This letter introduces a pre-rejection technique for wireless channel distorted speech with application to automatic speech recognition (ASR). Based on analysis of distorted speech signals over a wireless communication channel, we propose a method to reject the channel distorted speech with a small computational load. From a number of simulation results, we can discover that the pre-rejection algorithm enhances the robustness of speech recognition operation.

Iris recognition is used to identify a user based on the iris texture information which exists between the white sclera and the black pupil. For fast iris recognition, it is very important to capture user's focused eye image at fast speed. If not, the total recognition time is increased and it makes the user feel much inconvenience. In previous researches and systems, they use the focusing method which has been used for general landscape scene without considering the characteristics of iris image. So, they take much focusing time sometimes, especially in case of the user with glasses. To overcome such problems, we propose a new iris image acquisition method to capture user's focused eye image at very fast speed based on the corneal specular reflection. Experimental results show that the focusing time for both the users with glasses and without glasses is average 480 ms and we can conclude our method can be used for the real-time iris recognition camera.

Performance of selective Rake (SRake) receiver is evaluated for direct sequence ultra wideband (DS-UWB) communications considering an independent Rayleigh channel having exponentially decaying power delay profile (PDP). BEP performances are shown. The results obtained are compared with similar results in a channel having flat PDP. Assumption of a flat PDP is found to predict the optimum spreading bandwidth to be lower and sub-optimum operating performance beyond optimum spreading bandwidth to be severely worse than that is achievable in a channel having exponentially decaying PDP by employing an SRake receiver having fixed number of combined paths. Optimum spreading bandwidth for SRake in a channel having exponentially decaying PDP is shown to be much larger than the one in a channel having flat PDP; that is specifically a good-news for UWB communications. Effects of partial band interference are also investigated. Interference is found to be less effective in exponentially decaying PDP.

This paper proposes several novel hierarchical interconnection networks based on the (3, 3)-graphs, namely folded (3, 3)-networks, root-folded (3, 3)-networks, recursively expanded (3, 3)-networks, and flooded (3, 3)-networks. Just as the hypercubes, CCC, Peterson-based networks, and Heawood-based networks, these hierarchical networks have the following nice properties: regular topology, high scalability, and small diameters. Due to these important properties, these hierarchical networks seem to have the potential as alternatives for the future interconnection structures of multicomputer systems, especially massively parallel processors (MPPs). Furthermore, this paper will present the routing and broadcasting algorithms for these proposed networks to demonstrate that these algorithms are as elegant as the algorithms for hypercubes, CCC, and Petersen- or Heawood-based networks.

We have developed a novel basic technology for terahertz (THz) imaging, which allows detection and identification of chemicals by introducing the component spatial pattern analysis. The spatial distributions of the chemicals were obtained from terahertz multispectral transillumination images, using absorption spectra previously measured with a widely tunable THz-wave parametric oscillator. We have also separated the component spatial patterns of frequency-dependent absorptions in chemicals and frequency-independent components such as plastic, paper and measurement noise in THz spectroscopic images. Further we have applied this technique to the detection and identification of illicit drugs concealed in envelopes.

This paper proposes a new on-chip linearizer self-adapting to the input power and its implementation to high linear monolithic microwave integrated circuit (MMIC) power amplifier for 1.95 GHz wide-band code division multiple-access (W-CDMA) system. The linearizer consists of InGaP/GaAs heterojunction bipolar transistor (HBT) active bias circuit and reverse biased junction diode of which dynamic admittance to input power level functions adaptively to control the bias to the amplifier. The proposed linearizer has little insertion power loss, and more importantly, it consumes no additional die area and DC power. The HBT MMIC power amplifier with the integrated linearizer exhibits a maximum output power of 30.3 dBm, a power gain of 27.5 dB, a power added efficiency of 42% at the maximum output power under an operation voltage of 3.4 V, and adjacent channel leakage power ratio of -38 dBc at 27 dBm of output power.

In this paper we apply a parallel adaptive solution algorithm to simulate nanoscale double-gate metal-oxide-semiconductor field effect transistors (MOSFETs) on a personal computer (PC)-based Linux cluster with the message passing interface (MPI) libraries. Based on a posteriori error estimation, the triangular mesh generation, the adaptive finite volume method, the monotone iterative method, and the parallel domain decomposition algorithm, a set of two-dimensional quantum correction hydrodynamic (HD) equations is solved numerically on our constructed cluster system. This parallel adaptive simulation methodology with 1-irregular mesh was successfully developed and applied to deep-submicron semiconductor device simulation in our recent work. A 10 nm n-type double-gate MOSFET is simulated with the developed parallel adaptive simulator. In terms of physical quantities and refined adaptive mesh, simulation results demonstrate very good accuracy and computational efficiency. Benchmark results, such as load-balancing, speedup, and parallel efficiency are achieved and exhibit excellent parallel performance. On a 16 nodes PC-based Linux cluster, the maximum difference among CPUs is less than 6%. A 12.8 times speedup and 80% parallel efficiency are simultaneously attained with respect to different simulation cases.

