The paper presents a recognition method of character-position-free on-line handwritten Japanese text patterns to allow a user to overlay characters freely without confirming previously written characters. To develop this method, we first collected text patterns written without wrist or elbow support and without visual feedback and then prepared large sets of character-position-free handwritten Japanese text patterns artificially from normally handwritten text patterns. The proposed method sets each off-stroke between real strokes as undecided and evaluates the segmentation probability by SVM model. Then, the optimal segmentation-recognition path can be effectively found by Viterbi search in the candidate lattice, combining the scores of character recognition, geometric features, linguistic context, as well as the segmentation scores by SVM classification. We test this method on variously overlaid sample patterns, as well as on the above-mentioned collected handwritten patterns, and verify that its recognition rates match those of the latest recognizer for normally handwritten horizontal Japanese text with no serious speed restriction in practical applications.

In this paper, a vehicular antenna design scheme that considers vehicular body effects is proposed. A wire antenna for the global positioning system (GPS) and long-term evolution (LTE) systems is implemented on a plastic plate and then mounted on a windshield of the vehicle. Common outputs are used to allow feed sharing. It is necessary to increase the GPS right-hand circularly polarization (RHCP) gain near the zenith and to reduce the axis ratio (AR). For LTE, we need to increase the horizontal polarization (HP) gain. In addition, for LTE, multiband characteristics are required. In order to achieve the specified performance, the antenna shape is optimized via a Pareto genetic algorithm (PGA). When an antenna is mounted on the body, antenna performance changes significantly. To evaluate the performance of an antenna with complex shape mounted on a windshield, a commercial electromagnetic simulator (Ansoft HFSS) is used. To apply electromagnetic results output by HFSS to the PGA algorithm operating in the MATLAB environment, a MATLAB-to-HFSS linking program via Visual BASIC (VB) script was used. It is difficult to carry out the electromagnetic analysis on the entire body because of the limitations of the calculating load and memory size. To overcome these limitations, we consider only that part of the vehicle's body that influences antenna performance. We show that a series of optimization steps can minimize the degradation caused by the vehicle`s body. The simulation results clearly show that it is well optimized at 1.575GHz for GPS, and 0.74 ∼ 0.79GHz and 2.11 ∼ 2.16GHz for LTE, respectively.

The recent popularization of social network services (SNSs), such as YouTube, Dailymotion, and Facebook, enables people to easily publish their personal videos taken with mobile cameras. However, at the same time, such popularity has raised a new problem: video privacy. In such social videos, the privacy of people, i.e., their appearances, must be protected, but naively obscuring all people might spoil the video content. To address this problem, we focus on videographers' capture intentions. In a social video, some persons are usually essential for the video content. They are intentionally captured by the videographers, called intentionally captured persons (ICPs), and the others are accidentally framed-in (non-ICPs). Videos containing the appearances of the non-ICPs might violate their privacy. In this paper, we developed a system called BEPS, which adopts a novel conditional random field (CRF)-based method for ICP detection, as well as a novel approach to obscure non-ICPs and preserve ICPs using background estimation. BEPS reduces the burden of manually obscuring the appearances of the non-ICPs before uploading the video to SNSs. Compared with conventional systems, the following are the main advantages of BEPS: (i) it maintains the video content, and (ii) it is immune to the failure of person detection; false positives in person detection do not violate privacy. Our experimental results successfully validated these two advantages.

The recent cyber-attacks utilize various malware as a means of attacks for the attacker's malicious purposes. They are aimed to steal confidential information or seize control over major facilities after infiltrating the network of a target organization. Attackers generally create new malware or many different types of malware by using an automatic malware creation tool which enables remote control over a target system easily and disturbs trace-back of these attacks. The paper proposes a generation method of malware behavior patterns as well as the detection techniques in order to detect the known and even unknown malware efficiently. The behavior patterns of malware are generated with Multiple Sequence Alignment (MSA) of API call sequences of malware. Consequently, we defined these behavior patterns as a “feature-chain” of malware for the analytical purpose. The initial generation of the feature-chain consists of extracting API call sequences with API hooking library, classifying malware samples by the similar behavior, and making the representative sequences from the MSA results. The detection mechanism of numerous malware is performed by measuring similarity between API call sequence of a target process (suspicious executables) and feature-chain of malware. By comparing with other existing methods, we proved the effectiveness of our proposed method based on Longest Common Subsequence (LCS) algorithm. Also we evaluated that our method outperforms other antivirus systems with 2.55 times in detection rate and 1.33 times in accuracy rate for malware detection.

