Space-air-ground integrated networks (SAGINs) offer seamless coverage and have emerged as a promising solution for high-speed railway (HSR) communications, which traverse various environments. This paper investigates ...
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Transformer neural networks (TNN) have been widely utilized on a diverse range of applications, including natural language processing (NLP), machine translation, and computer vision (CV). Their widespread adoption has...
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The increasing dependence on smartphones with advanced sensors has highlighted the imperative of precise transportation mode classification, pivotal for domains like health monitoring and urban planning. This research...
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The increasing dependence on smartphones with advanced sensors has highlighted the imperative of precise transportation mode classification, pivotal for domains like health monitoring and urban planning. This research is motivated by the pressing demand to enhance transportation mode classification, leveraging the potential of smartphone sensors, notably the accelerometer, magnetometer, and gyroscope. In response to this challenge, we present a novel automated classification model rooted in deep reinforcement learning. Our model stands out for its innovative approach of harnessing enhanced features through artificial neural networks (ANNs) and visualizing the classification task as a structured series of decision-making events. Our model adopts an improved differential evolution (DE) algorithm for initializing weights, coupled with a specialized agent-environment relationship. Every correct classification earns the agent a reward, with additional emphasis on the accurate categorization of less frequent modes through a distinct reward strategy. The Upper Confidence Bound (UCB) technique is used for action selection, promoting deep-seated knowledge, and minimizing reliance on chance. A notable innovation in our work is the introduction of a cluster-centric mutation operation within the DE algorithm. This operation strategically identifies optimal clusters in the current DE population and forges potential solutions using a pioneering update mechanism. When assessed on the extensive HTC dataset, which includes 8311 hours of data gathered from 224 participants over two years. Noteworthy results spotlight an accuracy of 0.88±0.03 and an F-measure of 0.87±0.02, underscoring the efficacy of our approach for large-scale transportation mode classification tasks. This work introduces an innovative strategy in the realm of transportation mode classification, emphasizing both precision and reliability, addressing the pressing need for enhanced classification mechanisms in an eve
The integration of renewable energy resources has made power system management increasingly complex. DRL is a potential solution to optimize power system operations, but it requires significant time and resources duri...
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Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural network...
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Gait recognition is an active research area that uses a walking theme to identify the subject *** Gait Recognition(HGR)is performed without any cooperation from the ***,in practice,it remains a challenging task under ...
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Gait recognition is an active research area that uses a walking theme to identify the subject *** Gait Recognition(HGR)is performed without any cooperation from the ***,in practice,it remains a challenging task under diverse walking sequences due to the covariant factors such as normal walking and walking with wearing a ***,over the years,have worked on successfully identifying subjects using different techniques,but there is still room for improvement in accuracy due to these covariant *** paper proposes an automated model-free framework for human gait recognition in this *** are a few critical steps in the proposed ***,optical flow-based motion region esti-mation and dynamic coordinates-based cropping are *** second step involves training a fine-tuned pre-trained MobileNetV2 model on both original and optical flow cropped frames;the training has been conducted using static *** third step proposed a fusion technique known as normal distribution serially *** the fourth step,a better optimization algorithm is applied to select the best features,which are then classified using a Bi-Layered neural *** publicly available datasets,CASIA A,CASIA B,and CASIA C,were used in the experimental process and obtained average accuracies of 99.6%,91.6%,and 95.02%,*** proposed framework has achieved improved accuracy compared to the other methods.
In this paper, we propose a novel method for masked comparison using register rotation technique without masking conversions. In key encapsulation mechanisms (KEMs), ciphertext comparison is essential to ensure the se...
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To accommodate the wide range of input voltages supplied by redundant batteries and ensure an adequate hold-up time for communication systems during utility power failures, power supplies used in 5 G base stations typ...
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Decreasing the number of cables that bring heat into the cryostat is a critical issue for all cryoelectronic devices. In particular, arrays of superconducting nanowire single-photon detectors (SNSPDs) could require mo...
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Decreasing the number of cables that bring heat into the cryostat is a critical issue for all cryoelectronic devices. In particular, arrays of superconducting nanowire single-photon detectors (SNSPDs) could require more than 106 readout lines. Performing signal-processing operations at low temperatures could be a solution. Nanocryotrons, superconducting nanowire three-terminal devices, are good candidates for integrating sensing and electronics on the same technological platform as SNSPDs in photon-counting applications. In this work, we demonstrate that it is possible to read out, process, encode, and store the output of SNSPDs using exclusively superconducting nanowires patterned on niobium nitride thin films. In particular, we present the design and development of a nanocryotron ripple counter that detects input voltage spikes and converts the number of pulses to an N-digit value. The counting base can be tuned from 2 to higher values, enabling higher maximum counts without enlarging the circuit. As a proof of principle, we first experimentally demonstrate the building block of the counter, an integer-N frequency divider with N ranging from 2 to 5. Then, we demonstrate photon-counting operations at 405 nm and 1550 nm by coupling an SNSPD with a two-digit nanocryotron counter partially integrated on chip. The two-digit counter can operate in either base 2 or base 3, with a bit-error rate lower than 2×10−4 and a count rate of 107s−1. We simulate circuit architectures for integrated readout of the counter state and we evaluate the capabilities of reading out an SNSPD megapixel array that would collect up to 1012 counts per second. The results of this work, combined with our recent publications on a nanocryotron shift register and logic gates, pave the way for the development of nanocryotron processors, from which multiple superconducting platforms may benefit.
The increasing incidence of forest and land fires has become an urgent global concern in recent decades. In an effort to improve detection and understanding of forest fires, information technology-based approaches hav...
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