In this paper, we propose a low-power circuit-shared static flip-flop (CS2FF) for extremely low power digital VLSIs. The CS2FF consists of five static NORs and two inverters (INVs). The CS2FF utilizes a positive edge of a buffered clock signal, which is generated from a root clock, to take data into a master latch and a negative edge of the root clock to hold the data in a slave latch. The total number of transistors is only 24, which is the same as the conventional transmission-gate flip flop (TGFF) used in the most standard cell libraries. SPICE simulations in 0.18-µm standard CMOS process demonstrated that our proposed CS2FF achieved clock-to-Q delay of 18.3ns, setup time of 10.0ns, hold time of 5.5ns, and power dissipation of 9.7nW at 1-MHz clock frequency and 0.5-V power supply. The physical design area increased by 16% and power dissipation was reduced by 13% compared with those of conventional TGFF. Measurement results demonstrated that our proposed CS2FF can operate at 0.352V with extremely low energy of 5.93fJ.

Almost sure convergence coding theorems of one-shot and multi-shot Tunstall codes are proved for stationary memoryless sources. Coding theorem of one-shot Tunstall code is proved in the case that the leaf count of Tunstall tree increases. On the other hand, coding theorem is proved for multi-shot Tunstall code with increasing parsing count, under the assumption that the Tunstall tree grows as the parsing proceeds. In this result, it is clarified that the theorem for the one-shot Tunstall code is not a corollary of the theorem for the multi-shot Tunstall code. In the case of the multi-shot Tunstall code, it can be regarded that the coding theorem is proved for the sequential algorithm such that parsing and coding are processed repeatedly. Cartesian concatenation of trees and geometric mean of the leaf counts of trees are newly introduced, which play crucial roles in the analyses of multi-shot Tunstall code.

This paper addresses the problem of job scheduling in volunteer computing (VC) systems where each computation job is replicated and allocated to multiple participants (workers) to remove incorrect results by a voting mechanism. In the job scheduling of VC, the number of workers to complete a job is an important factor for the system performance; however, it cannot be fixed because some of the workers may secede in real VC. This is the problem that existing methods have not considered in the job scheduling. We propose a dynamic job scheduling method which considers the expected probability of completion (EPC) for each job based on the probability of worker's secession. The key idea of the proposed method is to allocate jobs so that EPC is always greater than a specified value (SPC). By setting SPC as a reasonable value, the proposed method enables to complete jobs without excess allocation, which leads to the higher performance of VC systems. We assume in this paper that worker's secession probability follows Weibull-distribution which is known to reflect more practical situation. We derive parameters for the distribution using actual trace data and compare the performance of the proposed and the previous method under the Weibull-distribution model, as well as the previous constant probability model. Simulation results show that the performance of the proposed method is up to 5 times higher than that of the existing method especially when the time for completing jobs is restricted, while keeping the error rate lower than a required value.

Amplify-and-forward (AF) relay multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems can achieve high data rate and high quality communications. On the other hand, it has to estimate all channels between the source-relay and relay-destination nodes in the destination node. In MIMO/OFDM systems, high time resolution carrier interferometry (HTRCI) has been proposed to achieve an accurate channel estimation (CE) with a small number of pilot signals. However, since it has many interferences, an accurate CE is not obtained and the system performance is degraded in AF relay MIMO/OFDM systems. Therefore, in this paper, we propose the complex HTRCI (C-HTRCI) pilot signal and the enhanced minimum mean square error (E-MMSE) equalization to achieve an accurate CE and to improve the system performance for AF relay MIMO/OFDM systems.

Orthogonal frequency division multiplexing (OFDM) channel estimation is the key technique used in broadband wireless networks. The Doppler frequency caused by fast mobility environments will cause inter-carrier interference (ICI) and degrade the performance of OFDM systems. Due to the severe ICI, channel estimation becomes a difficult task in higher mobility scenarios. Our aim is to propose a pilot-aided channel estimation method that is robust to high Doppler frequency with low computational complexity and pilot overheads. In this paper, the time duration of each estimate covers multiple consecutive OFDM symbols, named a “window”. A close-form of polynomial channel modeling is derived. The proposed method is initialized to the least squares (LS) estimates of the channels corresponding to the time interval of the pilot symbols within the window. Then, the channel interpolation is performed in the entire window. The results of computer simulations and computation complexity evaluations show that the proposed technique is robust to high Doppler frequency with low computation complexity and low pilot overheads. Compared with the state-of-the-art method and some conventional methods, the new technique proposed here has much lower computational complexity while offering comparable performance.

