Efficient multispectral texture segmentation using multivariatestatistics

A complete, low computational cost method is presented for multispectral textured image segmentation. The procedure performs a tesselation of the image into non-overlapped rectangular regions and decides about the homogeneity of each region, using statistical hypothesis testing. Regions labelled as homogeneous are used to estimate the parameters that are necessary to classify the pixels of the heterogeneous regions. The proposed scheme can also be used to estimate the number of different textures in the image. This represents an efficient alternative to other computationally expensive methods, such as those that employ clustering techniques     

Projection of circuit performance distributions by multivariatestatistics

Production test data from process monitoring test structures were utilized in a circuit simulator that accounts for the correlations between circuit elements. This `correlated' simulation is based on a principal component analysis technique that requires the means, the standard deviations, and the correlation coefficients of all circuit elements. A voltage reference subcircuit consisting of five n-p-n transistors and five based-diffused resistors was chosen for this simulation study. The statistical parameters of these circuit elements were approximated by those from the process monitor test structures and extracted from the test patterns of 1000 production wafers. The distributions of the reference voltage from this simulation were compared to a Monte Carlo simulation, and to the production data     

Accelerated training of backpropagation networks by using adaptive momentum step

Considerable research into the training of neural networks by the backpropagation technique has been undertaken in recent years. Introduction of a momentum term into the training equation can accelerate the training process. A new momentum step and a scheme for dynamically selecting the momentum rate are described. It is shown that these give improved acceleration of training and strong global convergence characteristics. Results are presented for four benchmark training tasks.<>     

An adaptive step size for backpropagation using linear lowerbounding functions

An adaptive step size is presented for the backpropagation algorithm in feedforward neural nets using linear lower bounding functions. Basically, a linear lower bounding function (LLBF) for a given function over an interval is a linear function that lies below the given function and matches the original function value at one end point. To search for an adaptive step size, an LLBF for the error function, which is expressed in terms of the step size, is derived. Since the error in a neural net can never be smaller than zero, it is plausible not to take a step larger than the step size when the associated LLBF reaches zero. In the paper, an adaptive learning algorithm based on the above idea is given. Numerical examples are used to illustrate its feasibility and to compare it with some previous results     

Extended backpropagation algorithm

A fast learning algorithm for feedforward neural networks is described. This algorithm is based on modifications to the backpropagation algorithm, The learning rate and momentum coefficient are adapted according to the coefficient of correlation between the downhill gradient and the previous weight update.<>     

Rapid backpropagation learning algorithms

One of the major drawbacks of the backpropagation algorithm is its slow rate of convergence. Researchers have tried several different approaches to speed up the convergence of backpropagation learning. In this paper, we present those rapid learning methods as three categories, and implement the representative methods of each category: (1) for the numerical method based approach, the Aitken's ² process, (2) for the heuristics based approach, the dynamic adaptation of learning rate, and (3) for the learning strategy based approach, the selective presentation of learning samples. Based on these implementations, the performance is evaluated with experiments and the merits and demerits are briefly discussed.This work was supported in part by a grant from the Korea Science and Engineering Foundation (KOSEF) and the Center for Artificial Intelligence Research (CAIR), the Engineering Research Center (ERC) of Excellence Program.     

The complex backpropagation algorithm

The backpropagation (BP) algorithm that provides a popular method for the design of a multilayer neural network to include complex coefficients and complex signals so that it can be applied to general radar signal processing and communications problems. It is shown that the network can classify complex signals. The generalization of the BP to deal with complex signals should make it possible to expand the line of applications of this powerful nonlinear signal processing algorithm     

Automated breath detection on long-duration signals using feedforward backpropagation artificial neural networks

A new breath-detection algorithm is presented, intended to automate the analysis of respiratory data acquired during sleep. The algorithm is based on two independent artificial neural networks (ANN(insp) and ANN(expi)) that recognize, in the original signal, windows of interest where the onset of inspiration and expiration occurs. Postprocessing consists in finding inside each of these windows of interest minimum and maximum corresponding to each inspiration and expiration. The ANN(insp) and ANN(expi) correctly determine respectively 98.0% and 98.7% of the desired windows, when compared with 29,820 inspirations and 29,819 expirations detected by a human expert, obtained from three entire-night recordings. Postprocessing allowed determination of inspiration and expiration onsets with a mean difference with respect to the same human expert of (mean +/- SD) 34 +/- 71 ms for inspiration and 5 +/- 46 ms for expiration. The method proved to be effective in detecting the onset of inspiration and expiration in full night continuous recordings. A comparison of five human experts performing the same classification task yielded that the automated algorithm was undifferentiable from these human experts, falling within the distribution of human expert results. Besides being applicable to adult respiratory volume data, the presented algorithm was also successfully applied to infant sleep data, consisting of uncalibrated rib cage and abdominal movement recordings. A comparison with two previously published algorithms for breath detection in respiratory volume signal shows that the presented algorithm has a higher specificity, while presenting similar or higher positive predictive values.     

