International Journal of Wavelets, Multiresolution and Information Processing

Wireless Sensor Networks (WSNs) with limited energy resource demand the implementation of energy-efficient techniques in applications. Recently, the scheme based on cooperative MIMO (Multiple-Input-Multiple-Output) has been proposed to enhance energy saving in WSNs. In this paper, we analyze the performance of energy consumption of inter-cluster communication based on cooperative MIMO techniques in cluster-based WSNs, and then propose a novel method to optimize the energy consumption analysis by changing the network topology into an equivalent double-string topology which can be easily solved by using optimization programming problem in SISO (Single-Input-Single-Output) system model, where cooperating nodes within each MIMO scheme transmission steps are treated as a virtual node. The simulation results show that our proposed data transmission with cooperative MIMO scheme outperforms the protocol without MIMO techniques significantly in terms of energy, lifetime and delay in multi-hop WSNs.

In view of the support vectors of ε-SVR that are not distributed in the ε belt and only located on the outskirts of the ε belt, a novel algorithm to construct ε-SVR of a large-scale training sample set is proposed in this paper. It computes firstly the ε-SVR hyper-plane of a small training sample set and the distances d of all samples to the hyper-plane, then deletes the samples not in field ε ≤ d ≤ d_max and searches SVs gradually in the scope ε ≤ d ≤ d_max, and trains step-by-step the final ε-SVR. Finally, it analyzes the time complexity of the algorithm, and verifies its convergence in the theory and tests its efficiency by the simulation.

In general, the sewer inspection usually employs a great number of CCTV images to discover sewer failures by human interpretation. A computer-aided program remains to be developed due to human's fatigue and subjectivity. To enhance the efficiency of sewer inspection, this paper attends to apply artificial intelligence to extract the failure features of the sewer systems that is demonstrated on the sewer system in the eastern Taichung City, Taiwan. Wavelet transform and gray-level co-occurrence matrix, which have been widely applied in many texture analyses, are adopted in this research to generate extracted features, which are the most valuable information in pattern recognition of failures on CCTV images. Wavelet transform is capable of dividing an image into four sub-images including approximation sub-image, horizontal detail sub-image, vertical detail sub-image, and diagonal detail sub-image. The co-occurrence matrices of horizontal orientation, vertical orientation, and 45° and 135° orientations, respectively, were calculated for the horizontal, vertical, and diagonal detail sub-images. Subsequently, the features including angular second moment, entropy, contrast, homogeneity, dissimilarity, correlation, and cluster tendency, can be obtained from the co-occurrence matrices. However, redundant features either decrease the accuracy of texture description or increase the difficulty of pattern recognition. Thus, the correlations of the features are estimated to search the appropriate feature sets according to the correlation coefficients between the features. In addition, a discriminant analysis was used to evaluate the discriminability of the features for the pipe failure defection, and entropy, correlation, and cluster tendency were found to be the best features based on the discriminant accuracy through an error matrix analysis.

Detecting outliers in high dimensional datasets is quite a difficult data mining task. Mining outliers in subspaces seems to be a promising solution, because outliers may be embedded in some interesting subspaces. Due to the existence of many irrelevant dimensions in high dimensional datasets, it is of great importance to eliminate the irrelevant or unimportant dimensions and identify outliers in interesting subspaces with strong correlation. Normally, the correlation among dimensions can be determined by traditional feature selection techniques and subspace-based clustering methods. The dimension-growth subspace clustering techniques find interesting subspaces in relatively lower possible dimension space, while dimension-growth approaches intend to find the maximum cliques in high dimensional datasets. This paper presents a novel approach by identifying outliers in correlated subspaces. The degree of correlation among dimensions is measured in terms of the mean squared residue. In doing so, we employ the frequent pattern algorithms to find the correlated subspaces. Based on the correlated subspaces obtained, outliers are distinguished from the projected subspaces by using classical outlier detection techniques. Empirical studies show that the proposed approach can identify outliers effectively in high dimensional datasets.

