Cloud computing is an emerging computing paradigm that may have a significant impact on various aspects of the development of information infrastructure. In a Cloud environment, different types of network resources need to be virtualized as a series of service components by network virtualization, and these service components should be further composed into Cloud services provided to end users. Therefore Quality of Service (QoS) aware service composition plays a crucial role in Cloud service provisioning. This paper addresses the problem on how to compose a sequence of service components for QoS guaranteed service provisioning in a virtualization-based Cloud computing environment. The contributions of this paper include a system model for Cloud service provisioning and two approximation algorithms for QoS-aware service composition. Specifically, a system model is first developed to characterize service provisioning behavior in virtualization-based Cloud computing, then a novel approximation algorithm and a variant of a well-known QoS routing procedure are presented to resolve QoS-aware service composition. Theoretical analysis shows that these two algorithms have the same level of time complexity. Comparison study conducted based on simulation experiments indicates that the proposed novel algorithm achieves better performance in time efficiency and scalability without compromising quality of solution. The modeling technique and algorithms developed in this paper are general and effective; thus are applicable to practical Cloud computing systems.

We propose a new analytical method for a composite dielectric grating embedded with conducting strips using scattering factors in the shadow theory. The scattering factor in the shadow theory plays an important role instead of the conventional diffraction amplitude. By specifying the relation between scattering factors and spectral-domain Green's functions, we derive expressions of the Green's functions directly for unit surface electric and magnetic current densities, and apply the spectral Galerkin method to our formulation. From some numerical results, we show that the expressions of the Green's functions are valid, and analyze scattering characteristics by composite gratings.

Electromagnetic emissions leak confidential data of cryptographic devices. Electromagnetic Analysis (EMA) exploits such emission for cryptanalysis. The performance of EMA dramatically decreases when correlated noise, which is caused by the interference of clock network and exhibits strong correlation with encryption signal, is present in the acquired EM signal. In this paper, three techniques are proposed to reduce the correlated noise. Based on the observation that the clock signal has a high variance at the signal edges, the first technique: single-sample Singular Value Decomposition (SVD), extracts the clock signal with only one EM sample. The second technique: multi-sample SVD is capable of suppressing the clock signal with short sampling length. The third one: averaged subtraction is suitable for estimation of correlated noise when background samplings are included. Experiments on the EM signal during AES encryption on the FPGA and ASIC implementation demonstrate that the proposed techniques increase SNR as much as 22.94 dB, and the success rates of EMA show that the data-independent information is retained and the performance of EMA is improved.

In this paper, we propose a novel incremental method for discovering latent variables from multivariate data with high efficiency. It integrates non-Gaussianity and an adaptive incremental model in an unsupervised way to extract informative features. Our proposed method discovers a small number of compact features from a very large number of features and can still achieve good predictive performance in EEG signals. The promising EEG signal classification results from our experiments prove that this approach can successfully extract important features. Our proposed method also has low memory requirements and computational costs.

A parameterization of perfect sequences over composition algebras over the real number field is presented. According to the proposed parameterization theorem, a perfect sequence can be represented as a sum of trigonometric functions and points on a unit sphere of the algebra. Because of the non-commutativity of the multiplication, there are two definitions of perfect sequences, but the equivalence of the definitions is easily shown using the theorem. A composition sequence of sequences is introduced. Despite the non-associativity, the proposed theorem reveals that the composition sequence from perfect sequences is perfect.

Geographic routing uses the geographical location information provided by nodes to make routing decisions. However, the nodes can not obtain accurate location information due to the effect of measurement error. A new routing strategy using maximum expected distance and angle (MEDA) algorithm is proposed to improve the performance and promote the successive transmission rate. We firstly introduce the expected distance and angle, and then we employ the principal component analysis to construct the object function for selecting the next hop node. We compare the proposed algorithm with maximum expectation within transmission range (MER) and greedy routing scheme (GRS) algorithms. Simulation results show that the proposed MEDA algorithm outperforms the MER and GRS algorithms with higher successive transmission rate.

Solving image recovery problems requires the use of some efficient regularizations based on a priori information with respect to the unknown original image. Naturally, we can assume that an image is modeled as the sum of smooth, edge, and texture components. To obtain a high quality recovered image, appropriate regularizations for each individual component are required. In this paper, we propose a novel image recovery technique which performs decomposition and recovery simultaneously. We formulate image recovery as a nonsmooth convex optimization problem and design an iterative scheme based on the alternating direction method of multipliers (ADMM) for approximating its global minimizer efficiently. Experimental results reveal that the proposed image recovery technique outperforms a state-of-the-art method.

