This study uses quantum-inspired techniques to ad-dress the DC optimal power flow problem considering frequency constraints. Although numerous analytical and data-driven meth-ods have been developed to solve DC-OPF un...
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The embracement of the wireless technology by the automobile industry has led to novel research interests in the field of Vehicular Ad-hoc Networks (VANETs). In addition to ad-hoc mode, a VANET supports infrastructure...
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A smart city is a fast-moving terrain that requires efficient and smart security mechanisms with resilience for solving the intricate challenges of modern urbanism. The current paper presents the critical review of ma...
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Human values capture what people and societies perceive as desirable, transcend specific situations and serve as guiding principles for action. People’s value systems motivate their positions on issues concerning the...
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Infrastructure development and its design are more complex because of its constraint on budget and efficiency. By remembering these difficulties, a modified electrical structure is necessary for the nation. The power ...
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The exponential growth of the metropolitan cities of the country has generated and magnified urban sprawl into the problematic proportions. Lack of the efficient traffic control and management has many a times lead to...
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Growing interest in integrating in-vehicle data processing and artificial intelligence (AI) has significantly improved performance. This improvement extends to technologies like object detection, with rapid progress, ...
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The self-driving car industry is gaining attention for its role in motion planning technology. Deep learning approaches have been implemented to plan autonomous vehicles' motion, but their effectiveness depends on...
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Machine learning with optical neural networks has featured unique advantages of the information processing including high speed,ultrawide bandwidths and low energy consumption because the optical dimensions(time,space...
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Machine learning with optical neural networks has featured unique advantages of the information processing including high speed,ultrawide bandwidths and low energy consumption because the optical dimensions(time,space,wavelength,and polarization)could be utilized to increase the degree of ***,due to the lack of the capability to extract the information features in the orbital angular momentum(OAM)domain,the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network ***,we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network(CNN)based on Laguerre-Gaussian(LG)beam modes with diverse diffraction *** proposed CNN architecture is composed of a trainable OAM mode-dispersion impulse as a convolutional kernel for feature extraction,and deep-learning diffractive layers as a *** resultant OAM mode-dispersion selectivity can be applied in information mode-feature encoding,leading to an accuracy as high as 97.2%for MNIST database through detecting the energy weighting coefficients of the encoded OAM modes,as well as a resistance to eavesdropping in point-to-point free-space ***,through extending the target encoded modes into multiplexed OAM states,we realize all-optical dimension reduction for anomaly detection with an accuracy of 85%.Our work provides a deep insight to the mechanism of machine learning with spatial modes basis,which can be further utilized to improve the performances of various machine-vision tasks by constructing the unsupervised learning-based auto-encoder.
Mobile devices face SQL injection, malware, and web-based threats. Current solutions lack real-time detection. This paper introduces an Android app with advanced algorithms for real-time threat scanning. During testin...
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