Silver electrical contacts are separated at constant speed and break arcs are generated in a DC100 V–450 V/10 A resistive circuit. The transverse magnetic field of a permanent magnet is applied to the break arcs. Dependences of the arc duration, arc dwell time and arc lengthening time on the strength of the magnetic field and supply voltage are investigated. The characteristics of the re-ignition of the break arc are also discussed. Following results are shown. The arc duration D is increased due to the increase of the arc lengthening time tm when the supply voltage E is increased for each magnetic flux density Bx, because the arc dwell time ts is almost constant. The arc duration D is increased due to the increase of both of the arc lengthening time tm and the arc dwell time ts when the magnetic flux density Bx is decreased. The arc lengthening time tended to become long when the re-ignition of the break arc is occurred. The lengthening time tends to become longer when the duration tm1 from the start of the arc lengthening to the start of the re-ignition is increased. Re-ignitions occurred frequently when the magnetic flux density of the transverse magnetic field is increased and the supply voltage is increased.

Break arcs are rotated by the radial magnetic field formed by a magnet embedded in the pipe-shaped cathode. The arcs are generated in switching a DC42 V resistive circuit. The closed contact current varies from 5 A to 21 A. The curvature of the anode surface is varied to study the dependence of the arc length and the positions of the break arcs in the contact gap. The following results are obtained: (i) as current decreases, there is more difference in arc duration among different curvatures; (ii) as current decreases, the arc duration decreases with decrease of the radius of curvature; (iii) in each contact curvature, the anode spots region is located nearer to the center axis than the cathode spots region; (iv) the arc length just before arc extinction is independent of the curvature of the contacts.

High-speed IP address lookup with fast prefix update is essential for designing wire-speed packet forwarding routers. The developments of optical fiber and 100 Gbps interface technologies have placed IP address lookup as the major bottleneck of high performance networks. In this paper, we propose a novel structure named Compressed Multi-way Prefix Tree (CMPT) based on B+ tree to perform dynamic and scalable high-speed IP address lookup. Our contributions are to design a practical structure for high-speed IP address lookup suitable for both IPv4 and IPv6 addresses, and to develop efficient algorithms for dynamic prefix insertion and deletion. By investigating the relationships among routing prefixes, we arrange independent prefixes as the search indexes on internal nodes of CMPT, and by leveraging a nested prefix compression technique, we encode all the routing prefixes on the leaf nodes. For any IP address, the longest prefix matching can be made at leaf nodes without backtracking. For a forwarding table with u independent prefixes, CMPT requires O(logmu) search time and O(mlogmu) dynamic insert and delete time. Performance evaluations using real life IPv4 forwarding tables show promising gains in lookup and dynamic update speeds compared with the existing B-tree structures.

Internet behavior is becoming more complex due to ever-changing networking technologies and applications. Thus, understanding and controlling the complex behavior of the Internet are important for designing future networks. One of the complex behaviors of the Internet is traffic dynamics. Previous studies revealed that flow control in the transport layer affects the traffic dynamics of the Internet. However, it is not clear how the topological structure impacts traffic dynamics. In this paper, we investigate packet delay dynamics and traffic fluctuation in ISP router-level topologies where the degree distribution exhibits a power-law nature, and the nodes interact via end-to-end feedback control functionality. We show the packet delay dynamics of the BA topologies generated by the Barabasi-Albert (BA) model and the ISP router-level topologies. Simulation results show that the end-to-end delay distributions exhibit a heavy tail in the TCP model. Moreover, the number of links with highly fluctuating queue length increases dramatically compared to that in the stop-and-wait model. Even in this case, the high-modularity structures of the ISP topologies reduce the number of highly fluctuating links compared with the BA topologies.

The minimized assumption generation has been recognized as an important improvement of the assume-guarantee verification method in order to generate minimal assumptions. The generated minimal assumptions can be used to recheck the whole component-based software at a lower computational cost. The method is not only fitted to component-based software but also has a potential to solve the state space explosion problem in model checking. However, the computational cost for generating the minimal assumption is very high so the method is difficult to be applied in practice. This paper presents an optimization as a continuous work of the minimized assumption generation method in order to reduce the complexity of the method. The key idea of this method is to find a smaller assumption in a sub-tree of the search tree containing the candidate assumptions using the depth-limited search strategy. With this approach, the improved method can generate assumptions with a lower computational cost and consumption memory than the minimized method. The generated assumptions are also effective for rechecking the systems at much lower computational cost in the context of software evolution. An implemented tool supporting the improved method and experimental results are also presented and discussed.

