The recently proposed distributed adaptive direct position determination (D-ADPD) algorithm provides an efficient way to locating a radio emitter using a sensor network. However, this algorithm may be suboptimal in the situation of colored emitted signals. We propose an enhanced distributed adaptive direct position determination (EDA-DPD) algorithm. Simulations validate that the proposed EDA-DPD outperforms the D-ADPD in colored emitted signals scenarios and has the similar performance with the D-ADPD in white emitted signal scenarios.

Web Service Composition (WSC) has been well recognized as a convenient and flexible way of service sharing and integration in service-oriented application fields. WSC aims at selecting and composing a set of initial services with respect to the Quality of Service (QoS) values of their attributes (e.g., price), in order to complete a complex task and meet user requirements. A major research challenge of the QoS-aware WSC problem is to select a proper set of services to maximize the QoS of the composite service meeting several QoS constraints upon various attributes, e.g. total price or runtime. In this article, a fast algorithm based on QoS-aware sampling (FAQS) is proposed, which can efficiently find the near-optimal composition result from sampled services. FAQS consists of five steps as follows. 1) QoS normalization is performed to unify different metrics for QoS attributes. 2) The normalized services are sampled and categorized by guaranteeing similar number of services in each class. 3) The frequencies of the sampled services are calculated to guarantee the composed services are the most frequent ones. This process ensures that the sampled services cover as many as possible initial services. 4) The sampled services are composed by solving a linear programming problem. 5) The initial composition results are further optimized by solving a modified multi-choice multi-dimensional knapsack problem (MMKP). Experimental results indicate that FAQS is much faster than existing algorithms and could obtain stable near-optimal result.

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.

A multisignature (MS) scheme enables a group of signers to produce a compact signature on a common message. In analyzing security of MS schemes, a key registration protocol with proof-of-possession (POP) is considered to prevent rogue key attacks. In this paper, we refine the POP-based security model by formalizing a new strengthened POP model and showing relations between the previous POP models and the new one. We next suggest a MS scheme that achieves: (1) non-interactive signing process, (2) O(1) pairing computations in verification, (3) tight security reduction under the co-CDH assumption, and (4) security under the new strengthened POP model. Compared to the tightly-secure BNN-MS scheme, the verification in ours can be at least 7 times faster at the 80-bit security level and 10 times faster at the 128-bit security level. To achieve our goal, we introduce a novel and simple POP generation method that can be viewed as a one-time signature without random oracles. Our POP technique can also be applied to the LOSSW-MS scheme (without random oracles), giving the security in the strengthened POP model.

In this paper, we propose an efficient regularized zero-forcing (RZF) precoding method that has lower hardware resource requirements and produces a shorter delay to the first transmitted symbol compared with truncated polynomial expansion (TPE) that is based on Neumann series in massive multiple-input multiple-output (MIMO) systems. The proposed precoding scheme, named matrix decomposition-polynomial expansion (MDPE), essentially applies a matrix decomposition algorithm based on polynomial expansion to significantly reduce full matrix multiplication computational complexity. Accordingly, it is suitable for real-time hardware implementations and high-mobility scenarios. Furthermore, the proposed method provides a simple expression that links the optimization coefficients to the ratio of BS/UTs antennas (β). This approach can speed-up the convergence to the matrix inverse by a matrix polynomial with small terms and further reduce computation costs. Simulation results show that the MDPE scheme can rapidly approximate the performance of the full precision RZF and optimal TPE algorithm, while adaptively selecting matrix polynomial terms in accordance with the different β and SNR situations. It thereby obtains a high average achievable rate of the UTs under power allocation.

This paper presents a novel MEMD interval thresholding denoising, where relevant modes are selected by the similarity measure between the probability density functions of the input and that of each mode. Simulation and measured EEG data processing results show that the proposed scheme achieves better performance than other traditional denoisings.

