Neural network decision directed edge-adaptive Kalman filter forimage estimation

A neural network-based scheme for decision directed edge-adaptive Kalman filtering is introduced in this work. A backpropagation neural network makes the decisions about the orientation of the edges based on the information in a window centered at the current pixel being processed. Then based upon the neural network output an appropriate image model which closely matches the local statistics of the image is chosen for the Kalman filter. This prevents the oversmoothing of the edges, which would have otherwise been caused by the standard Kalman filter. Simulation results are presented which show the effectiveness of the proposed scheme.     

天线阵CDMA系统中基于神经网络的盲空时信道估计

提出了天线阵ＣＤＭＡ系统中盲空时信道估计的约束优化神经网络模型,对其全局收敛性进行了分析,并对其性能进行了数值模拟。     

Neural network for demixing super-Gaussian signals

A new method for separating linear mixtures of statistically independent signals with super-Gaussian probability distributions, using a simple neural network, is proposed. The procedure is based on geometric properties, and it is shown that the maxima of the mixed density distribution belong to straight lines, the direction vectors of which, when taken as columns of a matrix, comprise a demixing matrix. The results obtained with synthetic mixtures of real speech signals are shown     

Neural network controller for cooperating robots

A co-ordinated position/force control system for two robot arms using neurocontrollers is proposed. The robots had to carry a load along a prescribed trajectory. Each robot is a SCARA arm with two driven joints. Simulation results demonstrate the performance of the proposed neurocontrol approach     

Neural network optimization for redundancy allocation

System reliability optimization problems such as redundancy allocation are hard to solve exactly. Neural networks offer an alternative computational model for obtaining good approximate solutions for such problems. In this paper we present a neural network for solving the redundancy allocation problem for a n-stage parallel redundant system with separable objective function and constraints. The problem is formulated as a 0–1 integer programming problem and solved using the network. The performance of the network compare favourably with that of the best fit algorithm. The number of iterations taken by the network increases very slowly with increase in number of variables. Hence the network can easily solve large problems.     

Neural network architectures for vector prediction

A vector predictor is an integral part of a predictive vector quantization coding scheme. The conventional techniques for designing a nonlinear predictor are extremely complex and suboptimal due to the absence of a suitable model for the source data. We investigated several neural network architectures that can be used to implement a nonlinear vector predictor, including the multilayer perceptron, the functional link network and the radial basis function network. We also evaluated and compared the performance of these neural network predictors with that of a linear vector predictor. Our experimental results show that a neural network predictor can predict the blocks containing edges with a higher accuracy than a linear predictor. However, the performance of a neural network predictor is comparable to that of a linear predictor for predicting the stationary and shade blocks     

Power spectrum of a recorded sinewave

Power spectrum and band speading of a recorded sinewave due to random flutter is analyzed for the general case, in contrast to a previous correspondence in which a rectangular flutter spectrum was assumed. Results are expressed in terms of mean-square flutter and time-base-error, which can be determined readily with proper instrumentationn.     

A NOVEL METHOD FOR SIGNAL PARAMETERS ESTIMATION

Based on uniform circular array and its element output signal delay delay a novel method for estimation of spatial frequency, azimuth and elevation is presented. Without any spectral peak search and parameters pairing, the resulting method is (?) resolution and small variance, even in short data length. Computer simulation(?) effectiveness of this method.     

Content-adaptive parameters estimation for multi-dimensional rate control

Multi-dimensional rate control schemes have been recently utilized to adapt video streams to dynamic network conditions and heterogeneous devices. However, current multi-dimensional rate control methods, which estimate the model coefficients using fixed update duration, usually yield inaccurate parameters for dynamically changing video content. To address this problem, a content-adaptive parameters estimation scheme is proposed for multi-dimensional rate control. Firstly, we propose to estimate the parameters using dynamical update duration based on video content and the update duration of the model coefficients is determined by jointly considering the varying picture complexity and feedback information from the actual encoding results, which can improve the model parameter estimation accuracy. Secondly, a coarse-to-fine initial parameter calculation method is proposed to refine the initial frame rate according to the channel condition and the video sequence characteristics. Extensive experimental results show that the proposed solutions outperform the state-of-the-art schemes, especially for video sequences with high temporal and spatial complexity. Furthermore, our algorithm also slightly reduces the computational complexity as compared to related algorithms.     

