In order to solve large Multidimensional Knapsack problems we examine a technique which decomposes a problem instance into two parts. The first part is solved using a traditional technique, such as Dynamic Programming, to reduce the number of variables in the problem by creating a single variable with many non-dominated states. In the second part the remaining variables are determined by an algorithm that repeatedly enumerates them with different constraint and objective requirements. The constraint and objective requirements are imposed by the various non-dominated states of the variable created in the first part of this technique. The main advantage of this approach is that when memory requirements prevent traditional techniques solving a problem instance, the enumeration provides a much less memory-intensive method, enabling a solution to be found. Two approaches are proposed for repeatedly enumerating a 0/1 Multidimensional Knapsack problem. It is demonstrated how these enumeration methods, in conjunction with the Modular Approach, were used to find the optimal solutions to a number of 500-variable, 5-constraint Multidimensional Knapsack problem instances proposed in the literature. The exact solutions to these instances were previously unknown.

In this paper, it is proved that there is a transformation between two types of nonlinear feedback shift register which can be regarded as implementation of the Bernoulli and tent maps with finite precision. This transformation can be interpreted as a sort of finite dimension version of topological conjugation between the Bernoulli and tent maps on continuous phase space. Several properties of periodic sequences generated from extended NFSRs are derived from the relation.

Grid computing is a state-of-the-art parallel computing technology which enables worldwide computers to dynamically share their computing powers and resource to each other. The grid takes advantage of Internet as a universal communication platform to carry messages. Basically, Internet doesn't guarantee loss-free and ordered transmission, hence, the grid should keep the cause and effect of events by itself to ensure the correct ordering of command invocations at the remote hosts. The ordering issue arises when the messages travel across the networks with unpredictable delay. Recent research has studied the security and resource control issues, but failed to address the requirements of transport layer on the grid communication platform. In this paper, we propose the Causal Ordered Grid (COG) architecture and implement it to study the transport performance issues when the grid is built over worldwide networks. The COG provides a novel service model to the applications with time-sensitive and causal-ordered transportation. From our experiments, the design of the grid middleware should use a causal-ordered, time-sensitive transportation rather than TCP. Our research will be beneficial to the improvement of the grid computing and can provide wealthy empirical results for the designer.

In this paper we present a 3D adaptive nonlinear filter, namely the 3D adaptive CPWLN, based on the Canonical Piece Wise-Linear Network with an LMS L-filter type of adaptation. This filter is used to equalize nonlinear channel effect and remove impulsive/or mixed impulsive and Additive White Gaussian noise from video sequences. First, motion compensation is performed by a robust estimator. Then, a 3-D CPWLN LMS L-filter is applied. The overall combination is able to adequately remove undesired effects of communication channel and noise. Computer simulations on real-world image sequences are included. The algorithm yields promising results in terms of both objective and subjective quality of the restored sequence.

We introduce a differential space-time block code (DSTBC) integrated with trellis coded modulation with two transmit antennas. Our scheme enables transmission of DSTBC encoded symbols as trellis metric rather than concatenating an outer code. Unlike conventional DSTBC, different transmit symbol phase rotations are used for each transmit antenna in order to obtain more options for trellis branch. The set partitioning for proposed codes is derived as well. The decoder computes decision statistic using Viterbi Algorithm with different number of states undergoing Rayleigh fading channels. This approach can provide full diversity gain as well as coding gain simultaneously remaining full transmit rate, which cannot be obtained by conventional DSTBC.

In this paper, we present a learning approach, positive correlation learning (PCL), that creates a multilayer neural network with good generalization ability. A correlation function is added to the standard error function of back propagation learning, and the error function is minimized by a steepest-descent method. During training, all the unnecessary units in the hidden layer are correlated with necessary ones in a positive sense. PCL can therefore create positively correlated activities of hidden units in response to input patterns. We show that PCL can reduce the information on the input patterns and decay the weights, which lead to improved generalization ability. Here, the information is defined with respect to hidden unit activity since the hidden unit plays a crucial role in storing the information on the input patterns. That is, as previously proposed, the information is defined by the difference between the uncertainty of the hidden unit at the initial stage of learning and the uncertainty of the hidden unit at the final stage of learning. After deriving new weight update rules for the PCL, we applied this method to several standard benchmark classification problems such as breast cancer, diabetes and glass identification problems. Experimental results confirmed that the PCL produces positively correlated hidden units and reduces significantly the amount of information, resulting improved generalization ability.

