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rabindra.tharu.np@gmail.com
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Neural Signature and Decoding of Unmanned Aerial Vehicle Operators in Emergency Scenarios Using Electroencephalography
Brain-computer interface (BCI) offers a novel means of communication and control for individuals with disabilities and can also enhance the interactions between humans and machines for the broader population. This paper explores the brain neural signatures of unmanned aerial vehicle (UAV) operators in emergencies and develops an operator’s electroencephalography (EEG) signals-based detection method for UAV…
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rabindra.tharu.np@gmail.com
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Θ-Net: A Deep Neural Network Architecture for the Resolution Enhancement of Phase-Modulated Optical Micrographs In Silico
Optical microscopy is widely regarded to be an indispensable tool in healthcare and manufacturing quality control processes, although its inability to resolve structures separated by a lateral distance under ~200 nm has culminated in the emergence of a new field named , while this too is prone to several caveats (namely phototoxicity, interference caused by…
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rabindra.tharu.np@gmail.com
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Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives
Electroencephalography (EEG) is useful for studying brain activity in major depressive disorder (MDD), particularly focusing on theta and alpha frequency bands via power spectral density (PSD). However, PSD-based analysis has often produced inconsistent results due to difficulties in distinguishing between periodic and aperiodic components of EEG signals. We analyzed EEG data from 114 young adults,…
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rabindra.tharu.np@gmail.com
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Calibration of Low-Cost Moisture Sensors in a Biochar-Amended Sandy Loam Soil with Different Salinity Levels
With the increasing focus on irrigation management, it is crucial to consider cost-effective alternatives for soil water monitoring, such as multi-point monitoring with low-cost soil moisture sensors. This study assesses the accuracy and functionality of low-cost sensors in a sandy loam (SL) soil amended with biochar at rates of 15.6 and 31.2 tons/ha by calibrating…
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rabindra.tharu.np@gmail.com
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Standard-Deviation-Based Adaptive Median Filter for Elimination of Batwing Effects in Step Microstructure Measurement Using Digital Holography
Digital holography has transformative potential for the measurement of stacked-chip microstructures due to its non-invasive, single-shot, full-field characteristics. However, significant light scattering and diffraction at steep edges in step microstructures cause the batwing effect, leading to measurement errors. Herein, we propose a standard-deviation-based adaptive median filter to eliminate batwing effects in step microstructure measurement using…
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rabindra.tharu.np@gmail.com
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Advanced Necklace for Real-Time PPG Monitoring in Drivers
Monitoring heart rate (HR) through photoplethysmography (PPG) signals is a challenging task due to the complexities involved, even during routine daily activities. These signals can indeed be heavily contaminated by significant motion artifacts resulting from the subjects’ movements, which can lead to inaccurate heart rate estimations. In this paper, our objective is to present an…
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rabindra.tharu.np@gmail.com
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Validity and Reliability of Movesense HR+ ECG Measurements for High-Intensity Running and Cycling
Low-cost, portable devices capable of accurate physiological measurements are attractive tools for coaches, athletes, and practitioners. The purpose of this study was primarily to establish the validity and reliability of Movesense HR+ ECG measurements compared to the criterion three-lead ECG, and secondarily, to test the industry leader Garmin HRM. Twenty-one healthy adults participated in running…
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rabindra.tharu.np@gmail.com
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Ada-LT IP: Functional Discriminant Analysis of Feature Extraction for Adaptive Long-Term Wi-Fi Indoor Localization in Evolving Environments
Wi-Fi fingerprint-based indoor localization methods are effective in static environments but encounter challenges in dynamic, real-world scenarios due to evolving fingerprint patterns and feature spaces. This study investigates the temporal variations in signal strength over a 25-month period to enhance adaptive long-term Wi-Fi localization. Key aspects explored include the significance of signal features, the effects…
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rabindra.tharu.np@gmail.com
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Auto-Scaling Techniques in Cloud Computing: Issues and Research Directions
In the dynamic world of cloud computing, auto-scaling stands as a beacon of efficiency, dynamically aligning resources with fluctuating demands. This paper presents a comprehensive review of auto-scaling techniques, highlighting significant advancements and persisting challenges in the field. First, we overview the fundamental principles and mechanisms of auto-scaling, including its role in improving cost efficiency,…
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rabindra.tharu.np@gmail.com
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Ultrasonic Assessment of Liver Fibrosis Using One-Dimensional Convolutional Neural Networks Based on Frequency Spectra of Radiofrequency Signals with Deep Learning Segmentation of Liver Regions in B-Mode Images: A Feasibility Study
The early detection of liver fibrosis is of significant importance. Deep learning analysis of ultrasound backscattered radiofrequency (RF) signals is emerging for tissue characterization as the RF signals carry abundant information related to tissue microstructures. However, the existing methods only used the time-domain information of the RF signals for liver fibrosis assessment, and the liver…
