Species interaction networks are a powerful tool for describing ecological communities;they typically contain nodes representing species, and edges representing interactions between those species. For the purposes of ...
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In this paper, we investigate the economic dispatch problem of smart grids in time-varying directed networks. The EDP essentially revolves around optimizing the distribution of generation power amongst multiple genera...
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Recent research has consistently indicated that the fusion of electroencephalography (EEG) features from multiple modalities can integrate cognitive state expressions across diverse dimensions, resulting in a substant...
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Training Large Language Models (LLMs) from scratch requires immense computational resources, making it prohibitively expensive. Model scaling-up offers a promising solution by leveraging the parameters of smaller mode...
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In recent years, neural networks have demonstrated substantial progress in medical image segmentation. However, accurately segmenting objects in medical images is often restricted by edge blurring, which complicates t...
In recent years, neural networks have demonstrated substantial progress in medical image segmentation. However, accurately segmenting objects in medical images is often restricted by edge blurring, which complicates the delineation of structures of interest. The purpose of this study is to develop a multi-branch hybrid CNN-transformer model to address the issue of edge blurring in medical image segmentation. This approach enables more precise edge detection and segmentation. Integrating hybrid CNN and transformer decoders facilitates detailed recovery while preserving global structures, thus minimizing errors in regions with blurred edges. Additionally, we incorporate a hybrid attention mechanism at the model’s bottleneck to enhance feature transfer between the encoder and decoder. The model was trained using the ACDC, Synapse, and polyp datasets, with a focus on improving segmentation accuracy in regions with blurry edges. Experimental results reveal that the proposed model surpasses existing state-of-the-art methods on the ACDC, Synapse, and polyp datasets, demonstrating its superior performance and effectiveness. These results suggest that the proposed model offers a significant improvement in handling edge blurring, with potential applications in enhancing medical image analysis and diagnostics.
A chaotic system is a highly volatile system characterized by its sensitive dependence on initial conditions and outside factors. Chaotic systems are prevalent throughout the world today: in weather patterns, disease ...
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This article presents a method for indirectly measuring the moisture content of paddy using a cylindrical capacitive sensor combined with Charge Integrator Circuits. The moisture measurement device operates based on t...
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ISBN:
(数字)9798331543952
ISBN:
(纸本)9798331543969
This article presents a method for indirectly measuring the moisture content of paddy using a cylindrical capacitive sensor combined with Charge Integrator Circuits. The moisture measurement device operates based on the principle of the dielectric constant of paddy, which changes according to the moisture content within them. The system consists of a moisture detection section utilizing a cylindrical capacitor, a signal generator operating at a frequency of 500 Hz with a microcontroller, a signal conditioning circuit converting the signal to voltage, and a processing unit. Testing was conducted with paddy at five different moisture levels, demonstrating the ability to accurately determine the moisture content of the rice. When compared to the commercially available PM-450TH moisture meter, the results showed a linear response across the measurement range, with an R 2 value of 0.9824 and an average error of 2.6%. This technology has the potential for further development and application to other types of seeds in the future.
Energy-based learning algorithms are alternatives to backpropagation and are well-suited to distributed implementations in analog electronic devices. However, a rigorous theory of convergence is lacking. We make a fir...
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The continuous advancement of communication systems necessitates the development of algorithms capable of identifying and correcting errors that may arise during data transmission and storage. This pursuit of reliabil...
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ISBN:
(数字)9798331522124
ISBN:
(纸本)9798331522131
The continuous advancement of communication systems necessitates the development of algorithms capable of identifying and correcting errors that may arise during data transmission and storage. This pursuit of reliability is particularly crucial in critical systems and sectors that are challenging to access, such as space exploration, passenger transportation, and financial services. In this context, the Error Correction Code (ECC) is a fundamental tool for providing a certain degree of reliability to these systems. This research proposes a novel technique to enhance the error correction capacity of ECCs by leveraging region overlapping. Specifically, we propose correcting data areas protected by more than one ECC, which allows for the inference of logical correlations between ECCs, thereby augmenting their error detection and correction capability. Our focus is bidimensional codeword structures, commonly known as 2D-ECCs, which entail a hierarchical arrangement of ECCs. We evaluated the ECC proposal, comparing its error correction and detection capabilities. Through this evaluation, we aim to demonstrate the technique's efficacy in bolstering the reliability and resilience of communication systems, particularly in critical domains where precision and accuracy are paramount.
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