Speaker verification is the task of determining whether two utterances represent the same person. After representing the utterances in the i-vector space, the crucial problem is only how to compute the similarity of two i-vectors. Metric learning has provided a viable solution to this problem. Until now, many metric learning algorithms have been proposed, but they are usually limited to learning a linear transformation. In this paper, we propose a nonlinear metric learning method, which learns an explicit mapping from the original space to an optimal subspace using deep Restricted Boltzmann Machine network. The proposed method is evaluated on the NIST SRE 2008 dataset. Since the proposed method has a deep learning architecture, the evaluation results show superior performance than some state-of-the-art methods.

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 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.

Packages are re-usable components for faster and effective software maintenance. To promote the re-use in object-oriented systems and maintenance tasks easier, packages should be organized to depict compact design. Therefore, understanding and assessing package organization is primordial for maintenance tasks like Re-usability and Changeability. We believe that additional investigations of prevalent basic design principles such as defined by R.C. Martin are required to explore different aspects of package organization. In this study, we propose package-organization framework based on reachable components that measures re-usability index. Package re-usability index measures common effect of change taking place over dependent elements of a package in an object-oriented design paradigm. A detailed quality assessment on different versions of open source software systems is presented which evaluates capability of the proposed package re-usability index and other traditional package-level metrics to predict fault-proneness in software. The experimental study shows that proposed index captures different aspects of package-design which can be practically integrated with best practices of software development. Furthermore, the results provide insights on organization of feasible software design to counter potential faults appearing due to complex package dependencies.

This paper proposes an effective signal processing scheme using a modulation code with two-dimensional (2D) run-length limited (RLL) constraints for bit-patterned media magnetic recording (BPMR). This 2D signal processing scheme is applied to be one of two-dimensional magnetic recording (TDMR) schemes for shingled magnetic recording on bit patterned media (BPM). A TDMR scheme has been pointed out an important key technology for increasing areal density toward 10Tb/in2. From the viewpoint of 2D signal processing for TDMR, multi-track joint decoding scheme is desirable to increase an effective transfer rate because this scheme gets readback signals from several adjacent parallel tracks and detect recorded data written in these tracks simultaneously. Actually, the proposed signal processing scheme for BPMR gets mixed readback signal sequences from the parallel tracks using a single reading head and these readback signal sequences are equalized to a frequency response given by a desired 2D generalized partial response system. In the decoding process, it leads to an increase in the effective transfer rate by using a single maximum likelihood (ML) sequence detector because the recorded data on the parallel tracks are decoded for each time slot. Furthermore, a new joint pattern-dependent noise-predictive (PDNP) sequence detection scheme is investigated for multi-track recording with media noise. This joint PDNP detection is embed in a ML detector and can be useful to eliminate media noise. Using computer simulation, it is shown that the joint PDNP detection scheme is able to compensate media noise in the equalizer output which is correlated and data-dependent.

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.

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.

This paper presents a novel control method based on predictions of a neural network in coordination with a conventional PID control to improve transient characteristics of digitally controlled switching dc-dc converters. Power supplies in communication systems require to achieve a superior operation for electronic equipment installed to them. Especially, it is important to improve transient characteristics in any required conditions since they affect to the operation of power supplies. Therefore, dc-dc converters in power supplies need a superior control method which can suppress transient undershoot and overshoot of output voltage. In the presented method, the neural network is trained to predict the output voltage and is adopted to modify the reference value in the PID control to reduce the difference between the output voltage and its desired one in the transient state. The transient characteristics are gradually improved as the training procedure of the neural network is proceeded repetitively. Furthermore, the timing and duration of neural network control are also investigated and devised since the time delay, which is one of the main issue in digital control methods, affects to the improvement of transient characteristics. The repetitive training and duration adjustment of the neural network are performed simultaneously to obtain more improvement of the transient characteristics. From simulated and experimental results, it is confirmed that the presented method realizes superior transient characteristics compared to the conventional PID control.

