Chronic Kidney Disease (CKD) is a major global health issue which is affecting million people around the world and with increasing rate of mortality. Mitigation of progression of CKD and better patient outcomes requir...
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Temperature control is of utmost importance in transmission systems. In this paper, a binary channel model is considered in which the transmission of a one causes a temperature increase while communicating a zero caus...
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This paper introduces a novel application of functional neural networks (FNNs) in the domain of electroencephalography-based (EEG-based) brain-computer interfaces (BCIs), targeting self-paced motor execution (ME) and ...
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This paper introduces a novel application of functional neural networks (FNNs) in the domain of electroencephalography-based (EEG-based) brain-computer interfaces (BCIs), targeting self-paced motor execution (ME) and motor imagery (MI). FNNs represent a neural network architecture tailored to smooth processes, and as such have been applied to EEG data classification recently. The paper proposes a comprehensive pipeline encompassing data acquisition, synchronization, pre-processing, training of FNNs, and real-time inference to enable the seamless integration of FNNs into real-world BCI applications. For the first time, FNNs are integrated into an end-to-end pipeline and serve for live inference outside a strict laboratory setting. In pursuit of a more accessible electroencephalography (EEG) artificial intelligence (AI) training scenario, the paper introduces a self-paced environment for auto-labeling EEG data. A custom-designed Pong game serves as the training task and enables subjects to engage in MI/ME tasks while receiving immediate visual feedback. To automate the labeling process of the recorded EEG data, the movements of both arms are captured with inertial measurement units (IMUs) and analyzed through gesture recognition. This novel training framework contributes to more natural and engaging data collection and reduces pre-processing for model training. To provide a comprehensive evaluation, the paper compares the performance of FNN and EEGNet in the self-paced MI/ME tasks. The comparative analysis addresses factors such as classification accuracy, real-time processing speed, and power consumption. Furthermore, the study explores various auto-labeling methods within the self-paced environment, analyzing their impact on the classification performances of both architectures. By evaluating these labeling methods, this work addresses the challenge of accurate and efficient EEG data labeling, crucial for training robust models for prediction of time critical events
The Swanepoel method is a widely used optical technique for characterizing thin films through normal-incidence transmission measurements. A critical step in this approach involves extracting the upper and lower envelo...
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Software defect classification is crucial for enhancing the quality and reliability of software. This research explores the integration of Locally Linear Embedding (LLE) into the preprocessing stages of classification...
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Electric vehicles (EVs) are one of the sustainable modes of transportation that can reduce the air pollution caused by fossil fuel-powered automobiles. The literature overlooks non-technical criteria, including enviro...
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Recent advances in artificial intelligence have prompted the use of machine learning methods in network security. In this paper, we address the issue of imbalanced data that is often present in network security datase...
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Our MEMS-based twistoptics device enables precise control of interlayer gaps and twist angles in photonic crystals, offering high-accuracy, multidimensional light manipulation. This finding holds potential for applica...
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In this letter we propose an optimization-based boundary controller for traffic flow dynamics capable of achieving both stability and invariance conditions. The approach is based on the definition of Boundary Control ...
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The inclusion-exclusion principle together with Legendre type theorems for number of distinct restricted partitions weighted by the parity of their length are used to give several recurrence relations for restricted p...
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