This paper proposes radio resource control scheme for ABR service that execute flow-control on the transmission rate and assignable bandwidth according to the congestion conditions in both wireless and wired networks. The proposed scheme is useful in improving frequency utilization and meeting the QoS requirements. There are two methods to realize the proposed scheme: explicit rate control (ERC) and binary control (BC). We estimate the performance of the proposed scheme by simulation in comparison with a scheme without flow control in a wireless network under the conditions of a finite buffer, wired network congestion, and RM-cell errors. Consequently, we confirm that the proposed scheme is more effective than the scheme without flow-control under all service conditions. In addition, we clarify that both ERC and BC are effective under the conditions of a finite buffer, wired network congestion, and RM-cell errors.

We study the joint optimization problem of a transmitter with multiple transmit antennas and a receiver with multiple receive antennas in a narrow-band communication system. We discuss the problem of designing a pre-filter at the transmitter, a post-filter at the receiver, and a bit allocation pattern to multiple symbols in the sense of minimizing the average bit error rate. With the optimized filters and the bit allocation, we could realize high efficiency and high data rate in band-limited channels.

In this article, an efficient vector quantization (VQ) scheme called side-match finite-state vector quantization with adaptive block classification is presented for image compression. It makes use of edge information contained in image in additional to the average values of blocks forming the image. In order to achieve low bit rate coding while preserving good quality images, neighboring blocks are utilized to predict the class of current block. Image blocks are mainly classified as edge blocks and non-edge blocks in this coding scheme. To improve the coding efficiency, edge blocks and non-edge blocks are further reclassified into different classes, respectively. Moreover, the number of bits for encoding an image is greatly reduced by foretelling the class of input block and applying small state codebook in corresponding class. The improvement of the proposed coding scheme is attractive as compared with other VQ techniques.

Efficient radio resource utilization and fairness are important goals that must be achieved since wireless ATM systems support various services with different traffic characteristics such as CBR and UBR. This paper proposes a novel delay-and-queuing data size-based MAC protocol for broadband wireless ATM. The proposed MAC protocol relies on a new resource scheduling algorithm that decides the priority of channel assignment based on both the queuing delay and the queuing data size in the transmission buffer. Simulation results confirm that the proposed MAC protocol is able to provide throughput fairness and to achieve excellent throughput performance for ATM services that experience dynamic traffic fluctuations.

Developments in new frequency bands for wireless communications make a broadband channel for new services possible. Great effort has been made researching and developing broadband wireless communication in the 60-GHz millimeter-wave band since the early 1990s. In this paper, we design an ATM (asynchronous transfer mode)-based indoor millimeter-wave wireless local area network (WLAN) that supports multimedia transmissions and focus on the wireless access topic for implementation of wireless ATM. We propose an integrated multimedia transmission protocol, based on the MAC (medium access control) protocol, called RS-ISMA (reservation-based slotted idle signal multiple access). It supports CBR (constant bit rate), VBR (variable bit rate), ABR (available bit rate) and UBR (unspecified bit rate) transmissions and provides QoS (quality of service)-dependent adaptive retransmissions. An RS-ISMA-based prototype full-duplex indoor high-speed WLAN in the 60-GHz band was developed.

A blind technique for adaptive signal suppression in multipath DS-CDMA communication channels for the downlink is considered. Its performance is degraded when mismatch problem occurs when multipath components arrive with fractional-chip delays. In order to surmount this problem, Multiple Finger Expansion Optimal Filter (MFE-OF) was recently proposed to estimate the received desired signal subspace using fractionally delayed despreading fingers. However, MFE-OF requires much computational complexity for good performance. In this paper, a modification to the MFE-OF is introduced by utilizing decision-directed steering vector to reduce the number of fingers required by MFE-OF down to that of the conventional OMF-RAKE without much performance degradation. This modified receiver is called Decision-Directed Optimal Filter (DDOF). Computer simulation validates the effectiveness of the new receiver to increase the downlink capacity of DS-CDMA systems.

A first-order investigation of the transport and energy-loss processes in silicon dioxide is worked out in the frame of the Spherical-Harmonics solution of the Boltzmann Transport Equation. The SiO2 conduction band is treated as a single-valley spherical and parabolic band. The relevant scattering mechanisms are modeled consistently: both the polar and nonpolar electron-phonon scattering mechanisms are considered. The scattering rates for each contribution are analyzed in comparison with Monte Carlo data. A number of macroscopic transport properties of electrons in SiO2 are worked out in the steady-state regime for a homogeneous bulk structure. The investigation shows a good agreement in comparison with experiments in the low-field regime and for different temperatures.

