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.

In this paper, a new method is proposed for supervised classification of ground cover types by using polarimetric synthetic aperture radar (SAR) data. The concept of similarity parameter between two scattering matrices is introduced for characterizing target scattering mechanism. Four similarity parameters of each pixel in image are used for classification. They are the similarity parameters between a pixel and a plane, a dihedral, a helix and a wire. The total received power of each pixel is also used since the similarity parameter is independent of the spans of target scattering matrices. The supervised classification is carried out based on the principal component analysis. This analysis is applied to each data set in image in the feature space for getting the corresponding feature transform vector. The inner product of two vectors is used as a distance measure in classification. The classification result of the new scheme is shown and it is compared to the results of principal component analysis with other decomposition coefficients, to demonstrate the effectiveness of the similarity parameters.

This paper presents a second-generation CMOS current conveyor (CCII) consisting of a rail-to-rail complementary n- and p-channel differential input stage for the voltage input, a class AB push-pull stage for the current input, and current mirrors for the current outputs. The CCII was implemented using a double-poly triple-metal 0.6 µm n-well CMOS process, to confirm its operation experimentally. A prototype chip achieves a rail-to-rail swing 2.3 V under 2.5 V power supplies and shows the exact voltage and current following performances up to 100 MHz. Because of its high performances, the CCII proposed herein is quite useful for a building block of current-mode circuits.

This paper describes our vehicle classification system, which is based on local-feature configuration. We have already demonstrated that our system works very well for vehicle recognition in outdoor environments. The algorithm is based on our previous work, which is a generalization of the eigen-window method. This method has the following three advantages: (1) It can detect even if parts of the vehicles are occluded. (2) It can detect even if vehicles are translated due to veering out of the lanes. (3) It does not require segmentation of vehicle areas from input images. However, this method does have a problem. Because it is view-based, our system requires model images of the target vehicles. Collecting real images of the target vehicles is generally a time consuming and difficult task. To ease the task of collecting images of all target vehicles, we apply our system to computer graphics (CG) models to recognize vehicles in real images. Through outdoor experiments, we have confirmed that using CG models is effective than collecting real images of vehicles for our system. Experimental results show that CG models can recognize vehicles in real images, and confirm that our system can classify vehicles.

This paper presents the transmission performance of a class-IV partial-response signaling SSB system and proposes a method that can improve its power efficiency. A line code that has no dc component has been used in the SSB transmission of digital signals. The type of line code, such as a partial-response signaling, increases the modulation states, and as a result, decreases the power efficiency. To overcome this obstacle, a new demodulation method called "re-filtering and combining" is proposed on the assumption of orthogonal phase detection. The demodulated quadrature channel is re-filtered by a Hilbert filter and is combined with the in-phase channel. It is confirmed by computer simulations that the new demodulation method improves the BER performance and a 3 dB improvement of the power efficiency is obtained.

The objective of this paper is to evaluate the impacts of impulsive class-A noise, co-channel interference due to other piconet, Rician fading on the packet error rate (PER), and throughput performance in the Bluetooth scatternet. Simulation results illustrate the significant difference in performance between synchronous and asynchronous Bluetooth systems. The paper also provides the insights on how to design Bluetooth scatternet for minimal PER and maximum throughput performance.

This paper presents an architecture for a table-lookup (TLU) engine that allows the real-time operation of complicated TLU for telecommunications, such as the longest prefix match (LPM) and the long-bit match in packet classification. The engine consists of many CAM (Content Addressable Memory) chips, which are classified into several groups. When actual TLU is performed, the entries in each CAM group are searched simultaneously, and the best entry candidate in each group is selected by an intra-group arbiter. The final output, the entry desired, is decided by an inter group arbiter that selects one group. This hierarchical structure of arbitration is the key to the scalability of the engine. To accelerate the operation speed of the engine, we introduce a novel mechanism called "hit-flag look-ahead" that sends a hit-flag signal from each matched CAM chip to the inter group arbiter before each intra group arbiter calculates the best CAM output in the group. We show that a TLU engine based on the above architecture achieves significantly fast performance compared to engines based on conventional techniques, especially in the case of a large number of entries with long-bit matching. Furthermore, our architecture can realize an 33.3 Mlps (lookups per second) within a 128 bit 300,000-entry table at wire speed.

