A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.

Mean shift: a robust approach toward feature space analysis

Digital watermarking is a key ingredient to copyright protection. It provides a solution to illegal copying of digital material and has many other useful applications such as broadcast monitoring and the recording of electronic transactions. Now, for the first time, there is a book that focuses exclusively on this exciting technology. Digital Watermarking covers the crucial research findings in the field: it explains the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied. As a result, additional groundwork is laid for future developments in this field, helping the reader understand and anticipate new approaches and applications.

Digital Watermarking

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A Break in the Clouds: Towards a Cloud Definition

A morphable model for the synthesis of 3D faces

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

A parameter adaptive DE algorithm on real-parameter optimization

With the increase in the number of private cars as well as the non-professional drivers, the current traffic environment is in urgent need of driving assist equipment to timely reminder and to rectify the incorrect driving behavior. To meet this requirement, this paper proposes an innovative algorithm of driving behavior analysis based on support vector machine SVM with a variety of driving operation and traffic information. The proposed driving behavior analysis algorithm will mainly monitor driver's driving operation behavior, including steering wheel angle, brake force, and throttle position. To increase the accuracy of driving behavior analysis, the proposed algorithm also takes road conditions, including urban roads, mountain roads, and highways into account. The proposed will make use of SVM to create a driving behavior model in various different road conditions, and then could determine whether the current driving behavior belongs to safe driving. Experimental results show the correctness of the proposed driving behavior analysis algorithm can achieve average 80% accuracy rate in various driving simulations. The proposed algorithm has the potential of applying to real-world driver assistance system.

Driving Behavior Analysis of Multiple Information Fusion Based on SVM

An important method in pattern recognition is k nearest-neighbor classification. However, its computational complexity limits its real-time applications. The partial distance search is a solution to this problem. Although it is not very effective, it can be combined with other algorithms to reduce the complexity. The paper proposes an indexing method that uses the variance vector of feature vectors of a design set to improve the efficiency of the partial distance search. Experimental results indicate the effectiveness of this indexing preprocessing

Improved partial distance search for k nearest-neighbor classification

Monkey King evolutionary algorithm (MKEA) is a new type and innovation of gene method that can be more effective evolution of the algorithm to reach goal or objective function. In this study and research is applied the monkey king evolutionary algorithm is used to apply the evolutionary particle to find the optimal power flow of system and calculate the complex power of each line, bus and to minimize power generation cost of the power plant. In order to study the practicability of the algorithm, it is applied to the standard IEEE 5bus load flow test system, and its convergence characteristic curve is observed and compared with the genetic algorithm (GA). The experimental results show that the MKEA can effectively solve the power system optimal power flow problem and this method is find the global solution not local solution that be confirmed in minimum fuel cost of generator of power plants. The minimum fuel cost obtained by MKE and GA is 5369.55 and 5422.0 US Dollars, respectively, when the number of population particles is 100 and the number of iterations is 300 that compared with GA which is 7.6% lower than GA. The results show that MKE has the obvious superiority to find the global solution.

Jeng-Shyang Pan

Papers

A parameter adaptive DE algorithm on real-parameter optimization

Driving Behavior Analysis of Multiple Information Fusion Based on SVM

Improved partial distance search for k nearest-neighbor classification

Optimal Economic Dispatch of Fuel Cost Based on Intelligent Monkey King Evolutionary Algorithm

Advances in Smart Vehicular Technology, Transportation, Communication and Applications