The moving source localization is very sensitive to the knowledge of sensor locations. In this paper, we propose a two-stage closed-form method for locating a moving source by using Time Difference of Arrival (TDOA) a...
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Ternary Content Addressable Memory (TCAM) is widely used in development areas such as network routers and machine learning, which are receiving significant attention. Ferroelectric field-effect transistors (FeFETs) ar...
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Recent research has focused on exploring uncrewed aerial vehicle (UAV)-to-ground communication channels, leading to the development of new fading channel models that incorporate both fading and shadowing phenomena. It...
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Defect classification in the aviation industry plays a crucial role in manufacturing. However, in non-standardized captured scenes, the object to be inspected and the background are often complex and variable, e.g., l...
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In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior(human standing) for the construction of a smart campus. Based on deep learning, we propose an intellige...
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In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior(human standing) for the construction of a smart campus. Based on deep learning, we propose an intelligentstanding human detection (ISHD) method based on an improved single shot multibox detector to detect thetarget of standing human posture in the scene frame of exam room video surveillance at a specific examinationstage. ISHD combines the MobileNet network in a single shot multibox detector network, improves the posturefeature extractor of a standing person, merges prior knowledge, and introduces transfer learning in the trainingstrategy, which greatly reduces the computation amount, improves the detection accuracy, and reduces the trainingdifficulty. The experiment proves that the model proposed in this paper has a better detection ability for the smalland medium-sized standing human body posture in video test scenes on the EMV-2 dataset.
The progress in technology has provided opportunities for innovative solutions to intricate challenges. One possible method is employing reinforcement learning to model flying trajectories in intricate environments. G...
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ISBN:
(纸本)9798331530938
The progress in technology has provided opportunities for innovative solutions to intricate challenges. One possible method is employing reinforcement learning to model flying trajectories in intricate environments. Game development is a discipline that involves intricate reasoning and dynamic interplay between the user and the game environment. By employing several gaming engines, developers are now able to replicate real-life situations through the implementation of diverse machine learning methods. Aircraft simulation in game creation using reinforcement learning involves creating a visual depiction of real-life settings where aircraft may navigate complex environments without direct input from a human user. Currently, reinforcement learning is not widely applied in game development, particularly in simulation-based path finding techniques. This algorithm approaches possess the efficacy and capacity to generate sophisticated neural networks capable of directing an agent to do certain tasks. The aim of this project is to create aircraft simulations for game development by utilizing reinforcement-learning techniques, so that it can provide a foundational idea of the usage of this algorithm in path-detection based decision-making techniques. The goal is to demonstrate the effectiveness of reinforcement learning in a real-world scenario, where the aircraft independently assesses and selects its flying trajectory. The system will undergo testing in three distinct phases, involving the utilization of Blender3D, Unity 3D, and Anaconda prompts. The results will then be compared using TensorFlow. Several training sessions will be conducted in various environments using the Anaconda environment to optimize the outcomes. In the latter stages of development, a dynamic user interface will be implemented to enhance the user's experience. The method is anticipated to produce 152% improved AI-trained data, which can be utilized for constructing extensive simulation and game-proj
Capacitive sensing technology is widely applied in ubiquitous sensing. Its low-power consumption enables it to be used in a wide variety of Industry 4.0 applications. Capacitive Sensors can be combined into Arrays (CS...
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In integrated sensing and communication (ISAC) systems, communication signals can easily be wiretapped by targets (potential eavesdroppers) or detected by wardens. Different from previous works that address a single s...
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When flying a satellite, various operations on-board the satellite require determining quite a precise orientation to properly function, such as Earth or Sun observation, downlink communication and similar. This resul...
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Given the large number of existing metaheuristic optimisation algorithms, there has been an increasing focus on improving benchmarking practices, to gain an improved understanding empirical performance, as well as mat...
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