This paper introduces a cutting-edge AI-empowered Unmanned Aerial Vehicle (UAV) system, enriched with stateof-the-art sensor technology, advanced image recognition algorithms, and autonomous navigation capabilities. T...
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ISBN:
(纸本)9798350386813;9798350386820
This paper introduces a cutting-edge AI-empowered Unmanned Aerial Vehicle (UAV) system, enriched with stateof-the-art sensor technology, advanced image recognition algorithms, and autonomous navigation capabilities. The system represents a transformative approach to search and rescue operations, offering unparalleled precision and rapid response times. Our methodology encompasses multifaceted data collection techniques, including surveys, interviews, data mining, Internet of Things (IoT) sensors, and sophisticated video analytics. Machine learning and deep learning models are then applied to process and analyze this data, enabling real-time imagerecognition for precise target identification. The system's AI-driven autonomous navigation algorithms optimize mission planning, resulting in significantly reduced response times and heightened mission success rates. Extensive real-world tests and simulations validate the exceptional performance of the proposed AI-empowered UAV system. These tests underscore its capacity to expedite emergency response efforts in dynamic and challenging environments. In parallel, this paper addresses critical ethical considerations, ememphasizing responsible data handling practices, and robust security measures to ensure the system's integrity in sensitive contexts. As exemplified through a compelling case study of successful rescue operations, this technology represents a groundbreaking advancement in the field. By bridging the gap between cutting- edge technology and life-saving applications, it holds the potential to redefine the landscape of search and rescue missions, ushering in an era of heightened efficiency, precision, and impact.
Analytic criterion for choice of the optimum phase in binary phase-only filters that will provide high correlation performance in optical imagerecognition is developed.
Analytic criterion for choice of the optimum phase in binary phase-only filters that will provide high correlation performance in optical imagerecognition is developed.
Microscopic control of BECs is realised through an atom -density based optimisation of optical potentials. These techniques make progress on the long-standing goal of producing smooth and arbitrary potentials for cold...
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ISBN:
(纸本)9780646825045
Microscopic control of BECs is realised through an atom -density based optimisation of optical potentials. These techniques make progress on the long-standing goal of producing smooth and arbitrary potentials for cold atom trapping, CO 2019 The Authors
The demo shows the effectiveness of a low latency remote control based on 5G and image processing at the edge exploiting artificial intelligence and GPUs to make a robot rover slalom between posts.
ISBN:
(数字)9781943580712
ISBN:
(纸本)9781728167626
The demo shows the effectiveness of a low latency remote control based on 5G and image processing at the edge exploiting artificial intelligence and GPUs to make a robot rover slalom between posts.
With the advancement in Tiny Machine Learning (ML) technologies, their application in enhancing unmanned aerial vehicles (UAVs) for hydraulic engineering surveying and mapping has become increasingly significant. Tiny...
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With the advancement in Tiny Machine Learning (ML) technologies, their application in enhancing unmanned aerial vehicles (UAVs) for hydraulic engineering surveying and mapping has become increasingly significant. TinyML's integration offers a leap in processing efficiency and capabilities, particularly in addressing challenges such as UAV search and monitoring due to loss of contact or forced landings. The usage of medical cyber-physical systems in healthcare can revolutionize existing service delivery methods. The study focuses into the spatial grid mapping technique for three-dimensional information, the PTZ camera spatial grid target locking algorithm, and the UAV detection and image correction algorithm. The UAV target is processed using the surveying UAV target tracking method. TinyML techniques are essential for processing and analyzing these photos quickly. Precise UAV identification and tracking are made possible by the combination of imagerecognition and radar data, which are then processed using TinyML algorithms. This study explores the complexities of algorithms designed specifically for TinyML, such as tracking, UAV detection, grid mapping, and 3D grid space division. Experimental results validate the enhanced capability of this. The results show how well the proposed technique maps and surveys water conservation regions while promptly catching, locking onto, and tracking drones. The algorithm in this study betters than the YOLO, SSD, and RetinaNet algorithms in the recognition and detection of image-oriented surveying and mapping drones.
An optical-digital approach to fabricating matched spatial filters for multiple imagerecognition is discussed. Multiplexing is accomplished in the Fourier plane following optical transformation. A screening filter is...
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An optical-digital approach to fabricating matched spatial filters for multiple imagerecognition is discussed. Multiplexing is accomplished in the Fourier plane following optical transformation. A screening filter is formed digitally by adding or subtracting the intensity spectra of the transformed images to be recognized or ignored. This screening filter is then used to construct optically a composite image which contains the essential features of the various images to be recognized and from which compromising features have been deleted. The composite image is then used to record the desired filter. Initial results for both recognition and discrimination are presented.
The feasibility of classification of stochastic images for color vision in real time has been investigated with two approaches. First, a hybrid incoherent optical correlator based on a quasi-monochromatic cathode ray ...
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The feasibility of classification of stochastic images for color vision in real time has been investigated with two approaches. First, a hybrid incoherent optical correlator based on a quasi-monochromatic cathode ray tube (CRT) is sequentially operated on red, green, and blue channels for statistical pattern recognition. Second, a color TV monitor is employed in the incoherent optical correlator to achieve spectral-spatial statistical pattern recognition in parallel in real time. The spectral-spatial statistical pattern recognition filter is designed with the least-squares linear mapping technique and is compensated for the smearing effects due to the finite spectral bandwidth of the light from CRTs. The experimental results provided demonstrate better recognition reliability when the spectral-spatial filters are used than when only spatial filters are employed.
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