In recent years, there has been increasing demand to miniaturize wiring harness connectors in automobiles due to the increasing volume of electronic equipment and so on. With this demand, contact failure caused by the fretting corrosion seems to become a serious problem in the future. In this report, three-dimensional observations using Focused Ion Beam (FIB)-SEM method have been made for tin plated fretting contacts before and after the contact resistance increase with tin plating thickness 5 µm. With these observations, the three dimensional structural transition from tin to tin oxide have been examined.

We review recently obtained results for CMOS (Complementary Metal Oxide Semiconductor) imaging devices used in biomedical applications. The topics include dish type image sensors, deep-brain implantation devices for small animals, and retinal prosthesis devices. Fundamental device structures and their characteristics are described, and the results of in vivo experiments are presented.

Providing quality-of service (QoS) guarantees in VoIP applications has become an urgent demand in wireless and mobile networks. One of the important issues is to find a simple quality metric fitted to low power mobile devices such as smart phones. This paper considers the gap ratio (the proportion of the accumulated gap periods over the whole call session) as a simple quality metric. Our study aims to find the optimum packet count threshold between two adjacent lost packets (referred to as Gmin in RTCP-XR), which is needed for the purpose of identifying whether the current packet at the receiver belongs to the gap state or the burst state, because quality prediction errors depend on the Gmin value when the gap ratio is used as a simple quality metric. Based on this metric, we propose an accounting model that can be a candidate accounting metric useful for a quality-based accounting mechanism.

With measuring the arc current, arc voltage and arc images, the high-current air arc commutation process across the separated electrodes was investigated. It shows that the existence of a short stable arc in the gap may increase the current commutation time. According to the energy balance of the arc column, the conditions to maintain the short stable arc were introduced and the effects of the current limiting resistance on the current commutation process were discussed.

With energy shortages and global climate change leading our concerns these days, the energy consumption of datacenters has become a key issue. Obviously, a substantial reduction in energy consumption can be made by powering down servers when they are not in use. This paper aims at designing, implementing and evaluating a Green Scheduler for reducing energy consumption of datacenters in Cloud computing platforms. It is composed of four algorithms: prediction, ON/OFF, task scheduling, and evaluation algorithms. The prediction algorithm employs a neural predictor to predict future load demand based on historical demand. According to the prediction, the ON/OFF algorithm dynamically adjusts server allocations to minimize the number of servers running, thus minimizing the energy use at the points of consumption to benefit all other levels. The task scheduling algorithm is responsible for directing request traffic away from powered-down servers and toward active servers. The performance is monitored by the evaluation algorithm to balance the system's adaptability against stability. For evaluation, we perform simulations with two load traces. The results show that the prediction mode, with a combination of dynamic training and dynamic provisioning of 20% additional servers, can reduce energy consumption by 49.8% with a drop rate of 0.02% on one load trace, and a drop rate of 0.16% with an energy consumption reduction of 55.4% on the other. Our method is also proven to have a distinct advantage over its counterparts.

Context awareness is viewed as one of the most important goals in the pervasive computing paradigm. As one kind of context awareness, danger awareness describes and detects dangerous situations around a user, and provides services such as warning to protect the user from dangers. One important problem arising in danger-aware systems is that the description/definition of dangerous situations becomes more and more complex, since many factors have to be considered in such description, which brings a big burden to the developers/users and thereby reduces the reliability of the system. It is necessary to develop a flexible reasoning method, which can ease the description/definition of dangerous situations by reasoning dangers using limited specified/predefined contexts/rules, and increase system reliability by detecting unspecified dangerous situations. Some reasoning mechanisms based on context similarity were proposed to address the above problems. However, the current mechanisms are not so accurate in some cases, since the similarity is computed from only basic knowledge, e.g. nature property, such as material, size etc, and category information, i.e. they may cause false positive and false negative problems. To solve the above problems, in this paper we propose a new flexible and accurate method from feature point of view. Firstly, a new ontology explicitly integrating basic knowledge and danger feature is designed for computing similarity in danger-aware systems. Then a new method is proposed to compute object similarity from both basic knowledge and danger feature point of views when calculating context similarity. The method is implemented in an indoor ubiquitous test bed and evaluated through experiments. The experiment result shows that the accuracy of system can be effectively increased based on the comparison between system decision and estimation of human observers, comparing with the existing methods. And the burden of defining dangerous situations can be decreased by evaluating trade-off between the system's accuracy and burden of defining dangerous situations.

