In the paper, we analyzed the system optimizationalgorithms in e-cash (electronic cash) by improving the efficiency of e-cash for software and hardware application. As for the fast asymmetric cryptography algorithms ...
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ISBN:
(纸本)9780878492879
In the paper, we analyzed the system optimizationalgorithms in e-cash (electronic cash) by improving the efficiency of e-cash for software and hardware application. As for the fast asymmetric cryptography algorithms in e-cash scheme, we presented basic interactive protocols based on discrete logarithm cryptosystem. In the protocol, the interacting algorithms achieve authenticated encryption in secret transmission algorithms, the verification of signature and transmission of secret message can be fulfilled in a single algorithm, and therefore the complexity of authentication algorithms in e-cash scheme is greatly reduced. As a comparison with traditional e-cash schemes, we presented an optimized e-cash scheme based on ECC (Elliptic Curves Cryptosystem). The cryptography algorithms of the scheme make full use of the superiority of ECC fast algorithms, thus the optimized e-cash scheme effectively avoids illegal distribution of e-cash and generalized forgery attack on system parameters with less system overheads.
This paper provides algorithms for the optimization of autonomous hybrid systems based on the geometrical properties of switching manifolds. The first and second sections of the paper introduce optimal hybrid control ...
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ISBN:
(纸本)9781424477456
This paper provides algorithms for the optimization of autonomous hybrid systems based on the geometrical properties of switching manifolds. The first and second sections of the paper introduce optimal hybrid control systems and the third section deals with the analysis of the Hybrid Maximum Principle (HMP) algorithm introduced in [8]. The HMP algorithm in [8] is then extended to a geometrical algorithm by employing the notion of geodesic curves on switching manifolds. The convergence analysis for the proposed algorithm is based on Lasalle Theory. To reduce the computational burden, a simplified version of the geodesic algorithm is formulated in the local coordinate system of the switching state. Simulation results show a significant improvement in terms of convergence rate and stability compared with the HMP algorithm.
Evaluation and optimization, with an ever increasing variety of material, are getting more and more time-consuming tasks in video algorithm development. An additional difficulty in moving video is that frame-by-frame ...
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ISBN:
(纸本)0819460974
Evaluation and optimization, with an ever increasing variety of material, are getting more and more time-consuming tasks in video algorithm development. An additional difficulty in moving video is that frame-by-frame perceived performance can significantly differ from real-time perceived performance. This paper proposes a way to handle this difficulty in a more systematic and objective way than with usual long tuning procedures. We take the example of interpolation algorithms where variations of sharpness or contrast look annoying in real-time whereas the frame-by-frame performance looks well acceptable. These variations are analyzed to get an objective measure for the real-time annoyance. We show that the reason for the problem is that most interpolation algorithms are optimized across intraframe criteria ignoring that the achievable intrinsic performance may vary from frame to frame. Our method is thus based on interframe optimization taking into account the measured annoyance. The optimization criteria are steered frame by frame depending on the achievable performance of the current interpolation and the achieved performance in previous frames. Our policy can be described as "better be good all time than very good from time to time". The advantage is that it is automatically controlled by the compromise wished in the given application.
The paper proposes a new approach and a system to develop parallel algorithms based on the joint use of the algebraic-algorithmic methodology of specification and development of programs and non-algorithmic (heuristic...
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The paper proposes a new approach and a system to develop parallel algorithms based on the joint use of the algebraic-algorithmic methodology of specification and development of programs and non-algorithmic (heuristic) techniques for code generation. The algebraic part of the methodology provides the formalized process of parallel program design through high-level algebraic-algorithmic specifications and automating transformations up to program code in a standard programming language. The heuristic part of the system is the dynamic adjustment of program code to a target platform and its optimization using self-learning code generation and heuristic technologies.
In this paper we propose a new feature extraction scheme for hyperspectral images based on mutual information. Relevance of extracted feature set to class label has been measured by average of mutual information betwe...
