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.

Facial Landmarks Detection under Occlusions via Extended Restricted Boltzmann Machine

Abstract: Facial landmarks encode critical information about face, which plays an important role in human communications. Accurate detecting and tracking facial landmarks have great potential value in intelligent user interfaces such as human-computer interactions. However, for face images with sever occlusions which may happen in real life such as hand occlusion, gesture occlusion and etc, detecting the facial landmarks is still a challenging problem. In this paper, we present a robust facial landmark detection method for image with occlusions based on Restricted Boltzmann Machine (RBM). We first present a face shape prior model which is constructed based on RBM to model the spatial shape patterns of the face. The detection process is accomplished by combining the prior shape model with the image measurements of facial landmarks. The low accuracy image measurements can be refined by the shape information embedded in the prior model. For the landmarks with severe occlusions, we firstly evaluate and determine the facial landmark occlusions, and replace their image measurements. The new image measurements are then fed into the prior model as evidence to predict the true locations. Evaluation on 3 databases demonstrates that the proposed method can detect facial landmarks accurately under severe occlusion, and achieved significant improvement over the current state of the art methods.

Hybrid Digit-Serial Multiplier for Shifted Polynomial Basis of GF(2 m )

Abstract: Recently, a shifted polynomial basis is a variation of polynomial basis representation. Such kind basis provides better performance in designing bit-parallel and subquadratic space complexity multipliers over binary extension fields. In this paper, we study a new shifted polynomial basis multiplication algorithm to implement a hybrid digit-serial multiplier. The proposed algorithm effectively integrates classic schoolbook multiplication, Karatsuba multiplication algorithms to reduce computational complexity, and the modular multiplication with the shifted polynomial basis reduction. We note that, comparably, the proposed architecture achieves lower computation time and higher bit-throughput compared to the best known digit-serial multipliers. Our proposed multipliers can be modular, regular, and suitable for very-large-scale integration (VLSI) implementations. The proposed digit-serial architecture makes the hardware implementations of cryptographic systems more high-performance, and are thus much suitable for efficient applications such as the elliptic curve cryptography (ECC) and pairing computation.

Compact Sine Cosine Algorithm applied in vehicle routing problem with time window

Abstract: In this paper, the compact Sine Cosine Algorithm (cSCA) is proposed. The cSCA algorithm is not based on population, but simulates the behavior of the actual population through a probability model called virtual population. Compared with the original algorithm, the cSCA algorithm takes up less memory space. However, frequent sampling may lead to poor solution quality. In view of this situation, this paper introduces the intergenerational generation sampling mechanism to improve the cSCA algorithm. Through the CEC2013 function set test, compared with the original SCA algorithm and other compact algorithms, the algorithm proposed in this paper can show strong solving ability. Finally, this paper describes how to apply the proposed algorithm and the SCA algorithm to solve the vehicle routing problem with time window in transportation. The quality of the solution is further improved by introducing the relocate operator. Through Solomon standard test data, the calculation performance of the algorithms is verified.

A Probability-Based Analytical Model Based on Deep Learning for Traffic Information Estimation

Abstract: This paper proposes a probability density function model based on deep learning to analyze the relationships between the number of call arrivals and vehicle speed. Furthermore, a vehicle speed estimation method based on deep learning is proposed to estimate vehicle speed in accordance with the number of call arrivals. A traffic flow estimation method is proposed to estimate traffic flow in accordance with the number of normal location updates. Finally, a traffic density estimation method is proposed to estimate the traffic density in accordance with the estimated vehicle speed and the estimated traffic flow. In experiments, the simulation results showed that the accuracies of estimated vehicle speed and estimated traffic density are 96.36% and 96.45%, respectively.

Indoor Water-Dispensing Robot with Two-Stage Localization

Abstract: With improvement in the quality of people's life and the change of their lifestyle, the demand for intelligent water dispensers is increased. This study, therefore, proposes to de-sign a service robot that can not only move autonomously but also serve people intelligently. In this paper, a solution to de-sign an intelligent water-dispensing service robot is proposed. A new integrated system architecture is introduced, and a two-stage localization, a novel method of indoor robot localization, is described to solve the localization problem of the robot. Ow-ing to the detailed description of the implementation, it is clearly indicated that the proposed solution is reasonable and feasible. Besides, comparisons of various indoor localization methods and indoor robot applications demonstrate that the water-dispensing service robot has advantages over the existing designs and application.

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.