As an important extension of high-efficiency video coding (HEVC), screen content coding (SCC) includes various new coding modes, such as Intra Block Copy (IBC), Palette-based coding (Palette), and Adaptive Color Transform (ACT). These new tools have improved screen content encoding performance. This paper proposed a novel and fast algorithm by classifying Code Units (CUs) as text CUs or non-text CUs. For text CUs, the Intra mode was skipped in the compression process, whereas for non-text CUs, the IBC mode was skipped. The current CU depth range was then predicted according to its adjacent left CU depth level. Compared with the reference software HM16.7+SCM5.4, the proposed algorithm reduced encoding time by 23% on average and achieved an approximate 0.44% increase in Bjøntegaard delta bit rate and a negligible peak signal-to-noise ratio loss.

This paper introduces a reversible watermarking algorithm that exploits an adaptable predictor and sorting parameter customized for each image and each payload. Our proposed method relies on a well-known prediction-error expansion (PEE) technique. Using small PE values and a harmonious PE sorting parameter greatly decreases image distortion. In order to exploit adaptable tools, Gaussian weight predictor and expanded variance mean (EVM) are used as parameters in this work. A genetic algorithm is also introduced to optimize all parameters and produce the best results possible. Our results show an improvement in image quality when compared with previous conventional works.

In this paper, the integration of dynamic plant-wide optimization and distributed generalized predictive control (DGPC) is presented for serially connected processes. On the top layer, chance-constrained programming (CCP) is employed in the plant-wide optimization with economic and model uncertainties, in which the constraints containing stochastic parameters are guaranteed to be satisfied at a high level of probability. The deterministic equivalents are derived for linear and nonlinear individual chance constraints, and an algorithm is developed to search for the solution to the joint probability constrained problem. On the lower layer, the distributed GPC method based on neighborhood optimization with one-step delay communication is developed for on-line control of the whole system. Simulation studies for furnace temperature set-points optimization problem of the walking-beam-type reheating furnace are illustrated to verify the effectiveness and practicality of the proposed scheme.

Authors have studied degradation phenomenon on electrical contacts under the influences of an external micro-oscillation. A new micro-sliding mechanism 2 (MSM2) has developed, which provides micro-sliding driven by a piezo-electric actuator and elastic hinges. The experimental results are obtained on “minimal sliding amplitudes” to make resistances fluctuate on electrical contacts under some conditions which are three types of inputwaveform (sinusoidal, rectangular, and impulsive) and three levels of frictional force (1.6, 1.0, and 0.3 N/pin) by using the MSM2. The dynamical characteristics are discussed under the conditions. The simple theoretical model on the input signal and the output of the mechanism is built and the theoretical expressions from the model are obtained. A natural angular frequency (ω0=12600[s-1]) and a damping ratio (ζ=0.03[-]) are evaluated using experimental dynamical responses. The waveforms of inputs and outputs are obtained and the characteristics between inputs and outputs are also obtained on the theoretical model using the above. The maximal gain between the input and the output in rectangular or impulsive (24.4) is much larger than that (0.0) in sinusoidal. The difference on the output-accelerations between in sinusoidal and in rectangular (impulsive) is discussed. It is shown that it is possible to cause the degradation phenomenon in sinusoidal only when the output displacement are enlarged. It is also shown that it is possible to cause the phenomenon in rectangular or in impulsive, in addition to the above, when the external force has sharper rising and falling waveforms even if the displacement and the frequency of the force is small. The difference on the output-amplitudes between in rectangular and in impulsive is discussed. It is not clear that there is the difference between the effect in rectangular and that in impulsive. It is indicated that it is necessary to discuss the other causes, for instance, another dynamical, thermal, and chemical process.

