Dynamic energy enabled differentiation (DEED) image watermarking based on human visual system and wavelet tree classification

In this paper, we present a novel dynamic energy enabled differentiation (DEED) watermarking algorithm based on the wavelet tree classification and human visual system (HVS). The wavelet coefficients of the image are divided into disjoint trees and a wavelet tree consists of 21 coefficients which are divided into 6 blocks. One watermark bit is embedded into one wavelet tree using the energy differentiation of positive and negative modulation between coefficients of each block. In addition, the contrast sensitive function (CSF) of human visual system is also considered for better weighting in watermarking since the wavelet coefficients across the subbands perform different characteristics and importance. As DEED still requires extra storage of side information during the extraction and results non-blind watermarking approach, a random direction differentiation approach called DEED_R is then proposed which is a truly blind watermarking technique. This study has performed intensive comparison for the proposed scheme with other tree energy differentiation based techniques like WTQ, ABW-TMD and WTGM under various geometric and nongeometric attacks. From the experimental results, the advantage of DEED based algorithms is not only with low complexity, but also outperforms WTGM and WTQ in terms of robustness and imperceptibility of watermarking.     

Does stock repurchase declaration affect stock price? Differences between the electrics industry and other industries

This study is to discuss the impact of stock repurchase declaration and purpose of repurchase on the stock price in the backdrop of listed companies on Taiwan’s stock market. Event Study Method is employed to discuss stock price fluctuations while GARCH (Generalized Autoregressive Conditional Heteroscedasticity) is applied to estimate the Market Model regressive coefficients. The samples consisted of companies declaring first stock repurchase are selected from August 9, 2000 to December 31, 2005 with a precondition that all the companies shall be listed ones 150 days prior to declaration. The study results reveal that companies from other industries have considerably bigger average CAR than companies of the electrics industry before and after the declaration of stock repurchase. Companies with application purpose of “maintain stockholders’ equities and corporate credit” have considerably bigger average CAR than companies with application purpose of “transferring stocks to employees”. In industries other than electrics, companies with application purpose of “maintain stockholders’ equities and corporate credit” have bigger accumulated abnormal return response than companies with application purpose of “transferring stocks to employees”. In case of “maintain stockholders’ equities and corporate credit” as the application purpose of stock repurchase, companies from industries other than electrics have relatively higher average CAR response. The empirical study results can serve as a reference for the listed company management and to related academic studies.     

Improved nonlinear trajectory tracking using RBFNN for a robotic helicopter

This paper presents a backstepping control method using radial‐basis‐function neural network (RBFNN) for improving trajectory tracking performance of a robotic helicopter. Many well‐known nonlinear controllers for robotic helicopters have been constructed based on the approximate dynamic model in which the coupling effect is neglected; their qualitative behavior must be further analyzed to ensure that the unmodeled dynamics do not destroy the stability of the closed‐loop system. In order to improve the controller design process, the proposed controller is developed based on the complete dynamic model of robotic helicopters by using an RBFNN function approximation to the neglected dynamic uncertainties, and then proving that all the trajectory tracking error variables are globally ultimately bounded and converge to a neighborhood of the origin. The merits of the proposal controller are exemplified by four numerical simulations, showing that the proposed controller outperforms a well‐known controller in (J. Robust Nonlinear Control 2004; 14 (12):1035–1059). Copyright © 2009 John Wiley & Sons, Ltd.     

Dynamical combination of byte level and Sub-Packet level FEC in HARQ mechanism to reduce error recovery overhead on video streaming over wireless networks

