We have investigated the influence of the ferromagnet magnetization on the transport properties of intrinsic Josephson junctions in Co/Au/BSCCO/Au/Co hybrid structure under applied magnetic fields. The current-voltage characteristic at 77K in a zero-field showed the multiple quasiparticle branches with hysteresis similar to that of conventional intrinsic Josephson junctions. On the other hand, it was observed that the critical current shows a clear asymmetric field dependence with respect to the direction of the field sweep, resulting in hysteretic behavior. By comparing the field dependence of critical current with magnetization curve of the sample, we found that the critical current is strongly suppressed in the antiparallel configuration of the relative magnetization orientation of two Co layers due to the accumulation of spin-polarized quasiparticles in intrinsic Josephson junctions. The observed suppression of the critical current is as large as more than 20%.

Cell phones with GPS function as well as GPS loggers are widely used and users' geographic information can be easily obtained. However, still battery consumption in these mobile devices is main concern and then obtaining GPS positioning data so frequently is not allowed. In this paper, a stayed location estimation method for sparse GPS positioning information is proposed. After generating initial clusters from a sequence of measured positions, the effective radius is set for every cluster based on positioning accuracy and the clusters are merged effectively using it. After that, short-time clusters are removed temporarily but measured positions included in them are not removed. Then the clusters are merged again, taking all the measured positions into consideration. This process is performed twice, in other words, two-stage short-time cluster removal is performed, and finally accurate stayed location estimation is realized even when the GPS positioning interval is five minutes or more. Experiments demonstrate that the total distance error between the estimated stayed location and the true stayed location is reduced by more than 33% and also the proposed method much improves F1 measure compared to conventional state-of-the-art methods.

This paper proposes to pre-compute approximate normal distribution functions and store them in textures such that real-time applications can process complex specular surfaces simply by sampling the textures. The proposed method is compatible with the GPU pipeline-based algorithms, and rendering is completed at real time. The experimental results show that the features of complex specular surfaces, such as the glinty appearance of leather and metallic flakes, are successfully reproduced.

Given a sequence of k convex polygons in the plane, a start point s, and a target point t, we seek a shortest path that starts at s, visits in order each of the polygons, and ends at t. We revisit this touring polygons problem, which was introduced by Dror et al. (STOC 2003), by describing a simple method to compute the so-called last step shortest path maps, one per polygon. We obtain an O(kn)-time solution to the problem for a sequence of pairwise disjoint convex polygons and an O(k2n)-time solution for possibly intersecting convex polygons, where n is the total number of vertices of all polygons. A major simplification is made on the operation of locating query points in the last step shortest path maps. Our results improve upon the previous time bounds roughly by a factor of log n.

Video reranking is an effective way for improving the retrieval performance of text-based video search engines. This paper proposes a graph-based Web video search reranking method with local and global consistency analysis. Generally, the graph-based reranking approach constructs a graph whose nodes and edges respectively correspond to videos and their pairwise similarities. A lot of reranking methods are built based on a scheme which regularizes the smoothness of pairwise relevance scores between adjacent nodes with regard to a user's query. However, since the overall consistency is measured by aggregating only the local consistency over each pair, errors in score estimation increase when noisy samples are included within query-relevant videos' neighbors. To deal with the noisy samples, the proposed method leverages the global consistency of the graph structure, which is different from the conventional methods. Specifically, in order to detect this consistency, the propose method introduces a spectral clustering algorithm which can detect video groups, in which videos have strong semantic correlation, on the graph. Furthermore, a new regularization term, which smooths ranking scores within the same group, is introduced to the reranking framework. Since the score regularization is performed by both local and global aspects simultaneously, the accurate score estimation becomes feasible. Experimental results obtained by applying the proposed method to a real-world video collection show its effectiveness.