Vector quantization (VQ) is an attractive image compression technique. VQ utilizes the high correlation between neighboring pixels in a block, but disregards the high correlation between the adjacent blocks. Unlike VQ, side-match VQ (SMVQ) exploits codeword information of two encoded adjacent blocks, the upper and left blocks, to encode the current input vector. However, SMVQ is a fixed bit rate compression technique and doesn't make full use of the edge characteristics to predict the input vector. Classified side-match vector quantization (CSMVQ) is an effective image compression technique with low bit rate and relatively high reconstruction quality. It exploits a block classifier to decide which class the input vector belongs to using the variances of neighboring blocks' codewords. As an alternative, this paper proposes three algorithms using gradient values of neighboring blocks' codewords to predict the input block. The first one employs a basic gradient-based classifier that is similar to CSMVQ. To achieve lower bit rates, the second one exploits a refined two-level classifier structure. To reduce the encoding time further, the last one employs a more efficient classifier, in which adaptive class codebooks are defined within a gradient-ordered master codebook according to various prediction results. Experimental results prove the effectiveness of the proposed algorithms.

This paper proposes a novel packet classification method using ternary content-addressable memory (TCAM), which can store very wide policy rules despite the limited width of TCAM. For IP version 6, policy rules could be 304 bits wide. This method enables us to use commercially available TCAM for packet classification and thus builds an ultra high-speed policy based packet forwarding engine for differentiated services on the Internet.

This paper proposes a compact, low-power, and rail-to-rail class-B output buffer for driving the large column line capacitance of LCDs. The comparator used as a nonlinear element in feedback path is modified from the current-mirror amplifier, which has area and power advantages. The output buffer was realized in a 0.35 m CMOS process. The active area of the buffer is 8673.5 m2. With a 3.3 V supply, the measured quiescent current is 7.4 A. The settling time for 0.05-3.25 V swing to within 0.2% is 8 s.

Most of the QoS-based routing schemes proposed so far focus on improving the performance of individual service classes. In a multi-class network where high priority QoS traffic coexists with best-effort traffic, routing decision for QoS sessions will have an effect on lower ones. A mechanism that allows dynamic sharing of link resources among multiple classes of traffic is needed. In this paper we propose a multi-class routing algorithm based on inter-class sharing resources among multiple class of traffic. Our algorithm is based on the concept of "the virtual residual bandwidth," which is derived from the real residual bandwidth. The virtual residual bandwidth is greater than the residual bandwidth when the load of lower priority traffic is light, and smaller when the load of lower priority traffic is heavy. The idea of our approach is simple since the routing algorithm for individual traffic doesn't change, but the only change is the definition of the link cost. We demonstrate through some extensive simulations the effectiveness of our approach when the best effort distribution is uneven and when its load is heavy. Also better performance is noticed when using topology with large number of alternative paths.

This paper presents the performance modeling, analysis, and simulation of SIP-T (Session Initiation Protocol for Telephones) signaling system in carrier class packet telephony network for NGN (Next Generation Networks). Until recently, fone of the greatest challenges in the migration from existing PSTN (Public Switched Telephone Network) toward NGN is to build a carrier class packet telephony network that preserves the ubiquity, quality, and reliability of PSTN services while allowing the greatest flexibility for use of new packet telephony technology. The SIP-T signaling system defined in IETF (Internet Engineering Task Force) draft is a mechanism that uses SIP (Session Initiation Protocol) to facilitate the interconnection of PSTN with carrier class packet telephony network. Based on IETF, the SIP-T signaling system not only promises scalability, flexibility, and interoperability with PSTN but also provides call control function of MGC (Media Gateway Controller) to set up, tear down, and manage VoIP (Voice over IP) calls in carrier class packet telephony network. In this paper, we derive the buffer size, the mean of queueing delay, and the variance of queueing delay of SIP-T signaling system that are the major performance evaluation parameters for improving QoS (Quality of Service) and system performance of MGC in carrier class packet telephony network focused on toll by-pass or tandem by-pass of PSTN. First, we assume a mathematical model of the M/G/1 queue with non-preemptive priority assignment to represent SIP-T signaling system. Second, we derive the formulas of buffer size, queueing delay, and delay variation for the non-preemptive priority queue by queueing theory respectively. Besides, some numerical examples of buffer size, queueing delay, and delay variation are presented as well. Finally, the theoretical estimates are shown to be in excellent consistence with simulation results.

We have recently developed a programmable composite noise generator (P-CNG) which can easily control noise parameters such as average power, time-based amplitude probability distribution (APD), crossing rate distribution, occurrence frequency distribution and burst duration. Two applications of the P-CNG are demonstrated to show its usefulness. For the first application, Middleton's Class A noise is simulated. A method of setting parameters for Class A noise is demonstrated. The APD of P-CNG output is in good agreement with that of true Class A noise. In the second application, the P-CNG is used for subjective evaluation test (opinion test) of TV picture degradation. Five simple composite noise models with two kinds of APD are used. Other parameters such as average power are kept constant. Experimental results show that the envelope and APD of composite noises do not greatly influence the subjective evaluation. Finally the capabilities of the P-CNG are shown.