Different from application-specific digital microfluidic biochips, a general-purpose design has several advantages such as dynamic reconfigurability, and fast on-line evaluation for real-time applications. To achieve such superiority, this design typically activates each electrode in the chip using an individual control pin. However, as the design complexity increases substantially, an order-of-magnitude increase in the number of control pins will significantly affect the manufacturing cost. To tackle this problem, several methods adopting a pin-sharing mechanism for general-purpose designs have been proposed. Nevertheless, these approaches sacrifice the flexibility of droplet movement, and result in an increase of bioassay completion time. In this paper, we present a novel pin-count reduction design methodology for general-purpose microfluidic biochips. Distinguished from previous approaches, the proposed methodology not only reduces the number of control pins significantly but also guarantees the full flexibility of droplet movement to ensure the minimal bioassay completion time.

Recently, object-proposal methods have attracted more and more attention of scholars and researchers for its utility in avoiding exhaustive sliding window search in an image. Object-proposal method is inspired by a concept that objects share a common feature. There exist many object-proposal methods which are either in segmentation fashion or engineering categories depending on low-level feature. Among those object-proposal methods, Edge Boxes, which is based on the number of contours that a bounding box wholly contains, has the state of art performance. Since Edge Boxes sometimes misses proposing some obvious objects in some images, we propose an appropriate version of it based on our two observations. We call the appropriate version as Improved Edge Boxes. The first of our observations is that objects have a property which can help us distinguish them from the background. It is called object saliency. An appropriate way we employ to calculate object saliency can help to retrieve some objects. The second of our observations is that objects ‘prefer’ to appear at the center part of images. For this reason, a bounding box that appears at the center part of the image is likely to contain an object. These two observations are going to help us retrieve more objects while promoting the recall performance. Finally, our results show that given just 5000 proposals we achieve over 89% object recall but 87% in Edge Boxes at the challenging overlap threshold of 0.7. Further, we compare our approach to some state-of-the-art approaches to show that our results are more accurate and faster than those approaches. In the end, some comparative pictures are shown to indicate intuitively that our approach can find more objects and more accurate objects than Edge Boxes.

This paper proposes an efficient video object segmentation approach that is tolerant to complex scene dynamics. Unlike existing approaches that rely on estimating object-like proposals on an intra-frame basis, the proposed approach employs temporally consistent foreground hypothesis using nonlinear regression of saliency guided proposals across a video sequence. For this purpose, we first generate salient foreground proposals at superpixel level by leveraging a saliency signature in the discrete cosine transform domain. We propose to use a random forest based nonlinear regression scheme to learn both appearance and shape features from salient foreground regions in all frames of a sequence. Availability of such features can help rank every foreground proposals of a sequence, and we show that the regions with high ranking scores are well correlated with semantic foreground objects in dynamic scenes. Subsequently, we utilize a Markov Random Field to integrate both appearance and motion coherence of the top-ranked object proposals. A temporal nonlinear regressor for generating salient object support regions significantly improves the segmentation performance compared to using only per-frame objectness cues. Extensive experiments on challenging real-world video sequences are performed to validate the feasibility and superiority of the proposed approach for addressing dynamic scene segmentation.

This paper presents two new types of markers of M-CubITS (M-sequence Multimodal Markers for ITS; M-Cubed for ITS) that is a ground-based positioning system, in order to advance the WYSIWYAS (What You See Is What You Are Suggested) navigation environments providing intuitive guidance. One of the new markers uses warning blocks of textured paving blocks that are often at important points as for pedestrian navigation, for example, the top and bottom of stairs, branch points, and so on. The other uses interlocking blocks that are often at wide spaces, e.g., pavements of plazas, parks, sidewalks and so on. Furthermore, we construct the integrated pedestrian navigation system equipped with the automatic marker-type identification function of the three types of markers (the warning blocks, the interlocking blocks, and the conventional marker using guidance blocks of textured paving blocks) in order to enhance the spatial availability of the whole M-CubITS and the navigation system. Consequently, we show the possibility to advance the WYSIWYAS navigation environments through the performance evaluation and the operation confirmation of the integrated system.