Byte level Forward Error Correction (B-FEC) is efficient for recovery from uniform bit errors, but not suitable to handle recovery from burst bit errors. Conversely, Sub-Packet level Forward Error Correction (SP-FEC) can alleviate the problem of large encoding/decoding delay jitter in Packet level Forward Error Correction (P-FEC) to efficiently handle recovery from burst bit errors, but has large error recovery overhead as P-FEC for recovery from uniform bit errors. This paper proposes a dynamic combination of byte level and Sub-Packet level Forward Error Correction (BSP-FEC) in the Hybrid Automatic Repeat reQuest (HARQ) mechanism to reduce the error recovery overhead. BSP-FEC not only can solve the problems appearing in B-FEC and SP-FEC, but also can get the advantages of B-FEC and SP-FEC in the HARQ mechanism. BSP-FEC replaces the SP-FEC checksum with B-FEC and uses Automatic Repeat reQuest (ARQ) when the network condition deteriorates. BSP-FEC not only utilizes an overhead cost model to dynamically decide the SP-FEC parameter and the B-FEC bit rate according to network conditions, but also utilizes a time constraint model to decide the ARQ retry limit. BSP-FEC dynamically adjusts the FEC redundancy to save bandwidth and improves the Decodable Frame Rate (DFR) and the Peak Signal to Noise Ratio (PSNR) of the delivered video streaming. Accordingly, BSP-FEC can improve multimedia communication performance to both avoid network congestion and shorten end-to-end delay by decreasing effective packet loss rate and packet recovery overhead. Because of the low packet recovery overhead, furthermore, BSP-FEC allows applications to transmit more application data in networks with limited bandwidth. Considering the compatibility, BSP-FEC is implemented in the application layer as the end-to-end protection method to protect packets from errors in wired/wireless networks. Numerical and simulation experimental results show that BSP-FEC obtains better recovery efficiency with the minimum error recovery overhead.     

Performance analysis for hierarchical resource allocation in multiplexed mobile packet data networks

The general packet radio service (GPRS) can be thought of as an overlay network above a Global Systems for Mobile (GSM) network. In cooperation with the GSM system, GPRS supports both voice and packet data services. Obviously, combining voice and data traffic implies the nature of contending with finite radio resources. The paper focuses on voice/data integrated GPRS downlink traffic and proposes two hierarchical resource allocation strategies by considering time slots and radio blocks simultaneously with two different TDMA frame configurations. The radio block based resource allocation can characterize the multiplexing scheme of the practical GPRS operation. The performance of the proposed strategies were evaluated analytically by multidimensional Markov chain and verified through intensive computer simulations. Based on both the analytical and simulation results, it showed that the proposed allocation strategies can offer high utilization of transmission resource, as well as low blocking probability to GPRS users. The results of the study provide not only a practical thinking for implementation but also a guideline for analysis.     

A tag-topic model for blog mining

Blog mining addresses the problem of mining information from blog data. Although mining blogs may share many similarities to Web and text documents, existing techniques need to be reevaluated and adapted for the multidimensional representation of blog data, which exhibit dimensions not present in traditional documents, such as tags. Blog tags are semantic annotations in blogs which can be valuable sources of additional labels for the myriad of blog documents. In this paper, we present a tag-topic model for blog mining, which is based on the Author-Topic model and Latent Dirichlet Allocation. The tag-topic model determines the most likely tags and words for a given topic in a collection of blog posts. The model has been successfully implemented and evaluated on real-world blog data.     

A multi-strategy knowledge interoperability framework for heterogeneous learning objects

This paper presents a knowledge exchange framework that can leverage the interoperability among semantically heterogeneous learning objects. With the release of various e-Learning standards, learning contents and digital courses are easy to achieve cross-platform sharing, exchanging, and even reorganizing. However, knowledge sharing in semantic level is still a challenge due to that the learning materials can be presented in any form, such as audios, videos, web pages, and even flash files. The proposed knowledge exchange framework allows users to share their learning materials (also called “learning objects”) in semantic level automatically. This framework contains two methodologies: the first is a semantic mapping between knowledge bases (i.e. ontologies) which have essentially similar concepts, and the second is an ontology-based classification algorithm for sharable learning objects. The proposed algorithm adopts the IMS DRI standard and classifies the sharable learning objects from heterogeneous repositories into a local knowledge base by their inner meaning instead of keyword matching. Significance of this research lies in the semantic inferring rules for ontology mapping and learning objects classification as well as the full automatic processing and self-optimizing capability. Focused on digital learning materials and contrasted to other traditional technologies, the proposed approach has experimentally demonstrated significantly improvement in performance.