This paper presents a novel method for unsupervised segmentation of objects with large displacements in high speed video sequences. Our general framework introduces a new foreground object predicting method that finds object hypotheses by encoding both spatial and temporal features via a semantic motion signature scheme. More specifically, temporal cues of object hypotheses are captured by the motion signature proposed in this paper, which is derived from sparse saliency representation imposed on magnitude of optical flow field. We integrate semantic scores derived from deep networks with location priors that allows us to directly estimate appearance potentials of foreground hypotheses. A unified MRF energy functional is proposed to simultaneously incorporate the information from the motion signature and semantic prediction features. The functional enforces both spatial and temporal consistency and impose appearance constancy and spatio-temporal smoothness constraints directly on the object hypotheses. It inherently handles the challenges of segmenting ambiguous objects with large displacements in high speed videos. Our experiments on video object segmentation benchmarks demonstrate the effectiveness of the proposed method for segmenting high speed objects despite the complicated scene dynamics and large displacements.

With the fast growth of the international tourism industry, it has been a challenge to forecast the tourism demand in the international tourism market. Traditional forecasting methods usually suffer from the prediction accuracy problem due to the high volatility, irregular movements and non-stationarity of the tourist time series. In this study, a novel single dendritic neuron model (SDNM) is proposed to perform the tourism demand forecasting. First, we use a phase space reconstruction to analyze the characteristics of the tourism and reconstruct the time series into proper phase space points. Then, the maximum Lyapunov exponent is employed to identify the chaotic properties of time series which is used to determine the limit of prediction. Finally, we use SDNM to make a short-term prediction. Experimental results of the forecasting of the monthly foreign tourist arrivals to Japan indicate that the proposed SDNM is more efficient and accurate than other neural networks including the multi-layered perceptron, the neuro-fuzzy inference system, the Elman network, and the single multiplicative neuron model.

This letter proposes a Light Space Partitioned Shadow Maps (LSPSMs) algorithm which implements shadow rendering based on a novel partitioning scheme in light space. In stead of splitting the view frustum like traditional Z-partitioning methods, we split partitions from the projection of refined view frustum in light space. The partitioning scheme is performed dual-directionally while limiting the wasted space. Partitions are created in dynamic number corresponding to the light and view directions. Experiments demonstrate that high quality shadows can be rendered in high efficiency with our algorithm.

The diffraction by a thin material strip is analyzed for the H-polarized plane wave incidence using the Wiener-Hopf technique together with approximate boundary conditions. An asymptotic solution is obtained for the case where the thickness and the width of the strip are small and large compared with the wavelength, respectively. The scattered field is evaluated asymptotically based on the saddle point method and a far field expression is derived. Scattering characteristics are discussed in detail via numerical results of the radar cross section.

Non-orthogonal multiple access (NOMA) enables multiple mobile devices to share the same frequency band. In a conventional NOMA scheme, the receiver of a far user detects its desired signal without canceling the signal for a near user. However, the signal for the near user acts as interference and degrades the accuracy of likelihood values for the far user. In this paper, a joint maximum likelihood detection scheme for the far user of the NOMA downlink is proposed. The proposed scheme takes the interference signal into account in calculating the likelihood values. Numerical results obtained through computer simulation show that the proposed scheme improves the performance by from 0.2dB to 3.1dB for power allocation coefficients of 0.2 to 0.4 at a bit error rate (BER) of 10-2 relative to the conventional scheme.

In this paper, we provided a new variant of Weil pairing on a family of genus 2 curves with the efficiently computable automorphism. Our pairing can be considered as a generalization of the omega pairing given by Zhao et al. We also report the algebraic cost estimation of our pairing. We then show that our pairing is more efficient than the variant of Tate pairing with the automorphism given by Fan et al. Furthermore, we show that our pairing is slightly better than the twisted Ate pairing on Kawazoe-Takahashi curve at the 192-bit security level.

This paper proposes novel simplified maximum likelihood detection for XOR physical layer network coding (XOR-PNC) in bi-directional wireless relay systems with Quaternary phase shift keying (QPSK). The proposed detection applies unitary precoding to achieve superior performance without computationally prohibitive exhaustive search. The performance of the XOR employing the proposed simplified MLD with the precoding is analyzed in relay systems with orthogonal frequency division multiplexing (OFDM). The performance of the XOR-PNC with the proposed techniques is also evaluated by computer simulation. The XOR-PNC with the proposed techniques achieves about 7dB better performance than the amplify-and-forward physical layer network coding in the 5-path fading channel at BER=10-4. It is also shown that the XOR-PNC with the proposed techniques achieves better performance than that without precoding.

We propose a novel improved characteristic basis function method (IP-CBFM) for accurately analysing the radar cross section (RCS). This new IP-CBFM incorporates the effect of higher-order multiple scattering and has major influences in analyzing monostatic RCS (MRCS) of single incidence and bistatic RCS (BRCS) problems. We calculated the RCS of two scatterers and could confirm that the proposed IP-CBFM provided higher accuracy than the conventional method while significantly reducing the number of CBF.