This paper presents a local learning framework in which the local classifiers can be pre-learned and the support size of each classifier can be selected to minimize the error bound. The proposed algorithm is compared with the conventional support vector machine (SVM). Experimental results show that our scheme using the user-defined parameters C and σ is more accurate and less sensitive than the conventional SVM.

A method is presented for selecting items asked for new users to input their preference rates on those items in recommendation systems based on the collaborative filtering. Optimal item selection is formulated by an integer programming problem and we solve it by using a kind of the Hopfield-network-like scheme for interior point methods.

We have proposed a speculative selection function for adaptive routing, which uses idle cycles of the network physical links to exchange network information between nodes, thus helps to decide the best selection. Previous study on the mesh network showed that SSR gives message selection flexibility that improves network performance by balancing the network traffic in both global and local scopes. This paper evaluates the speculative selection function on 2D torus network with simulation. The simulation compares the network throughput and latency with various traffic patterns. The visualization graphs show how the speculative selection eliminates hotspots and disperses traffic in the global scope. The simulation results demonstrate that by using speculative selection, the network performance is increased by around 7%. Compared to the mesh network, the torus's version has smaller gain due to the high performance nature of the torus network.

As the Internet has become the infrastructure for the global communication, the quality degradation due to network failures and illegal traffic such as DDoS (Distributed Denial of Service) have become a serious problem. In order to solve the problem, a network monitoring system that monitors the traffic of Internet in real time is strongly desired. Traffic monitors that collect the statistics from captured packets play a key roll in the system; however, they are not flexible enough for being used in the rapidly changing Internet. The traditional approach such that a new traffic monitor is developed for a new requirement results in a long turn around time of the development. Therefore, we have proposed a flexible network monitoring system that consists of programmable traffic monitors. Traffic monitors are made programmable by introducing active network techniques; therefore, we call the network monitoring system as the programmable monitor network. This paper describes the implementation of the programmable monitor network and its application to DDoS (Distributed Denial of Service) attack detection.

This paper proposes the use of the ratio of wavelet extrema numbers taken from the horizontal and vertical counts respectively as a texture feature, which is called aspect ratio of extrema number (AREN). We formulate the classification problem upon natural and synthesized texture images as an optimization problem and develop a coevolving approach to select both scalar wavelet and multiwavelet feature spaces of greater discriminatory power. Sequential searches and genetic algorithms (GAs) are comparatively investigated. The experiments using wavelet packet decompositions with the innovative packet-tree selection scheme ascertain that the classification accuracy of coevolutionary genetic algorithms (CGAs) is acceptable enough.

Orthogonal multicode direct sequence code division multiple access (DS-CDMA) has the flexibility in offering various data rate services. However, in a frequency-selective fading channel, the bit error rate (BER) performance is severely degraded since the othogonality among spreading codes is partially lost. In this paper, we apply frequency-domain equalization and antenna diversity combining, used in multi-carrier CDMA (MC-CDMA), to orthogonal multicode DS-CDMA in order to restore the code othogonality while achieving frequency and antenna diversity effect. It is found by computer simulations that the joint use of frequency-domain equalization and antenna diversity combining can significantly improve the BER performance of orthogonal multicode DS-CDMA in a frequency-selective fading channel.

Real-world IP networks are heterogeneous in terms of server and link capacities. A sophisticated and comprehensive load balancing method is essential if we are to avoid congestion in the servers and links of heterogeneous networks. If such a method is not available, network throughput is limited by bottleneck servers or links. This paper proposes an anycast technique that achieves load balancing under heterogeneity. The proposed method well suits implementation on active networks. By taking advantage of the processing ability provided by active nodes, the method can decide packet routes flexibly on the basis of various criteria to realize a variety of load balancing schemes. Some of these schemes can successfully prevent the congestion of heterogeneous networks by tackling bottlenecks in both server and link capacities. The method is also advantageous given its light control load even when using many mirrored servers. Computer simulations confirm the effectiveness of these features.

In this paper, we consider a location management scheme using Limited Pointer forwarding from Commonly visited sites (LPC) strategy for Personal Communication Services (PCS) networks. The Commonly Visited Site (CVS) is defined as a site in which a mobile user is found with high probability. A feature of the strategy is that it skips updating location information of the mobile user, provided that the mobile user moves within its CVSs. Such a strategy is expected to significantly reduce the location update cost. We evaluate the location management cost of the LPC scheme by employing a Continuous-Time Markov Chain (CTMC) model. We show that the LPC scheme can reduce the location management cost of a highly mobile user who is found in its CVS with high probability.