There is increasing evidence from in vivo recordings in monkeys trained to respond to stimuli by making left- or rightward eye movements, that firing rates in certain groups of neurons in oculo-motor areas mimic drift-diffusion processes, rising to a (fixed) threshold prior to movement initiation. This supplements earlier observations of psychologists, that human reaction-time and error-rate data can be fitted by random walk and diffusion models, and has renewed interest in optimal decision-making ideas from information theory and statistical decision theory as a clue to neural mechanisms. We review results from decision theory and stochastic ordinary differential equations, and show how they may be extended and applied to derive explicit parameter dependencies in optimal performance that may be tested on human and animal subjects. We then briefly describe a biophysically-based model of a pool of neurons in locus coeruleus, a brainstem nucleus implicated in widespread norepinephrine release. This neurotransmitter can effect transient gain changes in cortical circuits of the type that the abstract drift-diffusion analysis requires. We also describe how optimal gain schedules can be computed in the presence of time-varying noisy signals. We argue that a rational account of how neural spikes give rise to simple behaviors is beginning to emerge.

We propose a system that enables us to gather hundreds of images related to one set of keywords provided by a user from the World Wide Web. The system is called Image Collector II. The Image Collector, which we proposed previously, can gather only one or two hundreds of images. We propose the two following improvements on our previous system in terms of the number of gathered images and their precision: (1) We extract some words appearing with high frequency from all HTML files in which output images are embedded in an initial image gathering, and using them as keywords, we carry out a second image gathering. Through this process, we can obtain hundreds of images for one set of keywords. (2) The more images we gather, the more the precision of gathered images decreases. To improve the precision, we introduce word vectors of HTML files embedding images into the image selecting process in addition to image feature vectors.

As a representative of the linear discriminant analysis, the Fisher method is most widely used in practice and it is very effective in two-class classification. However, when it is expanded to a multi-class classification problem, the precision of its discrimination may become worse. A main reason is an occurrence of overlapped distributions on the discriminant space built by Fisher criterion. In order to take such overlaps among classes into consideration, our approach builds a new discriminant space by hierarchically classifying the overlapped classes. In this paper, we propose a new hierarchical discriminant analysis for texture classification. We divide the discriminant space into subspaces by recursively grouping the overlapped classes. In the experiment, texture images from many classes are classified based on the proposed method. We show the outstanding result compared with the conventional Fisher method.

Phonetic transcription of text is an indispensable component of text-to-speech (TTS) systems and is used in acoustic modeling for speech recognition and other natural language processing applications. One approach to the transcription of written text into phonetic entities or sounds is to use a set of well-defined context and language-dependent rules. The process of transcribing text into sounds starts by preprocessing the text and representing it by lexical items to which the rules are applicable. The rules can be segregated into phonemic and phonetic rules. Phonemic rules operate on graphemes to convert them into phonemes. Phonetic rules operate on phonemes and convert them into context-dependent phonetic entities with actual sounds. Converting from written text into actual sounds, developing a comprehensive set of rules, and transforming the rules into implementable algorithms for any language cause several problems that have their origins in the relative lack of correspondence between the spelling of the lexical items and their sound contents. For Standard Malay (SM) these problems are not as severe as those for languages of complex spelling systems, such as English and French, but they do exist. In this paper, developing a comprehensive computerized system for processing SM text and transcribing it into phonetic entities and evaluating the performance of this system, irrespective of the application, is discussed. In particular, the following issues are dealt with in this paper: (1) the spelling and other problems of SM writing and their impact on converting graphemes into phonemes, (2) the development of a comprehensive set of grapheme-to-phoneme rules for SM, (3) a description of the phonetic variations of SM or how the phonemes of SM vary in context and the development of a set of phoneme-to-phonetic transcription rules, (4) the formulation of the phonemic and phonetic rules into algorithms that are applicable to the computer-based processing of input SM text, and (5) the evaluation of the performance of the process of converting SM text into actual sounds by the above mentioned methods.