Given two data sets of user preferences and product attributes in addition to a set of query products, the aggregate reverse rank (ARR) query returns top-k users who regard the given query products as the highest aggregate rank than other users. ARR queries are designed to focus on product bundling in marketing. Manufacturers are mostly willing to bundle several products together for the purpose of maximizing benefits or inventory liquidation. This naturally leads to an increase in data on users and products. Thus, the problem of efficiently processing ARR queries become a big issue. In this paper, we reveal two limitations of the state-of-the-art solution to ARR query; that is, (a) It has poor efficiency when the distribution of the query set is dispersive. (b) It has to process a large portion user data. To address these limitations, we develop a cluster-and-process method and a sophisticated indexing strategy. From the theoretical analysis of the results and experimental comparisons, we conclude that our proposals have superior performance.

In this paper, an optimal method is proposed to design sparse-coefficient notch filters with principal basic vectors in the column space of a matrix constituted with frequency samples. The proposed scheme can perform in two stages. At the first stage, the principal vectors can be determined in the least-squares sense. At the second stage, with some components of the principal vectors, the notch filter design is formulated as a linear optimization problem according to the desired specifications. Optimal results can form sparse coefficients of the notch filter by solving the linear optimization problem. The simulation results show that the proposed scheme can achieve better performance in designing a sparse-coefficient notch filter of small order compared with other methods such as the equiripple method, the orthogonal matching pursuit based scheme and the L1-norm based method.

To prepare antireflection coating (ARC) by wet process is important technology for low cost fabrication of solar cells. In this research, we consider the optical reflectance of a three layer stack structure of ARC films on the pyramidally textured single-crystalline silicon substrates. Each layer of the ARC films is deposited by a spin-coating method. The triple layers consist of SiO2, SiO2-TiO2 mixture, and TiO2 films from air to the silicon substrate in that order, and the refractive index is slightly increased from air to the substrate. Light reflection can be reduced further mainly due to graded index effect. The optimized three layer structure ARC shows that the reflectance is below 0.048 at the wavelength of 600 nm.

In this paper we show a simple heuristic for constructing smaller automata for a set of regular expressions, based on suffix sorting: finding common prefixes and suffixes in regular expressions and merging them. It is an important problem in network security. We applied our approach to random and real-world regular expressions. Experimental results showed that our approach yields up to 12 times enhancement in throughput.

Understanding which refactoring transformations were performed is in demand in modern software constructions. Traditionally, many researchers have been tackling understanding code changes with history data derived from version control systems. In those studies, problems of the traditional approach are pointed out, such as entanglement of multiple changes. To alleviate the problems, operation histories on IDEs' code editors are available as a new source of software evolution data nowadays. By replaying such histories, we can investigate past code changes in a fine-grained level. However, the prior studies did not provide enough evidence of their effectiveness for detecting refactoring transformations. This paper describes an experiment in which participants detect refactoring transformations performed by other participants after investigating the code changes with an operation-replay tool and diff tools. The results show that both approaches have their respective factors that pose misunderstanding and overlooking of refactoring transformations. Two negative factors on divided operations and generated compound operations were observed in the operation-based approach, whereas all the negative factors resulted from three problems on tangling, shadowing, and out-of-order of code changes in the difference-based approach. This paper also shows seven concrete examples of participants' mistakes in both approaches. These findings give us hints for improving existing tools for understanding code changes and detecting refactoring transformations.

This paper proposes a sequentially iterative equalizer based on Kalman filtering and smoothing (SIEKFS) for multiple-input multiple-output (MIMO) systems under frequency selective fading channels. In the proposed SIEKFS, an iteration consists of sequentially executed subiterations, and each subiteration performs equalization and detection procedures of the symbols transmitted from a specific transmit antenna. During this subiteration, all available observations for the transmission block are utilized in the equalization procedures. Furthermore, the entire soft estimate of the desired symbols to be detected does not participate in the equalization procedures of the desired symbols, i.e., the proposed SIEKFS performs input-by-input equalization procedures for a priori information nulling. Therefore, compared with the original iterative equalizer based on Kalman filtering and smoothing, which performs symbol-by-symbol equalization procedures, the proposed SIEKFS can also perform iterative equalization based on the Kalman framework and turbo principle, with a significant reduction in computation complexity. Simulation results verify that the proposed SIEKFS achieves suboptimum error performance as the size of the antenna configuration and the number of iterations increase.

