scholarly journals Nonstationary signal extraction based on BatOMP sparse decomposition technique

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shuang-chao Ge ◽  
Shida Zhou
Keyword(s):  
Fitness Function ◽  
Random Noise ◽  
Cost Effective ◽  
Bat Algorithm ◽  
Signal Extraction ◽  
Decomposition Technique ◽  
Nonstationary Signal ◽  
Sparse Decomposition ◽  
Matching Pursuits ◽  
Adaptive Recognition

AbstractSparse decomposition technique is a new method for nonstationary signal extraction in a noise background. To solve the problem of accuracy and efficiency exclusive in sparse decomposition, the bat algorithm combined with Orthogonal Matching Pursuits (BatOMP) was proposed to improve sparse decomposition, which can realize adaptive recognition and extraction of nonstationary signal containing random noise. Two general atoms were designed for typical signals, and dictionary training method based on correlation detection and Hilbert transform was developed. The sparse decomposition was turned into an optimizing problem by introducing bat algorithm with optimized fitness function. By contrast with several relevant methods, it was indicated that BatOMP can improve convergence speed and extraction accuracy efficiently as well as decrease the hardware requirement, which is cost effective and helps broadening the applications.

scholarly journals Nonstationary Signal Extraction Based on Bat Algorithm Sparse Decomposition Technique

2021 ◽  
Author(s):  
Shuangchao Ge ◽  
Shida Zhou
Keyword(s):  
Fitness Function ◽  
Bat Algorithm ◽  
Signal Extraction ◽  
Nonstationary Signal ◽  
Sparse Decomposition ◽  
Natural Electromagnetic Field ◽  
Effective Signal ◽  
Short Time ◽  
Noisy Background ◽  
Adaptive Recognition

Abstract For nonstationary time series i.e. natural electromagnetic field and acoustical signal, effective signal extraction always requires prior knowledge or hypothesis, and hardly do without artificial judgment. We proposed bat algorithm sparse decomposition (BASD) to realize adaptive recognition and extraction of nonstationary signal in a noisy background. We designed two general atomics for typical signals, and developed dictionary training method based on correlation detection and Hilbert transform. The sparse decomposition was turned into an optimizing problem by introducing bat algorithm with optimized fitness function. By contrast with variational modal decomposition, it was indicated that BASD can effectively extract short time target without inducing global aliasing of local feature, and no preset mode number and late screening were needed.

Nonstationary signal inversion based on shaping regularization for random noise attenuation

2020 ◽  
Vol 17 (3) ◽  
pp. 432-442
Author(s):  
Wu-Yang Yang ◽  
Wei Wang ◽  
Guo-Fa Li ◽  
Xin-Jian Wei ◽  
Wan-Li Wang ◽  
...  
Keyword(s):  
Random Noise ◽  
Noise Attenuation ◽  
Nonstationary Signal ◽  
Random Noise Attenuation ◽  
Signal Inversion

scholarly journals Sparse Decomposition Technique for Segmentation and Compression of Compound Images

2018 ◽  
Vol 29 (1) ◽  
pp. 515-528
Author(s):  
V.N. Manju ◽  
A. Lenin Fred
Keyword(s):  
Huffman Coding ◽  
Gray Wolf ◽  
Decomposition Technique ◽  
Sparse Decomposition ◽  
Compound Images ◽  
Block Based ◽  
Compressed Data ◽  
Fuzzy C Means Algorithm ◽  
Original Information ◽  
Compound Image

Abstract Compression of compound records and images can be more cumbersome than the original information since they can be a mix of text, picture and graphics. The principle requirement of the compound record or images is the nature of the compressed data. In this paper, diverse procedures are used under block-based classification to distinguish the compound image segments. The segmentation process starts with separation of the entire image into blocks by spare decomposition technique in smooth blocks and non smooth blocks. Gray wolf-optimization based FCM (fuzzy C-means) algorithm is employed to segment background, text, graphics, images and overlap, which are then individually compressed using adaptive Huffman coding, embedded zero wavelet and H.264 coding techniques. Exploratory outcomes demonstrate that the proposed conspire expands compression ratio, enhances image quality and additionally limits computational complexity. The proposed method is implemented on the working platform of MATLAB.