This investigation proposes a fast wavelet-based color segmentation (FWCS) technique and a modified directional region-growing (DRG) technique for semantic image segmentation. The FWCS is a subsequent combination of progressive color truncation and histogram-based color extraction processes for segmenting color regions in images. By exploring specialized centroids of segmented fragments as initial growing seeds, the proposed DRG operates a directional 1-D region growing on pairs of color segmented regions based on those centroids. When the two examined regions are positively confirmed by DRG, the proposed framework subsequently computes the texture features extracted from these two regions to further check their relation using texture similarity testing (TST). If any pair of regions passes double checking with both DRG and TST, they are identified as associated regions. If two associated regions/areas are connective, they are unified to a union area enclosed by a single contour. On the contrary, the proposed framework merely acknowledges a linking relation between those associated regions/areas highlighted with any linking mark. Particularly, by the systematic integration of all proposed processes, the critical issue to decide the ending level of wavelet decomposition in various images can be efficiently solved in FWCS by a quasi-linear high-frequency analysis model newly proposed. The simulations conducted here demonstrate that the proposed segmentation framework can achieve a quasi-semantic segmentation without priori a high-level knowledge.

Asymmetric slope differential CMOS (ASD-CMOS) and asymmetric slope differential dynamic logic (ASDDL) surpass the highest speed that conventional CMOS logic circuits can achieve, resulting from deeply shortened rise time along with relatively prolonged fall time. ASD-CMOS is a static logic and ASDDL is a dynamic logic without per-gate synchronous clock signal, each of which needs two-phase operation as well as differential signaling, however, interleaved precharging hides the prolonged fall time and BDD-based compound logic design mitigates area increase. ASD-CMOS 16-bit multiplier in a 0.18-µm CMOS technology demonstrates 1.78 nsec per an operation, which reaches 34% reduction of the best delay time achieved by a multiplier using a CMOS standard cell library that is conventional yet tuned to the optimum in energy-delay products. ASDDL can be superior to DCVS-DOMINO circuits not only in delay time but also in area and even in power. ASDDL 16-bit multiplier achieves delay and power reduction of 4% and 20%, respectively, compared with DCVS-DOMINO realization. A prototype ASD-CMOS 16-bit multiplier with built-in test circuitry fabricated in a 0.13-µm CMOS technology operates with the delay time of 1.57 nsec at 1.2 V.

The millimeter-wave (MMW) broadband mixers that are useful for measurement instruments to analyze MMW high data rate signals have been investigated. At first, we propose the specialized RF front-end for analyses of MMW high data rate signals. Next, the required specifications for the 1st mixers of the front-end are estimated, and the design, fabrication, and testing results of Q, V, and W-band monolithic broadband resistive mixers are described. The testing results are compared with performances of the diode mixer designed for V-band. It was found that the resistive mixers have very attractive performances of low conversion loss, good frequency flatness and high third order intercept point (IP3) with low Local (LO) oscillators power. The developed resistive mixers are suitable for the proposed MMW band measurement instruments.

We considered pulse width dependence in a time-spreading Optical Code Division Multiplexing (OCDM) system using a phase encoder and decoder (127-chip, time-spreading 800 ps) by simulation. It follows that in a fully asynchronous OCDM transmission, the light source pulse width had a 20 ps degree of freedom.

This investigation proposes a virtual-FIFO (VFIFO) back-off algorithm for wireless networks. The proposed scheme takes advantage of the central unit (CU) in a wireless network to broadcast a common back-off window size to all the users, significantly alleviating the unfairness of bandwidth utilization in conventional binary exponential back-off (BEB) algorithms. The proposed scheme exploits the CU's capability for collision detection to estimate the number of simultaneously competing users. Additionally, packets generated in a given cycle are split into groups according to their times of arrivals and are guaranteed to be serviced one after another within the next cycle. Although the proposed algorithm is not strictly first come fist served, the FIFO principle is virtually accomplished. Simulation results demonstrate that the standard deviation of delay can be improved by more than two orders and the throughput can be maintained at 0.42 when the number of users approaches infinity. The capture effect even further improves system performance.