A new blocking artifact reduction algorithm is proposed that uses block classification and feedforward neural network filters in the spatial domain. At first, the existence of blocking artifact is determined using statistical characteristics of neighborhood block, which is then used to classify the block boundaries into one of four classes. Thereafter, adaptive inter-block filtering is only performed in two classes of block boundaries that include blocking artifact. That is, in smooth regions with blocking artifact, a two-layer feedforward neural network filters trained by an error back-propagation algorithm is used, while in complex regions with blocking artifact, a linear interpolation method is used to preserve the image details. Experimental results show that the proposed algorithm produces better results than the conventional algorithms.

This paper proposes a new blocking artifact reduction algorithm using an adaptive filter based on classifying the block boundary area. Generally, block-based coding, such as JPEG and MPEG, introduces blocking and ringing artifacts to an image, where the blocking artifact consists of grid noise, staircase noise, and corner outliers. In the proposed method, staircase noise and corner outliers are reduced by a 1D low-pass filter. Next, the block boundaries are divided into two classes based on the gradient of the pixel intensity in the boundary region. For each class, an adaptive filter is applied so that the grid noise is reduced in the block boundary regions. Thereafter, for those blocks with an edge component, the ringing artifact is removed by applying an adaptive filter around the edge. Finally, high frequency components are added to those block boundaries where the natural characteristics have been lost due to the adaptive filter. The computer simulation results confirmed a better performance by the proposed method in both the subjective and objective image qualities.

Properly representation of the complexity of class structure will be useful in object oriented software developments. Although some class complexity metrics have been proposed, they have ignored directions of coupling relationships among methods and attributes, such as whether a method writes data onto an attribute or reads data from the attribute. In this paper, we use a directed graph model to represent such coupling relationships. Based on the directed graph model, we propose a metric of class structural complexity. The proposed metric satisfies necessary conditions of complexity metric suggested by Briand and others. The following fact is showed by experimental data of Java classes. While the proposed metric follows a conventional metric, the proposed metric can capture an aspect of class structural complexity which is lost by the conventional one.

A novel rough neural network (RNN) structure and its application are proposed in this paper. We principally introduce its architecture and training algorithms: the genetic training algorithm (GA) and the tabu search training algorithm (TSA). We first compare RNN with the conventional NN trained by the BP algorithm in two-dimensional data classification. Then we compare RNN with NN by the same training algorithm (TSA) in functional approximation. Experiment results show that the proposed RNN is more effective than NN, not only in computation time but also in performance.

We propose a call admission control (CAC) scheme for the reverse link of direct sequence-code division multiple access (DS-CDMA) systems with multi-class traffic, in which the admissibility of the set of requested channels is decided by checking the outage probability of the total composite power at a cell-site receiver. The reverse link capacities under various traffic conditions are evaluated. From numerical results, we see that the proposed scheme can utilize a given radio resource more effectively as compared with the existing scheme using constraints on the individual power levels.

In this paper, we propose a method to reconstruct current distributions in the human brain from neuromagnetic measurements. The proposed method is based on the weighted lead-field synthetic (WLFS) filtering technique with the weighting factors calculated from the results of previous source space scanning. In this method, in addition to the depth normalization technique, weighting factors of the WLFS are determined by the cost values previously calculated based on the multiple signal classification (MUSIC) scan. We performed computer simulations of this method under noisy measurement conditions and compared the results to those obtained with the conventional WLFS method. The results of the simulations indicate that the proposed method is effective for the reconstruction of the current distributions in the human brain using magnetoencephalographic (MEG) measurements, even if the signal-to-noise ratio of the measured data is relatively low. We applied the proposed method to the magnetoencephalographic data obtained during a mental image processing task that included object recognition and mental rotation operations. The results suggest that the proposed method can extract the neural activity in the extrastriate visual region and the parietal region. These results are in agreement with the results of previous positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) studies.

Tree canopies contain various scattering elements such as leaves, branches and trunks, which contribute to complex backscattering, depending on frequency and polarization. In this paper, we propose to use the polarimetric correlation coefficient for classifying trees, forests, and vegetations. The polarimetric correlation coefficient can be derived by the elements of Sinclair scattering matrix. Since the scattering matrix can be defined in any polarization basis, we examined the coefficient in the linear HV, circular LR, and optimum polarization bases. First, the change of correlation coefficient inside trees along the range direction is examined using small trees in a laboratory. The wider the range, the better the index. The coefficient defined in the LR polarization basis showed the largest change within tree canopy, which also contribute to retrieve scattering mechanism. Second, this index for discrimination is applied to polarimetric SAR data sets (San Francisco and Briatia area) acquired by AIRSAR and SIR-C/X-SAR. It is shown that polarimetric correlation coefficient in the LR basis best serves to distinguish tree types.