Concatenated zigzag (CZ) codes are classified as one kind of parallel-concatenated codes with powerful performance and low complexity. This kind of codes has flexible implementation methods and a good application prospect. We propose a modified turbo-type decoder and adaptive extrinsic information scaling method based on the Max-Log-APP (MLA) algorithm, which can provide a performance improvement also under the relatively low decoding complexity. Simulation results show that the proposed method can effectively help the sub-optimal MLA algorithm to approach the optimal performance. Some contrasts with low-density parity-check (LDPC) codes are also presented in this paper.

Clustering is known to be an effective means of reducing energy dissipation and prolonging network lifetime in wireless sensor networks (WSNs). Recently, game theory has been used to search for optimal solutions to clustering problems. The residual energy of each node is vital to balance a WSN, but was not used in the previous game-theory-based studies when calculating the final probability of being a cluster head. Furthermore, the node payoffs have also not been expressed in terms of energy consumption. To address these issues, the final probability of being a cluster head is determined by both the equilibrium probability in a game and a node residual energy-dependent exponential function. In the process of computing the equilibrium probability, new payoff definitions related to energy consumption are adopted. In order to further reduce the energy consumption, an assistant method is proposed, in which the candidate nodes with the most residual energy in the close point pairs completely covered by other neighboring sensors are firstly selected and then transmit same sensing data to the corresponding cluster heads. In this paper, we propose an efficient energy-aware clustering protocol based on game theory for WSNs. Although only game-based method can perform well in this paper, the protocol of the cooperation with both two methods exceeds previous by a big margin in terms of network lifetime in a series of experiments.

We have proposed a fish-farm monitoring system. In our system, the transmission range of acoustic waves from sensors attached to the undersides of the fish is not omnidirectional because of obstruction from the bodies of the fish. In addition, energy-efficient control is highly important in our system to avoid the need to replace the batteries. In this letter, we propose a data-gathering method for fish-farm monitoring without the use of control packets so that energy-efficient control is possible. Instead, our method uses the transmission-range volume as calculated from the location of the sensor node to determine the timing of packet transmission. Through simulation evaluations, we show that the data-gathering performance of our proposed method is better than that of comparative methods.

We propose an algorithm for the gathering problem of mobile agents in arbitrary networks (graphs) with Byzantine agents. Our algorithm can make all correct agents meet at a single node in O(fm) time (f is the upper bound of the number of Byzantine agents and m is the number of edges) under the assumption that agents have unique ID and behave synchronously, each node is equipped with an authenticated whiteboard, and f is known to agents. Here, the whiteboard is a node memory where agents can leave information. Since the existing algorithm achieves gathering without a whiteboard in Õ(n9λ) time, where n is the number of nodes and λ is the length of the longest ID, our algorithm shows an authenticated whiteboard can significantly reduce the time for the gathering problem in Byzantine environments.

A 2nd-order ΔΣAD modulator architecture is proposed to simplify the operation phase using ring amplifier and SAR quantizer. The proposed modulator architecture can guarantee the reset time for ring amplifier and relax the speed requirement on asynchronous SAR quantizer. The SPICE simulation results demonstrate the feasibility of the proposed 2nd-order ΔΣAD modulator in 90nm CMOS technology. Simulated SNDR of 95.70dB is achieved while a sinusoid -1dBFS input is sampled at 60MS/s for the bandwidth is BW=470kHz. The power consumption of the analog part in the modulator is 1.67mW while the supply voltage is 1.2V.

