28324109
Precise Eye Localization with AdaBoost and Fast Radial Symmetry
29843270
A Self-adaptive Mixed Distribution Based Uni-variate Estimation of Distribution Algorithm for Large Scale Global Optimization
Large scale global optimization (LSGO), which is highly needed for many scientific and engineering applications, is a very important and very difficult task in optimization domain. Various algorithms have been proposed to tackle this challenging problem, but the use of estimation of distribution algorithms (EDAs) to it is rare. This chapter aims at investigating the behavior and performances of uni-variate EDAs mixed with different kernel probability densities via fitness landscape analysis. Based on the analysis, a self-adaptive uni-variate EDA with mixed kernels (MUEDA) is proposed. To assess the effectiveness and efficiency of MUEDA, function optimization tasks with dimension scaling from 30 to 1500 are adopted. Compared to the recently published LSGO algorithms, MUEDA shows excellent convergence speed, final solution quality and dimensional scalability.
3197098
A Multi-Objective Hw-sw Co-Synthesis Algorithm Based on Quantum-Inspired Evolutionary Algorithm
Hardware–Software (HW–SW) co-synthesis is one of the key steps in modern embedded system design. Generally, HW–SW co-synthesis is to optimally allocate processors, assign tasks to processors, and schedule the processing of tasks to achieve a good balance among performance, cost, power consumption, etc. Hence, it is a typical multi-objective optimization problem. In this paper, a new multi-objective HW–SW co-synthesis algorithm based on the quantum-inspired evolutionary algorithm (MQEAC) is proposed. MQEAC utilizes multiple quantum probability amplitude vectors to model the promising areas of solution space. Meanwhile, this paper presents a new crossover operator to accelerate the convergence to the Pareto front and introduces a PE slot-filling strategy to improve the efficiency of scheduling. Experimental results show that the proposed algorithm can solve the typical multi-objective co-synthesis problems effectively and efficiently.
26519336
Iris Recognition Algorithm Using Modified Log-Gabor Filters
In this paper, we presented an iris recognition algorithm based on modified Log-Gabor filters. The algorithm is similar as the method proposed by Daugman in general procedure while modified Log-Gabor filters are adopted to extract the iris phase information instead of complex Gabor filters used in Daugman's method. The advantage of Log-Gabor filters over complex Gabor filters is the former are strictly bandpass filters and the latter are not. The property of strictly bandpass makes the Log-Gabor filters more suitable to extract the iris phase features regardless of the background brightness. The comparison experiments between complex Gabor filters based methods and the proposed method are also presented in this paper
9214943
A restart univariate estimation of distribution algorithm: sampling under mixed Gaussian and Lévy probability distribution
A univariate EDA denoted as ldquoLSEDA-glrdquo for large scale global optimization (LSGO) problems is proposed in this paper. Three efficient strategies: sampling under mixed Gaussian and Levy probability distribution, standard deviation control strategy and restart strategy are adopted to improve the performance of classical univariate EDA on LSGO problems. The motivation of such work is to extend EDAs to LSGO domain reasonably. Comparison among LSEDA-gl, EDA with standard deviation control strategy only (EDA-STDC) and similar EDA version ldquocontinuous univariate marginal distribution algorithmrdquo UMDAc is carried out on classical test functions. Based on the general comparison standard, the strengths and weaknesses of the algorithms are discussed. Besides, LSEDA-gl is tested on 7 functions with 100, 500, 1000 dimensions provided in the CECpsila2008 Special Session on LSGO. This work is also expected to provide a comparison result for the CECpsila2008 special session.
