Accurate identification of protein-coding regions （exons） in DNA sequences has been a challenging task in bioinformatics. Particularly the coding regions have a 3-base periodicity, which forms the basis of all exon ...Accurate identification of protein-coding regions （exons） in DNA sequences has been a challenging task in bioinformatics. Particularly the coding regions have a 3-base periodicity, which forms the basis of all exon identifica- tion methods. Many signal processing tools and techniques have been applied successfully for the identification task but still improvement in this direction is needed. In this paper, we have introduced a new promising model-independent time-frequency filtering technique based on S-transform for accurate identification of the coding regions. The S-transform is a powerful linear time-frequency representation useful for filtering in time-frequency domain. The potential of the proposed technique has been assessed through simulation study and the results obtained have been compared with the existing methods using standard datasets. The comparative study demonstrates that the proposed method outperforms its counterparts in identifying the coding regions.展开更多
The aim of this paper is to present a new method for flight flutter modal parameter identification in noisy environment. This method employs a time-frequency （TF） filter to reduce the noise before identification, wh...The aim of this paper is to present a new method for flight flutter modal parameter identification in noisy environment. This method employs a time-frequency （TF） filter to reduce the noise before identification, which depends on the localization property of sweep excitation in TF domain. Then, a generalized total least square （GTLS） identification algorithm based on stochastic framework is applied to the enhanced data. System identification with noisy data is transformed into a generalized total least square problem, and the solution is carried out by the generalized singular value decomposition （GSVD） to avoid the intensive nonlinear optimization computation. A nearly maximum likelihood property can be achieved by ＇optimally＇ weighted generalized total least square. Finally, the efficiency of the method is illustrated by means of flight test data.展开更多
文摘干扰机在一个脉冲内精确复制、快速转发形成间歇采样转发干扰(interrupted-sampling repeater jamming,ISRJ),严重影响雷达工作性能。针对这一问题,从波形设计和滤波两个角度出发,提出一种频率捷变波形联合时频滤波器对抗间歇采样转发干扰的方法。首先,将常规线性调频(linear frequency modulation,LFM)信号划分为若干子段并随机重排,构建脉内频率捷变波形;然后,对回波信号进行时频分析得到时域投影分量,并利用大津(OTSU)算法计算最佳分割阈值,提取未被干扰的信号段;最后,通过卷积运算构建滤波器对脉压输出进行带通滤波,保留真实目标,实现干扰抑制。仿真结果表明,所提方法能够在干扰功率较大、信噪比低的情况下有效对抗间歇采样转发干扰,极大地提升了雷达抗干扰性能。
文摘Accurate identification of protein-coding regions （exons） in DNA sequences has been a challenging task in bioinformatics. Particularly the coding regions have a 3-base periodicity, which forms the basis of all exon identifica- tion methods. Many signal processing tools and techniques have been applied successfully for the identification task but still improvement in this direction is needed. In this paper, we have introduced a new promising model-independent time-frequency filtering technique based on S-transform for accurate identification of the coding regions. The S-transform is a powerful linear time-frequency representation useful for filtering in time-frequency domain. The potential of the proposed technique has been assessed through simulation study and the results obtained have been compared with the existing methods using standard datasets. The comparative study demonstrates that the proposed method outperforms its counterparts in identifying the coding regions.
文摘The aim of this paper is to present a new method for flight flutter modal parameter identification in noisy environment. This method employs a time-frequency （TF） filter to reduce the noise before identification, which depends on the localization property of sweep excitation in TF domain. Then, a generalized total least square （GTLS） identification algorithm based on stochastic framework is applied to the enhanced data. System identification with noisy data is transformed into a generalized total least square problem, and the solution is carried out by the generalized singular value decomposition （GSVD） to avoid the intensive nonlinear optimization computation. A nearly maximum likelihood property can be achieved by ＇optimally＇ weighted generalized total least square. Finally, the efficiency of the method is illustrated by means of flight test data.