The gate oxide of sub-0.1 µm MOSFETs channel length is expected to be reduced beyond 3 nm in spite of an increasing direct tunneling gate current. As tunnel injection modeling into SiO2 is expected to depend on the electron transport model adopted for the device description, a critical comparison is made in this paper between gate currents obtained from simulators based on Drift-Diffusion, Energy-Balance, and Monte Carlo models. The studied device is a 0.07 µm channel length n-MOSFET with 1.5 nm thick gate oxide. It is shown that positive drain voltage is responsible for two opposite effects on DT leakage: a carrier heating and a potential barrier hardening along the channel. It is proved by a careful study of Monte Carlo microscopic quantities that, contrary to what holds for thicker gate oxide transistors, the balance is favorable to the potential barrier effect. Injection into SiO2 is then dominated by near-thermal carriers injected at the channel beginning. For this reason, the gate current decreases when increasing the drain bias, with the maximum leakage obtained for (Vgs=Vdd, Vds=0), and a correct agreement is obtained between the Drift-Diffusion, Energy-Balance, and Monte Carlo approaches of gate current calculation, in spite of very different physical descriptions of transport at the microscopic level.

In this paper, we propose an effective SOI yield engineering methodology by practical usage of 2D simulations. Process design for systematic yield of Fully-Depleted SOI MOSFET requires specific consideration of floating-body effects and parasitic channel leakage currents. The influence of varied SOI layer thickness to such phenomena is also complicated and substantial. Instead of time-consuming 3D simulators, 2D simulators are used to optimize the process considering these effects in acceptable turn around time. Our methodology is more effective in future scaled-down process with decreased SOI layer thickness.

We consider a wait-free linearizable implementation of shared objects on a distributed message-passing system. We assume that the system provides each process with a local clock that runs at the same speed as global time and that all message delays are in the range [d-u,d] where d and u (0< u d) are constants known to every process. We present four wait-free linearizable implementations of read/write registers on reliable and unreliable broadcast models. We also present two wait-free linearizable implementations of general objects on a reliable broadcast model. The efficiency of an implementation is measured by the worst-case response time for each operation of the implemented object. Response times of our wait-free implementations of read/write registers on a reliable broadcast model is better than a previously known implementation in which wait-freedom is not taken into account.

Patterns exist in many contexts and can be considered the useful information for decision making. However, many patterns are not directly visible without careful presentation. Here, we describe an interactive visualization approach for browsing patterns in a history of interacting with a computer system. While a user is carrying out his/her business using computers, activities with respect to time and location are captured to determine the situational interactions. We first integrate the timeline and geographical map to create a structure to visualize spatiotemporal events in the interaction history. The spiral-based interactive visualization technique, presented in this paper, is then used to derive patterns according to the user-specified different spatial viewpoints on the map. In this study, we demonstrate how patterns can be used as visual statements for the analysis of a spatiotemporal data set in the information visualization.

A generalized algorithm for designing an optimum VQ source codec in systems with channel coding is presented. Based on an AWGN channel model, the algorithm derives the distribution of the channel decoder soft-output and substitutes it in the expression for the system end-to-end distortion. The VQ encoder/decoder pair is then optimized by minimizing this end-to-end distortion. For a Gauss-Markov source, the proposed algorithm outperforms the conventional SOVQ source coding scheme by 5.0 dB in the decoded source SNR. Application of this algorithm for designing optimum low-bit-rate speech codec is given. A 4.0 kbps VQ based CELP codec is designed for performance evaluations, where all the CELP parameter encoder/decoder pairs are optimized by minimizing their end-to-end distortions, respectively. As a result, the speech distortion over the noisy channel is minimized. Subjective tests show that the proposed algorithm improves the decoded speech quality by 2.5 MOS relative to a regular SOVQ CELP speech coding system. The performances of the algorithm under channel mismatch conditions are also shown and discussed.

It is known that cycle slip due to frequency selective fading causes a burst error by symbol deletion or insertion, and has a serious effect on mobile radio communication systems. In this paper, first, we show that phase rotating modulation is suitable for code synchronization error detection. Next, we consider a code synchronization controller using correlation estimator of received sequence, and the controller combines the estimator with 2π/3-shifted modulation to construct a new code synchronization error control scheme as a cycle slip cancelling system. Furthermore, we apply the scheme to the multilevel trellis coded modulation (TCM). Finally, computer simulation results confirm that proposed scheme is capable of code synchronization error correction.

The Tikhonov regularization theory converts ill-posed inverse problems into well-posed problems by putting penalty on the solution sought. Instead of solving an inverse problem, the regularization theory minimizes a weighted sum of "data error" and "penalty" function, and it has been successfully applied to a variety of problems including tomography, inverse scattering, detection of radiation sources and early vision algorithms. Since the function to be minimized is a weighted sum of functions, one should estimate appropriate weights. This is a problem of hyperparameter estimation and a vast literature exists. Another problem is how one should compare a particular penalty function (regularizer) with another. This is a special class of model comparison problems which are generally difficult. A Hierarchical Bayesian scheme is proposed with multiple hyperparameters in order to cope with data containing subsets which consist of different degree of smoothness. The scheme outperforms the previous scheme with single hyperparameter.

