Recently, probabilistic topic models have been applied to various types of data, including text, and their effectiveness has been demonstrated. Latent Dirichlet allocation (LDA) is a well known topic model. Variational Bayesian inference or collapsed Gibbs sampling is often used to estimate parameters in LDA; however, these inference methods incur high computational cost for large-scale data. Therefore, highly efficient technology is needed for this purpose. We use parallel computation technology for efficient collapsed Gibbs sampling inference for LDA. We assume a symmetric multiprocessing (SMP) cluster, which has been widely used in recent years. In prior work on parallel inference for LDA, either MPI or OpenMP has often been used alone. For an SMP cluster, however, it is more suitable to adopt hybrid parallelization that uses message passing for communication between SMP nodes and loop directives for parallelization within each SMP node. We developed an MPI/OpenMP hybrid parallel inference method for LDA, and evaluated the performance of the inference under various settings of an SMP cluster. We further investigated the approximation that controls the inter-node communications, and found out that it achieved noticeable increase in inference speed while maintaining inference accuracy.

Ultra-wideband (UWB) pulse radar has high range resolution and permeability in a dielectric medium, and has great potential for the non-destructive inspection or early-stage detection of breast cancer. As an accurate and high-resolution imaging method for targets embedded in a dielectric medium, extended range points migration (RPM) has been developed. Although this method offers an accurate internal target image in a homogeneous media, it assumes the permittivity of the dielectric medium is given, which is not practical for general applications. Although there are various permittivity estimation methods, they have essential problems that are not suitable for clear, dielectric boundaries like walls, or is not applicable to an unknown and arbitrary shape of dielectric medium. To overcome the above drawbacks, we newly propose a permittivity estimation method suitable for various shapes of dielectric media with a clear boundary, where the dielectric boundary points and their normal vectors are accurately determined by the original RPM method. In addition, our method iteratively compensates for the scattered waveform deformation using a finite-difference time domain (FDTD) method to enhance the accuracy of the permittivity estimation. Results from a numerical simulation demonstrate that our method achieves accurate permittivity estimation even for a dielectric medium of wavelength size.

This Letter presents a new feature named structured local binary Kirsch pattern (SLBKP) for image retrieval. Each input color image is decomposed into Y, Cb and Cr components. For each component image, eight 33 Kirsch direction templates are first performed pixel by pixel, and thus each pixel is characterized by an 8-dimenional edge-strength vector. Then a binary operation is performed on each edge-strength vector to obtain its integer-valued SLBKP. Finally, three SLBKP histograms are concatenated together as the final feature of each input colour image. Experimental results show that, compared with the existing structured local binary Haar pattern (SLBHP)-based feature, the proposed feature can greatly improve retrieval performance.

Acoustic energy harvesters that function in environments where sound pressure is extremely high (150 dB), such as in engine rooms of aircraft, are expected to be capable of powering wireless health monitoring systems. This paper presents the power generation performance of a lead-zirconate-titanate (PZT) acoustic energy harvester with a vibrating PZT diaphragm. The diaphragm had a diameter of 2 mm, consisting of Al (0.1 µm)/PZT (1 µm)/Pt (0.1 µm)/Ti (0.1 µm)/SiO2 (1.5 µm). The harvester generated a power of 510-14 W under a sound pressure level of 110 dB at the first resonance frequency of 6.28 kHz. It was found that the generated power was increased to 2.010-13 W using a sound-collecting Helmholtz resonator cone with a height of 60 mm. The cone provided a Helmholtz resonance at 5.8 kHz, and the generated power increased from 9.710-15 W to 7.310-13 W at this frequency. The cone was also effective in increasing the bandwidth of the energy harvester.

In this paper, we develop a novel two-sample test statistic for edge detection in CT image. This test statistic involves the non-parametric estimate of the samples' probability density functions (PDF's) based on the kernel density estimator and the calculation of the mean square error (MSE) distance of the estimated PDF's. In order to extract single-pixel-wide edges, a generic detection scheme cooperated with the non-maximum suppression is also proposed. This new method is applied to a variety of noisy images, and the performance is quantitatively evaluated with edge strength images. The experiments show that the proposed method provides a more effective and robust way of detecting edges in CT image compared with other existing methods.

We propose a novel threshold-free salient object detection approach which integrates both saliency density and edge response. The salient object with a well-defined boundary can be automatically detected by our approach. Saliency density and edge response maximization is used as the quality function to direct the salient object discovery. The global optimal window containing a salient object is efficiently located through the proposed saliency density and edge response based branch-and-bound search. To extract the salient object with a well-defined boundary, the GrabCut method is applied, initialized by the located window. Experimental results show that our approach outperforms the methods only using saliency or edge response and achieves a comparable performance with the best state-of-the-art method, while being without any threshold or multiple iterations of GrabCut.

