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Abstract: In this paper, a novel simplified iterative decoding scheme based on the so-called joint early detection-early stopping (JEDES) technique, is investigated for short-frame turbo coding applications. The proposed scheme attains a lower complexity than existing schemes by carefully integrating two known techniques: early detection and associated built-in cyclic redundancy check. Our analysis and simulation results show that for a frame size of 49bits, the proposed JEDES scheme can achieve a complexity saving of over 30% with little performance loss at practical bit and frame error rates compared with previous results. For a longer frame size 169, the proposed JEDES scheme can approach the GENIE scheme with nearly the same complexity reduction. [ABSTRACT FROM AUTHOR]

Abstract: An explicit expression is derived for the symbol error rate (SER) of the star MQAM system considered by Ioannis and Aris [Closed-form SER expressions for star MQAM in frequency non-selective Rician and Nakagami-m channels. Int J Electron Commun AEU 2005;59:417–20]. [Copyright &y& Elsevier]

Abstract: Phishing attack is growing significantly each year and is considered as one of the most dangerous threats in the Internet which may cause people to lose confidence in e-commerce. In this paper, we present a heuristic method to determine whether a webpage is a legitimate or a phishing page. This scheme could detect new phishing pages which black list based anti-phishing tools could not. We first convert a web page into 12 features which are well selected based on the existing normal and fishing pages. A training set of web pages including normal and fishing pages are then input for a support vector machine to do training. A testing set is finally fed into the trained model to do the testing. Compared to the existing methods, the experimental results show that the proposed phishing detector can achieve the high accuracy rate with relatively low false positive and low false negative rates. [Copyright &y& Elsevier]

Abstract: Radio frequency identification is a contactless, low power, and low cost wireless communication technology. Like barcode, it can be stuck to a product, animal, instrument and etc.; there are lots of applications being used practically in the real world. During communication, all tags and reader are shared with the same communication channel. The tag collision arbitration is a significant issue for reducing the communication overhead during tag identification. In this paper, we propose a fast tag identification algorithm named as Enhanced Anti-collision Algorithm (EAA). EAA uses counter and stack to reduce tag collisions efficiently. Compared to other existing methods, our method not only uses fewer timeslots but reduce the length of a timeslot for identifying tags. [Copyright &y& Elsevier]

Abstract: In this paper, we proposed a modified turbulent particle swarm optimization (named MTPSO) model for solving planar graph coloring problem based on particle swarm optimization. The proposed model is consisting of the walking one strategy, assessment strategy and turbulent strategy. The proposed MTPSO model can solve the planar graph coloring problem using four-colors more efficiently and accurately. Compared to the results shown in , not only the experimental results of the proposed model can get smaller average iterations but can get higher correction coloring rate when the number of nodes is greater than 30. [ABSTRACT FROM AUTHOR]

Abstract: In a multimodal biometric system, the effective fusion method is necessary for combining information from various single modality systems. In this paper the performance of sum rule-based score level fusion and support vector machines (SVM)-based score level fusion are examined. Three biometric characteristics are considered in this study: fingerprint, face, and finger vein. We also proposed a new robust normalization scheme (Reduction of High-scores Effect normalization) which is derived from min–max normalization scheme. Experiments on four different multimodal databases suggest that integrating the proposed scheme in sum rule-based fusion and SVM-based fusion leads to consistently high accuracy. The performance of simple sum rule-based fusion preceded by our normalization scheme is comparable to another approach, likelihood ratio-based fusion (Nandakumar et al., 2008), which is based on the estimation of matching scores densities. Comparison between experimental results on sum rule-based fusion and SVM-based fusion reveals that the latter could attain better performance than the former, provided that the kernel and its parameters have been carefully selected. [Copyright &y& Elsevier]

Abstract: This paper proposes a blind watermarking scheme based on wavelet tree quantization for copyright protection. In such a quantization scheme, there exists a large significant difference while embedding a watermark bit 1 and a watermark bit 0; it then does not require any original image or watermark during watermark extraction process. As a result, the watermarked images look lossless in comparison with the original ones, and the proposed method can effectively resist common image processing attacks; especially for JPEG compression and low-pass filtering. Moreover, by designing an adaptive threshold value in the extraction process, our method is more robust for resisting common attacks such as median filtering, average filtering, and Gaussian noise. Experimental results show that the watermarked image looks visually identical to the original, and the watermark can be effectively extracted. [Copyright &y& Elsevier]

Abstract: In this paper, a new forecasting model based on two computational methods, fuzzy time series and particle swarm optimization, is presented for academic enrollments. Most of fuzzy time series forecasting methods are based on modeling the global nature of the series behavior in the past data. To improve forecasting accuracy of fuzzy time series, the global information of fuzzy logical relationships is aggregated with the local information of latest fuzzy fluctuation to find the forecasting value in fuzzy time series. After that, a new forecasting model based on fuzzy time series and particle swarm optimization is developed to adjust the lengths of intervals in the universe of discourse. From the empirical study of forecasting enrollments of students of the University of Alabama, the experimental results show that the proposed model gets lower forecasting errors than those of other existing models including both training and testing phases. [Copyright &y& Elsevier]

Abstract: Mutual funds have become the most popular products for diversity of investment, since they are able to disperse investment risks to the smallest degree. In selecting mutual funds, the past performance of funds plays a central role in the expectations of the future performance of funds. In 2008, the U.S. sub-prime broke out; numerous investors lost more than half of the capitals donated. Therefore, a good trading strategy is necessary. In this paper, a new funds trading strategy that combines turbulent particle swarm optimization (named TPSO) and mixed moving average techniques is presented and used to find the proper content of technical indicator parameters to achieve high profit and low risk on a mutual fund. The time interval of moving average of the proposed method is adjustable and the trading model could avoid and reduce loss by providing several good buy and sell points. We tested the proposed model using the historical prices of last 10years and the experimental results show that the performance of the proposed model is far better than the best original performance. [Copyright &y& Elsevier]

Bus-based Vehicular Ad-hoc Network (VANET) is an excellent solution to cope with the highly dynamic and inconsistent structure of conventional vehicular network. In this paper, an algorithm for Internet of Energy in the context of Bus-based VANET is proposed, i.e., street-centric routing scheme (SCRS) to address the problem of optimal-route and relay-bus selection. Firstly, we propose a multipath routing scheme using probability of street consistency and probability of path consistency. The multipath improves performance concerning packet delivery ratio and end-to-end delay. Secondly, to enhance the packet forwarding, a new relay-bus selection mechanism is introduced by ant colony optimization (ACO)-based clustering. The relay-bus aims to transmit packets to next forwarding relay. Results show that clustering in ACO algorithm improves the relay-bus selection process by decreasing the computational cost, end-to-end delay, and unnecessary beacon messages. Thus, we conclude that the employment of multipath and clustering significantly improves performance of SCRS. [ABSTRACT FROM AUTHOR]