Polymeric thin films can be prepared by physical vapor deposition in several manners such as direct evaporation of the polymer, co-evaporation of two monomers followed by polyaddition or polycondensation reaction, or evaporation of single monomer followed by chain polymerization. The ionization-assisted deposition (IAD) was proposed as a new method of polymer deposition that has special features such as activation of polymerization reaction and aligning of the dipole orientation. These mechanisms were utilized for the formation of vinyl polymer and polyurea thin films aiming for such applications as organic light emitting diodes and piezoelectric devices.

Since odor sensing system is required in many fields, we have developed the system using QCM (Quartz Crystal Microbalance) sensor array and neural-network pattern recognition. In the present study, the mixed sensing films of two kinds of liquid-phase materials were characterized. As a result, it was found that the variety of sensing films were obtained by mixing two kinds of liquid-phase materials. The relative remnant of sensing film after repeated exposures was examined, and mixed films of two kinds of liquid-phase materials were found stable for the sensing materials.

We have developed a new type electrical probing system based on an atomic force microscope. This method enables us to measure simultaneously the surface topography and surface potential of thin films containing the crystal grains. The obtained local potential changes give an insight into conduction through the grains and their boundaries.

A novel adaptive motion vector quantization algorithm is presented in this letter. The algorithm effectively updates the set of motion vectors using gold-washing technique for block-matching according to the features of input image sequences. Simulation results show that the algorithm has both robust performance and low computational complexity for video coding.

For many military and civilian large-scale, real-world systems of interest, data are first acquired asynchronously, i. e. at irregular intervals of time, at geographically-dispersed sites, processed utilizing decision-making algorithms, and the processed data then disseminated to other appropriate sites. The term real-world refers to systems under computer control that relate to everyday life and are beneficial to the society in the large. The traditional approach to such problems consists of designing a central entity which collects all data, executes a decision making algorithm sequentially to yield the decisions, and propagates the decisions to the respective sites. Centralized decision making algorithms are slow and highly vulnerable to natural and artificial catastrophes. Recent literature includes successful asynchronous, distributed, decision making algorithm designs wherein the local decision making at every site replaces the centralized decision making to achieve faster response, higher reliability, and greater accuracy of the decisions. Two key issues include the lack of an approach to synthesize asynchronous, distributed, decision making algorithms, for any given problem, and the absence of a comparative analysis of the quality of their decisions. This paper proposes MFAD, a Mathematical Framework for Asynchronous, Distributed Systems, that permits the description of centralized decision-making algorithms and facilities the synthesis of distributed decision-making algorithms. MFAD is based on the Kohn-Nerode distributed hybrid control paradigm. It has been a belief that since the centralized control gathers every necessary data from all entities in the system and utilizes them to compute the decisions, the decisions may be "globally" optimal. In truth, however, as the frequency of the sensor data increases and the environment gets larger, dynamic, and more complex, the decisions are called into question. In the distributed decision-making system, the centralized decision-making is replaced by those of the constituent entities that aim at minimizing a Lagrangian, i. e. a local, non-negative cost criterion, subject to the constraints imposed by the global goal. Thus, computations are carried out locally, utilizing locally obtained dataand appropriate information that is propagated from other sites. It is hypothesized that with each entity engaged in optimizing its individual behavior, asynchronously, concurrently, and independent of other entities, the distributed system will approach "global" optimal behavior. While it does not claim that such algorithms may be synthesized for all centralized real-world systems, this paper implements both the centralized and distributed paradigms for a representative military battlefield command, control, and communication (C3) problem. It also simulates them on a testbed of a network of workstations for a comparative performance evaluation of the centralized and decentralized paradigms in the MFAD framework. While the performance results indicate that the decentralized approach consistently outperforms the centralized scheme, this paper aims at developing a quantitative evaluation of the quality of decisions under the decentralized paradigm. To achieve this goal, it introduces a fundamental concept, embodied through a hypothetical entity termed "Perfect Global Optimization Device (PGOD)," that generates perfect or ideal decisions. PGOD possesses perfect knowledge, i. e. the exact state information of every entity of the entire system, at all times, unaffected by delay. PGOD utilizes the same decision-making algorithm as the centralized paradigm and generates perfect globally-optimal decisions which, though unattainable, provide a fundamental and absolute basis for comparing the quality of decisions. Simulation results reveal that the quality of decisions in the decentralized paradigm are superior to those of the centralized approach and that they approach PGOD's decisions.

We describe a theoretically optimal algorithm for computing the homography between two images. First, we derive a theoretical accuracy bound based on a mathematical model of image noise and do simulation to confirm that our renormalization technique effectively attains that bound. Then, we apply our technique to mosaicing of images with small overlaps. By using real images, we show how our algorithm reduces the instability of the image mapping.