To analyze the structure of a set of high-dimensional perfect sequences over a composition algebra over R, we developed the theory of Fourier transforms of the set of such sequences. We define the discrete cosine transform and the discrete sine transform, and we show that there exists a relationship between these transforms and a convolution of sequences. By applying this property to a set of perfect sequences, we obtain a parameterization theorem. Using this theorem, we show the equivalence between the left perfectness and right perfectness of sequences. For sequences of real numbers, we obtain the parameterization without restrictions on the parameters.

In the literature, many cryptosystems have been proposed to be secure under the Strong Diffie-Hellman (SDH) and related problems. For example, there is a cryptosystem that is based on the SDH/related problem or allows the Diffie-Hellman oracle. If the cryptosystem employs general domain parameters, this leads to a significant security loss caused by Cheon's algorithm [14], [15]. However, all elliptic curve domain parameters explicitly recommended in the standards (e.g., ANSI X9.62/63 [1], [2], FIPS PUB 186-4 [43], SEC 2 [50], [51]) are susceptible to Cheon's algorithm [14], [15]. In this paper, we first prove that (q-1)(q+1) is always divisible by 24 for any prime order q>3. Based on this result and depending on small divisors d1,d2≤(log q)2, we classify primes q>3, such that both (q-1)/d1 and (q+1)/d2 are primes, into Perfect, Semiperfect, SEC1v2 and Acceptable. Then, we describe algorithmic procedures and show their simulation results of secure elliptic curve domain parameters over prime/character 2 finite fields resistant to Cheon's algorithm [14], [15]. Also, several examples of the secure elliptic curve domain parameters (including Perfect or Semiperfect prime q) are followed.

In this paper, we propose a method for designing finite impulse response (FIR) filters with canonic signed digit (CSD) coefficients using particle swarm optimization (PSO). In such a design problem, a large number of local minimums appear in an evaluation function for the optimization. An updating procedure of PSO tends to stagnate around such local minimums and thus indicates a premature convergence property. Therefore, a new framework for avoiding such a situation is proposed, in which the evaluation function is modified around the stagnation point. Several design examples are shown to present the effectiveness of the proposed method.

This paper presents a 20GHz Class-C VCO using a noise sensitivity mitigation technique. A radio frequency Class-C VCO suffers from the AM-PM conversion, caused by the non-linear capacitance of cross coupled pair. In this paper, the phase noise degradation mechanism is discussed, and a desensitization technique of AM-PM noise is proposed. In the proposed technique, AM-PM sensitivity is canceled by tuning the tail impedance, which consists of 4-bit resistor switches. A 65-nm CMOS prototype of the proposed VCO demonstrates the oscillation frequency from 19.27 to 22.4GHz, and the phase noise of -105.7dBc/Hz at 1-MHz offset with the power dissipation of 6.84mW, which is equivalent to a Figure-of-Merit of -183.73dBc/Hz.

Route guidance system is one of the essential components of a vehicle navigation system in ITS. In this paper, a centrally determined route guidance system is established to solve congestion problems. The Sarsa learning method is used to guide vehicles, and global and local parameter strategy is proposed to adjust the vehicle guidance by considering the whole traffic system and local traffic environment, respectively. The proposed method can save the average driving time and relieve traffic congestion. The evaluation was done using two cases on different road networks. The experimental results show the efficiency and effectiveness of the proposed algorithm.

A conventional HMM-based speech synthesis system for Hanoi Vietnamese often suffers from hoarse quality due to incomplete F0 parameterization of glottalized tones. Since estimating F0 from glottalized waveform is rather problematic for usual F0 extractors, we propose a pitch marking algorithm where pitch marks are propagated from regular regions of a speech signal to glottalized ones, from which complete F0 contours for the glottalized tones are derived. The proposed F0 parameterization scheme was confirmed to significantly reduce the hoarseness whilst slightly improving the tone naturalness of synthetic speech by both objective and listening tests. The pitch marking algorithm works as a refinement step based on the results of an F0 extractor. Therefore, the proposed scheme can be combined with any F0 extractor.

The RO (Ring-Oscillator)-based sensor is one of easily-implementable variation sensors, but for decomposing the observed variability into multiple unique device-parameter variations, a large number of ROs with different structures and sensitivities to device-parameters is required. This paper proposes an area efficient device parameter estimation method with sensitivity-configurable ring oscillator (RO). This sensitivity-configurable RO has a number of configurations and the proposed method exploits this property for reducing sensor area and/or improving estimation accuracy. The proposed method selects multiple sets of sensitivity configurations, obtains multiple estimates and computes the average of them for accuracy improvement exploiting an averaging effect. Experimental results with a 32-nm predictive technology model show that the proposed averaging with multiple estimates can reduce the estimation error by 49% or reduce the sensor area by 75% while keeping the accuracy. Compared to previous work with iterative estimation, 23% accuracy improvement is achieved.

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.

This paper introduces a fast image denoising algorithm by estimating noise parameters without prior information about the noise. Under the assumption that additive noise has a Gaussian distribution, the noise parameters were estimated from an observed degraded image, and were used to define the constraints of a noise detection process that was coupled with a Markov random field (MRF). In addition, an adaptive modified weighted Gaussian filter with variable window sizes defined by the constraints on noise detection was used to control the degree of smoothness of the reconstructed image. Experimental results demonstrate the capability of the proposed algorithm.

