With the rising popularity of delay-sensitive real-time multimedia applications (video, voice, and data) in IEEE 802.11 wireless local area networks (WLANs), it is becoming important to study the medium access control (MAC) layer delay performance of WLANs. The MAC layer delay can be classified into two categories: 1) medium access delay, and 2) delay at interface queue (IFQ). In this paper, based on a two-dimensional chain model, we analyze the medium access delay and give a method to calculate the IFQ delay. The proposed analysis is applicable to both the basic access and the RTS/CTS access mechanisms. Through extensive simulations, we evaluate our model. The simulation results show that our analysis is extremely accurate for both basic access and RTS/CTS access mechanism of the 802.11 DCF protocol.

With the rapid spread of information and ubiquitous access of browsers, flash crowds, a sudden, unanticipated surge in the volume of request rates, have become the bane of many Internet websites. This paper models and presents FCAN, an adaptive network that dynamically optimizes the system structure between peer-to-peer (P2P) and client-server (C/S) configurations to alleviate flash crowds effect. FCAN constructs P2P overlay on cache proxy server layer to distribute the flash traffic from origin server. It uses policy-configured DNS redirection to route the client requests in balance, and adopts strategy load detection to monitor and react the load changes. Our preliminary simulation results showed that the system is overall well behaved, which validates the correctness of our design.

A time domain (TD) speech enhancement technique to improve SNR in noise-contaminated speech is proposed. Additional supplementary scheme is applied to estimate the degree of noise of noisy speech. This is estimated from a function, which is previously prepared as the function of the parameter of the degree of noise. The function is obtained by least square (LS) method using the given degree of noise and the estimated parameter of the degree of noise. This parameter is obtained from the autocorrelation function (ACF) on frame-by-frame basis. This estimator almost accurately estimates the degree of noise and it is useful to reduce noise. The proposed method is based on two-stage processing. In the first stage, subtraction in time domain (STD), which is equivalent to ordinary spectral subtraction (SS), is carried out. In the result, the noise is reduced to a certain level. Further reduction of noise and by-product noise residual is carried out in the second stage, where blind source separation (BSS) technique is applied in time domain. Because the method is a single-channel speech enhancement, the other signal is generated by taking the noise characteristics into consideration in order to apply BSS. The generated signal plays a very important role in BSS. This paper presents an adaptive algorithm for separating sources in convolutive mixtures modeled by finite impulse response (FIR) filters. The coefficients of the FIR filter are estimated from the decorrelation of two mixtures. Here we are recovering only one signal of interest, in particular the voice of primary speaker free from interfering noises. In the experiment, the different levels of noise are added to the clean speech signal and the improvement of SNR at each stage is investigated. The noise types considered initially in this study consist of the synthesized white and color noise with SNR set from 0 to 30 dB. The proposed method is also tested with other real-world noises. The results show that the satisfactory SNR improvement is attained in the two-stage processing.

The k-edge-connectivity augmentation problem for a specified set of vertices of a graph with degree constraints, kECA-SV-DC, is defined as follows: "Given an undirected multigraph G = (V,E), a specified set of vertices S ⊆V and a function g: V → Z+ ∪{∞}, find a smallest set E' of edges such that (V,E ∪ E') has at least k edge-disjoint paths between any pair of vertices in S and such that, for any v ∈ V, E' includes at most g(v) edges incident to v, where Z+ is the set of nonnegative integers." This paper first shows polynomial time solvability of kECA-SV-DC and then gives a linear time algorithm for 2ECA-SV-DC.

This paper investigates the design curves of class E amplifier with nonlinear capacitance for any output Q and finite dc-feed inductance. The important results are; 1) the capacitance nonlinearity strongly affects the design parameters for low Q, 2) the value of dc-feed inductance is hardly affected by the capacitance nonlinearity, and 3) the input voltage is an important parameter to design class E amplifier with nonlinear capacitance. By carrying out PSpice simulations, we show that the simulated results agree with the desired ones quantitatively. It is expected that the design curves in this paper are useful guidelines for the design of class E amplifier with nonlinear capacitance.

