Weed infestation in cotton fields significantly challenges agricultural productivity by competing for essential nutrients and water resources. This study presents a comprehensive comparative analysis of two deep learn...
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Recently, the utilization of Radio Frequency (RF) devices has increased exponentially over numerous vertical platforms. This rise has led to an abundance of Radio Frequency Interference (RFI) continues to plague RF sy...
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The proliferation of Internet of Things (IoT) networks has significantly increased the complexity of software architectures, leading to heightened vulnerabilities and system inefficiencies. AI-infused Predictive Digit...
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Microscopic imaging is a critical tool in scientific research,biomedical studies,and engineering applications,with an urgent need for system miniaturization and rapid,precision autofocus ***,traditional microscopes an...
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Microscopic imaging is a critical tool in scientific research,biomedical studies,and engineering applications,with an urgent need for system miniaturization and rapid,precision autofocus ***,traditional microscopes and autofocus methods face hardware limitations and slow software speeds in achieving this *** response,this paper proposes the implementation of an adaptive Liquid Lens Microscope System utilizing Deep Reinforcement Learning-based Autofocus(DRLAF).The proposed study employs a custom-made liquid lens with a rapid zoom response,which is treated as an“agent.”Raw images are utilized as the“state”,with voltage adjustments representing the“actions.”Deep reinforcement learning is employed to learn the focusing strategy directly from captured images,achieving end-to-end *** contrast to methodologies that rely exclusively on sharpness assessment as a model’s labels or inputs,our approach involved the development of a targeted reward function,which has proven to markedly enhance the performance in microscope autofocus *** explored various action group design methods and improved the microscope autofocus speed to an average of 3.15 time ***,parallel“state”dataset lists with random sampling training are proposed which enhances the model’s adaptability to unknown samples,thereby improving its generalization *** experimental results demonstrate that the proposed liquid lens microscope with DRLAF exhibits high robustness,achieving a 79%increase in speed compared to traditional search algorithms,a 97.2%success rate,and enhanced generalization compared to other deep learning methods.
Vortices are whirling disturbances,commonly found in nature,ranging from tremendously small scales in Bose-Einstein condensations to cosmologically colossal scales in spiral *** optical vortex,generally associated wit...
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Vortices are whirling disturbances,commonly found in nature,ranging from tremendously small scales in Bose-Einstein condensations to cosmologically colossal scales in spiral *** optical vortex,generally associated with a spiral phase,can carry orbital angular momentum(OAM).The optical OAM can either be in the longitudinal direction if the spiral phase twists in the spatial domain or in the transverse direction if the phase rotates in the spatiotemporal *** this article,we demonstrate the intersection of spatiotemporal vortices and spatial vortices in a wave *** a result of this intersection,the wave packet hosts a tilted OAM that provides an additional degree of freedom to the applications that harness the OAM of photons.
Keyphrase extraction aims to extract important phrases that reflect the main topics of a document. Recently, deep learning methods are used to model semantic information and rank candidates based on the similarities b...
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Keyphrase extraction aims to extract important phrases that reflect the main topics of a document. Recently, deep learning methods are used to model semantic information and rank candidates based on the similarities between the n-grams and the document. However, existing keyphrase extraction methods mainly caused the keyphrase extraction task to be independent of the embedding. Based on the fact that phrases that are semantically closer to the document are more likely to become keyphrases, we propose a novel contrastive learning strategy for supervised keyphrase extraction by integrating local and global Information of the document. A pre-trained RoBERTa model is used to model contextual information of sub-words in the document. Then, the embedding vectors of n-grams and the document are calculated by the convolution neural layers. Finally, we propose a novel loss function for efficiently ranking candidate phrases by combining n-gram features and document embeddings during the training of the model.
One way to increase solar photovoltaic penetration in the grid is management of voltage fluctuations. This is because a photovoltaic plant cannot be interconnected to the grid if it causes voltage violations. Voltage ...
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The early diagnosis of diseases in fruits holds immense importance for agricultural industries, as it directly impacts production quality and quantity. This study introduces a novel approach utilizing Recursive Convol...
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In this letter, we propose a novel Movable Superdirective Pairs (MSP) approach that combines movable antennas with superdirective pair arrays to enhance millimeter-wave (mmWave) communications. By controlling the rota...
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Integrated sensing and communications (ISAC) are envisioned to be an integral part of future wireless networks, especially when operating at the millimeter-wave (mm-wave) and terahertz (THz) frequency bands. However, ...
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