In this paper, a VLSI architecture for lifting-based discrete wavelet transform (LDWT) is presented. Our architecture folds the computations of all resolution levels into the same low-pass and high-pass units to achieve higher hardware utilization. Due to the regular and flexible structure of the design, its area is independent of the length of the 1-D input sequence, and its latency is independent of the number of resolution levels. For the computations of analysis process of N-sample 1-D 3-level LDWT, our design takes about N clock cycles and requires 2 multipliers, 4 adders, and 22 registers. It is fabricated with TSMC 0.35-µm cell library and has a die size of 1.21.2 mm². The power dissipation of the chip is about 0.4 W at the clock rate of 80 MHz.

This paper presents a rough sets-based method for clustering nominal and numerical data. This clustering result is independent of a sequence of handling object because this method lies its basis on a concept of classification of objects. This method defines knowledge as sets that contain similar or dissimilar objects to every object. A number of knowledge are defined for a data set. Combining similar knowledge yields a new set of knowledge as a clustering result. Cluster validity selects the best result from various sets of combined knowledge. In experiments, this method was applied to nominal databases and numerical databases. The results showed that this method could produce good clustering results for both types of data. Moreover, ambiguity of a boundary of clusters is defined using roughness of the clustering result.

This paper considers a reconfiguration problem on a processor array model based on single-and-half-track switches, which is proposed for a fault tolerance technique at the fabrication time. The focus of this paper is to achieve the optimal reconfigurability, which means that whenever there exists a solution for successful reconfiguration, the designed method can find the solution. The paper consists of two parts. In the first part, we show two essential constraints that have been assumed in most of the previous studies, and make four reconfiguration classes that differ in the assumed essential constraints. Then, we present some inclusion relations among the four reconfiguration classes. As a result, it becomes clear that the most restrictive class including most of the previous methods never achieves the truly optimal reconfigurability. In the second part, we present a reconfiguration method based on sequential routing (RMSR). Although the worst-case time complexity of the RMSR is exponential in the number of processing elements, the reconfigurability of the RMSR is optimal within the most restrictive reconfiguration class. The effectiveness of the RMSR is shown by a computer simulation.

Distributed Denial of Service (DDoS) attacks are a pressing problem on the Internet as demonstrated by recent attacks on major e-commerce servers and ISPs. Since the attack is highly distributed, an effective solution must be formulated with a distributed approach. Recently, some solutions, in which intermediate network nodes filter or shape congested traffic, have been proposed. These solutions may decrease the congested traffic, but they still cause "collateral victims problem," that is, legitimate packets may be discarded mistakenly. In this paper, we propose Active Countermeasure Platform to minimize traffic congestion and to address the collateral victim problem using the Active Networks paradigm, which incorporates programmability into intermediate network nodes. Our platform can prevent overloading of the target and consuming the network bandwidth of both the backbone and the protected site autonomously. In addition, it can improve the collateral victim problem based on user policy. This paper shows the concept of our platform, system design and evaluation of the effectiveness using a prototype.

The existing methods for reconstruction of a super-resolution image from undersampled and shubpixel shifted image sequence have two disadvantages. One is that most of them have to perform a lot of computations which lead to taking a lot of time and cannot meet the need of realtime processing. Another is that they cannot achieve satisfactory results in the case that the undersampling rate is too low. This paper considers applying a pyramid structure method to the super-resolution of the image sequence since it has some iterative optimization and parallel processing abilities. Based on the Iterative Back-Projection proposed by Peleg, a practical implementation, called Pyramid Iterative Back-Projection, is presented. The experiments and the error analysis show the effectiveness of this method. The image resolution can be improved better even in the case of severely undersampled images. In addition, the proposed method can be done in parallel and meet the need of real-time processing. The implementation framework of the method can be easily extended to the other general super-resolution methods.

