scholarly journals IN-BODY RANGING FOR ULTRA-WIDE BAND WIRELESS CAPSULE ENDOSCOPY USING NEURAL NETWORKS BASED ON PARTICLE SWARM OPTIMIZATION

2018 ◽  
Vol 6 (2) ◽  
pp. 207-217
Author(s):  
Muzaffer KANAAN ◽  
Rüştü AKAY ◽  
Memduh SUVEREN
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Capsule Endoscopy ◽  
Particle Swarm ◽  
Wide Band ◽  
Wireless Capsule Endoscopy ◽  
Ultra Wide Band ◽  
Swarm Optimization ◽  
Wireless Capsule

scholarly journals Design and Simulation-Based Parametric Studies of a Compact Ultra-Wide Band Antenna for Wireless Capsule Endoscopy System at Inside Body Environment

2021 ◽  
pp. 186-195
Author(s):  
Md. Abdullah Al Rakib ◽  
◽  
Shamim Ahmad ◽  
Tareq Mohammad Faruqi ◽  
Mainul Haque ◽  
...  
Keyword(s):  
Capsule Endoscopy ◽  
High Speed ◽  
Wide Band ◽  
Wireless Capsule Endoscopy ◽  
Ultra Wide Band ◽  
Gi Tract ◽  
Time Data ◽  
Surface Current Density ◽  
Tissue Properties ◽  
Wireless Capsule

This paper focuses to design a compact (110mm³) Ultra-Wide Band (UWB) (3.1GHz to 10.6GHz) antenna, which covers almost the whole 10dB impedance matching bandwidth of the UWB range. Two of the main specialties of this article over other related articles are its antenna’s wider bandwidth (approx. 7.3GHz) and antenna’s simulation environment. No other papers consider such a realistic model to simulate their antenna, before. Due to its wider bandwidth, this antenna can be employed in the Wireless Capsule Endoscopy (WCE) system, which mainly requires a high-speed real-time data transfer-capable antenna. The antenna was examined inside simplified human Gastrointestinal (GI) tract phantoms (Colon, Esophagus, Small Intestine and Stomach) as well as the human Voxel GI tract model by maintaining proper tissue properties for the sake of accurate parametric results. Biocompatible material polyimide was used to construct the capsule wall to fulfill the system’s biocompatibility. In the result analysis part, the proposed antenna’s SAR (Specific Absorption Rate) or electromagnetic energy amount, consumed by near-side body tissue was considered and found in the acceptable region, according to Federal Communication Commission (FCC)’s regulation. Also, other crucial antenna parameters such as VSWR, reflection coefficient, radiation characteristics, efficiencies, directivity and surface current density were adoptable compare to other related articles. The Finite Integration Technique (FIT) of CST Microwave Studio Suite 2020 was used to investigate the antenna parameters.

scholarly journals Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1213
Author(s):  
Ahmed Aljanad ◽  
Nadia M. L. Tan ◽  
Vassilios G. Agelidis ◽  
Hussain Shareef
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Solar Irradiance ◽  
Particle Swarm ◽  
Time Interval ◽  
Time Intervals ◽  
Swarm Optimization ◽  
Backpropagation Neural Networks ◽  
Proposed Model ◽  
Short Time

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.

scholarly journals A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Mehmet Hacibeyoglu ◽  
Mohammed H. Ibrahim
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Artificial Neural Networks ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Continuous Optimization ◽  
Swarm Optimization ◽  
Feed Forward ◽  
Artificial Neural

Multilayer feed-forward artificial neural networks are one of the most frequently used data mining methods for classification, recognition, and prediction problems. The classification accuracy of a multilayer feed-forward artificial neural networks is proportional to training. A well-trained multilayer feed-forward artificial neural networks can predict the class value of an unseen sample correctly if provided with the optimum weights. Determining the optimum weights is a nonlinear continuous optimization problem that can be solved with metaheuristic algorithms. In this paper, we propose a novel multimean particle swarm optimization algorithm for multilayer feed-forward artificial neural networks training. The proposed multimean particle swarm optimization algorithm searches the solution space more efficiently with multiple swarms and finds better solutions than particle swarm optimization. To evaluate the performance of the proposed multimean particle swarm optimization algorithm, experiments are conducted on ten benchmark datasets from the UCI repository and the obtained results are compared to the results of particle swarm optimization and other previous research in the literature. The analysis of the results demonstrated that the proposed multimean particle swarm optimization algorithm performed well and it can be adopted as a novel algorithm for multilayer feed-forward artificial neural networks training.

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