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.

At FSE 2014, Grosso et al. proposed LS-designs which are a family of bitslice ciphers aiming at efficient masked implementations against side-channel analysis. They also presented two specific LS-designs, namely the non-involutive cipher Fantomas and the involutive cipher Robin. The designers claimed that the longest impossible differentials of these two ciphers only span 3 rounds. In this paper, for the two ciphers, we construct 4-round impossible differentials which are one round more than the longest impossible differentials found by the designers. Furthermore, with the 4-round impossible differentials, we propose impossible differential attacks on Fantomas and Robin reduced to 6 rounds (out of the full 12/16 rounds). Both of the attacks need 2119 chosen plaintexts and 2101.81 6-round encryptions.

A family of quaternary sequences over Z4 is defined based on the Ding-Helleseth generalized cyclotomic classes modulo pq for two distinct odd primes p and q. The linear complexity is determined by computing the defining polynomial of the sequences, which is in fact connected with the discrete Fourier transform of the sequences. The results show that the sequences possess large linear complexity and are “good” sequences from the viewpoint of cryptography.

Public safety networks need to more effectively meet the increasing demands for images or videos to be shared among first responders and incident commanders. Long term evolution (LTE) networks are considered to be candidates to achieve such broadband services. Capital expenditures in deploying base stations need to be decreased to introduce LTE for public safety. However, out-of-coverage areas tend to occur in cell edge areas or inside buildings because the cell areas of base stations for public safety networks are larger than those for commercial networks. The 3rd Generation Partnership Program (3GPP) in Release 13 has investigated device-to-device (D2D) based relay communication as a means to fill out-of-coverage areas in public safety LTE (PS-LTE). This paper proposes a relay selection scheme based on effective path throughput from an out-of-coverage terminal to a base station via an in-coverage relay terminal, which enables the optimal relay terminal to be selected. System level simulation results assuming on radii of 20km or less revealed that the proposed scheme could provide better user ratios that satisfied the throughput requirements for video transmission than the scheme standardized in 3GPP. Additionally, an evaluation that replicates actual group of fire-fighters indicated that the proposed scheme enabled 90% of out-of-coverage users to achieve the required throughput, i.e., 1.0Mbps, to transmit video images.

With the aim of characterizing the thermal conductivity for nanometer-scale thermoelectric materials, we have constructed a new measurement system based on ac calorimetry. Analysis of the obtained data requires time-evolution of temperature distribution in nanometer-scale material under periodic heating. In this study, we made a simulation using a C#-program for time-dependent temperature distribution, based on 2-dimensional heat-diffusion equation including the influence of heat emission from material edges. The simulation was applied to AlN with millimeter-scale dimensions for confirming the validity and accuracy. The simulated thermal diffusivity for 10×75-mm2-area AlN was 1.3×10-4 m2/s, which was larger than the value set in the heat-diffusion equation. This overestimation was also observed in the experiment. Therefore, our simulation can reproduce the unsteady heat conduction and be used for analyzing the ac calorimetry experiment.

We have studied on thermally assisted nano-structured transistors made of superconductor ultra-thin films. These transistors potentially work as interface devices for Josephson-CMOS (complementary metal oxide semiconductor) hybrid memory systems, because they can generate a high output voltage of sub-V enough to drive a CMOS transistor. In addition, our superconductor transistors are formed with very fine lines down to several tens of nm in widths, leading to very small foot print enabling us to make large capacity hybrid memories. Our superconductor transistors are made with niobium titanium nitride (NbTiN) thin films deposited on thermally-oxidized silicon substrates, on which other superconductor circuits or semiconductor circuits can be formed. The NbTiN thickness dependence of the critical temperature and of resistivity suggest thermally activated vortex or anti-vortex behavior in pseudo-two-dimensional superconducting films plays an important role for the operating principle of the transistors. To show the potential that the transistors can drive MOS transistors, we analyzed the driving ability of the superconductor transistors with HSPICE simulation. We also showed the turn-on behavior of a MOS transistor used for readout of a CMOS memory cell experimentally. These results showed the high potential of superconductor transistors for Josephson-CMOS hybrid memories.

The Localization of distributed sources has attracted significant interest recently. There mainly are two types of localization methods which are able to estimate distributed source positions: two-step methods and direct localization methods. Unfortunately, both fail to exploit the location information and so suffer a loss in localization accuracy. By utilizing the information not used in the above, a direct localization method of multiple distributed sources is proposed in this paper that offers improved location accuracy. We construct a direct localization model of multiple distributed sources and develop a direct localization estimator with the theory of multiple signal classification. The distributed source positions are estimated via a three-dimensional grid search. We also provide Cramer-Rao Bound, computational complexity analysis and Monte Carlo simulations. The simulations demonstrate that the proposed method outperforms the localization methods above in terms of accuracy and resolution.