This paper describes a fast and effective algorithm for refining the parameter estimates of multicomponent third-order polynomial phase signals (PPSs). The efficiency of the proposed algorithm is accompanied by lower signal-to-noise ratio (SNR) threshold, and computational complexity. A two-step procedure is used to estimate the parameters of multicomponent third-order PPSs. In the first step, an initial estimate for the phase parameters can be obtained by using fast Fourier transformation (FFT), k-means algorithm and three time positions. In the second step, these initial estimates are refined by a simple moving average filter and singular value decomposition (SVD). The SNR threshold of the proposed algorithm is lower than those of the non-linear least square (NLS) method and the estimation refinement method even though it uses a simple moving average filter. In addition, the proposed method is characterized by significantly lower complexity than computationally intensive NLS methods. Simulations confirm the effectiveness of the proposed method.

In the development of inter-vehicle communication systems for the prevention of car crashes, it is important to know radio propagation characteristics at blind intersections. In field experiments and numerical simulations to investigate radio propagation characteristics, a half wavelength dipole antenna is assumed to be the wave source in many cases. However, a directivity of car antenna is changed by the effect of both car body and antenna position on car. In this paper, path loss characteristics considering antenna positions on car body at a blind intersection in urban area for inter-vehicle communications using 700MHz band are investigated. Additionally, simplified car models are proposed for the efficient analysis of radio propagation. Here, the hybrid method using both FDTD and ray-tracing methods is used for the radio propagation analysis.

Piccolo is a lightweight block cipher proposed by Sony Corporation in 2011. The designers showed two key modes, Piccolo-80 and Piccolo-128, which use an 80-bit secret key and a 128-bit one, respectively. Isobe and Shibutani estimated the security of Piccolo-80, and they showed that 14-round (reduced) Piccolo-80 w/o whitening keys is vulnerable against the Meet-in-the-Middle attack. The time complexity of their attack is about 273, but unfortunately it requires 264 texts, namely, the full code book. In this paper, we propose a new impossible differential attack against 14-round Piccolo-80 w/o whitening keys, and it can recover the secret key without relying on the full code book. The time complexity is 268 and it uses 262.2 distinct know plaintexts.

Incoming GPS signals through windows can be often observed indoors. However, conventional indoor positioning systems do not use Global Positioning System (GPS) generally because the signals may come in NLOS (Non Line of Sight). In this paper, we propose a positioning method by fingerprinting based on the incoming GPS signals.

This paper presents a design methodology for positioning sub-platform from the viewpoint of positioning for smartphone-based location-based services (LBS). To achieve this, we analyze a mechanism of positioning error generation including principles of positioning sub-systems and structure of smartphones. Specifically, we carry out the experiments of smartphone positioning performance evaluation by the smartphone basic API (Application Programming Interface) and by the wireless LAN in various environments. Then, we describe the importance of considering three layers as follows: 1) the lower layer that caused by positioning sub-systems, e.g., GPS, wireless LAN, mobile base stations, and so on; 2) the middle layer that caused by functions provided from the platform such as Android and iOS; 3) the upper layer that caused by operation algorithm of applications on the platform.

The increasing demand for navigation and automation has led to the development of a number of accurate and precise navigation applications that make use of the Global Navigation Satellite System (GNSS) and additional sensors. One of the precise navigation techniques in GNSS, the real-time kinematic (RTK) technique, is well known. In this method, once the correct integer ambiguities are found in the carrier phase observation data, position can be determined to within 10cm. In particular, the advent of QZSS and BeiDou satellites can increase the availability of RTK-GNSS (relative to RTK using only GPS). It is understood that the increasing availability of RTK-GNSS will improve the performance of the integration of GNSS with additional sensors because the errors due to the inertial measurement unit (IMU) accumulate as time goes on. On the other hand, GNSS tends to suffer from multipath errors, especially in urban environments. To overcome this problem, a method was developed for improving RTK-GNSS using a low-cost IMU and conventional vehicle speed sensors. In this study, the quality of the complete observation data was assessed based on the carrier-to-noise ratio and satellite elevation angle, and the least-squares ambiguity decorrelation adjustment method and the ratio test were used to obtain fixed positions. We used speed information obtained from Doppler measurements as an alternative source of information; information from the IMU and vehicle speed sensor (integrated with the RTK-GNSS via a Kalman Filter) was used when there were no visible satellites. We also used the IMU and vehicle speed sensors to detect wrong fixes in the RTK-GNSS. A position and orientation system for land vehicles (Applanix) was used to estimate the reference positions. During GNSS outages, it is important to accurately determine the last heading of the car for precise navigation. In this study, it was found that GNSS Doppler-based direction data are required to obtain better direction information. The results of the experiment demonstrate that our proposed method is, to some extent, beneficial as an alternative to the conventional RTK-GPS in an urban environment.