In this paper, an associative memory model with a forgetting process proposed by Mezard et al. is investigated as a means of storing sparsely encoded patterns by the SCSNA proposed by Shiino and Fukai. Similar to the case of storing non-sparse (non-biased) patterns as analyzed by Mezard et al., this sparsely encoded associative memory model is also free from a catastrophic deterioration of the memory caused by memory pattern overloading. We theoretically obtain a relationship between the storage capacity and the forgetting rate, and find that there is an optimal forgetting rate leading to the maximum storage capacity. We call this the optimal storage capacity rate. As the memory pattern firing rate decreases, the optimal storage capacity increases and the optimal forgetting rate decreases. Furthermore, we shown that the capacity rate (i.e. the ratio of the storage capacity for the conventional correlation learning rule to the optimal storage capacity) is almost constant with respect to the memory pattern firing rate.

The jaw movements can be measured by estimating the position and orientation of two small permanent magnets attached on the upper and lower jaws. It is a difficult problem to estimate the positions and orientations of the magnets from magnetic field because it is a typical inverse problem. The back propagation neural networks (BPNN) are applicable to solve this problem in short processing time. But its precision is not enough to apply to practical measurement. In the other hand, precise estimation is possible by using the nonlinear least-square (NLS) method. However, it takes long processing time for iterative calculation, and the solutions may be trapped in the local minima. In this paper, we propose a precise and fast measurement system which makes use of the estimation algorithm combining BPNN with NLS method. In this method, the BPNN performs an approximate estimation of magnet parameters in short processing time, and its result is used as the initial value of iterative calculation of NLS method. The cost function is solved by Gauss-Newton iteration algorithm. Precision, processing time and noise immunity were examined by computer simulations. These results shows the proposed system has satisfactory ability to be applied to practical measurement.

Parallelization of the P-complete problem is known to be difficult. In this paper, we consider the parallelizability of a stack breadth-first search (stack BFS) problem, which is proved to be P-complete. We first propose the longest path length (LPL) as a measure for the P-completeness of the stack BFS. Next, using this measure, we propose an efficient parallel algorithm for the stack BFS. Assuming the size and LPL of an input graph are n and l, respectively, the complexity of the algorithm indicates that the stack BFS is in the class NC^k+1 if l = O(log^k n), where k is a positive integer. In addition, the algorithm is cost optimal if l=O(n^ε), where 0 < ε < 1.

We suggest a new probe message structure and an efficient probe-based deadlock detection and recovery algorithm that can be used in distributed database systems. We determine the characteristics of the probe messages and suggest an algorithm that can reduce the communication cost required for deadlock detection and recovery.

Most network systems provide an authentication mechanism based on a user identification number and a password. In such systems, it is easy to obtain a user's password using a sniffer program with illegal eavesdropping. The one-time password method and the challenge-response method are useful authentication schemes that protect a user's password against eavesdropping. In client/server environments, the one-time password scheme using time is especially useful because it solves the synchronization problem. However, it has a problem of time-slippage, and this problem causes the authentication to be failed. In this paper, we propose an effective one-time password algorithm, which solves the time-slippage problem through the use of 1-bit information, which denotes the duration in which the authentication could be failed because of time-slippage. This algorithm can be added easily and quickly to current one-time password systems using time without requiring any change of protocols.

A postprocessing algorithm is presented for blocking artifact reduction in block-coded images using the adaptive filters along the pattern of neighborhood blocks. Blocking artifacts appear as irregular high-frequency components at block boundaries, thereby reducing the noncorrelation between blocks due to the independent quantization process of each block. Accordingly, block-adaptive filtering is proposed to remove such components and enable similar frequency distributions within two neighborhood blocks and a high correlation between blocks. This type of filtering consists of inter-block filtering to remove blocking artifacts at the block boundaries and intra-block filtering to remove ringing noises within a block. First, each block is classified into one of seven classes based on the characteristics of the DCT coefficient and MV (motion vector) received in the decoder. Thereafter, adaptive intra-block filters, approximated to the normalized frequency distributions of each class, are applied adaptively according to the various patterns and frequency distributions of each block as well as the filtering directions in order to reduce the blocking artifacts. Finally, intra-block filtering is performed on those blocks classified as complex to reduce any ringing noise without blurring the edges. Experimental tests confirmed the effectiveness of the proposed algorithm.