In many multimedia applications, image encryption has to be conducted prior to image compression. This paper proposes a JPEG-friendly perceptual encryption method, which enables to be conducted prior to JPEG and Motion JPEG compressions. The proposed encryption scheme can provides approximately the same compression performance as that of JPEG compression without any encryption, where both gray scale images and color ones are considered. It is also shown that the proposed scheme consists of four block-based encryption steps, and provide a reasonably high level of security. Most of conventional perceptual encryption schemes have not been designed for international compression standards, but this paper focuses on applying the JPEG and Motion JPEG standards, as one of the most widely used image compression standards. In addition, this paper considers an efficient key management scheme, which enables an encryption with multiple keys to be easy to manage its keys.

Index compression is partially responsible for the current performance achievements of Internet search engines. Among many latest compression techniques, Simple9 can pack as many integers as possible into a single 32-bit machine word using 9 different padding modes. However, the number of wasted bits in Simple9 remains large. In previous works, researchers have focused on reducing the unused trailing bits of the padding modes and have proposed various additional modes that make full use of the cases of the status bits. Instead, we focus on the wasted bits in the integer list, padding extra zeros for a complete dense mode when the number of integers is not enough to fit a complete mode. More precisely, we first propose a novel index compression method called SimpleD with dense padding modes to achieve a more compact storage compared with that of Simple9. We then design an innovative metric for extracting the inserted extra zero integers during the decoding phase. Experiments on the TREC WT2G and GOV2 datasets show that our encoder outperforms Simple9 while still retaining a very fast decompression speed.

The conical area evolutionary algorithm (CAEA) has a very high run-time efficiency for bi-objective optimization, but it can not tackle problems with more than two objectives. In this letter, a conical hypervolume evolutionary algorithm (CHEA) is proposed to extend the CAEA to a higher dimensional objective space. CHEA partitions objective spaces into a series of conical subregions and retains only one elitist individual for every subregion within a compact elitist archive. Additionally, each offspring needs to be compared only with the elitist individual in the same subregion in terms of the local hypervolume scalar indicator. Experimental results on 5-objective test problems have revealed that CHEA can obtain the satisfactory overall performance on both run-time efficiency and solution quality.

With the growth of cloud-based services, cloud data centers are experiencing large growth. A key component in a cloud data center is the network technology deployed. In particular, Ethernet technology, commonly deployed in cloud data centers, is already envisioned for 10 Tbps Ethernet. In this paper, we study and analyze the makespan, workload execution times, and virtual machine migrations as the network speed increases. In particular, we consider homogeneous and heterogeneous data centers, virtual machine scheduling algorithms, and workload scheduling algorithms. Results obtained from our study indicate that the increase in a network's speed reduces makespan and workloads execution times, while aiding in the increase of the number of virtual machine migrations. We further observed that the number of migrations' behaviors in relation to the speed of the networks also depends on the employed virtual machines scheduling algorithm.

Due to its on-demand and pay-as-you-go properties, cloud computing has become an attractive alternative for HPC applications. However, communication-intensive applications with complex communication patterns still cannot be performed efficiently on cloud platforms, which are equipped with MapReduce technologies, such as Hadoop and Spark. In particular, one major obstacle is that MapReduce's simple programming model cannot explicitly manipulate data transfers between compute nodes. Another obstacle is cloud's relatively poor network performance compared with traditional HPC platforms. The traditional Strassen's algorithm of square matrix multiplication has a recursive and complex pattern on the HPC platform. Therefore, it cannot be directly applied to the cloud platform. In this paper, we demonstrate how to make Strassen's algorithm with complex communication patterns “cloud-friendly”. By reorganizing Strassen's algorithm in an iterative pattern, we completely separate its computations and communications, making it fit to MapReduce programming model. By adopting a novel data/task parallel strategy, we solve Strassen's data dependency problems, making it well balanced. This is the first instance of Strassen's algorithm in MapReduce-style systems, which also matches Strassen's communication lower bound. Further experimental results show that it achieves a speedup ranging from 1.03× to 2.50× over the classical Θ(n3) algorithm. We believe the principle can be applied to many other complex scientific applications.

Based on a unified representation of generalized cyclotomic classes, every generalized cyclotomic sequence of order d over $Z_{p_{1}^{e_{1}}p_{2}^{e_{2}}cdots p_{r}^{e_{r}}}$ is shown to be a sum of d-residue sequences over $Z_{p_{s}^{e_{s}}}$ for $sin {1,2,cdots,r }$. For d=2, by the multi-rate approach, several generalized cyclotomic sequences are explicitly expressed by Legendre sequences, and their linear complexity properties are analyzed.

Controlling fluid simulation is one of the important research topics in computer graphics. In this paper, we focus on controlling the simulation of cumuliform cloud formation. Using a previously proposed method for controlling cloud simulation the convergence speed is very slow; therefore, it takes a long time before the clouds form the desired shapes. We improved the method and accelerated the convergence by introducing a new mechanism for controlling the amount of water vapor added. We demonstrate the effectiveness of the proposed method by several examples.