In this letter, a design of inverse discrete cosine transform for energy-efficient watermarking mechanism based on DS-CDMA with significant energy and area reduction is presented. Taking advantage of converged input data value set as a precomputation concept, the proposed one-dimensional IDCT is a multiplierless hardware which differs from Loeffler architecture and has benefits of low complexity and low power consumption. The experimental results show that our design can reduce 85.2% energy consumption and 58.6% area. Various spectrum and spatial attacks are also tested to corroborate the robustness.

The nondeterminism of message-passing communication brings challenges to program debugging, testing and fault-tolerance. This paper proposes a novel deterministic message-passing implementation (DMPI) for parallel programs in the distributed environment. DMPI is compatible with the standard MPI in user interface, and it guarantees the reproducibility of message with high performance. The basic idea of DMPI is to use logical time to solve message races and control asynchronous transmissions, and thus we could eliminate the nondeterministic behaviors of the existing message-passing mechanism. We apply a buffering strategy to alleviate the performance slowdown caused by mismatch of logical time and physical time. To avoid deadlocks introduced by deterministic mechanisms, we also integrate DMPI with a lightweight deadlock checker to dynamically detect and solve these deadlocks. We have implemented DMPI and evaluated it using NPB benchmarks. The results show that DMPI could guarantee determinism with incurring modest runtime overhead (14% on average).

Oriental ink painting, called Sumi-e, is one of the most distinctive painting styles and has attracted artists around the world. Major challenges in Sumi-e simulation are to abstract complex scene information and reproduce smooth and natural brush strokes. To automatically generate such strokes, we propose to model the brush as a reinforcement learning agent, and let the agent learn the desired brush-trajectories by maximizing the sum of rewards in the policy search framework. To achieve better performance, we provide elaborate design of actions, states, and rewards specifically tailored for a Sumi-e agent. The effectiveness of our proposed approach is demonstrated through experiments on Sumi-e simulation.

This paper discusses the reduction of the amount of transmitted information for the efficient use of frequency resources in wireless feedback control systems, and clarify the effect of the reduction of the amount of transmitted information. As a typical example of the underactuated controlled object, a rotary inverted pendulum is considered. We propose a reduction method for state information fed back from the controller to the controlled object. It estimates angle or velocity state from the previous state. In addition, we propose a reduction method that temporally omits less important control information and state information. Numerical examples clarify the effect of the reduction methods on the control quality. And we show that the reduction methods achieve large reduction of the amount of transmitted information with small disadvantage of the control quality.

With the successful adoption of link analysis techniques such as PageRank and web spam filtering, current web search engines well support “navigational search”. However, due to the use of a simple conjunctive Boolean filter in addition to the inappropriateness of user queries, such an engine does not necessarily well support “informational search”. Informational search would be better handled by a web search engine using an informational retrieval model combined with enhancement techniques such as query expansion and relevance feedback. Moreover, the realization of such an engine requires a method to prosess the model efficiently. In this paper we propose a novel extension of an existing top-k query processing technique to improve search efficiency. We add to it the technique utilizing a simple data structure called a “term-document binary matrix,” resulting in more efficient evaluation of top-k queries even when the queries have been expanded. We show on the basis of experimental evaluation using the TREC GOV2 data set and expanded versions of the evaluation queries attached to this data set that the proposed method can speed up evaluation considerably compared with existing techniques especially when the number of query terms gets larger.

Cardinality estimation schemes of Radio Frequency IDentification (RFID) tags using Framed Slotted ALOHA (FSA) based protocol are studied in this paper. Not as same as previous estimation schemes, we consider tag cardinality estimation problem under not only detection errors but also capture effect, where a tag's IDentity (ID) might not be detected even in a singleton slot, while it might be identified even in a collision slot due to the fading of wireless channels. Maximum Likelihood (ML) approach is utilized for the estimation of the detection error probability, the capture effect probability, and the tag cardinality. The performance of the proposed method is evaluated under different system parameters via computer simulations to show the method's effectiveness comparing to other conventional approaches.

A novel and energy-efficient algorithm with Quality-of-Service (QoS) guarantee is proposed for cooperative spectrum sensing (CSS) with soft information fusion and hard information fusion. By weighting the sensing performance and the consumption of system resources in a utility function that is maximized with respect to the number of secondary users (SUs), it is shown that the optimal number of SUs is related to the price of these QoS requirements.

