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Company Investors and financial professionals mostly rely on quarterly reports to help them decide the ways to invest in stocks and assess the company's current performance. Quarterly company reports offer an abst...
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A method called deepfake produces fake video and films with artificial or substituted faces. Deepfakes are turning into a worrying societal phenomenon because they may be used maliciously to spread harmful information...
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This paper delves into the innovative application of quantum computing to solve complex multi-die FPGA (FieldProgrammable Gate Array) routing challenges. Leveraging the Quantum Approximate Optimization Algorithm (QAOA...
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With the wide application of deep convolutional neural networks (CNNs), higher requirements are put forward for feature extraction and discriminant ability in asset comparison tasks. The rapid development of CNNs has ...
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Recommendation Systems are ubiquitous, whether it is in social media posts, advertisements, or digital book and movie libraries. Many recommender systems suggest items based on the product or its use and do not consid...
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Accurate segmentation of skin lesion areas is significant for the diagnosis and analysis of skin diseases. Due to the irregular and blurred boundaries between healthy skin and lesion areas and sometimes the interferen...
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Accurate pollutant forecasting serves as a crucial component in air quality monitoring and control in a smart city. Traditional pollutant forecasting models such as statistical, and machine learning models follow a si...
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The pharmaceutical industry's supply chain management faces critical challenges, including tampering of vaccine details and a lack of transparency from regulatory authorities to end customers. These issues comprom...
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Over one billion people worldwide are affected with neurological disorders and their economic impact is approximately $800 billion annually, which constitutes major medical challenge. Using neuromodulation systems cur...
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ISBN:
(纸本)9798331543617
Over one billion people worldwide are affected with neurological disorders and their economic impact is approximately $800 billion annually, which constitutes major medical challenge. Using neuromodulation systems currently available suffers from sensitivity, reaction time as well as energy consumption. The proposal in this research is to address these major issues in closed loop neuromodulation by using a Quantum enhanced Spiking Neural Network (QESNN) architecture. This paper represents the interfacing of two major fields: quantum sensing and neuromorphic computing. The QESNN architecture comprises three core components: This is implemented as an array of quantum sensors, a quantum classical hybrid interface, and a spiking neural network (SNN). Taking advantage of quantum superposition and entanglement principles, the quantum sensor array noninvasively images neural activity at the level of single action potentials using NV centers. These sensors work at ambient temperatures, which is unlike superconducting devices. For processing with neuromorphic processing, quantum-classical hybrid converts quantum sensor data into classical signals with advanced signal process such as quantum state estimation and noise reduction. By modeling biological neurons with leaky integrate and fire neurons, the SNN serves as a low power, timed neural dynamics modulation component that emulates biological event driven behavior. A key innovation in our architecture is adaptive thresholding, which dynamically adjusts detection thresholds based on signal distributions, improving sensitivity and reducing false positives by 45.6%. The system also achieves 20-30% higher power efficiency through techniques like adaptive sensor frequency control and low-power processing. Simulation results that show how the QESNN performs better than classical systems with less false positives and greater energy efficiency are presented. A new platform is demonstrated that integrates quantum sensing with neurom
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