Local spatio-temporal features are popular in the human action recognition task. In practice, they are usually coupled with a feature encoding approach, which helps to obtain the video-level vector representations that can be used in learning and recognition. In this paper, we present an efficient local feature encoding approach, which is called Approximate Sparse Coding (ASC). ASC computes the sparse codes for a large collection of prototype local feature descriptors in the off-line learning phase using Sparse Coding (SC) and look up the nearest prototype's precomputed sparse code for each to-be-encoded local feature in the encoding phase using Approximate Nearest Neighbour (ANN) search. It shares the low dimensionality of SC and the high speed of ANN, which are both desired properties for a local feature encoding approach. ASC has been excessively evaluated on the KTH dataset and the HMDB51 dataset. We confirmed that it is able to encode large quantity of local video features into discriminative low dimensional representations efficiently.

Distributed compressed video sensing (DCVS), combining advantages of compressed sensing and distributed video coding, is developed as a novel and powerful system to get an encoder with low complexity. Nevertheless, it is still unclear how to explore the method to achieve an effective video recovery through utilizing realistic signal characteristics as much as possible. Based on this, we present a novel spatiotemporal dictionary learning (DL) based reconstruction method for DCVS, where both the DL model and the l1-analysis based recovery with correlation constraints are included in the minimization problem to achieve the joint optimization of sparse representation and signal reconstruction. Besides, an alternating direction method with multipliers (ADMM) based numerical algorithm is outlined for solving the underlying optimization problem. Simulation results demonstrate that the proposed method outperforms other methods, with 0.03-4.14 dB increases in PSNR and a 0.13-15.31 dB gain for non-key frames.

The recent increase in the use of intelligent devices such as smartphones has enhanced the relationship between daily human behavior sensing and useful applications in ubiquitous computing. This paper proposes a novel method inspired by personal sensing technologies for collecting and visualizing road accessibility at lower cost than traditional data collection methods. To evaluate the methodology, we recorded outdoor activities of nine wheelchair users for approximately one hour each by using an accelerometer on an iPod touch and a camcorder, gathered the supervised data from the video by hand, and estimated the wheelchair actions as a measure of street level accessibility in Tokyo. The system detected curb climbing, moving on tactile indicators, moving on slopes, and stopping, with F-scores of 0.63, 0.65, 0.50, and 0.91, respectively. In addition, we conducted experiments with an artificially limited number of training data to investigate the number of samples required to estimate the target.

The rapid development of a global navigation satellite system (GNSS) has raised the demand for spacecraft navigation, particularly for lunar spacecraft (LS). First, instead of the traditional approach of combining the united X-band system (UXB) with very-long-baseline interferometry (VLBI) by a terrestrial-based observing station in Chinese deep-space exploration, the spacecraft navigation based on inter-satellite link (ISL) is proposed because the spatial coverage of the GNSS downstream signals is too narrow to be used for LS navigation. Second, the feasibility of LS navigation by using ISL wide beam signals has been analyzed with the following receiving parameters: the geometrical dilution of precision (GDOP) and the carrier-to-noise ratio (C/N0) for satellites autonomously navigation of ISL and LS respectively; the weighting distance root-mean-square (wdrms) for the combination of both navigation modes. Third, to be different from all existing spacecraft ISL and GNSS navigation methods, an ISL annular beam transmitting antenna has been simulated to minimize the wdrms (1.138m) to obtain the optimal beam coverage: 16°-47° on elevation angle. Theoretical calculations and simulations have demonstrated that both ISL autonomous navigation and LS navigation can be satisfied at the same time. Furthermore, an onboard annular wide beam ISL antenna with optimized parameters has been designed to provide a larger channel capacity with a simpler structure than that of the existing GPS ISL spot beam antenna, a better anti-jamming performance than that of the former GPS ISL UHF-band wide band antenna, and a wider effectively operating area than the traditional terrestrial-based measurement. Lastly, the possibility and availability of applying an ISL receiver with an annular wide beam antenna on the Chinese Chang'E-5T (CE-5T) reentry experiment for autonomous navigation are analyzed and verified by simulating and comparing the ISL receiver with the practiced GNSS receiver.