It is well known that TCP does not fully utilize the available bandwidth in fast long-distance networks. To solve this scalability problem, several high speed transport protocols have been proposed. They include HighSpeed TCP (HS-TCP), Scalable TCP (S-TCP), Binary increase control TCP (BIC-TCP), and H-TCP. These protocols increase (decrease) their window size more aggressively (slowly) compared to standard TCP (STD-TCP). This paper aims at evaluating and comparing these high speed transport protocols through computer simulations. We select six metrics that are important for high speed protocols; scalability, buffer requirement, TCP friendliness, TCP compatibility, RTT fairness, and responsiveness. Simulation scenarios are carefully designed to investigate the performance of these protocols in terms of the metrics. Results clarify that each high speed protocol successfully solves the problem of STD-TCP. In terms of the buffer requirement, S-TCP and BIC-TCP have better performance. For TCP friendliness and compatibility, HS-TCP and H-TCP offer better performance. For RTT fairness, BIC-TCP and H-TCP are superior. For responsiveness, HS-TCP and H-TCP are preferred. However, H-TCP achieves a high degree of fairness at the expense of the link utilization. Thus, we understand that all the proposed high speed transport protocols have their own shortcomings. Thus, much more research is needed on high speed transport protocols.

Rendering realistic trees is quite important for simulating a 3D natural scene. Separating the trunk from its background is the first step toward the 3D model construction of the tree. In this paper, a three-phase algorithm is developed to extract the trunk structure of the tree and hence segment the trunk from the image. Some experiments were conducted and results confirmed the feasibility of proposed algorithm.

This paper shows that the Surrounding Gate Transistor (SGT) can be scaled down to decananometer gate lengths by using an intrinsically-doped body and gate work function engineering. Strong gate controllability is an essential characteristics of the SGT. However, by using an intrinsically-doped body, the SGT can realize a higher carrier mobility and stronger gate controllability of the silicon body. Then, in order to adjust the threshold voltage, it is necessary to adopt gate work function engineering in which a metal or metal silicide gate is used. Using a three-dimensional (3D) device simulator, we analyze the short-channel effects and current characteristics of the SGT. We compare the device characteristics of the SGT to those of the Tri-gate transistor and Double-Gate (DG) MOSFET. When the silicon pillar diameter (or silicon body thickness) is 10 nm, the gate length is 20 nm, and the oxide thickness is 1 nm, the SGT shows a subthreshold swing of 63 mV/dec and a DIBL of -17 mV, whereas the Tri-gate transistor and the DG MOSFET show a subthreshold swing of 71 mV/dec and 77 mV/dec, respectively, and a DIBL of -47 mV and -75 mV, respectively. By adjusting the value of the gate work function, we define the off current at VG = 0 V and VD = 1 V. When the off current is set at 1 pA/µm, the SGT can realize a high on current of 1020 µA/µm at VG = 1 V and VD = 1 V. Moreover, the on current of the SGT is 21% larger than that of the Tri-gate transistor and 52% larger than that of the DG MOSFET. Therefore, the SGT can be scaled reliably toward the decananometer gate length for high-speed and low-power ULSI.

The Goal-Orientation is one of the key features in agent systems. This paper proposes a new methodology for multi-agent system development based on Goal Net model. The methodology covers the whole life cycle of the agent system development, from requirement analysis, architecture design, detailed design to implementation. A Multi-Agent Development Environment (MADE) that facilitates the design and implementation of agent systems is presented. A case study on an agent-based e-learning system developed using the proposed methodology is illustrated in this paper.

We developed a CMOS watchdog sensor that simulates the changes in quality of perishables such as farm and marine products. The sensor can imitate a chemical reaction that causes the changes in the quality of perishables, with a wide range of activation energy from 0.1 eV to 0.7 eV. Attached to perishable goods, the sensor simulates the deterioration of the goods caused by surrounding temperatures. By reading the output of the sensor, consumers can determine whether the goods are fresh or not. This sensor consists of subthreshold CMOS circuits with a low-power consumption of 5 µW or less.