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ISBN:
(纸本)9781424467600
In this paper we propose a new feature extraction scheme for hyperspectral images based on mutual information. Relevance of extracted feature set to class label has been measured by average of mutual information between each of them and class label and Redundancy of them is measured by average of mutual information between each pair of them. Based on relevance of features and redundancy between them, we propose a cost function that maximize relevance of extracted features and simultaneously minimize redundancy between them. This cost function has been already used for feature selection. In this paper we will find the parameters of an optimal linear mapping by optimizing the proposed cost function with respect them. Linear methods are attractive due to their simplicity. Because of nonlinear and nonconvex relation between proposed cost function and the parameters, we use genetic algorithm for optimization. Mutual information accounts for higher order statistics, not just for second order as PCA and LDA do. Hence mutual information is a better criterion for hyperspectral images because they have higher order statistics than two. Our classification results for AVARIS data shows proposed method has better performance over PCA and LDA.
The principles of G.729 algorithm are analyzed. It proposes an optimal approach of adaptive codebook search. Realized on fixed point DSP TMS320VC5410,the searching time of the optimal algorithm is thus significantly d...
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The principles of G.729 algorithm are analyzed. It proposes an optimal approach of adaptive codebook search. Realized on fixed point DSP TMS320VC5410,the searching time of the optimal algorithm is thus significantly decreased,and the result shows that the speech quality is not decreased.
This paper presents the design and implementation of a low power digital signal processor （THUCIDSP-1 ） targeting at application for cochlear implants. Multi-level low power strategies including algorithm optimizati...
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This paper presents the design and implementation of a low power digital signal processor （THUCIDSP-1 ） targeting at application for cochlear implants. Multi-level low power strategies including algorithm optimization, operand isolation, clock gating and memory partitioning are adopted in the processor design to reduce the power consumption. Experimental results show that the complexity of the Continuous Interleaved Sampling （CIS） algorithm is reduced by more than 80 % and the power dissipation of the hardware alone is reduced by about 25% with the low power methods. The THUCIDSP-1 prototype, fabricated in 0.18-μm standard CMOS process, consumes only 1.91 mW when executing the CIS algorithm at 3 MHz.
An improved genetic K-means clustering algorithm is proposed and is applied to image segmentation. According to the characteristics of the image, the feature vector of the pixel is properly chosen and the weight facto...
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ISBN:
(纸本)9780769535579
An improved genetic K-means clustering algorithm is proposed and is applied to image segmentation. According to the characteristics of the image, the feature vector of the pixel is properly chosen and the weight factors of the feature vector are adjusted, which enhances the segmentation precision. The selection of conventional genetic algorithm and the modification of mutation operations improve the speed of convergence. Computing time is reduced due to combining the membership matrix with the coding of chromosomes skillfully. The results of the experiments demonstrate that in the image segmentation the proposed algorithm is better than traditional genetic K-means algorithm.
In this paper, we explore face detection and face recognition algorithms for ubiquitous computing environment. We develop algorithms for application programming interface (API) suitable for embedded system. The basic ...
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ISBN:
(纸本)9783642027093
In this paper, we explore face detection and face recognition algorithms for ubiquitous computing environment. We develop algorithms for application programming interface (API) suitable for embedded system. The basic requirements include appropriate data format and collection of feature data to achieve efficiency of algorithm. Our experiment presents a face detection and face recognition algorithm for handheld devices. The essential part for proposed system includes;integer representation from floating point calculation. optimization of memory management scheme and efficient face detection performance on complex background scene.
This paper presents a two-degree-of-freedom controller structure for electric power steering systems. The controller is synthesized using a hybrid linear matrix inequality and genetic algorithms optimization. Robust s...
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ISBN:
(纸本)9781424445233
This paper presents a two-degree-of-freedom controller structure for electric power steering systems. The controller is synthesized using a hybrid linear matrix inequality and genetic algorithms optimization. Robust stability is studied for both sector-bounded and passive uncertainties resulting in a system of linear matrix inequalities (LMIs) and a linear matrix equality (LME). This system of LMIs/LME defines a guaranteed cost H_(2) optimization subject to an H_(infinity)-norm performance as well as a strict-positive-real constraints. Experimental results involving human-in-the-loop show that the control design did satisfy the criteria for robust control and performance. Furthermore, the ease-of-tuning of the proposed controller structure makes it possible to improve the steering "feel".
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