This paper presents a parallelization method utilizing dead time to implement higher precision feedback control systems in multicore processors. The feedback control system is known to be difficult to parallelize, and it is difficult to deal with the dead time in control systems. In our method, the dead time is explicitly represented as delay elements. Then, these delay elements are distributed to the overall systems with equivalent transformation so that the system can be simulated or executed in parallel pipeline operation. In addition, we introduce a method of delay-element addition for parallelization. For a spring-mass-damper model with a dead time, parallel execution of the model using our technique achieves 3.4 times performance acceleration compared with its sequential execution on an ideal four-core simulation and 1.8 times on a cycle-accurate simulator of a four-core embedded processor as a threaded application on a real-time operating system.

Nonnegative component representation (NCR) is a mid-level representation based on nonnegative matrix factorization (NMF). Recently, it has attached much attention and achieved encouraging result for action recognition. In this paper, we propose a novel hierarchical dictionary learning strategy (HDLS) for NMF to improve the performance of NCR. Considering the variability of action classes, HDLS clusters the similar classes into groups and forms a two-layer hierarchical class model. The groups in the first layer are disjoint, while in the second layer, the classes in each group are correlated. HDLS takes account of the differences between two layers and proposes to use different dictionary learning methods for this two layers, including the discriminant class-specific NMF for the first layer and the discriminant joint dictionary NMF for the second layer. The proposed approach is extensively tested on three public datasets and the experimental results demonstrate the effectiveness and superiority of NCR with HDLS for large-scale action recognition.

In recent years, society has experienced several changes in its ways and methods of consuming. Nowadays, the diversity and the customization of products and services have provoked that the consumer needs continuously change. Hence, the database systems support e-business processes are required to be timeliness and adaptable to the changing preferences. Autonomous Decentralized Database System (ADDS), has been proposed in order to satisfy the enhanced requirements of current on-line e-business applications. Autonomy and decentralization of subsystems help to achieve short response times in highly competitive situations and an autonomous Coordination Mobile Agent (CMA) has been proposed to achieve flexibility in a highly dynamic environment. However, a problem in ADDS is as the number of sites increases, the distribution and harmonization of product information among the sites are turning difficult. Therefore, many users cannot be satisfied quickly. As a result, system timeliness is inadequate. To solve this problem, a self configuration technology is proposed. This technology can configure the system to the evolving situation dynamically for achieving high response. A simulation shows the effectiveness of the proposed technology in a large-scale system. Finally, an implementation of this technology is presented.

Distributed antenna systems (DASs) combined with multi-user multiple-input multiple-output (MU-MIMO) transmission techniques have recently attracted significant attention. To establish MU-MIMO DASs that have wide service areas, the use of a dynamic clustering scheme (CS) is necessary to reduce computation in precoding. In the present study, we propose a simple method for dynamic clustering to establish a single cell large-scale MU-MIMO DAS and investigate its performance. We also compare the characteristics of the proposal to those of other schemes such as exhaustive search, traditional location-based adaptive CS, and improved norm-based CS in terms of sum rate improvement. Additionally, to make our results more universal, we further introduce spatial correlation to the considered system. Computer simulation results indicate that the proposed CS for the considered system provides better performance than the existing schemes and can achieve a sum rate close to that of exhaustive search but at a lower computational cost.

This paper realized the automatic recognition of Mycobacterium tuberculosis in Ziehl-Neelsen stained images by the conventional light microscopy, which can be used in the computer-aided diagnosis of the tuberculosis. We proposed a novel recognition method based on active shape model. First, the candidate bacillus objects are segmented by a method of marker-based watershed transform. Next, a point distribution model of the object shape is proposed to label the landmarks on the object automatically. Then the active shape model is performed after aligning the training set with a weight matrix. The deformation regulation of the object shape is discovered and successfully applied in recognition without using geometric and other commonly used features. During this process, a width consistency constraint is combined with the shape parameter to improve the accuracy of the recognition. Experimental results demonstrate that the proposed method yields high accuracy in the images with different background colors. The recognition accuracy in object level and image level are 92.37% and 97.91% respectively.

