Ground-Based Synthetic Aperture Radar (GBSAR) is crucial for applications like geological surveys and infrastructure monitoring, but data acquisition is often time-consuming and expensive. This paper explores using Ge...
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This paper evaluates the performance of three forecasting algorithms, specifically Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and Exponential Smoothing (ES), in forecasting energy consu...
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The present work describes the hydrothermal synthesis of fractal-like graphitic carbon nitride nanostructures which was characterized in detailed by different spectroscopic and microscopic techniques. XRD endorsed the...
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This paper introduces a deep learning method for lung cancer segmentation through Computed Tomography (CT) image analysis, with the goal of aiding radiologists in the early detection of lung cancer. For this purpose, ...
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We present a model and a computation of signal losses in satellite-to-ground optical communication links for an low Earth orbit (LEO) satellite operating in the C-band, with an optical ground station (OGS) located in ...
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When multiple phase-shifting transformers (PSTs) are installed in a meshed electrical grid, careful coordination of these devices is crucial. Poor coordination can result in inefficient infrastructure use and may even...
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This paper presents a smart walker designed to address mobility issues for individuals with various impairments. The prototype comes with autonomous and manual navigation options;it uses an NVIDIA Jetson Orin Nano wit...
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Assessing the vital signs of small laboratory animals can pose challenges due to their faint heart rates and limited lung capacity. This becomes particularly important during their post-surgery recovery when continuou...
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In the domain of credit risk modeling, logistic regression remains the de facto industry standard. Yet, machine learning and deep learning techniques hold the promise of significantly enhancing current predictive capa...
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State-space graphs and automata serve as fundamental tools for modeling and analyzing the behavior of computational systems. Recurrent neural networks (RNNs) and language models are deeply intertwined, as RNNS provide...
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