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Dual-Core Photonic Crystal Fiber Plasmonic Refractive Index Sensor: A Numerical Analysis

Photonic Sensors 2019年第2期9卷 151-161页

作者： Alok Kumar PAUL ajay krishno sarkar Department of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi-6204 Bangladesh

A numerical analysis on dual core photonic crystal fiber (DC-PCF) based surface plasmon resonance (SPR) refractive index sensor is presented. The guiding parameters and required sensing performances are examined with finite element method (FEM) based software under MATLAB environment. According to simulation, it is warranted that the proposed refractive index sensor offers the maximum amplitude sensitivity of 554.9 refractive index unit (RIU-1) and 636.5 RIU 1 with the maximum wavelength sensitivity of 5800nm/RIU and 11 500nm/RIU, and the sensor resolutions of 1.72 ×10^-5RIU and 8.7× 10^-6 RIU, at analyte refractive index (RI) of 1.40 for x- and y-polarized modes, respectively. As the sensing performance in different wavelength ranges is quite high, the proposed sensor can be used in simultaneous detection for different wavelength ranges. Therefore, the proposed device is of a suitable platform for detecting biological, chemical, biochemical, and organic chemical analytes.

关键词： Photonic crystal fiber biosensor refractive index sensor finite element method plasmonic material

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Prediction of Attention and Short-Term Memory Loss by EEG Workload Estimation

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Journal of Biosciences and Medicines 2023年第4期11卷 304-318页

作者： Md. Ariful Islam ajay krishno sarkar Md. Imran Hossain Md. Tofail Ahmed A. H. M. Iftekharul Ferdous Department of Electrical and Electronic Engineering Rajshshi University of Engineering and Technology Rajshshi Bangladesh Department of Electrical Electronic and Communication Engineering Pabna University of Science and Technology Pabna Bangladesh Department of Information and Communication Engineering Pabna University of Science and Technology Pabna Bangladesh Department of Electrical and Electronic Engineering Pabna University of Science and Technology Pabna Bangladesh

Mental workload plays a vital role in cognitive impairment. The impairment refers to a person’s difficulty in remembering, receiving new information, learning new things, concentrating, or making decisions that seriously affect everyday life. In this paper, the simultaneous capacity (SIMKAP) experiment-based EEG workload analysis was presented using 45 subjects for multitasking mental workload estimation with subject wise attention loss calculation as well as short term memory loss measurement. Using an open access preprocessed EEG dataset, Discrete wavelet transforms (DWT) was utilized for feature extraction and Minimum redundancy and maximum relevancy (MRMR) technique was used to select most relevance features. Wavelet decomposition technique was also used for decomposing EEG signals into five sub bands. Fourteen statistical features were calculated from each sub band signal to form a 5 × 14 window size. The Neural Network (Narrow) classification algorithm was used to classify dataset for low and high workload conditions and comparison was made using some other machine learning models. The results show the classifier’s accuracy of 86.7%, precision of 84.4%, F1 score of 86.33%, and recall of 88.37% that crosses the state-of-the art methodologies in the literature. This prediction is expected to greatly facilitate the improved way in memory and attention loss impairments assessment.

关键词： Attention Loss Cognitive Impairment EEG Feature Selection SIMKAP Short Term Memory Loss Machine Learning Workload

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Impact assessment of the seasonal hydrological loading on geodetic movement and seismicity in Nepal Himalaya using GRACE and GNSS measurements

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Geodesy and Geodynamics 2022年第5期13卷 445-455页

作者： Devendra Shashikant Nagale Suresh Kannaujiya Param K.Gautam ajay K.Taloor Tandrila sarkar Indian Institute of Remote Sensing ISRODehradunUttarakhand 248001India Wadia Institute of Himalayan Geology DehradunUttarakhand 248001India Department of Remote Sensing and GIS University of JammuJammu 180006India Indian Institute of Technology(Indian School of Mines) Dhanbad 826004India

The Himalayan terrain is an epitome of ongoing convergence and geodetic deformation where both tectonic and non-tectonic forces *** this study,the Gravity Recovery and Climate Experiment(GRACE)and Global Positioning System(GPS)datasets are used to assess the impact of seasonal loading on deformation with seismicity in *** recorded GPS data from 21 Global Navigation Satellite System(GNSS)stations during 2017-2020 are processed with respect to ITRF14 and the Indian reference frame,and the Center for Space Research(CSR)mascon RL06 during 2002-2020 is adopted to estimate the terrestrial water storage(TWS)change over the Ganga-Brahmaputra River *** results indicate that the hydrological loading effect or TWS change shows high negative,high positive,and moderately positive values in pre-monsoon,co-monsoon,and post-monsoon months,*** detrended GPS data of both horizontal and vertical components correlate with the seasonal TWS change using the Pearson correlation coefficient at each GNSS *** addition,the correlation coefficient has been interpolated using inverse distance weighting to investigate the regional TWS influence on geodetic *** the north component,the correlation coefficient ranges from-0.6 to *** the same time,the TWS is positively correlated with geodetic displacement(0.82)in the east component,and the correlation coefficient is negative(-0.69)in the vertical *** negative correlation signifies an inverse relationship between seasonal TWS variation and geodetic *** strain rate is estimated,which shows higher negative values in pre-monsoon than in ***,the effect of seismicity is 47.90%for pre-monsoon,15.97%for co-monsoon,and 17.56%for *** we can infer that the seismicity decreases with the increase of seasonal hydrological ***,the effect of strain is much higher in pre-monsoon than in post-monsoon since the impact of co-monsoon continues to

关键词： Non-tectonic Terrestrial water storage GRACE Strain rate Seismicity

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Artificial Neural Networks for Event Based Rainfall-Runoff Modeling

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Journal of Water Resource and Protection 2012年第10期4卷 891-897页

作者： Archana sarkar Rakesh Kumar National Institute of Hydrology Roorkee India

The Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model the event-based rainfall-runoff process. A case study has been done for ajay river basin to develop event-based rainfall-runoff model for the basin to simulate the hourly runoff at Sarath gauging site. The results demonstrate that ANN models are able to provide a good representation of an event-based rainfall-runoff process. The two important parameters, when predicting a flood hydrograph, are the magnitude of the peak discharge and the time to peak discharge. The developed ANN models have been able to predict this information with great accuracy. This shows that ANNs can be very efficient in modeling an event-based rainfall-runoff process for determining the peak discharge and time to the peak discharge very accurately. This is important in water resources design and management applications, where peak discharge and time to peak discharge are important input

关键词： Artificial Neural Networks (ANNs) Event Based Rainfall-Runoff Process Error Back Propagation Neural Power

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