The speech noise reduction system based on the frequency domain adaptive line enhancer using a windowed modified DFT (MDFT) pair is presented. The adaptive line enhancer (ALE) is effective for extracting sinusoidal signals blurred by a broadband noise. In addition, it utilizes only one microphone. Therefore, it is suitable for the realization of speech noise reduction in portable electronic devices. In the ALE, an input signal is generated by delaying a desired signal using the decorrelation parameter, which makes the noise in the input signal decorrelated with that in the desired one. In the present paper, we propose to set decorrelation parameters in the frequency domain and adjust them to optimal values according to the relationship between speech and noise. Such frequency domain decorrelation parameters enable the reduction of the computational complexity of the proposed system. Also, we introduce the window function into MDFT for suppressing spectral leakage. The performance of the proposed noise reduction system is examined through computer simulations.

This paper shows the recent standardization activities on Ethernet OAM functions. First, it briefly introduces recent carrier class Ethernet services indicating their characteristics and operational issues. Then, it explains current standardization status on Ethernet OAM functions. Finally it shows the requirements for Ethernet OAM functions and details of the OAM mechanisms currently being standardized by ITU-T SG13, SG15 and IEEE 802.1 WG.

This paper proposes a new optical access network architecture that differs from those of conventional Point-to-Point (PP) and Passive Optical Networks (PON). The proposed architecture, Optical Switched Access Network (OSAN), uses Optical Switching Modules (OSMs) that connect an Optical Line Terminal (OLT) to Optical Network Units (ONUs) in a virtual point to point configuration so that it offers the merits of both PP and PON while overcoming their demerits. Each OSM optically switches packets of variable length one by one under electrical control. To allow the elimination of optical buffers from OSM, OSAN uses the Multi-Point Control Protocol (MPCP) defined in IEEE 802.3ah. We evaluate the transmission distances between OLT and ONUs, and consider a network synchronization scheme and discovery mechanism that supports MPCP.

This paper discusses propagation delay error, transient response, and power consumption distribution due to inductive effects in optimal buffered on-chip interconnects. Inductive effect is said to be important to consider in deep submicron (DSM) VLSI design. However, study shows that the effect decreases and can be neglected in next technology nodes for such conditions.

As computer hardware components are achieving greater speeds, network link bandwidths are becoming wider. A number of enhancements to TCP have been developed in order to fully exploit these improvements in network infrastructures, including TCP window scale option, SACK option, and HighSpeed TCP (HSTCP) modifications. However, even with these enhancements, TCP cannot provide satisfactory performance in high-speed long-delay networks. As a means addressing this problem, gentle HighSpeed TCP (gHSTCP) has been proposed in [1]. However, its effectiveness has only been demonstrated in simulation experiments. In the present paper, a refined gHSTCP algorithm is proposed for application to real networks. The performance of the refined gHSTCP algorithm is then assessed experimentally. The refined gHSTCP algorithm is based on the original algorithm, which uses two modes (Reno mode and HSTCP mode) in the congestion avoidance phase and switches modes based on RTT increasing trends. The refined gHSTCP algorithm compares two RTT thresholds and judges which mode will be used. The performance of gHSTCP is compared with TCP Reno/HSTCP and parallel TCP mechanisms. The experimental results demonstrate that gHSTCP can provide a better tradeoff in terms of utilization and fairness against co-existing traditional TCP Reno connections, whereas HSTCP and parallel TCP suffer from the trade-off problem.