For real-time services, such as VoIP and videoconferencing supplied through a multi-domain MPLS network, it is vital to guarantee end-to-end QoS of the inter-domain paths. Thus, it is important to allocate an appropriate QoS class to the inter-domain paths in each transit domain. Because each domain has its own policy for QoS class allocation, each domain must then allocate an appropriate QoS class adaptively based on the estimation of the QoS class allocation policies adopted in other domains. This paper proposes an adaptive method for acquiring a QoS class allocation policy through the use of reinforcement learning. This method learns the appropriate policy through experience in the actual QoS class allocation process. Thus, the method can adapt to a complex environment where the arrival of inter-domain path requests does not follow a simple Poisson process and where the various QoS class allocation policies are adopted in other domains. The proposed method updates the allocation policy whenever a QoS class is actually allocated to an inter-domain path. Moreover, some of the allocation policies often utilized in the real operational environment can be updated and refined more frequently. For these reasons, the proposed method is designed to adapt rapidly to variances in the surrounding environment. Simulation results verify that the proposed method can quickly adapt to variations in the arrival process of inter-domain path requests and the QoS class allocation policies in other domains.

In this letter, a quantization error-aware Tomlinson-Harashinma Precoding (THP) is proposed based on the equivalent zero-forcing (ZF) criterion in Multiuser Multiple-Input Single-Output (MU-MISO) systems with limited feedback, where the transmitter has only quantized channel direction information (CDI). This precoding scheme is robust to the channel uncertainties arising from the quantization error and the lack of channel magnitude information (CMI). Our simulation results show that the new THP scheme outperforms the conventional precoding scheme in limited feedback systems with respect to Bit Error Ratio (BER).

The research on driverless cars has been making much progress lately. In this paper, we propose a new traffic control system without traffic lights at an intersection. We assume a system with fully autonomous driverless cars, and infrastructure to avoid collision completely. When automobiles approach an intersection, they communicates with the access point in both random access mode and polling mode, and the movement of the automobiles will be coordinated by the infra structure (access point). Traffic congestion is very difficult to predict and deal with because it is a function of many unknown factors such as number of cars, weather, road conditions, accidents, etc. The proposed algorithm is designed for urban road networks to ease the congestion, and make it more predictable at the same time. A key idea of this paper is that IEEE 802.11 DCF/PCF mechanisms are used to control traffic flow for driverless cars when there are no traffic lights at an intersection. The algorithm uses the concept of contention/contention-free period of IEEE 802.11 to find a balance between the efficiency of traffic flow and the fairness between users.

Decision tree-based clustering and parameter estimation are essential steps in the training part of an HMM-based speech synthesis system. These two steps are usually performed based on the maximum likelihood (ML) criterion. However, one of the drawbacks of the ML criterion is that it is sensitive to outliers which usually result in quality degradation of the synthesized speech. In this letter, we propose an approach to detect and remove outliers for HMM-based speech synthesis. Experimental results show that the proposed approach can improve the synthetic speech, particularly when the available training speech database is insufficient.

Out-of-vocabulary (OOV) words create serious problems for automatic speech recognition (ASR) systems. Not only are they miss-recognized as in-vocabulary (IV) words with similar phonetics, but the error also causes further errors in nearby words. Language models (LMs) for most open vocabulary ASR systems treat OOV words as a single entity, ignoring the linguistic information. In this paper we present a class-based n-gram LM that is able to deal with OOV words by treating each of them individually without retraining all the LM parameters. OOV words are assigned to IV classes consisting of similar semantic meanings for IV words. The World Wide Web is used to acquire additional data for finding the relation between the OOV and IV words. An evaluation based on adjusted perplexity and word-error-rate was carried out on the Wall Street Journal corpus. The result suggests the preference of the use of multiple classes for OOV words, instead of one unknown class.

We propose a novel scheme that aims to determine the optimal number of clusters based on the field conditions and the positions of mobile sink nodes. In addition, we merge algorithms of tree-based index structures to form an energy-efficient cluster structure. A performance evaluation shows that the proposed method produces highly-balanced clusters that are energy efficient and achieves up to 1.4 times higher survival rates than the previous clustering schemes, under various operational conditions.

The performance of English automatic speech recognition systems decreases when recognizing spontaneous speech mainly due to multiple pronunciation variants in the utterances. Previous approaches address this problem by modeling the alteration of the pronunciation on a phoneme to phoneme level. However, the phonetic transformation effects induced by the pronunciation of the whole sentence have not yet been considered. In this article, the sequence-based pronunciation variation is modeled using a noisy channel approach where the spontaneous phoneme sequence is considered as a “noisy” string and the goal is to recover the “clean” string of the word sequence. Hereby, the whole word sequence and its effect on the alternation of the phonemes will be taken into consideration. Moreover, the system not only learns the phoneme transformation but also the mapping from the phoneme to the word directly. In this study, first the phonemes will be recognized with the present recognition system and afterwards the pronunciation variation model based on the noisy channel approach will map from the phoneme to the word level. Two well-known natural language processing approaches are adopted and derived from the noisy channel model theory: Joint-sequence models and statistical machine translation. Both of them are applied and various experiments are conducted using microphone and telephone of spontaneous speech.