We describe the design and simulation of a suitable branching ratio for an optical coupler for a cable re-routing operation support system with no service interruption, and report our experimental results. We also show the risk analysis, and report that the branching ratio was 0.47 where the probability was 99.7% that the maximum acceptable cable loss of a detour line was more than that of the current line.

3D ball tracking is of great significance in ping-pong game analysis, which can be utilized to applications such as TV contents and tactic analysis, with some of them requiring real-time implementation. This paper proposes a CPU-GPU platform based Particle Filter for multi-view ball tracking including 4 proposals. The multi-peak estimation and the ball-like observation model are proposed in the algorithm design. The multi-peak estimation aims at obtaining a precise ball position in case the particles' likelihood distribution has multiple peaks under complex circumstances. The ball-like observation model with 4 different likelihood evaluation, utilizes the ball's unique features to evaluate the particle's similarity with the target. In the GPU implementation, the double-queue structure and the vectorized data combination are proposed. The double-queue structure aims at achieving task parallelism between some data-independent tasks. The vectorized data combination reduces the time cost in memory access by combining 3 different image data to 1 vector data. Experiments are based on ping-pong videos recorded in an official match taken by 4 cameras located in 4 corners of the court. The tracking success rate reaches 99.59% on CPU. With the GPU acceleration, the time consumption is 8.8 ms/frame, which is sped up by a factor of 98 compared with its CPU version.

We propose an estimation method of pedestrian detectability considering the driver's visual adaptation to drastic illumination change, which has not been studied in previous works. We assume that driver's visual characteristics change in proportion to the elapsed time after illumination change. In this paper, as a solution, we construct multiple estimators corresponding to different elapsed periods, and estimate the detectability by switching them according to the elapsed period. To evaluate the proposed method, we construct an experimental setup to present a participant with illumination changes and conduct a preliminary simulated experiment to measure and estimate the pedestrian detectability according to the elapsed period. Results show that the proposed method can actually estimate the detectability accurately after a drastic illumination change.

Pedestrian attribute information is important function for an advanced driver assistance system (ADAS). Pedestrian attributes such as body pose, face orientation and open umbrella indicate the intended action or state of the pedestrian. Generally, this information is recognized using independent classifiers for each task. Performing all of these separate tasks is too time-consuming at the testing stage. In addition, the processing time increases with increasing number of tasks. To address this problem, multi-task learning or heterogeneous learning is performed to train a single classifier to perform multiple tasks. In particular, heterogeneous learning is able to simultaneously train a classifier to perform regression and recognition tasks, which reduces both training and testing time. However, heterogeneous learning tends to result in a lower accuracy rate for classes with few training samples. In this paper, we propose a method to improve the performance of heterogeneous learning for such classes. We introduce a rarity rate based on the importance and class probability of each task. The appropriate rarity rate is assigned to each training sample. Thus, the samples in a mini-batch for training a deep convolutional neural network are augmented according to this rarity rate to focus on the classes with a few samples. Our heterogeneous learning approach with the rarity rate performs pedestrian attribute recognition better, especially for classes representing few training samples.

This paper aims to establish expressions for IEEE 802.11 string-topology multi-hop networks with transmission control protocol (TCP) traffic flow. The relationship between the throughput and transport-layer function in string-topology multi-hop network is investigated. From the investigations, we obtain an analysis policy that the TCP throughput under the TCP functions is obtained by deriving the throughput of the network with simplified into two asymmetric user datagram protocol flows. To express the asymmetry, analytical expressions in medium access control-, network-, and transport layers are obtained based on the airtime expression. The expressions of the network layer and those of transport layer are linked using the “delayed ACK constraint,” which is a new concept for TCP analysis. The analytical predictions agree well with the simulation results, which prove the validity of the obtained analytical expressions and the analysis policy in this paper.

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.

