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The iterative property of inverse butterfly permutation network makes it possible to implement shift operation with simple routing algorithm, which has high application value in cryptography, digital image processing and other fields. Based on the inverse butterfly network, this paper proposes a subword shift unit, which integrates the operations of subword rotation shift, subword logical shift and...
In this paper, we hybridize the improved gravitational search algorithm (IGSA) with kernel based extreme learning machine (KELM) method. Based on this, a novel hybrid system IGSA-KELM is proposed to improve the generalization performance for classification problems. In this system, IGSA is designed by combining the search strategy of particle swarm optimization and GSA to effectively reduce the problem...
Using multi-channel EEG acquisition equipment for mental fatigue detection has several limitations to its application. Therefore, this paper will explore single-channel EEG signals based on portable EEG acquisition equipment to detect mental fatigue. In addition, improved single sub-band reconstruction of wavelet packet algorithm is proposed to eliminate frequency aliasing. This aliasing phenomenon...
The blind separation of mixed images is a very exciting area of research. However, classical techniques such as eigen and singular value decomposition, which are based on second order statistics, fail to blindly separate mixed signals in many circumstances. A rapidly developed statistical method during last few years, Independent Component Analysis (ICA), which is based on higher order statistics,...
Sequence alignment is one of the most fundamental and important operation in bioinformatics. Through sequence alignment, we can find the sequence's information of function, structure and evolution. BLAST is one of the most popular algorithms in the field of sequence alignment. In this paper, we have designed a GPU-based parallel BLAST algorithm and implemented it on the brook+ platform. The main task...
A generic method for multi-objective optimization for bad data identification is presented based on multi-objective vector evaluated particle swarm optimization (VEPSO) algorithm. This multi-objective VEPSO is made adaptive in nature by allowing its vital parameters to change with iterations. This adaptability helps the algorithms to explore the search space more efficiently. After the bad data are...
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