Moth Flame Optimization Algorithm Range-Based for Node Localization Challenge in Decentralized Wireless Sensor Network

2019 ◽  
Vol 10 (1) ◽  
pp. 82-109 ◽  
Author(s):  
Mihoubi Miloud ◽  
Rahmoun Abdellatif ◽  
Pascal Lorenz
Keyword(s):  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Dimensional Space ◽  
Fitness Function ◽  
Low Cost ◽  
Bat Algorithm ◽  
Wireless Sensor ◽  
Flower Pollination Algorithm ◽  
Node Localization ◽  
Moth Flame Optimization Algorithm

Recently developments in wireless sensor networks (WSNs) have raised numerous challenges, node localization is one of these issues. The main goal in of node localization is to find accurate position of sensors with low cost. Moreover, very few works in the literature addressed this issue. Recent approaches for localization issues rely on swarm intelligence techniques for optimization in a multi-dimensional space. In this article, we propose an algorithm for node localization, namely Moth Flame Optimization Algorithm (MFOA). Nodes are located using Euclidean distance, thus set as a fitness function in the optimization algorithm. Deploying this algorithm on a large WSN with hundreds of sensors shows pretty good performance in terms of node localization. Computer simulations show that MFOA converge rapidly to an optimal node position. Moreover, compared to other swarm intelligence techniques such as Bat algorithm (BAT), particle swarm optimization (PSO), Differential Evolution (DE) and Flower Pollination Algorithm (FPA), MFOA is shown to perform much better in node localization task.

Efficient feature selection using one-pass generalized classifier neural network and binary bat algorithm with a novel fitness function

2019 ◽  
Vol 24 (6) ◽  
pp. 4575-4587 ◽  
Author(s):  
Akshata K. Naik ◽  
Venkatanareshbabu Kuppili ◽  
Damodar Reddy Edla
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Fitness Function ◽  
Bat Algorithm

BAT algorithm based feature selection: Application in credit scoring

2021 ◽  
pp. 1-10
Author(s):  
Diwakar Tripathi ◽  
B. Ramachandra Reddy ◽  
Y.C.A. Padmanabha Reddy ◽  
Alok Kumar Shukla ◽  
Ravi Kant Kumar ◽  
...  
Keyword(s):  
Feature Selection ◽  
Fitness Function ◽  
Credit Scoring ◽  
Optimization Technique ◽  
Predictive Performance ◽  
Bat Algorithm ◽  
Vital Role ◽  
Support Vector ◽  
Comparative Performance

Credit scoring plays a vital role for financial institutions to estimate the risk associated with a credit applicant applied for credit product. It is estimated based on applicants’ credentials and directly affects to viability of issuing institutions. However, there may be a large number of irrelevant features in the credit scoring dataset. Due to irrelevant features, the credit scoring models may lead to poorer classification performances and higher complexity. So, by removing redundant and irrelevant features may overcome the problem with large number of features. In this work, we emphasized on the role of feature selection to enhance the predictive performance of credit scoring model. Towards to feature selection, Binary BAT optimization technique is utilized with a novel fitness function. Further, proposed approach aggregated with “Radial Basis Function Neural Network (RBFN)”, “Support Vector Machine (SVM)” and “Random Forest (RF)” for classification. Proposed approach is validated on four bench-marked credit scoring datasets obtained from UCI repository. Further, the comprehensive investigational results analysis are directed to show the comparative performance of the classification tasks with features selected by various approaches and other state-of-the-art approaches for credit scoring.

scholarly journals Performance of Parallel Distributed Bat Algorithm using MPI on a PC Cluster

2020 ◽  
Vol 4 (1) ◽  
pp. 19-27 ◽  
Author(s):  
Fazal Noor ◽  
Abdulghani Ibrahim ◽  
Mohammed M. AlKhattab
Keyword(s):  
Message Passing ◽  
Message Passing Interface ◽  
Fitness Function ◽  
Nonlinear Problems ◽  
Bat Algorithm ◽  
C Language ◽  
Pc Cluster ◽  
Elapsed Time ◽  
Speed Up ◽  
Linear Problems

Optimization algorithms are often used to obtain optimal solutions to complex nonlinear problems and appear in many areas such as control, communication, computation, and others. Bat algorithm is a heuristic optimization algorithm and efficient in obtaining approximate best solutions to non-linear problems. In many situations complex problems involve large amount of computations that may require simulations to run for days or weeks or even years for an algorithm to converge to a solution. In this research, a Parallel Distributed Bat Algorithm (PDBA) is formulated using Message Passing Interface (MPI) in C language code for a PC Cluster. The time complexity of PDBA is determined and presented. The performance in terms of speed-up, efficiency, elapsed time, and number of times fitness function is executed is also presented.

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