Although sparse coding has emerged as an extremely powerful tool for texture and image classification, it neglects the relationship of coding coefficients from the same class in the training stage, which may cause a decline in the classification performance. In this paper, we propose a novel coding strategy named compact sparse coding for ground-based cloud classification. We add a constraint on coding coefficients into the objective function of traditional sparse coding. In this way, coding coefficients from the same class can be forced to their mean vector, making them more compact and discriminative. Experiments demonstrate that our method achieves better performance than the state-of-the-art methods.

The advanced front-end (AFE) for automatic speech recognition (ASR) was standardized by the European Telecommunications Standards Institute (ETSI). The AFE provides speech enhancement realized by an iterative Wiener filter (IWF) in which a smoothed FFT spectrum over adjacent frames is used to design the filter. We have previously proposed robust time-varying complex Auto-Regressive (TV-CAR) speech analysis for an analytic signal and evaluated the performance of speech processing such as F0 estimation and speech enhancement. TV-CAR analysis can estimate more accurate spectrum than FFT, especially in low frequencies because of the nature of the analytic signal. In addition, TV-CAR can estimate more accurate speech spectrum against additive noise. In this paper, a time-invariant version of wide-band TV-CAR analysis is introduced to the IWF in the AFE and is evaluated using the CENSREC-2 database and its baseline script.

This letter presents a method for active noise cancelation (ANC) for headphone application. The method improves the performance of ANC by deriving a flexible independent component analysis (ICA) algorithm in a hybrid structure combining feedforward and feedback configurations with correlation-based wind detection. The effectiveness of the method is demonstrated through simulation.

A field multiplication in the extended binary field is often expressed using Toeplitz matrix-vector products (TMVPs), whose matrices have special properties such as symmetric or triangular. We show that such TMVPs can be efficiently implemented by taking advantage of some properties of matrices. This yields an efficient multiplier when a field multiplication involves such TMVPs. For example, we propose an efficient multiplier based on the Dickson basis which requires the reduced number of XOR gates by an average of 34% compared with previously known results.

Crowd collectiveness, i.e., a quantitative metric for collective motion, has received increasing attention in recent years. Most of existing methods build a collective network by assuming each agent in the crowd interacts with neighbors within fixed radius r region or fixed k nearest neighbors. However, they usually use a universal r or k for different crowded scenes, which may yield inaccurate network topology and lead to lack of adaptivity to varying collective motion scenarios, thereby resulting in poor performance. To overcome these limitations, we propose a compressive sensing (CS) based method for measuring crowd collectiveness. The proposed method uncovers the connections among agents from the motion time series by solving a CS problem, which needs not specify an r or k as a priori. A descriptor based on the average velocity correlations of connected agents is then constructed to compute the collectiveness value. Experimental results demonstrate that the proposed method is effective in measuring crowd collectiveness, and performs on par with or better than the state-of-the-art methods.

The next generation high efficiency video coding (HEVC) standard achieves high performance by extending the encoding block to 64×64. There are some parallel tools to improve the efficiency for encoder and decoder. However, owing to the dependence of the current prediction block and surrounding block, parallel processing at CU level and Sub-CU level are hard to achieve. In this paper, focusing on the spatial motion vector prediction (SMVP) and temporal motion vector prediction (TMVP), parallel improvement for spatio-temporal prediction algorithms are presented, which can remove the dependency between prediction coding units and neighboring coding units. Using this proposal, it is convenient to process motion estimation in parallel, which is suitable for different parallel platforms such as multi-core platform, compute unified device architecture (CUDA) and so on. The simulation experiment results demonstrate that based on HM12.0 test model for different test sequences, the proposed algorithm can improve the advanced motion vector prediction with only 0.01% BD-rate increase that result is better than previous work, and the BDPSNR is almost the same as the HEVC reference software.

Isosurface extraction is one of the most popular techniques for visualizing scalar volume data. However, volume data contains infinitely many isosurfaces. Furthermore, a single isosurface might contain many connected components, or contours, with each representing a different object surface. Hence, it is often a tedious and time-consuming manual process to find and extract contours that are interesting to users. This paper describes a novel method for automatically extracting salient contours from volume data. For this purpose, we propose a contour gradient tree (CGT) that contains the information of salient contours and their saliency magnitude. We organize the CGT in a hierarchical way to generate a sequence of contours in saliency order. Our method was applied to various medical datasets. Experimental results show that our method can automatically extract salient contours that represent regions of interest in the data.

This paper presents a new connected component labeling algorithm. The proposed algorithm scans image lines every three lines and processes pixels three by three. When processing the current three pixels, we also utilize the information obtained before to reduce the repeated work for checking pixels in the mask. Experimental results demonstrated that our method is more efficient than the fastest conventional labeling algorithm.