Silver electrical contacts are separated to generate break arcs in a DC48 V/6-24 A resistive circuit. The transverse magnetic field formed by a permanent magnet is applied to the break arcs. A series of experiments are carried out for two different experimental conditions. One condition is a constant contact separating speed while the magnetic flux density is changed to investigate the shortening effect of the arc duration. Another condition is a constant magnetic flux density while the contact separating speed is changed to investigate the changes in the arc duration and the contact gap when the break arc is extinguished. As a result, with constant separating speed, it is confirmed that the duration of break arcs is shortened by the transverse magnetic field and the break arcs are extinguished when the arc length reaches a certain value L. Under the condition of constant transverse magnetic field, (i) the arc duration is shortened by increasing the separation speed; (ii) the contact gap when the break arc is extinguished is almost constant when the separating speed v is sufficiently faster than 5 mm/s.

In order to study the influences of contact opening speeds on arc extinction gap length characteristics, Ag contacts were operated to break DC inductive load currents from 0.1 A to 2.0 A at 14 V with contact opening speeds of 0.5 mm/s, 1 mm/s, 2 mm/s, 5 mm/s and 10 mm/s in a switching mechanism employing a stepping motor, and arc voltage waveforms were observed at each opening of the contacts. From the results, the average arc durations were determined at each current level under the respective contact opening speeds, and the average arc extinction gap lengths were calculated by multiplying the average arc duration value and the contact opening speed value. It was found that average arc durations showed no significant differences with increasing contact opening speeds. Thus, arc extinction gaps became larger at faster opening speeds in the inductive load conditions of this study.

With exponentially increasing power densities due to technology scaling and ever increasing demand for performance, chip temperature has become an important issue that limits the performance of computer systems. Typically, it is essential to use a set of on-chip thermal sensors to monitor temperatures during the runtime. The runtime thermal measurements are then employed by dynamic thermal management techniques to manage chip performance appropriately. In this paper, we propose an inverse distance weighting method based on a dynamic Voronoi diagram for the reconstruction of full thermal characterization of integrated circuits with non-uniform thermal sensor placements. Firstly we utilize the proposed method to transform the non-uniformly spaced samples to virtual uniformly spaced data. Then we apply three classical interpolation algorithms to reconstruct the full thermal signals in the uniformly spaced samples mode. To evaluate the effectiveness of our method, we develop an experiment for reconstructing full thermal status of a 16-core processor. Experimental results show that the proposed method significantly outperforms spectral analysis techniques, and can obtain full thermal characterization with an average absolute error of 1.72% using 9 thermal sensors per core.

This paper proposes a built-in self-test (BIST) scheme for noise-tolerant testing of a digital-to-analogue converter (DAC). The proposed BIST calculates the differences in output voltages between a DAC and test modules. These differences are used as the inputs of an integrator that determines integral nonlinearity (INL). The proposed method has an advantage of random noise cancelation and achieves a higher test accuracy than do the conventional BIST methods. The simulation results show high standard noise-immunity and fault coverage for the proposed method.

We present a new multi-path routing methodology, MLB-routing, that is based on the multinomial logit model, which is well known in the random utility field. The key concept of the study is to set multiple paths from the origin to the destination, and distribute packets in accordance with multinomial logit type probability. Since MLB-routing is pure multi-path routing, it reduces the convergence on some links and increases bandwidth utilization in the network. Unlike existing multi-path routing schemes, which pre-set alternate paths, the proposed method can dynamically distribute packets to every possible path and thus is more efficient. Furthermore, it should be mentioned that this methodology can be implemented as either a link-state protocol or a distance-vector protocol. Therefore, it well supports the existing Internet. Simulations show that this methodology raises network utilization and significantly reduces end-to-end delay and jitter.

Sender-based message logging (SBML) with checkpointing has its well-known beneficial feature, lowering highly failure-free overhead of synchronous logging with volatile logging at sender's memory. This feature encourages it to be applied into many distributed systems as a low-cost transparent rollback recovery technique. However, the original SBML recovery algorithm may no longer be progressing in some transient communication error cases. This paper proposes a consistent recovery algorithm to solve this problem by piggybacking small log information for unstable messages received on each acknowledgement message for returning the receive sequence number assigned to a message by its receiver. Our algorithm also enables all messages scheduled to be sent, but delayed because of some preceding unstable messages to be actually transmitted out much earlier than the existing ones.

Boosting over weak classifiers is widely used in pedestrian detection. As the number of weak classifiers is large, researchers always use a sampling method over weak classifiers before training. The sampling makes the boosting process harder to reach the fixed target. In this paper, we propose a partial derivative guidance for weak classifier mining method which can be used in conjunction with a boosting algorithm. Using weak classifier mining method makes the sampling less degraded in the performance. It has the same effect as testing more weak classifiers while using acceptable time. Experiments demonstrate that our algorithm can process quicker than [1] algorithm in both training and testing, without any performance decrease. The proposed algorithms is easily extending to any other boosting algorithms using a window-scanning style and HOG-like features.