In recent years, there has been an increasing demand with regard to available elemental services provided by independent firms for compositing new services. Currently, however, whenever it is difficult to maintain the required level of quality of a new composite web service, assignment of the new computer's resources as provisioning at the data center is not always effective, especially in the area of performance for composite web service providers. Thus, a new approach might be required. This paper presents a new control method aiming to maintain the performance requirements for composite web services. There are three aspects of our method that are applied: first of all, the theory of constraints (TOC) proposed by E.M. Goldratt ; secondly, an evaluation process in the non-linear feed forward controlling method: and finally multiple trials in applying policies with verification. In particular, we will discuss the architectural and theoretical aspects of the method in detail, and will show the insufficiency of combining the feedback controlling approach with TOC as a result of our evaluation.

Link prediction is the task of inferring the existence or absence of certain relationships among data objects such as identity, interaction, and collaboration. Link prediction is found in various applications in the fields of information integration, recommender systems, bioinformatics, and social network analysis. The increasing interest in dynamically changing networks has led to growing interest in a more general link prediction problem called temporal link prediction in the data mining and machine learning communities. However, only links among nodes at the same time point are considered in temporal link prediction. We propose a new link prediction problem called cross-temporal link prediction in which the links among nodes at different time points are inferred. A typical example of cross-temporal link prediction is cross-temporal entity resolution to determine the identity of real entities represented by data objects observed in different time periods. In dynamic environments, the features of data change over time, making it difficult to identify cross-temporal links by directly comparing observed data. Other examples of cross-temporal links are asynchronous communications in social networks such as Facebook and Twitter, where a message is posted in reply to a previous message. We adopt a dimension reduction approach to cross-temporal link prediction; that is, data objects in different time frames are mapped into a common low-dimensional latent feature space, and the links are identified on the basis of the distance between the data objects. The proposed method uses different low-dimensional feature projections in different time frames, enabling it to adapt to changes in the latent features over time. Using multi-task learning, it jointly learns a set of feature projection matrices from the training data, given the assumption of temporal smoothness of the projections. The optimal solutions are obtained by solving a single generalized eigenvalue problem. Experiments using a real-world set of bibliographic data for cross-temporal entity resolution and a real-world set of emails for unobserved asynchronous communication inference showed that introducing time-dependent feature projections improved the accuracy of link prediction.

In this paper, an imaging components transmission scheme for the improvement of multipath delay resolution in a Fractional Sampling (FS) OFDM receiver is proposed. FS has been proposed as a diversity scheme and achieves path diversity by enlarging the bandwidth of the baseband filters in order to transmit the imaging components of the desired signal. However, FS is not able to achieve diversity with very short delay multipaths because of its low multipath delay resolution. Wider bandwidth of the transmission signal is required to improve the resolution of the delay. On the other hand, cognitive radio is an emerging technology to utilize frequency spectrum flexibly through dynamic spectrum access (DSA). To resolve the small delay multipaths and to use the spectrum flexibly with DSA, this paper investigates the FS path diversity with the imaging components on the separated frequency channel. The correlation between the 2 FS branches is analyzed theoretically on the 2 path channel under the conditions of sampling interval, delay spread, and frequency separation. Numerical results through computer simulation show that the proposed scheme improves the multipath resolution and the bit error rate (BER) performance under the existence of small delay multipaths.

Blind source separation (BSS) techniques are required for various signal decomposing issues. Independent component analysis (ICA), assuming only a statistical independence among stochastic source signals, is one of the most useful BSS tools because it does not need a priori information on each source. However, there are many requirements for decomposing multiple deterministic signals such as complex sinusoidal signals with different frequencies. These requirements may include pulse compression or clutter rejection. It has been theoretically shown that an ICA algorithm based on maximizing non-Gaussianity successfully decomposes such deterministic signals. However, this ICA algorithm does not maintain a sufficient separation performance when the frequency difference of the sinusoidal waves becomes less than a nominal frequency resolution. To solve this problem, this paper proposes a super-resolution algorithm for complex sinusoidal signals by extending the maximum likelihood ICA, where the probability density function (PDF) of a complex sinusoidal signal is exploited as a priori knowledge, in which the PDF of the signal amplitude is approximated as a Gaussian distribution with an extremely small standard deviation. Furthermore, we introduce an optimization process for this standard deviation to avoid divergence in updating the reconstruction matrix. Numerical simulations verify that our proposed algorithm remarkably enhances the separation performance compared to the conventional one, and accomplishes a super-resolution separation even in noisy situations.

Fast and accurate estimation of missing relations, e.g., similarity, distance and kernel, among objects is now one of the most important techniques required by major data mining tasks, because the missing information of the relations is needed in many applications such as economics, psychology, and social network communities. Though some approaches have been proposed in the last several years, the practical balance between their required computation amount and obtained accuracy are insufficient for some class of the relation estimation. The objective of this paper is to formalize a problem to quickly and efficiently estimate missing relations among objects from the other known relations among the objects and to propose techniques called “PSD Estimation” and “Row Reduction” for the estimation problem. This technique uses a characteristic of the relations named “Positive Semi-Definiteness (PSD)” and a special assumption for known relations in a matrix. The superior performance of our approach in both efficiency and accuracy is demonstrated through an evaluation based on artificial and real-world data sets.