On the complex backpropagation algorithm

A recursive algorithm for updating the coefficients of a neural network structure for complex signals is presented. Various complex activation functions are considered and a practical definition is proposed. The method, associated to a mean-square-error criterion, yields the complex form of the conventional backpropagation algorithm     

An improved scheme for direct adaptive control of dynamical systems using backpropagation neural networks

This paper presents an improved direct control architecture for the on-line learning control of dynamical systems using backpropagation neural networks. The proposed architecture is compared with the other direct control schemes. In this scheme the neural network interconnection strengths are updated based on the output error of the dynamical system directly, rather than using a transformed version of the error employed in other schemes. The ill effects of the controlled dynamics on the on-line updating of the network weights are moderated by including a compensating gain layer. An error feedback is introduced to improve the dynamic response of the control system. Simulation studies are performed using the nonlinear dynamics of an underwater vehicle and the promising results support the effectiveness of the proposed scheme.     

Single-step digital backpropagation for nonlinearity mitigation

Nonlinearity mitigation based on the enhanced split-step Fourier method (ESSFM) for the implementation of low-complexity digital backpropagation (DBP) is investigated and experimentally demonstrated. After reviewing the main computational aspects of DBP and of the conventional split-step Fourier method (SSFM), the ESSFM for dual-polarization signals is introduced. Computational complexity, latency, and power consumption of DBP based on the SSFM and ESSFM algorithms are estimated and compared. Effective low-complexity nonlinearity mitigation in a 112 Gb/s polarization-multiplexed QPSK system is experimentally demonstrated by using a single-step DBP based on the ESSFM. The proposed DBP implementation requires only a single step of the ESSFM algorithm to achieve a transmission distance of 3200 km over a dispersion-unmanaged link. In comparison, a conventional DBP implementation requires 20 steps of the SSFM algorithm to achieve the same performance. An analysis of the computational complexity and structure of the two algorithms reveals that the overall complexity and power consumption of DBP are reduced by a factor of 16 with respect to a conventional implementation, while the computation time is reduced by a factor of 20. Similar complexity reductions can be obtained at longer distances if higher error probabilities are acceptable. The results indicate that the proposed algorithm enables a practical and effective implementation of DBP in real-time optical receivers, with only a moderate increase in the computational complexity, power consumption, and latency with respect to a simple feed-forward equalizer for bulk dispersion compensation.     

An adaptive backpropagation neural network for real-time ischemiaepisodes detection: development and performance analysis using theEuropean ST-T database

A supervised neural network (NN)-based algorithm was used for automated detection of ischemic episodes resulting from ST segment elevation or depression. The performance of the method was measured using the European ST-T database. In particular, the performance was measured in terms of beat-by-beat ischemia detection and in terms of the detection of ischemic episodes. The algorithm used to train the NN was an adaptive backpropagation (BP) algorithm. This algorithm drastically reduces training time (tenfold decrease in our case) when compared to the classical BP algorithm. The recall phase of the NN is then extremely fast, a fact that makes it appropriate for real-time detection of ischemic episodes. The resulting NN is capable of detecting ischemia independent of the lead used. It was found that the average ischemia episode detection sensitivity is 88.62% while the ischemia duration sensitivity is 72.22%. The results show that NN can be used in electrocardiogram (EGG) processing in cases where fast and reliable detection of ischemic episodes is desired as in the case of critical care units (CCUs)     

Fingerprint recognition using DCT features

A method of fingerprint recognition based on the DCT features of a discrete image is proposed. Its performance is evaluated by the k-nearest neighbour (k-NN) classifier. Experimental results show the achievement of the proposed method in terms of the recognition rate and the low computational effort.     

基于多尺度几何分析的目标描述和识别

结合多尺度几何分析和局部二值模式算子,构造了一种新的多尺度、多方向局部特征描述子--局部Contourlet二值模式(LCBP).通过对尺度内、尺度间及同一尺度不同方向子带内LCBP直方图统计分析,同时考虑到LCBP的四叉树结构特点和模型的简单性,用两状态HMT描述LCBP系数,得到LCBP-HMT模型.在此基础上,提...     

