Semantic segmentation plays an important role in computer perception tasks. Integrating the rich details of RGB images with the illumination robustness of thermal infrared (TIR) images is a promising approach for achi...
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Semantic segmentation plays an important role in computer perception tasks. Integrating the rich details of RGB images with the illumination robustness of thermal infrared (TIR) images is a promising approach for achieving reliable semantic scene understanding. Current approaches for RGB-Thermal semantic segmentation often overlook the unique characteristics exhibited by each modality at different encoding layers and underutilize the complementary information between the two modalities during decoding. To acquire complementary cross-modality encoding and decoding features, we propose a multi-branch differential bidirectional fusion network known as MDBFNet. Firstly, it models the dependencies between the modality-specific characteristics and the different encoding layers, and designs a TIR-led detail enhancement module (TDE) and an RGB-led semantic enhancement module (RSE) to guide distinguishable fusion for different layer features. Secondly, a three-branch fusion decoder with three supervision (TFDS) is proposed to thoroughly explore the complementary decoding features between two modalities. Experiments on MFNet and PST900 datasets show that our method surpasses state-of-the-art methods by a clear margin. IEEE
This paper presents a new and efficient tree data structure for sorting and collision detection of disks in 2D based on a new tree-based data structure, called hexatree, which is introduced for the first time in this ...
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In this paper, a single-phase dynamic voltage restorer (DVR) based on a Z-source AC-AC converter is proposed. The Z-source converter has a shared ground and continuous input current. Using the impedance network filter...
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The Causal Broadcast (CB) algorithms are presented for message passing between processes of distributed systems (DSs). These algorithms play an essential role in distributed computing. Therefore, the correctness of th...
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Mobile Crowdsensing (MCS) has emerged as a compelling paradigm for data sensing and collection, leveraging the widespread adoption of mobile devices and the active participation of numerous users. Despite its potentia...
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The growing demand for reliable and sustainable energy has driven researchers and engineers to explore renewable energy sources to complement traditional power grids. Wind energy conversion systems (WECS), particularl...
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Addressing the global challenge of ensuring a consistent and abundant supply of fresh fruit, particularly in the context of fruit crops, is hindered by the prevalence of plant diseases. These diseases directly impact ...
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Addressing the global challenge of ensuring a consistent and abundant supply of fresh fruit, particularly in the context of fruit crops, is hindered by the prevalence of plant diseases. These diseases directly impact the quality of fruits, leading to a decline in overall agricultural production. Mango leaf diseases pose significant threats to global mango production, necessitating accurate and efficient classification techniques for timely disease management. Our study focuses on introducing MangoLeafXNet, a customized Convolutional Neural Network (CNN) architecture specifically tailored for the classification of mango leaf diseases, along with a healthy class. Our proposed model comprises six layers optimized to capture intricate disease patterns, demonstrating superior performance compared with prevalent pre-trained models. The model is trained and evaluated on three publicly available datasets: MangoLeafBD (4000 images across 8 classes), MangoPest (16 pest classes including healthy leaves), and MLDID (3000 high-resolution images across 5 classes). Our model demonstrated exceptional classification performance, attaining 99.8% accuracy, 99.62% recall, 99.5% precision, and an F1-score of 99.56%. Further validation on the MangoPest dataset and the Mango Leaf Disease Identification Dataset (MLDID) resulted in accuracies of 96.31% and 96.33%, respectively, confirming the robustness and adaptability of MangoLeafXNet across different datasets. Additionally, we incorporate Explainable AI techniques, including GRAD-CAM, Saliency Map, and LIME to enhance the interpretability of our model. We deployed Gradio web interface to create an interactive interface that allows users to upload images of mango leaves and get real-time classification and validation results along with confidence scores. This contribution not only advances the state-of-the-art in mango leaf disease classification but also offers promising prospects for real-time disease diagnosis and precision agriculture
In this research, A novel approach for optimizing load shedding during power system stress conditions is introduced by combining gravitational search and particle swarm optimization (GSA-PSO) with Deep Learning. This ...
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The rapid integration and expanding use of Electric Public Transport Vehicles (EPTVs) have become pivotal in the evolution of Modern Urban Power Distribution Networks (MUPDNs). As urban centers transition towards elec...
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