Error bounds for stochastic estimation of signal parameters

This paper is concerned with stochastic-approximation algorithms for estimating signal parameters. Emphasis will be on the performance of the algorithm for a finite number of observations as opposed to the asymptotic convergence rate. We use as an upper bound a result due to Dvoretzky. A lower bound on the average mean-square error is derived. This new bound is based on the Cramér-Rao inequality. The conventional Cramér-Rao bound is not directly applicable, because it requires the knowledge of the bias function, which is difficult to find in a recursive estimation scheme. To avoid this difficulty, we introduce the concept of most favorable bias function and use the calculus of variations to derive the lower bound. The new bound also serves as a standard to judge the merits of the stochastic-estimation algorithm, since under some general conditions no estimate can yield smaller error. It is shown that under some conditions the two bounds are nearly equal, and hence the algorithm is near optimal. The asymptotic efficiency of the algorithm is compared with Sakrison's result. A stochastic-estimation algorithm is derived for estimating Doppler frequency, and performance curves in terms of the error bounds are presented.     

Clustering-based label estimation for network anomaly detection

A substantial body of work has been done to identify network anomalies using supervised and unsupervised learning techniques with their unique strengths and weaknesses. In this work, we propose a new approach that takes advantage of both worlds of unsupervised and supervised learnings. The main objective of the proposed approach is to enable supervised anomaly detection without the provision of the associated labels by users. To this end, we estimate the labels of each connection in the training phase using clustering. The “estimated” labels are then utilized to establish a supervised learning model for the subsequent classification of connections in the testing stage. We set up a new property that defines anomalies in the context of network anomaly detection to improve the quality of estimated labels. Through our extensive experiments with a public dataset (NSL-KDD), we will prove that the proposed method can achieve performance comparable to one with the “original” labels provided in the dataset. We also introduce two heuristic functions that minimize the impact of the randomness of clustering to improve the overall quality of the estimated labels.     

Hierarchical neural network for hand pose estimation

Hand pose estimation plays an important role in human–computer interaction and augmented reality. Regressing the joints coordinates is a difficult task due to the flexibility of the joint, self-occlusion and so on. In this paper, we propose a novel and simple hierarchical neural network for hand pose estimation. The hand joint coordinates are divided into six parts and each part is regressed in sequence with this hierarchical architecture. This can divide the complex task of regressing all hand joints coordinates into several sub-tasks which can make the estimation more accurate. When regress the joint coordinates of one part, the features of other parts may bring negative influence to this part due to the similarity among the fingers, so we use an interference cancellation operation in our hierarchical architecture. At the time the joint coordinates of one part are regressed, the corresponding features will be removed from the hand global feature to eliminate the interference of this part. The obtained features will be used as input for regressing the joints coordinates of the next part. The ablation study verifies the effectiveness of our hierarchical architecture. The experimental results demonstrate that our method can achieve state-of-the-art or comparable results relative to existing methods on four public hand pose datasets.     

Neural network control for DC motor micromaneuvering

The application of a neural network controller for compensating the effects induced by the friction in a DC motor micromaneuvering system is considered in this article. A backpropagation neural network operating in the specialized learning mode, using the sign gradient descent algorithm, is employed. The input vector to the neural network controller consists of the time history of the motor angular shaft velocity within a prespecified time window. The on-line training of the neural network is performed in the region of interest of the output domain. The neural network output resembles that of a pulse width modulated controller. The effect of the number of neurons in the input and hidden layers on the transient system response is explored. Experimental studies are presented to indicate the effectiveness of the proposed algorithm     

Neural network control for dynamic voltage restorer

The dynamic voltage restorer is a power electronic device which has demonstrated its ability to protect sensitive loads from the effects of voltage sags. This compensator is connected in series with the distribution feeder. A neural network control is proposed. Simulation results are shown to validate these control methods.     