An increasing number of psychological studies have demonstrated that the six basic expressions of emotions are not culturally universal. However, automatic facial expression recognition (FER) systems disregard these findings and assume that facial expressions are universally expressed and recognized across different cultures. Therefore, this paper presents an analysis of Western-Caucasian and East-Asian facial expressions of emotions based on visual representations and cross-cultural FER. The visual analysis builds on the Eigenfaces method, and the cross-cultural FER combines appearance and geometric features by extracting Local Fourier Coefficients (LFC) and Facial Fourier Descriptors (FFD) respectively. Furthermore, two possible solutions for FER under multicultural environments are proposed. These are based on an early race detection, and independent models for culture-specific facial expressions found by the analysis evaluation. HSV color quantization combined with LFC and FFD compose the feature extraction for race detection, whereas culture-independent models of anger, disgust and fear are analyzed for the second solution. All tests were performed using Support Vector Machines (SVM) for classification and evaluated using five standard databases. Experimental results show that both solutions overcome the accuracy of FER systems under multicultural environments. However, the approach which individually considers the culture-specific facial expressions achieved the highest recognition rate.

CMOS microprocessors are limited in their capacity for clock speed improvement because of increasing computing power, i.e., they face a power-wall problem. Single-flux-quantum (SFQ) circuits offer a solution with their ultra-fast-speed and ultra-low-power natures. This paper introduces our contributions towards ultra-high-speed cryogenic SFQ computing. The first step is to design SFQ microprocessors. From qualitatively and quantitatively evaluating past-designed SFQ microprocessors, we have found that revisiting the architecture of SFQ microprocessors and on-chip caches is the first critical challenge. On the basis of cross-layer discussions and analysis, we came to the conclusion that a bit-parallel gate-level pipeline architecture is the best solution for SFQ designs. This paper summarizes our current research results targeting SFQ microprocessors and on-chip cache architectures.

We report room-temperature atomic layer deposition (ALD) of SnO2 using tetramethyltin (TMT) as a precursor and plasma-excited humidified argon as an oxidizing gas and investigate the saturation behaviors of these gases on SnO2-covered Si prisms by IR absorption spectroscopy to determine optimal precursor/oxidizer injection conditions. TMT is demonstrated to adsorb on the SnO2 surface by reacting with surface OH groups, which are regenerated by oxidizing the TMT-saturated surface by plasma-excited humidified argon. We provide a detailed discussion of the growth mechanism. We also report the RT ALD application to the RT TFT fabrication.

Based on imperfect channel state information (CSI), the energy efficiency (EE) of downlink distributed antenna systems (DASs) with multiple receive antennas is investigated assuming composite Rayleigh fading channels. A new EE is introduced which is defined as the ratio of the average transmission rate to the total consumed power. According to this definition, an optimal power allocation (PA) scheme is developed for maximizing EE in a DAS subject to the maximum transmit power constraint. It is shown that a PA solution for the constrained EE optimization does exist and is unique. A Newton method based practical iterative algorithm is presented to solve PA. To avoid the iterative calculation, a suboptimal PA scheme is derived by means of the Lambert function, which yields a closed-form PA. The developed schemes include the ones under perfect CSI as special cases, and only need the statistical CSI. Thus, they have low overhead and good robustness. Moreover, the theoretical EE under imperfect CSI is derived for performance evaluation, and the resulting closed-form EE expression is obtained. Simulation results indicate that the theoretical EE can match the corresponding simulated value well, and the developed suboptimal scheme has performance close to optimal one, but with lower complexity.

This paper describes why we require access system virtualization. The purpose of access system virtualization is different from that of core network virtualization. Therefore, a specific approach should be considered such as the separation of software and hardware, interface standardization, or deep softwarization.

The problem with distributed representations generated by neural networks is that the meaning of the features is difficult to understand. We propose a new method that gives a specific meaning to each node of a hidden layer by introducing a manually created word semantic vector dictionary into the initial weights and by using paragraph vector models. We conducted experiments to test the hypotheses using a single domain benchmark for Japanese Twitter sentiment analysis and then evaluated the expandability of the method using a diverse and large-scale benchmark. Moreover, we tested the domain-independence of the method using a Wikipedia corpus. Our experimental results demonstrated that the learned vector is better than the performance of the existing paragraph vector in the evaluation of the Twitter sentiment analysis task using the single domain benchmark. Also, we determined the readability of document embeddings, which means distributed representations of documents, in a user test. The definition of readability in this paper is that people can understand the meaning of large weighted features of distributed representations. A total of 52.4% of the top five weighted hidden nodes were related to tweets where one of the paragraph vector models learned the document embeddings. For the expandability evaluation of the method, we improved the dictionary based on the results of the hypothesis test and examined the relationship of the readability of learned word vectors and the task accuracy of Twitter sentiment analysis using the diverse and large-scale benchmark. We also conducted a word similarity task using the Wikipedia corpus to test the domain-independence of the method. We found the expandability results of the method are better than or comparable to the performance of the paragraph vector. Also, the objective and subjective evaluation support each hidden node maintaining a specific meaning. Thus, the proposed method succeeded in improving readability.