In this paper, the performances of texture classification based on pyramidal and uniform decomposition are comparatively studied with and without feature selection. This comparison using the subband variance as feature explores the dependence among features. It is shown that the main problem when employing 2-D non-separable wavelet transforms for texture classification is the determination of the suitable features that yields the best classification results. A Max-Max algorithm which is a novel evaluation function based on genetic algorithms is presented to evaluate the classification performance of each subset of selected features. It is shown that the performance with feature selection in which only about half of features are selected is comparable to that without feature selection. Moreover, the discriminatory characteristics of texture spread more in low-pass bands and the features extracted from the pyramidal decomposition are more representative than those from the uniform decomposition. Experimental results have verified the selectivity of the proposed approach and its texture capturing characteristics.

ATM networks are connection-oriented. Making a call requires first sending a message to do an admission control to guarantee the connections' QoS (quality of service) in the network. In this paper, we focus on the problem of translating a global QoS requirement into a set of local QoS requirements in ATM networks. Usually, an end-user is only concerned with the QoS requirements on end-to-end basis and does not care about the local switching node QoS. Most of recent research efforts only focus on worst-case end-to-end delay bound but pay no attention to the problem of distributing the end-to-end delay bound to local switching node. After admission control, when the new connection is admitted to enter the network, they equally allocate the excess delay and reserve the same bandwidth at each switch along the path. But, this can not improve network utilization efficiently. It motivates us to design a novel local QoS requirement allocation scheme to get better performance. Using the number of maximum supportable connections as the performance index, we derive an optimal delay allocation (OPT) policy. In addition, we also proposed an analysis model to evaluate the proposed allocation scheme and equal allocation (EQ) scheme in a series of switching nodes with the Rate-controlled scheduling architecture, including a traffic shaper and a non-preemptive earliest-deadline-first scheduler. From the numerical results, we have shown the importance of allocation policy and explored the factors that affect the performance index.

The wavelet transform (WT) has recently emerged as a powerful tool for image compression. In this paper, a new image compression technique combining the genetic algorithm (GA) and grey-based competitive learning network (GCLN) in the wavelet transform domain is proposed. In the GCLN, the grey theory is applied to a two-layer modified competitive learning network in order to generate optimal solution for VQ. In accordance with the degree of similarity measure between training vectors and codevectors, the grey relational analysis is used to measure the relationship degree among them. The GA is used in an attempt to optimize a specified objective function related to vector quantizer design. The physical processes of competition, selection and reproduction operating in populations are adopted in combination with GCLN to produce a superior genetic grey-based competitive learning network (GGCLN) for codebook design in image compression. The experimental results show that a promising codebook can be obtained using the proposed GGCLN and GGCLN with wavelet decomposition.

A method to recognize planar objects undergoing affine transformation is proposed in this paper. The method is based upon wavelet multiscale features and Hopfield neural networks. The feature vector consists of the multiscale wavelet transformed extremal evolution. The evolution contains the information of the contour primitives in a multiscale manner, which can be used to discriminate dominant points, hence a good initial state of the Hopfield network can be obtained. Such good initiation enables the network to converge more efficiently. A wavelet normalization scheme was applied to make our method scale invariant and to reduce the distortion resulting from normalizing the object contours. The Hopfield neural network was employed as a global processing mechanism for feature matching and made our method suitable to recognize planar objects whose shape distortion arising from an affine transformation. The Hopfield network was improved to guarantee unique and more stable matching results. A new matching evaluation scheme, which is computationally efficient, was proposed to evaluate the goodness of matching. Two sets of images, noiseless and noisy industrial tools, undergoing affine transformation were used to test the performance of the proposed method. Experimental results showed that our method is not only effective and robust under affine transformation but also can limit the effect of noises.

Image coding with vector quantization (VQ) reveals several defects which include edge degradation and high encoding complexity. This paper presents an edge-preserving coding system based on VQ to overcome these defects. A signal processing unit first classifies image blocks into low-activity or high-activity class. A high-activity block is then decomposed into a smoothing factor, a bit-plane and a smoother (lower variance) block. These outputs can be more efficiently encoded by VQ with lower distortion. A set of visual patterns is used to encode the bit-planes by binary vector quantization. We also develop a modified search-order coding to further reduce the redundancy of quantization indexes. Simulation results show that the proposed algorithm achieves much better perceptual quality with higher compression ratio and significant lower computational complexity, as compared to the direct VQ.

In this paper, a classified vector quantization (CVQ) method using a novel direction based classifier is proposed. The new classifier uses a distortion measure related to the angle between vectors to determine the similarity of vectors. The distortion measure is simple and adequate to classify various edge types other than single and straight line types, which limit the size of image block to a rather small size. Simulation results show that the proposed technique can achieve better perceptual quality and edge integrity at a larger block size, as compared to other CVQs. It is shown when the vector dimension is changed from 16(4 4) to 64(8 8), the average bit rate can be reduced from 0. 684 bpp to 0.191, whereas the PSNR degradation is only about 1.2 dB.

Fractional calculus is the generalization of the operators of differential and integration to non-integer order, and a differential equation involving the fractional calculus operators such as d1/2/dt1/2 and d-1/2/dt-1/2 is called the fractional differential equation. They have many applications in science and engineering. But not only its analytical solutions exist only for a limited number of cases, but also, the numerical methods are difficult to solve. In this paper we propose a new numerical method based on the operational matrices of the orthogonal functions for solving the fractional calculus and fractional differential equations. Two classical fractional differential equation examples are included for demonstration. They show that the new approach is simper and more feasible than conventional methods. Advantages of the proposed method include (1) the computation is simple and computer oriented; (2) the scope of application is wide; and (3) the numerically unstable problem never occurs in our method.

A typical user is concerned only with the quality of service of a network on an end-to-end basis. Therefore, how end-to-end requirements are mapped into the local switching node requirements and maximum network utilization is a function of network internal design. In this paper, we address the problem of QOS allocation. We derived an optimal QOS allocation policy and decided the maximum utilization bound in a deterministic traffic model. We adopted the worst case delay bound as the end-to-end and local QOS requirement. With (σ, ρ) traffic model, we derived a formula for delay bound and the number of connections. We found that with the delay bound as the QOS metric, there is a significant difference in the performance of allocation policies. We also developed an evaluation strategy to analyze allocation policies. The numerical results for two simple network topologies: tandem network model and uneven traffic load model, compare the equal allocation policy with the optimal allocation policy and show the correctness and efficiency of QOS allocation policy.

Traffic markers differentiate among packets from senders based on their service profile in the differentiated service networks. Researchers have previously revealed that the existing marking mechanism causes the unfairness in aggregates. This study presents a new marking algorithm. Simulation results demonstrate that the fairness of the proposed scheme exceeds that of SRTCM, TRTCM, TSWTCM and ITSWTCM for medium to high network provision levels.