Neural network techniques for adaptive multiuser demodulation

Adaptive methods for performing multiuser demodulation in a direct-sequence spread-spectrum multiple-access (DS/SSMA) communication environment are investigated. In this scenario, the noise is characterized as being the sum of the interfering users' signals and additive Gaussian noise. The optimal receiver for DS/SSMA systems has a complexity that is exponential in the number of users. This prohibitive complexity has spawned the area of research on suboptimal receivers with moderate complexity. Adaptive algorithms for detection allow for reception when the communication environment is either unknown or changing. Motivated by previous work with radial basis functions (RBF's) for performing equalization, RBF networks that operate with knowledge of only a subset of the system parameters are studied. Although this form of detection has been previously studied (group detection) when the system parameters are known, in this work, neural network techniques are employed to adaptively determine unknown system parameters. This approach is further bolstered by the fact that the optimal detector in the synchronous case can be implemented by a RBF network when all of the system parameters are known. The RBF network's performance (with estimated parameters) is compared with the optimal synchronous detector, the decorrelating detector and the single layer perceptron detector. Clustering techniques and adaptive least mean squares methods are investigated to determine the unknown system parameters. This work shows that the adaptive radial basis function network attains near optimal performance and is robust in realistic communication environments     

Adaptive resistive network for stereo depth estimation

Starting from a resistive network that performs, at each node, the convolution between an image and a Gabor function of any phase, the author presents a resistive network able to extract phase information from an image. An adaptation mechanism is used to find, for each node, the Gabor function that best matches the image at the node. This network is useful to evaluate the disparity between two stereo images for depth estimation     

无线传感器网络的神经网络模型

无线传感器网络是复杂的无线网络。无线传感器网络拥有大量的网络节点。网络节点是无线传感器网络的基础。为了研究复杂的无线传感器网络，采用了神经元描述了WSN的网络节点，用神经元模型表示了无线传感器网络。给出了无线待感器网络节点的神经元模型和无线传感器网络的神经网络模型，并将神经网络应用于无线传感器网络的数据融合应用。结果表明，基于神经网络的无线传感器网络研究可以使得复杂研究变得简单，利于开展WSN的深入研究。     

Neural network optimization for multi-target multi-sensor passivetracking

In this paper, we review a number of neural network approaches to combinatorial optimization. We specifically address the difficult problem of localizing multiple targets using only passive sensors, i.e. the sensors detect only bearing angles. Thus, target positions must be found through triangulation. An efficient solution to this problem has been of particular interest in air defence applications. In this paper, we describe two different neural network based approaches for solving this passive tracking problem. In particular, we demonstrate the use of a Hopfield neural network to preface the subsequent development of the multiple elastic modules (MEM) model. The MEM model is presented as a significant extension to current self-organizing neural networks. We describe the unique features of the MEM model, including nonhomogeneous adaptive temperature field for escaping from poor local optima, and locking and expectation features used for dealing with dynamic real-world problems. Applications of the MEM model to other areas including computer vision, are also briefly described     

Neural network technique for parametric testing of mixed-signalcircuits

Feedforward neural networks have been used to identify unknown parameters of an active filter excited by a voltage step. It has been demonstrated that the proposed technique is much faster and more robust in the presence of noise when compared to least square-error model fitting     

Neural network training enhancement

In this study we explore the use of nonlinear embedding maps to expand the dimension of the input space. The efficacy of such maps to speed training and to enhance performance is illustrated through several examples. A natural connection to nonlinear synaptic interconnects is also developed.