Acoustic monitoring of rare and elusive bird species presents significant challenges due to the complexity of environmental noise and the variability in bird vocalizations. This study proposes a novel deep learning fr...
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This paper presents a technique to design and implementation of 4-bit digital calculator using Verilog on FPGA. The calculator is aimed at providing basic arithmetic functionalities including addition, subtraction, mu...
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The adoption of solar-powered water pumping system in agriculture has been helpful due to its potential to mitigate energy costs and reduce carbon emissions. However, the conventional control mechanisms lack real-time...
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Human life is advancing and getting better in every way as a result of the industry’s rapid progress in this area. In the current environment, automated systems are preferred to non-automated approaches. The Internet...
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The burgeoning field of crowd behavior analysis necessitates advanced methodologies to ensure public safety and enhance event management strategies. Traditional approaches often fall short in addressing the complexity...
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The burgeoning field of crowd behavior analysis necessitates advanced methodologies to ensure public safety and enhance event management strategies. Traditional approaches often fall short in addressing the complexity and dynamics of large crowd gatherings, leading to inaccuracies in density estimation, individual tracking, and behavior analysis. In response to these limitations, this research introduces a comprehensive suite of algorithms designed to revolutionize crowd behavior analysis through improved accuracy and adaptability. Firstly, we propose a dynamic density estimation algorithm integrating Faster R-CNN with Kernel Density Estimation, tailored for real-time adaptation to fluctuating crowd densities. This method not only refines person counting but also offers detailed spatial insights into crowd distribution, demonstrating a Mean Absolute Error of less than 5% when compared to ground truth data. Addressing the challenges in crowd tracking, we present a novel multi-object tracking framework that synergizes YOLOv8 object detection with the Hungarian algorithm to mitigate occlusions and trajectory ambiguities. This model maintains high tracking accuracy, achieving an Identity F1 score of over 0.8 even in densely populated environments. In the realm of crowd behavior recognition, our research introduces a hierarchical framework that amalgamates 3D Convolutional Neural Networks and Graph Convolutional Networks to dissect and understand both individual actions and collective behaviors. This innovative approach not only deciphers complex crowd dynamics but also sets a new benchmark in behavior recognition accuracy, exceeding 90% on UCSD Crowd and Avenue Dataset Samples. Furthermore, we pioneer a self-supervised abnormality detection framework leveraging Variational Adversarial Autoencoders, which circumvents the limitations of traditional anomaly detection by eliminating the need for labeled data samples. This model showcases remarkable adaptability and scalabil
This paper presents a novel approach for air pollution monitoring using Photonic Crystal Fiber (PCF) with unique optical properties. Through Finite Element Method (FEM) simulations in COMSOL, the study explores how th...
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Memories form a very large part of VLSI circuits. A significant portion of VLSI circuits are made of memories. The design of memory systems aims to store enormous volumes of data. "MBIST" refers to Memory Bu...
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The predator has the ability to quickly respond to the misjudged decision and hunt the camouflaged target by analyzing its movement. Those decision compensation and movement analysis for hunting are closely tied to te...
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Wireless sensor networks (WSNs) represent a critical research domain within the Internet of Things (IoT) technology. The distributed Kalman filter (DKF) has garnered significant attention as an information fusion meth...
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Multi-purpose surveillance rovers are dynamic technological innovations intended to meet diverse surveillance requirements across security, exploration, disaster management, and environmental monitoring domains. This ...
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