Graph Learning-Based Power System Health Assessment Model by Koji Yamashita, Nanpeng Yu, Evangelos Farantatos, Lin Zhu

“…The proposed framework leverages a physics-informed graph convolution network and graph attention network with ordinal encoders, which are benchmarked with multi-layer perceptron models. …”
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Burned Area Segmentation in Optical Remote Sensing Images Driven by U-Shaped Multistage Masked Autoencoder by Yuxiang Fu, Wei Fang, Victor S. Sheng

“…DCNet has three major components: the ViT encoder (global branch), the convolution encoder (local branch), and the decoder. …”
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A Three-Dimensional Phenotype Extraction Method Based on Point Cloud Segmentation for All-Period Cotton Multiple Organs by Pengyu Chu, Bo Han, Qiang Guo, Yiping Wan, Jingjing Zhang

“…To address the challenge of significant structural variations in cotton organs across different growth stages, we designed an innovative point cloud segmentation algorithm, ResDGCNN, which integrates residual learning with dynamic graph convolution to enhance organ segmentation performance throughout all developmental stages. …”
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High‐order multilayer attention fusion network for 3D object detection by Baowen Zhang, Yongyong Zhao, Chengzhi Su, Guohua Cao

“…To enhance the expressive power between different modality features, we introduce a high‐order feature fusion module that performs multi‐level convolution operations on the element‐wise summed features. …”
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A Hybrid Deep Learning Paradigm for Robust Feature Extraction and Classification for Cataracts by Akshay Bhuvaneswari Ramakrishnan, Mukunth Madavan, R. Manikandan, Amir H. Gandomi

“…ABSTRACT The study suggests using a hybrid convolutional neural networks‐support vector machines architecture to extract reliable characteristics from medical images and classify them as an ensemble using four different models. …”
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Transferable Deep Learning Models for Accurate Ankle Joint Moment Estimation during Gait Using Electromyography by Amged Elsheikh Abdelgadir Ali, Dai Owaki, Mitsuhiro Hayashibe

“…Transferable prediction across different subjects is advantageous for calibration-free, practical clinical applications. …”
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Influence of Target Surface BRDF on Non-Line-of-Sight Imaging by Yufeng Yang, Kailei Yang, Ao Zhang

“…The reconstructed NLOS images were classified via a convolutional neural network to assess how different surface materials impacted imaging quality. …”
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Overview of Deep Learning Algorithms and Optimizers for Brain Tumor Segmentation by Nisha Purohit, Chandi Prasad Bhatt

“…This review focuses on analyzing different deep learning architectures and explores their performance when optimized using different optimizers. …”
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Unsupervised Anomaly Detection for Volcanic Deformation in InSAR Imagery by Robert Popescu, Nantheera Anantrasirichai, Juliet Biggs

“…To tackle these issues, this paper explores the use of unsupervised deep learning on InSAR images for the purpose of identifying volcanic deformation as anomalies. We test three different state‐of‐the‐art architectures, one convolutional neural network Patch Distribution Modeling (PaDiM) and two generative models (GANomaly and Denoising diffusion probabilistic models (DDPM)). …”
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FruitNet: Lightweight CNN for High-Throughput Image-Based Fruit Yield Estimation by Yadav Kamlesh Kumar, Tandan Gajendra

“…Therefore, in order to ensure that the model is robust to different scenarios the model is trained on a robust dataset involving fruit of different variety, growth stage and under different environmental conditions. …”
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Dynamic Path Planning of Unknown Environment Based on Deep Reinforcement Learning by Xiaoyun Lei, Zhian Zhang, Peifang Dong

“…Considering lidar signal and local target position as the inputs, convolutional neural networks (CNNs) are used to generalize the environmental state. …”
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Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper by Kiranmayee Janardhan, Vinay Martin D’Sa Prabhu, T. Christy Bobby

“…Classification of brain gliomas is also essential because different types require different treatment approaches. …”
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Multimode Flex-Interleaver Core for Baseband Processor Platform by Rizwan Asghar, Dake Liu

“…Algorithmic level optimizations like 2D transformation and realization of recursive computation are applied, which appear to be the key to reach to an efficient hardware multiplexing among different interleaver implementations. The presented hardware enables the mapping of vital types of interleavers including multiple block interleavers and convolutional interleaver onto a single architecture. …”
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Complex-Valued CNN Nonlinear Equalization Enabled 36-Tbit/s (45×800-Gbit/s) WDM Transmission Over 3150 Km Using Silicon-Based IC-TROSA by Yuhan Gong, Xiaoshuo Jia, Ying Zhu, Kailai Liu, Ming Luo, Jin Tao, Zhixue He, Chao Li, Zichen Liu, Yan Li, Jian Wu, Chao Yang

“…The paper also demonstrates the application of CVCNN in WDM systems, enhancing system performance across different WDM encoding schemes. Finally, the experiment verified that CVCNN requires fewer computational resources than real-valued convolutional neural networks (RVCNN).…”
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Multiscale Feature Filtering Network for Image Recognition System in Unmanned Aerial Vehicle by Xianghua Ma, Zhenkun Yang, Shining Chen

“…These branches employ multiple atrous convolutions at different scales, respectively, and further adaptively generate channel-wise feature responses by emphasizing channel-wise dependencies. …”
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WISP: Workframe for Interferogram Signal Phase-Unwrapping by Timofey F. Khirianov, Aleksandra I. Khirianova, Egor V. Parkevich, Ilya Makarov

“…Iterations continue until the difference between the reconstructed and experimental phase distributions reaches an asymptotic minimum. …”
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Infrared object detection for robot vision based on multiple focus diffusion and task interaction alignment by Jixu Zhang, Li Wang, Hung-Wei Li, Meng-Yen Hsieh, Shunxiang Zhang, Hua Wen, Meng Chen

“…However, the small gray-scale difference between the object and the background region in the infrared grayscale image and the single gray-scale information lead to the blurring of the semantic information of the image, which makes the robot unable to detect the object effectively. …”
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An improved CNN model in image classification application on water turbidity by Ying Nie, Yuqiang Chen, Jianlan Guo, Shufei Li, Yu Xiao, Wendong Gong, Ruirong Lan

“…Due to the subtle changes in water turbidity images, the differences captured are often too subtle to be classified. …”
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Long-Term Neonatal EEG Modeling with DSP and ML for Grading Hypoxic–Ischemic Encephalopathy Injury by Leah Twomey, Sergi Gomez, Emanuel Popovici, Andriy Temko

“…First, the EEG signal is transformed into an amplitude and frequency modulated audio spectrogram, which enhances its relevant signal properties. The difference between EEG Grades 1 and 2 is enhanced. A convolutional neural network is then designed as a regressor to map the input image into an EEG grade, by utilizing an optimized rounding module to leverage the monotonic relationship among the grades. …”
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YOLO-WAD for Small-Defect Detection Boost in Photovoltaic Modules by Yin Wang, Wang Yun, Gang Xie, Zhicheng Zhao

“…Firstly, we replace C2f (CSP bottleneck with two convolutions) with C2f-WTConv (CSP bottleneck with two convolutions–wavelet transform convolution) in the backbone network to enlarge the receptive field and better extract the features of small-target defects (hot spots). …”
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