This letter presents a simple and explicit formulation of non-unique Wiener filters associated with the linear predictor for processing of sinusoids. It was shown in the literature that, if the input signal consists of only sinusoids and does not include a white noise, the input autocorrelation matrix in the Wiener-Hopf equation becomes rank-deficient and thus the Wiener filter is not uniquely determined. In this letter we deal with this rank-deficient problem and present a mathematical description of non-unique Wiener filters in a simple and explicit form. This description is directly obtained from the tap number, the frequency of sinusoid, and the delay parameter. We derive this result by means of the elementary row operations on the augmented matrix given by the Wiener-Hopf equation. We also show that the conventional Wiener filter for noisy input signal is included as a special case of our description.

Deploying a group of mobile sensor (MS) nodes to monitor a moving phenomenon in an unknown and open area includes a lot of challenges if the phenomenon moves quickly and due to the limited capabilities of MS nodes in terms of mobility, sensing and communication ranges. To address these challenges and achieve a high weighted sensing coverage, in this paper, we propose a new algorithm for moving-phenomenon monitoring, namely VirFID-MP (Virtual Force (VF)-based Interest-Driven phenomenon monitoring with Mobility Prediction). In VirFID-MP, the future movement of the phenomenon is first predicted using the MS nodes' movement history data. Then, the prediction information is used to calculate a virtual force, which is utilized to speed up MS nodes toward the moving phenomenon. In addition, a prediction-based oscillation-avoidance algorithm is incorporated with VirFID-MP movement control to reduce the nodes' energy consumption. Our simulation results show that VirFID-MP outperforms original VirFID schemes in terms of weighted coverage efficiency and energy consumption.

It is qualitatively considered that emergency information processing by using UTM grids is effective in generating COP (Common Operational Pictures). Here, we conducted a numerical evaluation based on emergency information-processing training to examine the efficiency of the use of UTM grid maps by staff at the Tagajo City Government office. The results of the demonstration experiment were as follows: 1) The time required for information propagation and mapping with UTM coordinates was less than that with address text consisting of area name and block number. 2) There was no measurable difference in subjective estimates of the training performance of participants with or without the use of UTM grids. 3) Fear of real emergency responses decreased among training participants using UTM grids. 4) Many of the negative free answers on a questionnaire evaluation of participants involved requests regarding the reliability and operability of UTM tools.

We arrange disaster mitigation activities into temporal order and discuss the contribution of information and communications technology (ICT) to the reduction of disaster damage in the stages of precaution, emergency response, and post-mortem study. Examples of the current contribution of ICT are introduced and future possible uses of ICT are discussed. We focus on the contribution of ICT to decision-making in emergency responses by augmenting human intelligence. Research directions of ICT for disaster mitigation technology are summarized in the categories “tough ICT”, “intelligence amplification for decision-making in disaster mitigation” and “safe ICT.”

Most of the existing algorithms cannot effectively solve the data sparse problem of trajectory prediction. This paper proposes a novel sparse trajectory prediction method based on L-Z entropy estimation. Firstly, the moving region of trajectories is divided into a two-dimensional plane grid graph, and then the original trajectories are mapped to the grid graph so that each trajectory can be represented as a grid sequence. Secondly, an L-Z entropy estimator is used to calculate the entropy value of each grid sequence, and then the trajectory which has a comparatively low entropy value is segmented into several sub-trajectories. The new trajectory space is synthesised by these sub-trajectories based on trajectory entropy. The trajectory synthesis can not only resolve the sparse problem of trajectory data, but also make the new trajectory space more credible. In addition, the trajectory scale is limited in a certain range. Finally, under the new trajectory space, Markov model and Bayesian Inference is applied to trajectory prediction with data sparsity. The experiments based on the taxi trajectory dataset of Microsoft Research Asia show the proposed method can make an effective prediction for the sparse trajectory. Compared with the existing methods, our method needs a smaller trajectory space and provides much wider predicting range, faster predicting speed and better predicting accuracy.

This paper presents a novel data compression method for testing integrated circuits within the selective dictionary coding framework. Due to the inverse value of dictionary indices made use of for the compatibility analysis with the heuristic algorithm utilized to solve the maximum clique problem, the method can obtain a higher compression ratio than existing ones.

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.

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.