Dynamic resource management has become an active area of research in the Cloud Computing paradigm. Cost of resources varies significantly depending on configuration for using them. Hence efficient management of resources is of prime interest to both Cloud Providers and Cloud Users. In this work we suggest a probabilistic resource provisioning approach that can be exploited as the input of a dynamic resource management scheme. Using a Video on Demand use case to justify our claims, we propose an analytical model inspired from standard models developed for epidemiology spreading, to represent sudden and intense workload variations. We show that the resulting model verifies a Large Deviation Principle that statistically characterizes extreme rare events, such as the ones produced by “buzz/flash crowd effects” that may cause workload overflow in the VoD context. This analysis provides valuable insight on expectable abnormal behaviors of systems. We exploit the information obtained using the Large Deviation Principle for the proposed Video on Demand use-case for defining policies (Service Level Agreements). We believe these policies for elastic resource provisioning and usage may be of some interest to all stakeholders in the emerging context of cloud networking.

Reduction of the intensity noise in semiconductor lasers is an important subject for the higher performance of an application. Simultaneous usage of the superposition of high frequency current and the electric negative feedback loop was proposed to suppress the noise for the higher power operation of semiconductor lasers. Effective noise reduction of more than 25 dB with 80 mW operation was experimentally demonstrated.

In this paper, we propose Hidden Conditional Neural Fields (HCNF) for continuous phoneme speech recognition, which are a combination of Hidden Conditional Random Fields (HCRF) and a Multi-Layer Perceptron (MLP), and inherit their merits, namely, the discriminative property for sequences from HCRF and the ability to extract non-linear features from an MLP. HCNF can incorporate many types of features from which non-linear features can be extracted, and is trained by sequential criteria. We first present the formulation of HCNF and then examine three methods to further improve automatic speech recognition using HCNF, which is an objective function that explicitly considers training errors, provides a hierarchical tandem-style feature and includes a deep non-linear feature extractor for the observation function. We show that HCNF can be trained realistically without any initial model and outperforms HCRF and the triphone hidden Markov model trained by the minimum phone error (MPE) manner using experimental results for continuous English phoneme recognition on the TIMIT core test set and Japanese phoneme recognition on the IPA 100 test set.

Digital still cameras emerged following the introduction of the Sony Mavica analog prototype camera in 1981. These early cameras produced poor image quality and did not challenge film cameras for overall quality. By 1995 digital still cameras in expensive SLR formats had 6 mega-pixels and produced high quality images (with significant image processing). In 2005 significant improvement in image quality was apparent and lower prices for digital still cameras (DSCs) started a rapid decline in film usage and film camera sells. By 2010 film usage was mostly limited to professionals and the motion picture industry. The rise of DSCs was marked by a “pixel war” where the driving feature of the cameras was the pixel count where even moderate cost, ∼ $120, DSCs would have 14 mega-pixels. The improvement of CMOS technology pushed this trend of lower prices and higher pixel counts. Only the single lens reflex cameras had large sensors and large pixels. The drive for smaller pixels hurt the quality aspects of the final image (sharpness, noise, speed, and exposure latitude). Only today are camera manufactures starting to reverse their course and producing DSCs with larger sensors and pixels. This paper will explore why larger pixels and sensors are key to the future of DSCs.

In the IEEE 802.11e standard, a reference scheduler is presented. The reference scheduler uses the same service interval (SI) to poll all stations in a polling list, and uses separate scheduling and admission algorithms. This increases the polling overhead and decreases TXOP utilization. Many papers have attempted to enhance the reference scheduler, but its admission control is similar to reference scheduler. In this paper, we propose a scheduling and admission algorithm that assigns each station an SI close to its maximum SI (MSI). As a result, the proposed algorithm shows higher performance than that of the reference scheduler.

Our research is focused on examining a stereoscopic quality assessment model for stereoscopic images with disparate quality in left and right images for glasses-free stereo vision. In this paper, we examine the objective assessment model of 3-D images, considering the difference in image quality between each view-point generated by the disparity-compensated coding. A overall stereoscopic image quality can be estimated by using only predicted values of left and right 2-D image qualities based on the MPEG-7 descriptor information without using any disparity information. As a result, the stereoscopic still image quality is assessed with high prediction accuracy with correlation coefficient=0.98 and average error=0.17.

The convergence behavior of turbo APPM (TAPPM) decoding is analyzed by using a three-dimensional extrinsic information transfer (EXIT) chart and the decoding trajectory. The signal-to-noise ratio (SNR) threshold, below which iterative decoding fails to converge, is predicted by using the 3-D EXIT chart analysis. Bit error rate performances of TAPPM schemes validate the EXIT-chart-based SNR threshold predictions. Outer constituent codes of TAPPM are chosen to show the lowest SNR threshold with the aid of EXIT chart analysis.

Novel thermopiles based on modulation doped AlGaAs/InGaAs and AlGaN/GaN heterostructures are proposed and developed for the first time, for uncooled infrared FPA (Focal Plane Array) image sensor application. The high responsivity with the high speed response time are designed to 4,900 V/W with 110 µs for AlGaAs/InGaAs, and to 460 V/W with 9 µs for AlGaN/GaN thermopiles, respectively. Based on integrated HEMT-MEMS technology, the AlGaAs/InGaAs 3232 matrix FPAs are fabricated to demonstrate its enhanced performances by black body measurement. The technology presented here demonstrates the potential of this approach for low-cost uncooled infrared FPA image sensor application.