Reducing the energy consumption of wireless communication systems with new technologies and solutions continues to be an important concern in developing future standards. In this paper, we study the routing strategies in multi-hop relaying networks. For a 2-way assignment routing method, an efficient feedback scheme is presented to minimize the power consumption over the whole system. Compared with the full channel information in traditional feedback scheme, only the backward accumulated feedback metrics are required. If the proposed routing calculation is used, there is no performance loss. When the number of the hops and the relays is large, the new scheme achieves a significant feedback overhead reduction. Moreover, we show a proof for the optimality of the presented routing strategy based on mathematical induction.

Since retina blood vessels (RBV) are a major factor in ophthalmological diagnosis, it is essential to detect RBV from a fundus image. In this letter, we proposed the detection method of RBV using a morphological analysis and support vector machine classification. The proposed RBV detection method consists of three strategies: pre-processing, features extraction and classification. In pre-processing, noises were reduced and RBV were enhanced by anisotropic diffusion filtering and illumination equalization. Features were extracted by using the image intensity and morphology of RBV. And a support vector machine (SVM) classification algorithm was used to detect RBV. The proposed RBV detection method was simulated and validated by using the DRIVE database. The averages of accuracy and TPR are 0.94 and 0.78, respectively. Moreover, by comparison, we confirmed that the proposed RBV detection method detected RBV better than the recent RBV detections methods.

We present a new structure for parallel affine projection (AP) filters with different step-sizes. By observing their error signals, the proposed alternating AP (A-AP) filter selects one of the two AP filters and updates the weights of the selected filter for each iteration. As a result, the total computations required for the proposed structure is almost the same as that for a single AP filter. Experimental results show that the proposed alternating selection scheme extracts the best properties of each component filter, namely fast convergence and small steady-state error.

This paper firstly analysis the coherent correlation, non-coherent accumulation detector used in weak satellite signal detection mathematically and statistically, and derives its single threshold based on the CFAR (constant false alarm rate). And then the paper improved the detector under the situation of more than one satellite existing with different signal power. Based on this new type of detector, a threshold calculation method is introduced considering the effect of near-far problem in the weak signal detection. Finally the method is verified and compared to the traditional single threshold with simulated data and collected intermediate frequency real data. The results show that this new threshold method can detect signal efficiently with lower false alarm possibility and larger detection possibility.

A translucent wavelength switched optical network (WSON) is a cost-efficient infrastructure between opaque networks and transparent optical networks, which aims at seeking a graceful balance between network cost and service provisioning performance. In this paper, we experimentally present a resilient translucent WSON with the control of an enhanced path computation element (PCE) and extended generalized multi-protocol label switching (GMPLS) controllers. An adaptive routing and wavelength assignment scheme with the consideration of accumulated physical impairments, wavelength availabilities and regenerator allocation is experimentally demonstrated and evaluated for dynamic provisioning of lightpaths. By using two different network scenarios, we experimentally verify the feasibility of the proposed solutions in support of translucent WSON, and quantitatively evaluate the path computation latency, network blocking probability and service disruption time during end-to-end lightpath restoration. We also deeply analyze the experimental results and discuss the synchronization between the PCE and the network status. To the best of our knowledge, the most significant progress and contribution of this paper is that, for the first time, all the proposed methodologies in support of PCE/GMPLS controlled translucent WSON, including protocol extensions and related algorithms, are implemented in a network testbed and experimentally evaluated in detail, which allows verifying their feasibility and effectiveness when being potentially deployed into real translucent WSON.

Manufacturing defects in the deep sub-micron VLSI process and aging resulted problems of devices during lifecycle are inevitable, and fault-tolerant routing algorithms are important to provide the required communication for NoCs in spite of failures. The proposed algorithm, referred to as scalable and reconfigurable fault-tolerant distributed routing (RFDR), partitions the system into nine regions using the concept of divide-and-conquer. It is a distributed algorithm, and each router guarantees fault-tolerance within one's own region and the system can be still sustained with multiple fault areas. The proposed RFDR has excellent scalability with hardware cost keeping constant independent of system size. Also it is completely reconfigurable when new nodes fail. Simulations under various synthetic traffic patterns show its better performance compared to Extended-XY routing algorithm. Moreover, there is almost no hardware overhead compared to Logic-Based Distributed Routing (LBDR), but the fault-tolerance capacity is enhanced in the proposed algorithm. Hardware cost is reduced 37% compared to Reconfigurable Distributed Scalable Predictable Interconnect Network (R-DSPIN) which only supports single fault region.

In this letter, a novel adaptive total variation (ATV) model is proposed for image inpainting. The classical TV model is a partial differential equation (PDE)-based technique. While the TV model can preserve the image edges well, it has some drawbacks, such as staircase effect in the inpainted image and slow convergence rate. By analyzing the diffusion mechanism of TV model and introducing a new edge detection operator named difference curvature, we propose a novel ATV inpainting model. The proposed ATV model can diffuse the image information smoothly and quickly, namely, this model not only eliminates the staircase effect but also accelerates the convergence rate. Experimental results demonstrate the effectiveness of the proposed scheme.