Jeng-Shyang Pan

Bio: Jeng-Shyang Pan is an academic researcher from Shandong University of Science and Technology. The author has contributed to research in topics: Digital watermarking & Watermark. The author has an hindex of 50, co-authored 789 publications receiving 11645 citations. Previous affiliations of Jeng-Shyang Pan include National Kaohsiung Normal University & Technical University of Ostrava.

Interactive Artificial Bee Colony Supported Passive Continuous Authentication System

Abstract: In this paper, an initiative passive continuous authentication (CA) system based on both hard and soft biometrics is presented. Human facial features are used as hard biometric information for the authentication process, and the clothes' color of a user is employed as the soft biometric information. The passive CA system keeps verifying, without interrupting the user from concentrating on his work. It also provides the capacity for the machine to recognize who is in front of the terminal, reduces the potential security leaks, and denies access to the invader with the stolen account and password. In this system, the face recognition core is implemented not only by the Eigenface method, but also assisted by the interactive artificial bee colony optimization algorithm. The proposed method is evaluated by the ORL face database and tested on the prototype CA system for computer security. The experimental results indicate that the accuracy of recognition is raised up to 3.13%, i.e., from 83.75% to 86.88%, with data from the ORL database, and it is improved by 34.53% on average in the real-time continuous authentication environment.

Quasi-Affine Transformation Evolutionary Algorithm With Communication Schemes for Application of RSSI in Wireless Sensor Networks

Abstract: QUasi-Affine TRansformation Evolutionary algorithm (QUATRE) is a new optimization algorithm based on population for complex multiple real parameter optimization problems in real world. In this paper, a novel multi-group multi-choice communication strategy algorithm for QUasi-Affine TRansformation Evolutionary (MM-QUATRE) algorithm is proposed to solve the disadvantage that the original QUATRE is always easily to fall into local optimization in the strategy of updating bad nodes with multiple groups and multiple choices. We compared it with other intelligent algorithms, the most advanced PSO variant, parallel PSO (P-PSO) variant, native QUATRE and parallel QUATRE (P-PSO) under CEC2013 large-scale optimization test suite. Thus, the performance of MM-QUATRE was verified. The conclusion that the MM-QUATRE algorithm is superior to other intelligent algorithms is proved by the experimental results. In addition, the application results of MM-QUATRE algorithm (MM-QUATRE-RSSI) based on RSSI in WSN node localization were analyzed and studied. The results appear that this method has higher localization accuracy than other similar algorithms.

An improvement to the nearest neighbor classifier and f ace recognition experiments

Abstract: The conventional nearest neighbor classier (NNC) directly exploits the dis- tances between the test sample and training samples to perform classication. NNC independently evaluates the distance between the test sample and a training sample. In this paper, we propose to use the classication procedure of sparse representation to im- prove NNC. The proposed method has the following basic idea: the training samples are not uncorrelated and the \distance" between the test sample and a training sample should not be independently calculated and should take into account the relationship between dif- ferent training samples. The proposed methodrst uses a linear combination of all the training samples to represent the test sample and then exploits modied \distance" to classify the test sample. The method obtains the coefficients of the linear combination by solving a linear system. The method then calculates the distance between the test sample and the result of multiplying each training sample by the corresponding coefficient and assumes that the test sample is from the same class as the training sample that has the minimum distance. The method elaborately modies NNC and considers the relationship between different training samples, so it is able to produce a higher classication accu- racy. A large number of face recognition experiments on three face image databases show that the maximum difference between the accuracies of the proposed method and NNC is greater than 10%.

Fault Node Recovery Algorithm for a Wireless Sensor Network

Abstract: This paper proposes a fault node recovery algorithm to enhance the lifetime of a wireless sensor network when some of the sensor nodes shut down. The algorithm is based on the grade diffusion algorithm combined with the genetic algorithm. The algorithm can result in fewer replacements of sensor nodes and more reused routing paths. In our simulation, the proposed algorithm increases the number of active nodes up to 8.7 times, reduces the rate of data loss by approximately 98.8%, and reduces the rate of energy consumption by approximately 31.1%.

Mean shift: a robust approach toward feature space analysis

Abstract: 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.

Digital Watermarking

Abstract: 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.

PORs: Proofs of Retrievability for Large Files

Abstract: 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.