This paper presents a prototype of a 3D imaging step-frequency radar system at 10-20GHz suitable for the nondestructive inspection of the walls of wooden houses. Using this prototype, it is possible to obtain data for 3D imaging with a single simple scan and make 3D volume images of braces — broken or not — in the walls of wooden houses using synthetic aperture radar processing. The system is a multistatic radar composed of a one-dimensional array antenna (32 transmitting and 32 receiving antennas, which are resistively loaded printed bowtie antennas) and is able to acquire frequency domain data for all the transmitting and receiving antenna pairs, i.e., 32×32=1024 pairs, in 33ms per position. On the basis of comparisons between two array antenna prototype designs, we investigated the optimal distance between a transmitting array and a receiving array to reduce the direct coupling effect. We produced a prototype multistatic radar system and used it to measure different types of wooden targets in two experiments. In the first experiment, we measured plywood bars behind a decorated gypsum board, simulating a broken wooden brace inside a house wall. In the second experiment, we measured a wooden brace made of Japanese cypress as a target inside a model of a typical (wooden) Japanese house wall. The results of both experiments demonstrate the imaging capability of the radar prototype for nondestructive inspection of the insides of wooden house walls.

In this letter, we propose a garbage collection technique for non-volatile memory systems, called Migration Cost Sensitive Garbage Collection (MCSGC). Considering the migration overhead from selecting victim blocks, MCSGC increases the lifetime of memory systems and improves response time in garbage collection. Additionally, the proposed algorithm also improves the efficiency of garbage collection by separating cold data from hot data in valid pages. In the experimental evaluation, we show that MCSGC yields up to a 82% improvement in lifetime prolongation, compared with existing garbage collection, and it also reduces erase and migration operations by up to 30% and 29%, respectively.

We propose a framework to perform auto-radiometric calibration in photometric stereo methods to estimate surface orientations of an object from a sequence of images taken using a radiometrically uncalibrated camera under varying illumination conditions. Our proposed framework allows the simultaneous estimation of surface normals and radiometric responses, and as a result can avoid cumbersome and time-consuming radiometric calibration. The key idea of our framework is to use the consistency between the irradiance values converted from pixel values by using the inverse response function and those computed from the surface normals. Consequently, a linear optimization problem is formulated to estimate the surface normals and the response function simultaneously. Finally, experiments on both synthetic and real images demonstrate that our framework enables photometric stereo methods to accurately estimate surface normals even when the images are captured using cameras with unknown and nonlinear response functions.

In this paper a hardware-efficient local extrema detection (LED) method used for scale-space extrema detection in the SIFT algorithm is proposed. By reformulating the reuse of the intermediate results in taking the local maximum and minimum, the necessary operations in LED are reduced without degrading the detection accuracy. The proposed method requires 25% to 35% less logic resources than the conventional method when implemented in an FPGA with a slight increase in latency.

In statistical methods, such as statistical static timing analysis, Gaussian mixture model (GMM) is a useful tool for representing a non-Gaussian distribution and handling correlation easily. In order to repeat various statistical operations such as summation and maximum for GMMs efficiently, the number of components should be restricted around two. In this paper, we propose a method for reducing the number of components of a given GMM to two (2-GMM). Moreover, since the distribution of each component is represented often by a linear combination of some explanatory variables, we propose a method to compute the covariance between each explanatory variable and the obtained 2-GMM, that is, the sensitivity of 2-GMM to each explanatory variable. In order to evaluate the performance of the proposed methods, we show some experimental results. The proposed methods minimize the normalized integral square error of probability density function of 2-GMM by the sacrifice of the accuracy of sensitivities of 2-GMM.

In this paper, we deal with the fixed-length lossy compression, where a fixed-length sequence emitted from the information source is encoded into a codeword, and the source sequence is reproduced from the codeword with a certain distortion. We give lower and upper bounds on the minimum number of codewords such that the probability of exceeding a given distortion level is less than a given probability. These bounds are characterized by using the α-mutual information of order infinity. Further, for i.i.d. binary sources, we provide numerical examples of tight upper bounds which are computable in polynomial time in the blocklength.

Wireless Local Area Networks (WLANs) based on the IEEE 802.11 standard have been increasingly used. Access Points (APs) are being established in various public places, such as railway stations and airports, as well as private residences. Moreover, the rate of public WLAN services continues to increase. Throughput prediction of an AP in a multi-rate environment, i.e., predicting the amount of receipt data (including retransmission packets at an AP), is an important issue for wireless network design. Moreover, it is important to solve AP placement and selection problems. To realize the throughput prediction, we have proposed an AP throughput prediction method that considers terminal distribution. We compared the predicted throughput of the proposed method with a method that uses linear order computation and confirmed the performance of the proposed method, not by a network simulator but by the numerical computation. However, it is necessary to consider the impact of CSMA/CA in the MAC layer, because throughput is greatly influenced by frame collision. In this paper, we derive an effective transmission rate considering CSMA/CA and frame collision. We then compare the throughput obtained using the network simulator NS2 with a prediction value calculated by the proposed method. Simulation results show that the maximum relative error of the proposed method is approximately 6% and 15% for UDP and TCP, respectively, while that is approximately 17% and 21% in existing method.