Low-complexity joint subcarrier and power allocation is considered. The applied criterion is to minimize the transmission power while satisfying the users' rate requirements. Subcarrier and power allocation are separately applied. Fixed spectrum efficiency is assumed to simplify the subcarrier allocation. We show that under fixed spectrum efficiency, power allocation can be obtained by solving some sets of linear equations. Simulation result shows the effectiveness of the proposed algorithm.

Solid-state disks (SSDs) have received much attention as replacements for hard disk drives (HDDs). One of their noticeable advantages is their high-speed read/write operation. To achieve good performance, SSDs have an internal memory hierarchy which includes several volatile memories, such as DRAMs and SRAMs. Furthermore, many SSDs adopt aggressive memory management schemes under the assumption of stable power supply. Unfortunately, the data stored in the volatile memories are lost when the power supplied to SSDs is abruptly shut off. Such power failure is often observed in portable devices. For this reason, it is critical to provide a power failure protection scheme for reliable SSDs. In this work, we propose a power-failure protection scheme for SSDs to increase their reliability. The contribution of our work is three-fold. First, we design a power failure protection circuit which incorporates super-capacitors as well as rechargeable batteries. Second, we provide a method to determine the capacity of backup power sources. Third, we propose a data backup procedure when the power failure occurs. We implemented our method on a real board and applied it to a notebook PC with a contemporary SSD. The board measurement and simulation results prove that our method is robust in cases of sudden power failure.

In this paper, negative bias temperature instability (NBTI) reliability of pFETs is analyzed under the post-fabrication SRAM self-improvement scheme that we have developed recently, where cell stability is self-improved by simply applying high stress voltage to supply voltage terminal (VDD) of SRAM cells. It is newly found that there is no significant difference in both threshold voltage and drain current degradation by NBTI stress between fresh PFETs and PFETs after self-improvement scheme application, indicating that the self-improvement scheme has no critical reliability problem.

High-resolution time of arrival (TOA) estimation techniques have great promise for the high range resolution required in recently developed radar systems. A widely known super-resolution TOA estimation algorithm for such applications, the multiple-signal classification (MUSIC) in the frequency domain, has been proposed, which exploits an orthogonal relationship between signal and noise eigenvectors obtained by the correlation matrix of the observed transfer function. However, this method suffers severely from a degraded resolution when a number of highly correlated interference signals are mixed in the same range gate. As a solution for this problem, this paper proposes a novel TOA estimation algorithm by introducing a maximum likelihood independent component analysis (MLICA) approach, in which multiple complex sinusoidal signals are efficiently separated by the likelihood criteria determined by the probability density function (PDF) of a complex sinusoid. This MLICA schemes can decompose highly correlated interference signals, and the proposed method then incorporates the MLICA into the MUSIC method, to enhance the range resolution in richly interfered situations. The results from numerical simulations and experimental investigation demonstrate that our proposed pre-processing method can enhance TOA estimation resolution compared with that obtained by the original MUSIC, particularly for lower signal-to-noise ratios.

The performance of a mobile database management system (DBMS) in which most queries are made up of random data accesses if the NAND flash memory is used as storage media of the DBMS is degraded. The reason for this is that the performance of NAND flash memory is good for writing sequentially but poor when writing randomly. Thus, a new storage structure and querying policies are needed in mobile DBMS when flash memory is used as the storage media. In this letter, we propose a new policy of database page management to enhance the frequent random update performance, and then evaluate the performance experimentally.

A variety of ubiquitous computing devices, such as radio frequency identification (RFID) and wireless sensor network (WSN), are generating huge and significant events that should be rapidly processed for business excellence. In this paper, we describe how complex event processing (CEP) technology can be applied to ubiquitous process management based on context-awareness. To address the issue, we propose a method for context-aware event processing using event processing language (EPL) statement. Specifically, the semantics of a situation drive the transformation of EPL statement templates into executable EPL statements. The proposed method is implemented in the domain of ubiquitous cold chain logistics management. With the proposed method, context-aware event processing can be realized to enhance business performance and excellence in ubiquitous computing environments.

With the rapid expansion of vector data model application to digital content such as drawings and digital maps, the security and retrieval for vector data models have become an issue. In this paper, we present a vector data-hashing algorithm for the authentication, copy protection, and indexing of vector data models that are composed of a number of layers in CAD family formats. The proposed hashing algorithm groups polylines in a vector data model and generates group coefficients by the curvatures of the first and second type of polylines. Subsequently, we calculate the feature coefficients by projecting the group coefficients onto a random pattern, and finally generate the binary hash from binarization of the feature coefficients. Based on experimental results using a number of drawings and digital maps, we verified the robustness of the proposed hashing algorithm against various attacks and the uniqueness and security of the random key.