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Xu M.,Shanghai Maritime University | Xu M.,Shanghai Key Laboratory of Intelligent Information Processing | Liu G.,Shanghai Maritime University
International Journal of Distributed Sensor Networks | Year: 2013

Low data delivery efficiency and high energy consumption are the inherent problems in Underwater Wireless Sensor Networks (UWSNs) characterized by the acoustic channels. Existing energy-efficient routing algorithms have been shown to reduce energy consumption of UWSNs to some extent, but still neglect the correlation existing in the local data of sensor nodes. In this paper, we present a Multi-population Firefly Algorithm (MFA) for correlated data routing in UWSNs. We design three kinds of fireflies and their coordination rules in order to improve the adaptability of building, selecting, and optimization of routing path considering the data correlation and their sampling rate in various sensor nodes. Different groups of fireflies conduct their optimization in the evolution in order to improve the convergence speed and solution precision of the algorithm. Moreover, after the data packets are merged during the process of routing path finding, MFA can also eliminate redundant information before they are sent to the sink node, which in turn saves energy and bandwidth. Simulation results have shown that MFA achieves better performance than existing protocols in metrics of packet delivery ratio, energy consumption, and network throughput. © 2013 Ming Xu and Guangzhong Liu. Source

Liu B.,Harbin Institute of Technology | Liu B.,Shanghai Key Laboratory of Intelligent Information Processing | Wang X.,Harbin Institute of Technology | Zou Q.,Xiamen University | And 2 more authors.
Molecular Informatics | Year: 2013

Protein remote homology detection is a key problem in bioinformatics. Currently the discriminative methods, such as Support Vector Machine (SVM) can achieve the best performance. The most efficient approach to improve the performance of SVM-based methods is to find a general protein representation method that is able to convert proteins with different lengths into fixed length vectors and captures the different properties of the proteins for the discrimination. The bottleneck of designing the protein representation method is that native proteins have different lengths. Motivated by the success of the pseudo amino acid composition (PseAAC) proposed by Chou, we applied this approach for protein remote homology detection. Some new indices derived from the amino acid index (AAIndex) database are incorporated into the PseAAC to improve the generalization ability of this method. Finally, the performance is further improved by combining the modified PseAAC with profile-based protein representation containing the evolutionary information extracted from the frequency profiles. Our experiments on a well-known benchmark show this method achieves superior or comparable performance with current state-of-theart methods. © 2013 Wiley-VCH Verlag GmbH and Co. Source

Ding Y.,Shanghai University | Ding Y.,Shanghai Key Laboratory of Intelligent Information Processing
IEEE Transactions on Information Theory | Year: 2016

Burst errors are a type of distortion in many data communications and data storage channels. In this paper, we consider the list decodability of codes for single burst error case and phased-burst error case independently. Firstly, we analyze the list decodability of random codes, and we show that the burst list decoding radius and the rate of random codes achieve the Singleton bound and the Gilbert-Varshamov bound for single case and phased case, respectively. Second, we illustrate that cyclic codes and algebraic geometry codes are good burst list-decodable codes for single case and phased case, respectively. © 2015 IEEE. Source

Liu B.,Harbin Institute of Technology | Liu B.,Shanghai Key Laboratory of Intelligent Information Processing | Liu B.,Gordon Life Science Institute | Xu J.,Harbin Institute of Technology | And 6 more authors.
PLoS ONE | Year: 2014

Playing crucial roles in various cellular processes, such as recognition of specific nucleotide sequences, regulation of transcription, and regulation of gene expression, DNA-binding proteins are essential ingredients for both eukaryotic and prokaryotic proteomes. With the avalanche of protein sequences generated in the postgenomic age, it is a critical challenge to develop automated methods for accurate and rapidly identifying DNA-binding proteins based on their sequence information alone. Here, a novel predictor, called "iDNA-Prot|dis", was established by incorporating the amino acid distancepair coupling information and the amino acid reduced alphabet profile into the general pseudo amino acid composition (PseAAC) vector. The former can capture the characteristics of DNA-binding proteins so as to enhance its prediction quality, while the latter can reduce the dimension of PseAAC vector so as to speed up its prediction process. It was observed by the rigorous jackknife and independent dataset tests that the new predictor outperformed the existing predictors for the same purpose. As a user-friendly web-server, iDNA-Prot|dis is accessible to the public at http://bioinformatics.hitsz.edu.cn/iDNAProt-dis/. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step protocol guide is provided on how to use the web-server to get their desired results without the need to follow the complicated mathematic equations that are presented in this paper just for the integrity of its developing process. It is anticipated that the iDNAProt| dis predictor may become a useful high throughput tool for large-scale analysis of DNA-binding proteins, or at the very least, play a complementary role to the existing predictors in this regard. © 2014 Liu et al. Source

Jin L.,Shanghai Key Laboratory of Intelligent Information Processing
IEEE Transactions on Information Theory | Year: 2014

It has been a great challenge to construct new quantum maximum-distance- separable (MDS) codes. In particular, it is very hard to construct the quantum MDS codes with relatively large minimum distance. So far, except for some sparse lengths, all known q-ary quantum MDS codes have minimum distance ≤q/2+1. In this paper, we provide a construction of the quantum MDS codes with minimum distance >q2+1. In particular, we show the existence of the q -ary quantum MDS codes with length n=q2+1 and minimum distance d for any d≤q+1 (this result extends those given in [10], [12], and [13]); and with length (q2+2)/3 and minimum distance d for any d≤ (2q+2)/3 if 3\vert (q+1). Our method is through Hermitian self-orthogonal codes. The main idea of constructing the Hermitian self-orthogonal codes is based on the solvability in Fq of a system of homogenous equations over F q2. © 2014 IEEE. Source

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