High efficiency video coding (HEVC) is a video compression standard that outperforms the predecessor H.264/AVC by doubling the compression efficiency. To enhance the coding accuracy, HEVC adopts sample adaptive offset (SAO), which reduces the distortion of reconstructed pixels using classification based non-linear filtering. In the traditional coding tree unit (CTU) grain based VLSI encoder implementation, during the pixel classification stage, SAO cannot use the raw samples in the boundary of the current CTU because these pixels have not been processed by deblocking filter (DF). This paper proposes a hardware-oriented category determination algorithm based on estimating the deblocking strengths on CTU boundaries and selectively adopting the promising samples in these areas during SAO classification. Compared with HEVC test mode (HM11.0), experimental results indicate that the proposed method achieves an average 0.13%, 0.14%, and 0.12% BD-bitrate reduction (equivalent to 0.0055dB, 0.0058dB, and 0.0097dB increases in PSNR) in CTU sizes of 64 × 64, 32 × 32, and 16 × 16, respectively.

The growing trends in Internet usage for data and knowledge sharing calls for dynamic classification of web contents, particularly at the edges of the Internet. Rather than considering Linked Data as an integral part of Big Data, we propose Autonomous Decentralized Semantic-based Content Classifier (ADSCC) for dynamic classification of unstructured web contents, using Linked Data and web metadata in Content Delivery Network (CDN). The proposed framework ensures efficient categorization of URLs (even overlapping categories) by dynamically mapping the changing user-defined categories to ontologies' category/classes. This dynamic classification is performed by the proposed system that mainly involves three main algorithms/modules: Dynamic Mapping algorithm, Autonomous coordination-based Inference algorithm, and Context-based disambiguation. Evaluation results show that the proposed system achieves (on average), the precision, recall and F-measure within the 93-97% range.

Some networks, such as wireless sensor networks, vehicle networks, etc., are often disconnected and thus fail to provide an end-to-end route for transmission. As a result, a new kind self-organized wireless network, i.e., Delay Tolerant Network (DTN) is proposed to transmit messages using a store-carry-forward method. To efficiently process aggregation queries, this paper proposes a subscription aggregation query processing method that combines query processing and transfer protocols. The basic idea is reducing the number of redundant copy transmissions, increasing the message delivery rate and reducing the transmission delay by matrix summation. Theoretical and experimental results show that the method can attain a good performance in the delay tolerant networks.

East Japan Railway Company has created new businesses such as life-style business and information technology business on the basis of railway business for sustainable growth. These businesses generate and provide synergy to one another effectively because each business is autonomous decentralized system based on diversified infrastructure. The infrastructure includes not just structure but management, technology, operation and maintenance: we call this “MTOMI Model.” The MTOMI Model is the key concept of JR East's businesses and can generate JR East's ecosystem.

A novel activation method for a B dopant implanted in a Si substrate using a soft X-ray undulator was examined. As the photon energy of the irradiated soft X-ray approached the energy of the core level of Si 2p, the activation ratio increased. The effect of soft X-ray irradiation on B activation was remarkable at temperatures lower than 400°C. The activation energy of B activation by soft X-ray irradiation (0.06 eV) was lower than that of B activation by furnace annealing (0.18 eV). The activation of the B dopant by soft X-ray irradiation occurs at low temperature, although the activation ratio shows small values of 6.2×10-3 at 110°C. The activation by soft X-ray is caused not only by thermal effects, but also electron excitation and atomic movement.

This paper introduces low-power and small area injection-locking clock and data recovery circuit (CDR) for the wireline and wireless proximity link. By using signal conversion from differential input to common-mode output, the newly proposed edge detector can eliminate the usually used delay line and XOR-based edge detector, and provided low power operation and a small circuit area. The CDR test chip fabricated in a 65-nm CMOS process consumes 30mW from a 1.2- V supply at 12.5Gbps. The fabricated CDR achieved a BER lower than 10-12 and the recovered clock had an rms jitter of 0.87ps. The CDR area is 0.165mm2.

An on-chip monitoring circuit using a sub-sampling scheme, which consists of a 6-bit flash analog-to-digital converter (ADC) and a 51-phase phase-locked loop (PLL)-based frequency synthesizer, is proposed to analyze the signal integrity of a single-ended 8-Gb/s octal data rate (ODR) chip-to-chip interface with a source synchronous clocking scheme.