基于纠错编码的自适应图像水印算法

采用三维Lorenz映射对水印加密以提高安全性,并通过纠正水印误码来降低检测误码率,提高水印的稳健性.充分考虑了纠错编码中引入的信息冗余将导致水印嵌入强度的降低等问题,采用基于蚁群算法的图像内容边缘检测方法,使水印的分布集中在图像显著的边缘特征中,嵌入强度随着图像特征的变化而自适应地变化.在水印的检测中,利用Kalman滤波法对水印信息进行预测和估计,在没有原始图像数据的情况下,能够恢复嵌入的信息.实验结果表明,该方案具有较好的抗JPEG压缩、高斯噪声和剪切的鲁棒性.     

Spectral entropy feature subset selection using SEPCOR to detect alcoholic impact on gamma sub band visual event related potentials of multichannel electroencephalograms (EEG)

The problem of analyzing and identifying regions of high discrimination between alcoholics and controls in a multichannel electroencephalogram (EEG) signal is modeled as a feature subset selection technique that can improve the recognition rate between both groups. Several studies have reported efficient detection of alcoholics by feature extraction and selection in gamma band visual event related potentials (ERP) of a multichannel EEG signal. However, in these studies the correlation between features and their class information is not considered for feature selection. This may lead to redundancy in the feature set and result in over fitting. Therefore in this study, a statistical feature selection technique based on Separability & Correlation analysis (SEPCOR) is proposed to select an optimal feature subset automatically that possesses minimum correlation between selected channels and maximum class separation. The optimal feature selection consists of a ranking method that assigns ranks to channels based on a variability measure (V-measure). From the ranked feature set of highly discriminative features, different subsets are automatically selected by heuristically applying a correlation threshold in steps from 0.02 to 0.1. These subsets are applied as input features to multilayer perceptron (MLP) neural network and k-nearest neighbor (k-NN) classifiers to discriminate alcoholic and control visual ERP. Prior to feature selection, spectral entropy features are computed in gamma sub band (30–55 Hz) interval of a 61-channel multi-trial EEG signal with multiple object recognition tasks. Independent Component Analysis (ICA) is performed on raw EEG data to remove eye blink, motion and muscle artifacts. Results indicate that both classifiers exhibit excellent classification accuracy of 99.6%, for a feature subset of 22 optimal channels with correlation threshold of 0.1. In terms of computation time, k-NN classifier outperforms multilayer perceptron-back propagation (MLP-BP) network with 7.93 s whereas MLP network takes 55 s to perform the recognition task with the same accuracy. Compared to feature section methods used in previous studies on the same EEG alcoholic database, there is a significant improvement in classification accuracy based on the proposed SEPCOR method.     

Neuro-Flight Controllers for Aircraft Using Minimal Resource Allocating Networks (MRAN)

This paper presents the application of the recently developed Minimal Resource Allocating Network (MRAN) for aircraft flight control, with special emphasis on its robustness and fault tolerance properties. MRAN is a dynamic Radial Basis Function network (RBFN) incorporating a growing and pruning strategy resulting in a compact network structure. For the aircraft control application presented here, a simple scheme in which MRAN aids a conventional controller using a feedback error learning mechanism is presented. The robustness and the fault tolerant nature of the neuro controller is illustrated using a F8 fighter aircraft model in an autopilot mode. The objective of the controller is to follow the velocity and pitch rate pilot commands under large parameter variations and sudden changes in actuator time constants. Simulation results demonstrate the satisfactory performance of the MRAN neuro-flight controller even under these faulty conditions.