Petri Net (PN) is a frequently-used model for deadlock detection. Among various detection methods on PN, reachability analysis is the most accurate one since it never produces any false positive or false negative. Although suffering from the well-known state space explosion problem, reachability analysis is appropriate for small- and medium-scale programs. In order to mitigate the explosion problem several kinds of techniques have been proposed aiming at accelerating the reachability analysis, such as net reduction and abstraction. However, these techniques are for general PN and do not take the particularity of application into consideration, so their optimization potential is not adequately developed. In this paper, the feature of mutual exclusion-based program is considered, therefore several strategies are proposed to accelerate the reachability analysis. Among these strategies a customized net reduction rule aims at reducing the scale of PN, two marking compression methods and two pruning methods can reduce the volume of reachability graph. Reachability analysis on PN can only report one deadlock on each path. However, the reported deadlock may be a false alarm in which situation real deadlocks may be hidden. To improve the detection efficiency, we proposed a deadlock recovery algorithm so that more deadlocks can be detected in a shorter time. To validate the efficiency of these methods, a prototype is implemented and applied to SPLASH2 benchmarks. The experimental results show that these methods accelerate the reachability analysis for mutual exclusion-based deadlock detection significantly.

In the field of computer science, the network reliability problem for evaluating the network failure probability has been extensively investigated. For a given undirected graph G, the network failure probability is the probability that edge failures (i.e., edge erasures) make G unconnected. Edge failures are assumed to occur independently with the same probability. The main contributions of the present paper are the upper and lower bounds on the expected network failure probability. We herein assume a simple random graph ensemble that is closely related to the Erds-Rényi random graph ensemble. These upper and lower bounds exhibit the typical behavior of the network failure probability. The proof is based on the fact that the cut-set space of G is a linear space over F2 spanned by the incident matrix of G. The present study shows a close relationship between the ensemble analysis of the expected network failure probability and the ensemble analysis of the error detection probability of LDGM codes with column weight 2.

State-of-the-art parallel computers, which are growing in parallelism, require a lot of things in their interconnection networks. Although wide spectrum of efforts in research and development for effective and practical interconnection networks are reported, the problem is still open. One of the largest issues is congestion control that intends to maximize the network performance in terms of throughput and latency. Throttling, or injection limitation, is one of the center ideas of congestion control. We have proposed a new class of throttling method, Entropy Throttling, whose foundation is entropy concept of packets. The throttling method is successful in part, however, its potentials are not sufficiently discussed. This paper aims at exploiting capabilities of the Entropy Throttling method via comprehensive evaluation. Major contributions of this paper are to introduce two ideas of hysteresis function and guard time and also to clarify wide performance characteristics in steady and unsteady communication situations. By introducing the new ideas, we extend the Entropy throttling method. The extended methods improve communication performance at most 3.17 times in the best case and 1.47 times in average compared with non-throttling cases in collective communication, while the method can sustain steady communication performance.

Lock-based thread synchronization techniques have been commonly used in parallel programming on multi-core processors. However, lock can cause deadlocks and poor scalabilites, and Transactional Memory (TM) has been proposed and studied for lock-free synchronization. On TMs, transactions are executed speculatively in parallel as long as they do not encounter any conflicts on shared variables. On general HTMs: hardware implementations of TM, transactions which have conflicted once each other will conflict repeatedly if they will be executed again in parallel, and the performance of HTM will decline. To address this problem, in this paper, we propose a conflict prediction to avoid conflicts before executing transactions, considering historical data of conflicts. The result of the experiment shows that the execution time of HTM is reduced 59.2% at a maximum, and 16.8% on average with 16 threads.

The three dimensional (3D) reconstruction of a medical image sequence can provide intuitive morphologies of a target and help doctors to make more reliable diagnosis and give a proper treatment plan. This paper aims to reconstruct the surface of a renal corpuscle from the microscope renal biopsy image sequence. First, the contours of renal corpuscle in all slices are extracted automatically by using a context-based segmentation method with a coarse registration. Then, a new coevolutionary-based strategy is proposed to realize a fine registration. Finally, a Gauss-Seidel iteration method is introduced to achieve a non-rigid registration. Benefiting from the registrations, a smooth surface of the target can be reconstructed easily. Experimental results prove that the proposed method can effectively register the contours and give an acceptable surface for medical doctors.