There have been many researches on providing mobility under mobile/wireless environment. However, previous researches had several problems as disruption and unnecessary traffic. Disruption happens when messages are exchanged between nodes as registration is made after handoff, and unnecessary traffic occurs because of the use of Random-walk model, in which the probability for MN to move to the neighboring cells is equal. In order to solve these problems, this study proposes a technique and algorithm for composing Directional Shadow Registration Region (DSRR) that provides seamless mobility. The core of DSRR is to prevent disruption and unnecessary traffic by shadow registration at neighboring cells with a high probability of handoff (AAAF). We are introduced a cell division scheme and decided minimal DSRR. DSRR can sensed the optimal time for handoff through Regional Cell Division and applied Direction Vector (DV) obtained through Directional Cell Sectoring. According to the result of the experiment, the proposed DSRR processes message exchange between nodes within the intra-domain, the frequency of disruptions decreased significantly compared to that in previous researches held in inter-domain environment. In addition, traffic that occurs at every handoff happened twice in DSRR compared to n (the number of neighboring cells) times in previous researches.

In CC-NUMA multiprocessor systems, it is important to reduce the remote memory access time. Based upon the fact that increasing the size of the LRU second-level (L2) cache more than a certain value does not reduce the cache miss rate significantly, in this paper, we propose two split L2 caches to utilize the surplus of the L2 cache. The split L2 caches are composed of a traditional LRU cache and another cache to reduce the remote memory access time. Both work together to reduce total L2 cache miss time by keeping remote (or long-distance) blocks as well as recently used blocks. For another cache, we propose two alternatives: an L2-RVC (Level 2 - Remote Victim Cache) and an L2-DAVC (Level 2 - Distance-Aware Victim Cache). The proposed split L2 caches reduce total execution time by up to 27%. It is also found that the proposed split L2 caches outperform the traditional single LRU cache of double size.

Multiple-input multiple-output (MIMO) systems can improve the spectral efficiency of a wireless link, by transmitting several data streams simultaneously from different transmit antennas. However, at the receiver, multi-stream detection is needed for extracting the transmitted data streams from the received signals. This letter considers ordered successive detection (OSD) for multi-stream detection. OSD consists of several stages, and at each stage only one data stream is chosen to be detected among the remaining streams according to a specified ordering metric. OSD has been formulated using both the zero forcing (ZF) and minimum mean square error (MMSE) criteria. This letter clarifies the reason behind the superiority of OSD using the MMSE criterion to OSD using the ZF criterion through the investigation of the relation between their ordering metrics. For uncorrelated MIMO channels, we show that both ordering metrics yield the same performance for OSD using either ZF or MMSE criterion. Accordingly, the superiority of OSD using the MMSE criterion to OSD using the ZF criterion is clarified to be a direct result of the inherent superiority of MMSE nulling to ZF nulling, and to be independent of the ordering operation. Performance comparisons of OSD and maximum likelihood detection are also given for modulation schemes of different sizes.

We have developed Multi-modal Neural Networks (MNN) to improve the accuracy of symbolic sequence pattern classification. The basic structure of the MNN is composed of several sub-classifiers using neural networks and a decision unit. Two types of the MNN are proposed: a primary MNN and a twofold MNN. In the primary MNN, the sub-classifier is composed of a conventional three-layer neural network. The decision unit uses the majority decision to produce the final decisions from the outputs of the sub-classifiers. In the twofold MNN, the sub-classifier is composed of the primary MNN for partial classification. The decision unit uses a three-layer neural network to produce the final decisions. In the latter type of the MNN, since the structure of the primary MNN is folded into the sub-classifier, the basic structure of the MNN is used twice, which is the reason why we call the method twofold MNN. The MNN is validated with two benchmark tests: EPR (English Pronunciation Reasoning) and prediction of protein secondary structure. The reasoning accuracy of EPR is improved from 85.4% by using a three-layer neural network to 87.7% by using the primary MNN. In the prediction of protein secondary structure, the average accuracy is improved from 69.1% of a three-layer neural network to 74.6% by the primary MNN and 75.6% by the twofold MNN. The prediction test is based on a database of 126 non-homologous protein sequences.

This paper proposes an algorithm for detecting 3-way interactions. As far as the authors know, this is the first proposal ever made for a detection algorithm of 3-way interactions. In this paper, by analyzing examples, the mechanism of 3-way interactions is clarified and a detection algorithm of 3-way interactions is proposed. Namely the proposed detection algorithm is heuristic. To evaluate the algorithm, we implemented a detection system based on the proposed algorithm and applied it to 12 services, and 82 3-way interactions were detected. This shows the proposed algorithm is effective.