Middle-level parts have attracted great attention in the computer vision community, acting as discriminative elements for objects. In this paper we propose an unsupervised approach to mine discriminative parts for object detection. This work features three aspects. First, we introduce an unsupervised, exemplar-based training process for part detection. We generate initial parts by selective search and then train part detectors by exemplar SVM. Second, a part selection model based on consistency and distinctiveness is constructed to select effective parts from the candidate pool. Third, we combine discriminative part mining with the deformable part model (DPM) for object detection. The proposed method is evaluated on the PASCAL VOC2007 and VOC2010 datasets. The experimental results demons-trate the effectiveness of our method for object detection.

When there are multiple component predictors, it is promising to integrate them into one predictor for advanced reasoning. If each component predictor is given as a stochastic model in the form of probability distribution, an exponential mixture of the component probability distributions provides a good way to integrate them. However, weight parameters used in the exponential mixture model are difficult to estimate if there is no training samples for performance evaluation. As a suboptimal way to solve this problem, weight parameters may be estimated so that the exponential mixture model should be a balance point that is defined as an equilibrium point with respect to the distance from/to all component probability distributions. In this paper, we propose a weight parameter estimation method that represents this concept using a symmetric Kullback-Leibler divergence and generalize this method.

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.

This letter presents a two-stage method to extend the degenerate unmixing estimation technique (DUET) for reverberant speech separation. First, frequency-bin-wise attenuation and delay parameters are introduced and estimated by online update rules, to handle early reflections. Next, a mask reestimation algorithm based on the precedence effect is developed to detect and fix the errors on binary masks caused by late reflections. Experimental results demonstrate that the proposed method improves separation performance significantly.

The technique of partial transmit sequences (PTS) is effective in reducing the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals. However, the conventional PTS (CPTS) scheme has high computation complexity because it needs several inverse fast Fourier transform (IFFT) units and an optimization process to find the candidate signal with the lowest PAPR. In this paper, we propose a new low-complexity PTS scheme for OFDM systems, in which a hybrid subblock partition method (SPM) is used to reduce the complexity that results from the IFFT computations and the optimization process. Also, the PAPR reduction performance of the proposed PTS scheme is further enhanced by multiplying a selected subblock with a predefined phase rotation vector to form a new subblock. The time-domain signal of the new subblock can be obtained simply by performing a circularly-shift-left operation on the IFFT output of the selected subblock. Computer simulations show that the proposed PTS scheme achieves a PAPR reduction performance close to that of the CPTS scheme with the pseudo-random SPM, but with much lower computation complexity.

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.

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.

Transparent organic light-emitting diodes (TOLEDs) were investigated with top electrode of indium-tin-oxide (ITO) by ion-plating method. High deposition rate of 4.4 nm/s was realized without plasma damage of under organic layer. In the TOLEDs with inverted structure, high transmittance of over 75% at 550 nm and bright emission of 1,850 and 1,410 cd/m2, from bottom and top side at 163 mA/cm2, respectively, were obtained.

In this paper, a real-valued reweighted l1 norm minimization method based on data reconstruction in monostatic multiple-input multiple-output (MIMO) radar is proposed. Exploiting the special structure of the received data, and through the received data reconstruction approach and unitary transformation technique, a one-dimensional real-valued received data matrix can be obtained for recovering the sparse signal. Then a weight matrix based on real-valued MUSIC spectrum is designed for reweighting l1 norm minimization to enhance the sparsity of solution. Finally, the DOA can be estimated by finding the non-zero rows in the recovered matrix. Compared with traditional l1 norm-based minimization methods, the proposed method provides better angle estimation performance. Simulation results are presented to verify the effectiveness and advantage of the proposed method.

Sparse Matrix-Vector Multiplication (SpMxV) is widely used in many high-performance computing applications, including information retrieval, medical imaging, and economic modeling. To eliminate the overhead of zero padding in SpMxV, prior works have focused on partitioning a sparse matrix into row vectors sets (RVS's) or sub-matrices. However, performance was still degraded due to the sparsity pattern of a sparse matrix. In this letter, we propose a heuristics, called recursive merging, which uses a greedy approach to recursively merge those row vectors of nonzeros in a matrix into the RVS's, such that each set included is ensured a local optimal solution. For ten uneven benchmark matrices from the University of Florida Sparse Matrix Collection, our proposed partitioning algorithm is always identified as the method with the highest mean density (over 96%), but with the lowest average relative difference (below 0.07%) over computing powers.

Sparsity-based methods have been recently applied to abnormal event detection and have achieved impressive results. However, most such methods suffer from the problem of dimensionality curse; furthermore, they also take no consideration of the relationship among coefficient vectors. In this paper, we propose a novel method called consistent sparse representation (CSR) to overcome the drawbacks. We first reconstruct each feature in the space spanned by the clustering centers of training features so as to reduce the dimensionality of features and preserve the neighboring structure. Then, the consistent regularization is added to the sparse representation model, which explicitly considers the relationship of coefficient vectors. Our method is verified on two challenging databases (UCSD Ped1 database and Subway batabase), and the experimental results demonstrate that our method obtains better results than previous methods in abnormal event detection.