The optical network is a promising approach for realizing a scalable backbone network. In backbone networks, survivability is very important because great volumes of traffic incur damage from faulty equipment. To address this issue, various recovery schemes have been proposed for optical backbone networks. Among those schemes, shared mesh restoration utilizes link bandwidth efficiently because the backup lightpaths share link bandwidth if they protect against different failures and are never utilized simultaneously. However, a route computation method for the backup lightpaths that promotes such bandwidth sharing is necessary to achieve efficient bandwidth utilization. This paper proposes a distributed route computation method for the backup lightpaths in shared mesh restoration. In this method, the link weight is estimated to be smaller if a backup lightpath newly established can share the link bandwidth with the backup lightpaths already accommodated in that link. The link weight can be calculated using the Markov Decision Theory. The bandwidth sharing between the backup lightpaths can be promoted by selecting the shortest route based on such modified link weights. The proposed method effectively realizes efficient utilization of the link bandwidth and achieves low loss rate of reliable lightpath establishment requests under the same traffic load. The proposed method restricts the amount of link state information advertised by the routing protocol and achieves a sufficiently small amount of route calculation.

In this paper, a semi-dynamic protection path configuration method is proposed for WDM optical networks. In the method, the protection path is established by connecting several sub-lightpaths from the source node to the destination node of the original working lightpath, as opposed to conventional path restoration method where a single protection lightpath between the source-destination pair performs restoration. The proposed method provides enhanced flexibility in protection path configuration and relieves the cost of spare capacity reservation. This paper also studies the effects of wavelength conversion capability of intermediate optical cross-connect nodes on protection path routing and spare capacity utilization. In terms of spare capacity utilization, the proposed method shows substantial reduction of spare capacity overhead compared with dedicated path restoration in all optical networks without wavelength conversion, and shows similar capacity efficiency compared with shared path restoration in opaque networks with full wavelength conversion capability. In terms of robustness, the proposed method shows nearly the same restoration ratio for double-link failure as that of dynamic restoration method.

IP-over-optical multilayer networks are capable of flexibly dealing with traffic increases and fluctuations because they support both high-speed transmission using lightpaths and scalable IP hop-by-hop transmission. This paper introduces an architecture for quality of service (QoS) control in such networks, based on the differentiated services (DiffServ) concept. The architecture supports both class-based queues and class-based lightpaths to efficiently handle multiple-QoS-class traffic. QoS schemes based on the proposed architecture are categorized into four types according to their traffic-differentiation and transmission mechanisms. Through simulation, the schemes are evaluated in terms of measures that largely determines network costs. Finally, the conditions under which each scheme is feasible are clarified in terms of the traffic volume and the cost of class-based queues for DiffServ.

In this paper, we shall construct mathematical theory based on the concept of set-valued mappings, suitable for available operation of extraordinarily complicated large-scale network systems by introducing some connected-block structures. A fine estimation technique for availability of system behaviors of such network systems are obtained finally in the form of fixed point theorem for a special system of fuzzy-set-valued mappings.

Millimeter-wave (MMW) communication systems, specially from 60 GHz band, are expected to play a key role in realizing the intelligent transportation system (ITS) for high date rate and mulitmedia communications. During the last years, System-in-package (SiP) solutions for MMW RF-transceiver have become an attractive alternative to System-on-Chip (SoC) approaches. In the scheme, active circuits are integrated with passive circuit much tightly. Traditional distributed hybrid circuit concept, such as ADS and Microwave Office, is not quite applicable for SiP, specially in MMW frequency. In this paper, an improved finite-difference time-domain formulation using the matrix theory and an improved integral transform is presented to solve hybrid high-order linear lumped circuit and passive circuit problem implicitly instead of explicit method used for traditional FDTD method. In this improved method, general high-order linear lumped circuit is expressed firstly as not only one equation but also equations set according kirchhoff's laws. Then, a local iterative matrix expression connecting with each active circuit can be built when EM fields, current/current density and interior variable of equations set are treated as separated vector element. In order to make this formulation stable, an improved integral transform is proposed to reform the matrix's condition number. Simulations show that the results by our improved FDTD formulation can effectively not only keep the accuracy of passive circuit, but also integrate high-order linear lumped circuit expressed by equations set as well as one equation.