Face recognition using position-dictionaries and region covariance feature

In this paper, a new sparsity formulation called position-dictionary based sparse representation is developed for frontal face recognition. Different from the sparse representation based classification (SRC) method and the Gabor-feature based SRC (GSRC) method which both employ a global dictionary to decompose image patches, the proposed method constructs a position-dictionary for each location using training patches in the corresponding location since they resemble each other and are more likely to favor the same atoms. Sparse coefficients of each position-patch can be obtained by solving an \(l_{1}\) -norm minimization problem. For each face image, sparse coefficients of position-patches are pooled to construct a discriminative upper level feature to represent face image. PCA is used to perform dimension reduction. Each testing sample is represented as a sparse linear combination of all training samples, and recognition is accomplished by evaluating which class of training samples leads to the minimum reconstruction error. We compared the proposed method with SRC and GSRC method on three benchmark face databases. Experimental results show that the proposed method achieves higher recognition rates and is robust to a certain degree of occlusions.     

Personal recognition using hand shape and texture.

This paper proposes a new bimodal biometric system using feature-level fusion of hand shape and palm texture. The proposed combination is of significance since both the palmprint and hand-shape images are proposed to be extracted from the single hand image acquired from a digital camera. Several new hand-shape features that can be used to represent the hand shape and improve the performance are investigated. The new approach for palmprint recognition using discrete cosine transform coefficients, which can be directly obtained from the camera hardware, is demonstrated. None of the prior work on hand-shape or palmprint recognition has given any attention on the critical issue of feature selection. Our experimental results demonstrate that while majority of palmprint or hand-shape features are useful in predicting the subjects identity, only a small subset of these features are necessary in practice for building an accurate model for identification. The comparison and combination of proposed features is evaluated on the diverse classification schemes; naive Bayes (normal, estimated, multinomial), decision trees (C4.5, LMT), k-NN, SVM, and FFN. Although more work remains to be done, our results to date indicate that the combination of selected hand-shape and palmprint features constitutes a promising addition to the biometrics-based personal recognition systems.     

结合雷达目标特征的红外成像目标识别

由于单传感器在导弹的目标识别和跟踪方面存在的固有缺陷,近年来,数据融合技术在这一方面得到广泛地应用和研究。文中提出了在双模（ 雷达,红外） 数据融合方案中,结合雷达目标特征的红外成像目标识别方法,并详细讨论了所涉及的图像处理技术（ 区域分割,骨架提取） 和人工神经网络分类技术。最后给出了实验结果和结论。     

Face recognition algorithm using local and global information

A successful algorithm for the recognition of partially distorted human face images is presented. The key to this approach is to combine both local and global information. The results are compared with traditional elastic matching algorithms and a decreased mismatch rate is reported     

Braille document recognition using Belief Propagation

There is a significant need for a system to recognize Braille documents in order to preserve them and make them available to a larger group of visually impaired people. A new system for Braille document recognition is proposed. We introduce a highly-adaptive Braille documents parameters estimation method to automatically determine the rotation angle, indentations, and spacing in both vertical and horizontal orientation. The key element in determining the rotation angle of the images is based on Radon transform. Also we introduce the method of Braille translation using Belief Propagation on the assumption that the Braille document is a Hidden Markov Model. We demonstrate the effectiveness of rotation angle correction as well as the accuracy of indentation and spacing in both orientations. We also prove that the translation algorithm is efficient and robust to errors made in the dot detection. The proposed method may be used for further applications.     

Micro-expression recognition using advanced genetic algorithm

In recent years, numerous facial expression recognition related applications had been commercialized in the market. Many of them achieved promising and reliable performance results in real-world applications. In contrast, the automated micro-expression recognition system relevant research analysis is still greatly lacking. This is because of the nature of the micro-expression that is usually appeared with relatively lesser duration and lower intensity. However, due to its uncontrollable, subtlety, and spontaneity properties, it is capable to reveal one’s concealed genuine feelings. Therefore, this paper attempts to improve the performance of current micro-expression recognition systems by introducing an efficient and effective algorithm. Particularly, we employ genetic algorithms (GA) to discover an optimal solution in order to facilitate the computational process in producing better recognition results. Prior to the GA implementation, the benchmark preprocessing method and feature extractors are directly adopted herein. Succinctly, the complete proposed framework composes three main steps: the apex frame acquisition, optical flow approximation, and feature extraction with CNN architecture. Experiments are conducted on the composite dataset that is made up of three publicly available databases, viz, CASME II, SMIC, and SAMM. The recognition performance tends to prevail the state-of-the-art methods by attaining an accuracy of 85.9% and F1-score of 83.7%.