We propose part-segment (PS) features for estimating an articulated pose in still images. The PS feature evaluates the image likelihood of each body part (e.g. head, torso, and arms) robustly to background clutter and nuisance textures on the body. While general gradient features (e.g. HOG) might include many nuisance responses, the PS feature represents only the region of the body part by iterative segmentation while updating the shape prior of each part. In contrast to similar segmentation features, part segmentation is improved by part-specific shape priors that are optimized by training images with fully-automatically obtained seeds. The shape priors are modeled efficiently based on clustering for fast extraction of PS features. The PS feature is fused complementarily with gradient features using discriminative training and adaptive weighting for robust and accurate evaluation of part similarity. Comparative experiments with public datasets demonstrate improvement in pose estimation by the PS features.

In PQCrypto 2013, Yasuda, Takagi and Sakurai proposed a new signature scheme as one of multivariate public key cryptosystems (MPKCs). This scheme (called YTS) is based on the fact that there are two isometry classes of non-degenerate quadratic forms on a vector space with a prescribed dimension. The advantage of YTS is its efficiency. In fact, its signature generation is eight or nine times faster than Rainbow of similar size. For the security, it is known that the direct attack, the IP attack and the min-rank attack are applicable on YTS, and the running times are exponential time for the first and the second attacks and sub-exponential time for the third attack. In the present paper, we give a new attack on YTS whose approach is to use the diagonalization of matrices. Our attack works in polynomial time and it actually recovers equivalent secret keys of YTS having 140-bits security against min-rank attack in around fifteen seconds.

This letter proposes a novel post-processing method for self-similarity based super-resolution (SR). Existing back-projection (BP) methods enhance SR images by refining the reconstructed coarse high-frequency (HF) information. However, it causes artifacts due to interpolation and excessively smoothes small HF signals, particularly in texture regions. Motivated by these observations, we propose a novel post-processing method referred to as middle-frequency (MF) based refinement. The proposed method refines the reconstructed HF information in the MF domain rather than in the spatial domain, as in BP. In addition, it does not require an internal interpolation process, so it is free from the side-effects of interpolation. Experimental results show that the proposed algorithm provides superior performance in terms of both the quantity of reproduced HF information and the visual quality.

Recent work on joint word segmentation, POS (Part Of Speech) tagging, and dependency parsing in Chinese has two key problems: the first is that word segmentation based on character and dependency parsing based on word were not combined well in the transition-based framework, and the second is that the joint model suffers from the insufficiency of annotated corpus. In order to resolve the first problem, we propose to transform the traditional word-based dependency tree into character-based dependency tree by using the internal structure of words and then propose a novel character-level joint model for the three tasks. In order to resolve the second problem, we propose a novel semi-supervised joint model for exploiting n-gram feature and dependency subtree feature from partially-annotated corpus. Experimental results on the Chinese Treebank show that our joint model achieved 98.31%, 94.84% and 81.71% for Chinese word segmentation, POS tagging, and dependency parsing, respectively. Our model outperforms the pipeline model of the three tasks by 0.92%, 1.77% and 3.95%, respectively. Particularly, the F1 value of word segmentation and POS tagging achieved the best result compared with those reported until now.