17592710
Full-Space LDA With Evolutionary Selection for Face Recognition
Linear discriminant analysis (LDA) is a popular feature extraction technique for face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional face data. Some approaches have been proposed to overcome this problem, but they usually utilize all eigenvectors of null or range subspaces of within-class scatter matrix (Sw). However, experimental results testified that not all the eigenvectors in the full space of S w are positive to the classification performance, some of which might be negative. As far as we know, there have been no effective ways to determine which eigenvectors should be adopted. This paper proposes a new method EDA+Full-space LDA, which takes full advantage of the discriminative information of the null and range subspaces of Sw by selecting an optimal subset of eigenvectors. An estimation of distribution algorithm (EDA) is used to pursuit a subset of eigenvectors with significant discriminative information in full space of Sw middot EDA+Full-space LDA is tested on ORL face image database. Experimental results show that our method outperforms other LDA methods
3459442
Steganalysis of YASS
A promising steganographic method-yet another steganography scheme (YASS)-was designed to resist blind steganalysis via embedding data in randomized locations. In addition to a concrete realization which is named the YASS algorithm in this paper, a few strategies were proposed to work with the YASS algorithm in order to enhance the data embedding rate and security. In this work, the YASS algorithm and these strategies, together referred to as YASS, have been analyzed from a warden's perspective. It is observed that the embedding locations chosen by YASS are not randomized enough and the YASS embedding scheme causes detectable artifacts. We present a steganalytic method to attack the YASS algorithm, which is facilitated by a specifically selected steganalytic observation domain (SO-domain), a term to define the domain from which steganalytic features are extracted. The proposed SO-domain is not exactly, but partially accesses, the domain where the YASS algorithm embeds data. Statistical features generated from the SO-domain have demonstrated high effectiveness in detecting the YASS algorithm and identifying some embedding parameters. In addition, we discuss how to defeat the above-mentioned strategies of YASS and demonstrate a countermeasure to a new case in which the randomness of the embedding locations is enhanced. The success of detecting YASS by the proposed method indicates a properly selected SO-domain is beneficial for steganalysis and confirms that the embedding locations are of great importance in designing a secure steganographic scheme.
20301405
Unequal-arm Adaptive Rood Pattern Search with Early Terminations For Fast Block-matching Motion Estimation on H.264
Based on a rood-shaped pattern of the motion-vector displacement distribution inherently resulted from block-matching motion estimation, a series of adaptive rood pattern search (ARPS) algorithms have been introduced. Among them, the unequal-arm ARPS has achieved superior performance on many accounts while maintaining a fairly close rate-distortion performance compared with that of the full search. In this paper, an enhanced version of the unequal-arm ARPS is introduced by incorporating early terminations at both the initial search and the refined local search stages. Its performance was compared with that of the UMHexagonS fast block-matching motion estimation algorithm based on the JM-9.3 software encoder. The experimental results show that the unequal-arm ARPS with early terminations has yielded significant search efficiency gain while consistently maintaining a fairly close rate-distortion performance compared with that of the UMHexagonS. Furthermore, the proposed algorithm is fairly simple on implementation
3439449
Classification of functional data: A segmentation approach
We suggest a classification and feature extraction method on functional data where the predictor variables are curves. The method, called functional segment discriminant analysis (FSDA), combines the classical linear discriminant analysis and support vector machine. FSDA is particularly useful for irregular functional data, characterized by spatial heterogeneity and local patterns like spikes. FSDA not only reduces the computation and storage burden by using a fraction of the spectrum, but also identifies important predictors and extracts features. FSDA is highly flexible, easy to incorporate information from other data sources and/or prior knowledge from the investigators. We apply FSDA to two public domain data sets and discuss the understanding developed from the study.
16772027
CCL-SVC: Optimizing user experience of broadcasting video on computation capability limited handheld devices
In this paper, we propose a novel scheme using computing complexity layered scalable video coding (CCLSVC) to optimize the user experience of broadcasting video in the computing capability limited handheld terminals. To address the heterogeneity of computing capability among different handheld devices, we employ hierarchal B reference structure of SVC to divide the frames into multiple computing complexity layers (CC Layers) in server side. The handheld clients simply choose to decode the frames in their corresponding layers in terms of their computation capability to maximize the video PSNR. We have proved that the optimal CC Layers division problem is a precedence constrained scheduling problem, which is an NP-complete problem. And we further propose our fast greedy method to approximately get optimized broadcasting video playback PSNR. The simulation shows that our method is superior to temporal SVC and random frame discarding method.
267541
Impact of Process and Temperature Variations on Network-on-Chip Design Exploration
With the continuing scaling of CMOS technologies, process variation is becoming a key factor highly impacting system-level power and temperature. Traditional methods of assuming a uniform temperature and no process variation can lead to gross inaccuracies even for system-level design, thus it is critical to consider the effects of process variation and temperature variation during early design exploration. In this paper, we describe the implementation of an architecture- level early-stage design space exploration tool that incorporates the effect of process and temperature variation for network-on-chips(NoC). The tool is used to study the impact of process and temperature variations on power and energy-delay-product-per-flit metrics for different NoC architectures, and our simulation results show that design choices are influenced by the effects of process and temperature variation, thus demonstrating the importance of considering, and enabling the high- level impact analysis of process and temperature variation early in the design flow.