摘要：Owing to rapid advances in the next-generation sequencing technology, the cost of DNA sequencing has been reduced by over several orders of magnitude. However, genomic sequencing of individuals at the population scale is still restricted to a few model species due to the huge challenge of constructing libraries for thousands of samples. Meanwhile, pooled sequencing provides a cost-effective alternative to sequencing individuals separately, which could vastly reduce the time and cost for DNA library preparation. Technological improvements, together with the broad range of biological research questions that require large sample sizes, mean that pooled sequencing will continue to complement the sequencing of individual genomes and become increasingly important in the foreseeable future. However, simply mixing samples together for sequencing makes it impossible to identify reads that belongs to each sample. Barcoding technology could help to solve this problem, nonetheless, currently, barcoding every sample is costly especially for large-scale samples. An alternative to barcoding is combinatorial pooled sequencing which employs pooling pattern rather than short DNA barcodes to encode each sample. In combinatorial pooled sequencing, samples are mixed into few pools according to a carefully designed pooling strategy which allows the sequencing data to be decoded to identify the reads that belongs to the sample that are unique or rare in the population. In this review, we mainly survey the experiment design and decoding procedure for the combinatorial pooled sequencing applied in rare variant and rare haplotype carriers screening, complex genome assembling and single individual haplotyping.

摘要：Various efficient generalized sphere decoding (GSD) algorithms have been proposed to approach optimal ML performance for underdetermined linear systems, by transforming the original problem into the full-column-rank one so that standard SD can be fully applied. However, their design parameters are heuristically set based on observation or the possibility of an ill-conditioned transformed matrix can affect their searching efficiency. This paper presents a better transformation to alleviate the ill-conditioned structure and provides a systematic approach to select design parameters for various GSD algorithms in order to high efficiency. Simulation results on the searching performance confirm that the proposed techniques can provide significant improvement.

摘要：Coding techniques have always been a major area of scientific interest. Due to this interest, many coding schemes were invented. Eventually, their implementation in various systems contributed in the evolvement of Wireless Communications. A breakthrough was definitely Turbo coding. Particularly, the concept of joining two or more convolutional encoders in parallel (PCCC) or in serial (SCCC), along with the iterative decoding technique, literally raised the expectations of the anticipated BER performance. In fact, Concatenated Convolutional Codes clearly outperform convolutional codes. Moreover, various systems, either under development or either for future use, will have high standards. The previous systems should present exceptional tolerance of noise effects and consequently a low overall number of received errors. For this purpose a new PCCC design was developed. The system’s performance analysis, using an AWGN channel, showed better results for various iterations compared to other schemes such as typical PCCC, SCCC and finally a Convolutional encoder with a Viterbi decoder.

摘要：How to identify topological entities during rebuilding features is a critical problem in Feature-Based Parametric Modeling System (FBPMS). In the article, authors proposes a new coding approach to distinguish different entities. The coding mechanism is expatiated,and some typical examples are presented. At last, the algorithm of decoding is put forward based on set theory.

摘要：Turbo code has been shown to have ability to achieve performance that is close to Shannon limit. It has been adopted by various commercial communication systems. Both universal mobile telecommunications system (UMTS) TDD and FDD have also employed turbo code as the error correction coding scheme. It outperforms convolutional code in large block size, but because of its time delay, it is often only used in the non-real-time service. In this paper, we discuss the encoder and decoder structure of turbo code in B3G mobile communication System. In addition, various decoding techniques, such as the Log-MAP, Max-log-MAP and SOVA algorithm for non-real-time service are deduced and compared. The performance results of decoder and algorithms in different configurations are also shown.