The growth of Internet traffic and the variety of traffic classes make network performance extremely difficult to evaluate. Even though most current methods rely on complex or costly hardware, recent research on bandwidth sharing has suggested the possibility of defining evaluation methods that simply require basic statistics on aggregated link utilization, such as mean and variance. This would greatly simplify monitoring systems as these statistics are easily calculable from Simple Network Management Protocol (SNMP) calls. However, existing methods require knowledge of certain fixed information about the network being monitored (e.g. link capacities). This is usually unavailable when the operator's view is limited to its share of leased links or when shared links carry traffic with different priorities. In this paper, departing from the analysis of aggregated link utilization statistics obtainable from SNMP requests, we propose a method that detects traffic degradation based on link utilization samples. It does not require knowledge of the capacity of the aggregated link or any other network parameters, giving network operators the possibility to control network performance in a more reliable and cost-effective way.

To efficiently use network resources, internet service providers need to conduct traffic engineering that dynamically controls traffic routes to accommodate traffic change with limited network resources. The performance of traffic engineering (TE) depends on the accuracy of traffic prediction. However, the size of traffic change has been drastically increasing in recent years due to the growth in various types of network services, which has made traffic prediction difficult. Our approach to tackle this issue is to separate traffic into predictable and unpredictable parts and to apply different control policies. However, there are two challenges to achieving this: dynamically separating traffic according to predictability and dynamically controlling routes for each separated traffic part. In this paper, we propose a macroflow-based TE scheme that uses different routing policies in accordance with traffic predictability. We also propose a traffic-separation algorithm based on real-time traffic analysis and a framework for controlling separated traffic with software-defined networking technology, particularly OpenFlow. An evaluation of actual traffic measured in an Internet2 network shows that compared with current TE schemes the proposed scheme can reduce the maximum link load by 34% (at the most congested time) and the average link load by an average of 11%.

This paper proposes a novel method for two-dimensional (2-D) direction-of-arrival (DOA) estimation of multiple signals employing a sparse L-shaped array structured by a sparse linear array (SLA), a sparse uniform linear array (SULA) and an auxiliary sensor. In this method, the elevation angles are estimated by using the SLA and an efficient search approach, while the azimuth angle estimation is performed in two stages. In the first stage, the rough azimuth angle estimates are obtained by utilizing a noise-free cross-covariance matrix (CCM), the estimated elevation angles and data from three sensors including the auxiliary sensor. In the second stage, the fine azimuth angle estimates can be achieved by using the shift-invariance property of the SULA and the rough azimuth angle estimates. Without extra pair-matching process, the proposed method can achieve automatic pairing of the 2-D DOA estimates. Simulation results show that our approach outperforms the compared methods, especially in the cases of low SNR, snapshot deficiency and multiple sources.

A random forest (RF) is a kind of ensemble machine learning algorithm used for a classification and a regression. It consists of multiple decision trees that are built from randomly sampled data. The RF has a simple, fast learning, and identification capability compared with other machine learning algorithms. It is widely used for application to various recognition systems. Since it is necessary to un-balanced trace for each tree and requires communication for all the ones, the random forest is not suitable in SIMD architectures such as GPUs. Although the accelerators using the FPGA have been proposed, such implementations were based on HDL design. Thus, they required longer design time than the soft-ware based realizations. In the previous work, we showed the high-level synthesis design of the RF including the fully pipelined architecture and the all-to-all communication. In this paper, to further reduce the amount of hardware, we use k-means clustering to share comparators of the branch nodes on the decision tree. Also, we develop the krange tool flow, which generates the bitstream with a few number of hyper parameters. Since the proposed tool flow is based on the high-level synthesis design, we can obtain the high performance RF with short design time compared with the conventional HDL design. We implemented the RF on the Xilinx Inc. ZC702 evaluation board. Compared with the CPU (Intel Xeon (R) E5607 Processor) and the GPU (NVidia Geforce Titan) implementations, as for the performance, the FPGA realization was 8.4 times faster than the CPU one, and it was 62.8 times faster than the GPU one. As for the power consumption efficiency, the FPGA realization was 7.8 times better than the CPU one, and it was 385.9 times better than the GPU one.