In this paper, we propose a successive signal-to-leakage-plus-noise ratio (SLNR) based precoding with geometric mean decomposition (GMD) for the downlink multi-user multiple-input multiple-output (MU-MIMO) systems. The known leakages are canceled at the transmit side, and SLNR is calculated with the unknown leakages. GMD is applied to cancel the known leakages, so the subchannels for each receiver have equal gain. We further improve the proposed precoding scheme by ordering users. Simulation results show that the proposed schemes have a considerable bit error rate (BER) improvement over the original SLNR scheme.

A method for spatio-temporal resolution enhancement of video sequences based on super-resolution reconstruction is proposed. A new observation model is defined for accurate resolution enhancement, which enables subpixel motion in intermediate frames to be obtained. A modified optimization formula for obtaining a high-resolution sequence is also adopted.

Accurate estimation of Software Code Size is important for developing cost-efficient embedded systems. The Code Size affects the amount of system resources needed, like ROM and RAM memory, and processing capacity. In our previous work, we have estimated the Code Size based on CFP (COSMIC Function Points) within 15% accuracy, with the purpose of deciding how much ROM memory to fit into products with high cost pressure. Our manual CFP measurement process would require 2.5 man years to estimate the ROM size required in a typical car. In this paper, we want to investigate how the manual effort involved in estimation of Code Size can be minimized. We define a UML Profile capturing all information needed for estimation of Code Size, and develop a tool for automated estimation of Code Size based on CFP. A case study will show how UML models save manual effort in a realistic case.

Non-negative matrix factorization (NMF) is widely used for music transcription because of its efficiency. However, the conventional NMF-based music transcription algorithm often causes harmonic confusion errors or time split-up errors, because the NMF decomposes the time-frequency data according to the activated frequency in its time. To solve these problems, we proposed an NMF with temporal continuity and harmonicity constraints. The temporal continuity constraint prevented the time split-up of the continuous time components, and the harmonicity constraint helped to bind the fundamental with harmonic frequencies by reducing the additional octave errors. The transcription performance of the proposed algorithm was compared with that of the conventional algorithms, which showed that the proposed method helped to reduce additional false errors and increased the overall transcription performance.

In this letter, a post-detection signal to noise ratio (SNR) is considered for transmit antenna selection, when a sorted QR decomposition (SQRD) algorithm is used for signal detection in spatial multiplexing (SM) ultra-wideband (UWB) multiple input multiple output systems. The post-detection SNR expression is obtained using a QR factorization algorithm based on a sorted Gram-Schmidt process. The employed antenna selection criterion is to utilize the largest minimum post-detection SNR value. It is shown via simulations that the antenna selection significantly enhances the BER performance of the SQRD-based SM UWB systems on a log-normal multipath fading channel.

This paper proposes a new first-scan method for two-scan labeling algorithms. In the first scan, our proposed method first scans every fourth image line, and processes the scan line and its two neighbor lines. Then, it processes the remaining lines from top to bottom one by one. Our method decreases the average number of times that must be checked to process a foreground pixel will; thus, the efficiency of labeling can be improved.

In this paper, we propose a novel gait recognition framework - Spherical Space Model with Human Point Clouds (SSM-HPC) to recognize front view of human gait. A new gait representation - Marching in Place (MIP) gait is also introduced which preserves the spatiotemporal characteristics of individual gait manner. In comparison with the previous studies on gait recognition which usually use human silhouette images from image sequences, this research applies three dimensional (3D) point clouds data of human body obtained from stereo camera. The proposed framework exhibits gait recognition rates superior to those of other gait recognition methods.

Mel Frequency Cepstral Coefficients (MFCC) are the most popular acoustic features used in automatic speech recognition (ASR), mainly because the coefficients capture the most useful information of the speech and fit well with the assumptions used in hidden Markov models. As is well known, MFCCs already employ several principles which have known counterparts in the peripheral properties of human hearing: decoupling across frequency, mel-warping of the frequency axis, log-compression of energy, etc. It is natural to introduce more mechanisms in the auditory periphery to improve the noise robustness of MFCC. In this paper, a k-nearest neighbors based frequency masking filter is proposed to reduce the audibility of spectra valleys which are sensitive to noise. Besides, Moore and Glasberg's critical band equivalent rectangular bandwidth (ERB) expression is utilized to determine the filter bandwidth. Furthermore, a new bandpass infinite impulse response (IIR) filter is proposed to imitate the temporal masking phenomenon of the human auditory system. These three auditory perceptual mechanisms are combined with the standard MFCC algorithm in order to investigate their effects on ASR performance, and a revised MFCC extraction scheme is presented. Recognition performances with the standard MFCC, RASTA perceptual linear prediction (RASTA-PLP) and the proposed feature extraction scheme are evaluated on a medium-vocabulary isolated-word recognition task and a more complex large vocabulary continuous speech recognition (LVCSR) task. Experimental results show that consistent robustness against background noise is achieved on these two tasks, and the proposed method outperforms both the standard MFCC and RASTA-PLP.