We present a novel trigger-based language model adaptation method oriented to the transcription of meetings. In meetings, the topic is focused and consistent throughout the whole session, therefore keywords can be correlated over long distances. The trigger-based language model is designed to capture such long-distance dependencies, but it is typically constructed from a large corpus, which is usually too general to derive task-dependent trigger pairs. In the proposed method, we make use of the initial speech recognition results to extract task-dependent trigger pairs and to estimate their statistics. Moreover, we introduce a back-off scheme that also exploits the statistics estimated from a large corpus. The proposed model reduced the test-set perplexity considerably more than the typical trigger-based language model constructed from a large corpus, and achieved a remarkable perplexity reduction of 44% over the baseline when combined with an adapted trigram language model. In addition, a reduction in word error rate was obtained when using the proposed language model to rescore word graphs.

This paper describes a hands-free speech recognition technique based on acoustic model adaptation to reverberant speech. In hands-free speech recognition, the recognition accuracy is degraded by reverberation, since each segment of speech is affected by the reflection energy of the preceding segment. To compensate for the reflection signal we introduce a frame-by-frame adaptation method adding the reflection signal to the means of the acoustic model. The reflection signal is approximated by a first-order linear prediction from the observation signal at the preceding frame, and the linear prediction coefficient is estimated with a maximum likelihood method by using the EM algorithm, which maximizes the likelihood of the adaptation data. Its effectiveness is confirmed by word recognition experiments on reverberant speech.

Conventional confidence measures for assessing the reliability of ASR (automatic speech recognition) output are typically derived from "low-level" information which is obtained during speech recognition decoding. In contrast to these approaches, we propose a novel utterance verification framework which incorporates "high-level" knowledge sources. Specifically, we investigate two application-independent measures: in-domain confidence, the degree of match between the input utterance and the application domain of the back-end system, and discourse coherence, the consistency between consecutive utterances in a dialogue session. A joint confidence score is generated by combining these two measures with an orthodox measure based on GPP (generalized posterior probability). The proposed framework was evaluated on an utterance verification task for spontaneous dialogue performed via a (English/Japanese) speech-to-speech translation system. Incorporating the two proposed measures significantly improved utterance verification accuracy compared to using GPP alone, realizing reductions in CER (confidence error-rate) of 11.4% and 8.1% for the English and Japanese sides, respectively. When negligible ASR errors (that do not affect translation) were ignored, further improvement was achieved for the English side, realizing a reduction in CER of up to 14.6% compared to the GPP case.

In this paper, we describe a parallel decoding-based ASR system developed of ATR that is robust to noise type, SNR and speaking style. It is difficult to recognize speech affected by various factors, especially when an ASR system contains only a single acoustic model. One solution is to employ multiple acoustic models, one model for each different condition. Even though the robustness of each acoustic model is limited, the whole ASR system can handle various conditions appropriately. In our system, there are two recognition sub-systems which use different features such as MFCC and Differential MFCC (DMFCC). Each sub-system has several acoustic models depending on SNR, speaker gender and speaking style, and during recognition each acoustic model is adapted by fast noise adaptation. From each sub-system, one hypothesis is selected based on posterior probability. The final recognition result is obtained by combining the best hypotheses from the two sub-systems. On the AURORA-2J task used widely for the evaluation of noise robustness, our system achieved higher recognition performance than a system which contains only a single model. Also, our system was tested using normal and hyper-articulated speech contaminated by several background noises, and exhibited high robustness to noise and speaking styles.

We address issues for improving hands-free speech enhancement and speech recognition performance in different car environments using a single distant microphone. This paper describes a new single-channel in-car speech enhancement method that estimates the log spectra of speech at a close-talking microphone based on the nonlinear regression of the log spectra of noisy signal captured by a distant microphone and the estimated noise. The proposed method provides significant overall quality improvements in our subjective evaluation on the regression-enhanced speech, and performed best in most objective measures. Based on our isolated word recognition experiments conducted under 15 real car environments, the proposed adaptive nonlinear regression approach shows an advantage in average relative word error rate (WER) reductions of 50.8% and 13.1%, respectively, compared to original noisy speech and ETSI advanced front-end (ETSI ES 202 050).