This paper develops GMRA-based construction procedures of Parseval framelets in the setting of reducing subspaces of L²(ℝ^d). A unitary extension principle is established; in particular, for a general expansive matrix A with |det A| = 2, an explicit construction of Parseval framelets is obtained. Some examples are also provided to illustrate the generality of our theory.

Wavelets are the powerful tool for signal processing especially bio-signal processing. Wavelet transform is used to represent the signal to some other time frequency representation better suited for detecting and removing redundancies. In this paper, electrocardiogram (ECG) signal coding using biorthogonal wavelet-based Burrows–Wheeler Coder is discussed. Biorthogonal wavelet transform is used to decompose the ECG signal. Then the Burrows–Wheeler Coder is applied in order to compress the decomposed ECG signal. The Burrows–Wheeler Coder is the combination of Burrows–Wheeler Transformation (BWT), Move-to-Front (MTF) coder and Huffman coder. Compression Ratio (CR) and Percent Root mean square Difference (PRD) are used as performance measures. ECG signals/records from MIT-BIH arrhythmic database are used to evaluate the performance of this coder. This algorithm is tested with 25 different records from MIT-BIH arrhythmia database and obtained the average PRD as 0.0307% to 3.8706% for the average CR of 3.6362 : 1 to 280.48 : 1. For record 117, the CR of 8.1638 : 1 is achieved with PRD 0.1652%. This experimental results show that this coder outperforms other coders such as Djohn, EZW, SPIHT, Novel algorithm etc. that exist in the literature in terms of coding efficiency and computation.

The demand for high data rate transmission is ever increasing every day. Multi-carrier code division multiple access (MC-CDMA) system is considered as the forerunner and advancement in the mobile communication system. In this paper, two types of JPEG2000 lossily-compressed test images are transmitted through an MC-CDMA channel in low SNR (as low as 4 dB) environment and their quality are evaluated objectively by using peak signal-to-noise ratio (PSNR) and root mean square error (RMSE). The test images are all compressed from ratio of 10 : 1 up to 70 : 1 and the system involves multi-user image transmission in near real-time low SNR (±5 dB). It is found that JPEG2000 image compression technique that applies wavelet transform performed quite well in the low SNR multipath fading channel — as low as 4 dB, and this looks promising for future applications.

We consider a Cauchy problem for a modified Helmholtz equation, especially when we give the optimal error bound for this problem. Some spectral regularization methods and a revised Tikhonov regularization method are used to stabilize the problem from the viewpoint of general regularization theory. Hölder-type stability error estimates are provided for these regularization methods. According to the optimality theory of regularization, the error estimates are order optimal.

Resource-constrained wireless IP networks require compression based encryption techniques for secure archival, transmission and distribution of multimedia data. A partial encryption technique based on SPIHT encoder is thus proposed that provides enhanced security level by encrypting selected bit values and keeping the encrypted bit locations as secret. The proposed technique preserves the scalability property of the encoder and provides high data security without adversely affecting the compression efficiency. Two new edge based parameters, edge ratio and edge differential ratio are also proposed to measure the degradation in the encrypted image. Thorough performance and security analysis as regards to various evaluation metrics ascertains its high performance and ability to withstand cryptanalytic attacks.

In this paper, a Wavelet Neuro-Fuzzy model has been proposed. The proposed work caters an application of wavelet network used in fuzzy systems for forecasting of dynamic systems. A wavelet network approximates the consequent part of each fuzzy rule. The wavelet network is a feed-forward neural network with one hidden layer that uses a combination of Wavelet and Sigmoid Activation Function. A hybrid learning method composed of genetic algorithm and gradient descent is proposed to tune the learning parameters of the proposed Wavelet Neuro-Fuzzy model. Further, an analysis regarding the convergence and stability of gradient descent learning is presented for the proposed Wavelet Neuro-Fuzzy model. To evaluate the effectiveness of proposed model and learning strategy, three different classes of benchmark problems have been considered.