Advancements in next-generation sequencer(NGS)platforms have improved NGS sequence data production and reduced the cost involved,which has resulted in the production of a large amount of genome data.The downstream ana...Advancements in next-generation sequencer(NGS)platforms have improved NGS sequence data production and reduced the cost involved,which has resulted in the production of a large amount of genome data.The downstream analysis of multiple associated sequences has become a bottleneck for the growing genomic data due to storage and space utilization issues in the domain of bioinformatics.The traditional string-matching algorithms are efficient for small sized data sequences and cannot process large amounts of data for downstream analysis.This study proposes a novel bit-parallelism algorithm called BitmapAligner to overcome the issues faced due to a large number of sequences and to improve the speed and quality of multiple sequence alignment(MSA).The input files(sequences)tested over BitmapAligner can be easily managed and organized using the Hadoop distributed file system.The proposed aligner converts the test file(the whole genome sequence)into binaries of an equal length of the sequence,line by line,before the sequence alignment processing.The Hadoop distributed file system splits the larger files into blocks,based on a defined block size,which is 128 MB by default.BitmapAligner can accurately process the sequence alignment using the bitmask approach on large-scale sequences after sorting the data.The experimental results indicate that BitmapAligner operates in real time,with a large number of sequences.Moreover,BitmapAligner achieves the exact start and end positions of the pattern sequence to test the MSA application in the whole genome query sequence.The MSA’s accuracy is verified by the bitmask indexing property of the bit-parallelism extended shifts(BXS)algorithm.The dynamic and exact approach of the BXS algorithm is implemented through the MapReduce function of Apache Hadoop.Conversely,the traditional seeds-and-extend approach faces the risk of errors while identifying the pattern sequences’positions.Moreover,the proposed model resolves the largescale data challenges that are covered through MapReduce in the Hadoop framework.Hive,Yarn,HBase,Cassandra,and many other pertinent flavors are to be used in the future for data structuring and annotations on the top layer of Hadoop since Hadoop is primarily used for data organization and handles text documents.展开更多
Recently, cryptographic applications based on finite fields have attracted much attention. The most demanding finite field arithmetic operation is multiplication. This investigation proposes a new multiplication algor...Recently, cryptographic applications based on finite fields have attracted much attention. The most demanding finite field arithmetic operation is multiplication. This investigation proposes a new multiplication algorithm over GF（2^m） using the dual basis representation. Based on the proposed algorithm, a parallel-in parallel-out systolic multiplier is presented, The architecture is optimized in order to minimize the silicon covered area （transistor count）. The experimental results reveal that the proposed bit-parallel multiplier saves about 65% space complexity and 33% time complexity as compared to the traditional multipliers for a general polynomial and dual basis of GF（2^m）.展开更多
SPIHT和无链表SPIHT(Not List SPIHT)是高效的图像压缩算法,但是抗误码性差、压缩速度慢等缺点限制了其在航天领域的应用。文章针对上述两个缺点对算法进行了改进,采用Le Gall5/3小波对遥感图像进行小波分解,将小波域系数分家族块进行...SPIHT和无链表SPIHT(Not List SPIHT)是高效的图像压缩算法,但是抗误码性差、压缩速度慢等缺点限制了其在航天领域的应用。文章针对上述两个缺点对算法进行了改进,采用Le Gall5/3小波对遥感图像进行小波分解,将小波域系数分家族块进行索引、扫描和码率分配,按照比特平面或运算进行重要性预测,实现了N个位平面同时编码。改进算法与SPIHT相比易于硬件编程实现,仿真结果显示,解压后图像峰值信噪比(PSNR)提高了0.2～0.6db,压缩速度提高了4～6倍。用硬件实现时如果采用并行和流水线操作,速度还可以进一步提高。展开更多
基金This work was supported in part by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2018R1C1B5084424)in part by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2019R1A6A1A03032119).
文摘Advancements in next-generation sequencer(NGS)platforms have improved NGS sequence data production and reduced the cost involved,which has resulted in the production of a large amount of genome data.The downstream analysis of multiple associated sequences has become a bottleneck for the growing genomic data due to storage and space utilization issues in the domain of bioinformatics.The traditional string-matching algorithms are efficient for small sized data sequences and cannot process large amounts of data for downstream analysis.This study proposes a novel bit-parallelism algorithm called BitmapAligner to overcome the issues faced due to a large number of sequences and to improve the speed and quality of multiple sequence alignment(MSA).The input files(sequences)tested over BitmapAligner can be easily managed and organized using the Hadoop distributed file system.The proposed aligner converts the test file(the whole genome sequence)into binaries of an equal length of the sequence,line by line,before the sequence alignment processing.The Hadoop distributed file system splits the larger files into blocks,based on a defined block size,which is 128 MB by default.BitmapAligner can accurately process the sequence alignment using the bitmask approach on large-scale sequences after sorting the data.The experimental results indicate that BitmapAligner operates in real time,with a large number of sequences.Moreover,BitmapAligner achieves the exact start and end positions of the pattern sequence to test the MSA application in the whole genome query sequence.The MSA’s accuracy is verified by the bitmask indexing property of the bit-parallelism extended shifts(BXS)algorithm.The dynamic and exact approach of the BXS algorithm is implemented through the MapReduce function of Apache Hadoop.Conversely,the traditional seeds-and-extend approach faces the risk of errors while identifying the pattern sequences’positions.Moreover,the proposed model resolves the largescale data challenges that are covered through MapReduce in the Hadoop framework.Hive,Yarn,HBase,Cassandra,and many other pertinent flavors are to be used in the future for data structuring and annotations on the top layer of Hadoop since Hadoop is primarily used for data organization and handles text documents.
文摘Recently, cryptographic applications based on finite fields have attracted much attention. The most demanding finite field arithmetic operation is multiplication. This investigation proposes a new multiplication algorithm over GF（2^m） using the dual basis representation. Based on the proposed algorithm, a parallel-in parallel-out systolic multiplier is presented, The architecture is optimized in order to minimize the silicon covered area （transistor count）. The experimental results reveal that the proposed bit-parallel multiplier saves about 65% space complexity and 33% time complexity as compared to the traditional multipliers for a general polynomial and dual basis of GF（2^m）.
文摘SPIHT和无链表SPIHT(Not List SPIHT)是高效的图像压缩算法,但是抗误码性差、压缩速度慢等缺点限制了其在航天领域的应用。文章针对上述两个缺点对算法进行了改进,采用Le Gall5/3小波对遥感图像进行小波分解,将小波域系数分家族块进行索引、扫描和码率分配,按照比特平面或运算进行重要性预测,实现了N个位平面同时编码。改进算法与SPIHT相比易于硬件编程实现,仿真结果显示,解压后图像峰值信噪比(PSNR)提高了0.2～0.6db,压缩速度提高了4～6倍。用硬件实现时如果采用并行和流水线操作,速度还可以进一步提高。
