nonlinear estimation. A class of stable square-root nonlinear information filters is then proposed in this technical note. In addition, the boundedness of their estimation errors is also proven. Results from simulations of filtering a chaotic map demonstrate that the proposed square-root nonlinear filters can improve numerical stability, and has better filtering performance than other information filters.

Kernel Affine Projection Sign Algorithms for Combating Impulse Interference

The kernel method has been successfully applied to nonlinear adaptive filtering. This brief presents a kernel affine projection sign algorithm (KAPSA), which is a nonlinear extension of the affine projection sign algorithm (APSA). The proposed KAPSA combines the benefits of the kernel method and the APSA, and is robust against non-Gaussian impulse interference. In order to further improve the filtering performance, a variable step-size adjustment is incorporated into the KAPSA, resulting in a new variable step-size kernel APSA (VSS-KAPSA) without increasing the computational burden. Simulations in the context of time-series prediction show that both the KAPSA and the VSS-KAPSA are robust against impulse interference and that both outperform other affine projection algorithms in terms of steady-state mean square errors.

Source Extraction in Bandwidth Constrained Wireless Sensor Networks

Source extraction was traditionally done by sensor arrays. Recently, sensor networks have been considered as promising candidates for extraction of multiple sources. In a sensor network, each sensor observes an instantaneous linear mixture of the sources and their observations are corrupted by additive white Gaussian noise. Two sensor network models are adopted. The first one is cluster based, in which a sensor acts as cluster head and performs local extraction of the sources based on its own observation and the received quantized data from the cluster members. Then, the extracted signal is quantized and the quantized data are sent to the sink while the sink performs global extraction of the sources. The other one is cluster free, in which data collected by the sensors are quantized and sent to the sink directly. Then, the sink performs global extraction of the sources. The proposed schemes are evaluated against the benchmarking case where the sensor observations are undistorted.

An adaptive demodulator for the chaotic modulation communication system with RBF neural network

Chaotic modulation is an important spread spectrum (SS) technique amongst chaotic communications. The logistic chaotic signal acts as the modulation signal in this paper. An adaptive demodulator based on the radial basis function (RBF) neural network is proposed. The demodulator makes use of the good approximant capacity of RBF network for a nonlinear dynamical system. Using the proposed adaptive learning algorithm, the source message can be recovered from the received SS signal. The recovering procedure is on line and adaptive. The simulated examples are included to demonstrate the new method. For the purpose of comparison, the extended-Kalman-filter-based (EKF) demodulator was also analysed. The results indicate that the mean square error (MSE) of the recovered source signal by the proposed demodulator Is significantly reduced, especially for the SS signal with a higher signal-to noise ratio (SNR).

A chaos-based steganography algorithm for H.264 standard video sequences

It is important to embed secret messages into video sequences by using steganography technique. Based on the H.264 video coding standard, a chaotic steganography algorithm is proposed and realized in this paper. The results indicate that the steganography algorithm can be implemented fast and effectively, and vision effect and peak signal to noise ratios (PSNR) of the video sequences are also nearly not affected after decoding.

A modified quantized kernel least mean square algorithm for prediction of chaotic time series

We propose a new method to predict the chaotic time series.The gradient descent method is used in M-QKLMS to reduce the steady-state MSE.The modified quantization method is incorporated in M-QKLMS to reduce the network size.The energy conservation relation of M-QKLMS in RKHS is derived.A sufficient condition for mean square convergence of M-QKLMS is provided. A modified quantized kernel least mean square (M-QKLMS) algorithm is proposed in this paper, which is an improvement of quantized kernel least mean square (QKLMS) and the gradient descent method is used to update the coefficient of filter. Unlike the QKLMS method which only considers the prediction error, the M-QKLMS method uses both the new training data and the prediction error for coefficient adjustment of the closest center in the dictionary. Therefore, the proposed method completely utilizes the knowledge hidden in the new training data, and achieves a better accuracy. In addition, the energy conservation relation and a sufficient condition for mean-square convergence of the proposed method are obtained. Simulations on prediction of chaotic time series show that the M-QKLMS method outperforms the QKLMS method in terms of steady-state mean square errors.