Sparse Convolution FPGA Accelerator Based on Multi-Bank Hash Selection by Jia Xu, Han Pu, Dong Wang

“…The main contributions are as follows: (1) Most neural network inference tasks are typically executed on general-purpose computing devices, which often fail to deliver high energy efficiency and are not well-suited for accelerating sparse convolutional models. In this work, we propose a specialized computational circuit for the convolutional operations of sparse neural networks. …”
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Voice activity detection in noisy conditions using tiny convolutional neural network by R. S. Vashkevich, E. S. Azarov

“…The properties of the model are achieved by using a special convolutional layer that considers the harmonic structure of vocal speech. …”
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EFCNet: Expert Feature-Based Convolutional Neural Network for SAR Ship Detection by Zheng Chen, Yuxiang Zhang, Jing Bai, Biao Hou

“…Due to the special properties of synthetic aperture radar (SAR) images, they are widely used in maritime applications, such as detecting ships at sea. …”
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Application of the Algebraic Extension Method to the Construction of Orthogonal Bases for Partial Digital Convolutions by Aruzhan Kadyrzhan, Akhat Bakirov, Dina Shaltykova, Ibragim Suleimenov

“…To solve this problem, the method of algebraic extensions was used, a special case of which is the transition from real numbers to complex numbers. …”
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Fast computation of cyclic convolutions and their applications in code-based asymmetric encryption schemes by Andrey N. Sushko, Boris Y. Steinberg, Kirill V. Vedenev, Anton A. Glukhikh, Yury V. Kosolapov

“…The proposed algorithms achieve high speed by compactly storing sparse vectors, using hardware-supported XOR instructions, and replacing modulo operations with specialized loop transformations. These fast algorithms have potential applications not only in cryptography, but also in other areas where convolutions are used.…”
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Enhancing the robustness of solar photovoltaic fault classification based on depthwise separable group equivariant CNNs by Jielong Guo, Chak Fong Chong, Pedro Henriques Abreu, Chao Mao, Chan-Tong Lam, Benjamin K. Ng

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Fluorescence images of skin lesions and automated diagnosis using convolutional neural networks by Matheus B. Rocha, Sebastiao Pratavieira, Renan Souza Vieira, Juliana Duarte Geller, Amanda Lima Mutz Stein, Fernanda Sales Soares de Oliveira, Tania R.P. Canuto, Luciana de Paula Vieira, Renan Rossoni, Maria C.S. Santos, Patricia H.L. Frasson, Renato A. Krohling

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Vanilla Convolutional Neural Network is all you Need for Online and Offline Signature Verification by Yilmaz Mustafa Berkay, Öztürk Kağan

“…On the other hand, those models are designed and hand-crafted specializing in the problem, online or offline SV. In this work, we suggest and show on popular datasets that similar and simple convolutional neural network (CNN) models can achieve state-of-the-art results both for offline and online SV problems. …”
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Effective Skin Cancer Diagnosis Through Federated Learning and Deep Convolutional Neural Networks by Mabrook S. Al-Rakhami, Salman A. AlQahtani, Abdulaziz Alawwad

“…However, detecting it can be a challenging task, even for specialized dermatologists. Early detection is crucial for successful treatment, and deep learning techniques, particularly deep convolutional neural networks (DCNNs), have shown tremendous potential in this area. …”
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Deep Content-Dependent 3-D Convolutional Sparse Coding for Hyperspectral Image Denoising by Haitao Yin, Hao Chen

“…Furthermore, by exploiting the lightweight of separable convolution and the adaptability of hypernetwork, we design a separable content-dependent 3D Convolution (SCD-Conv) to carry out CD-CSCNet. …”
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Training Fully Convolutional Neural Networks for Lightweight, Non-Critical Instance Segmentation Applications by Miguel Veganzones, Ana Cisnal, Eusebio de la Fuente, Juan Carlos Fraile

“…We compare two common fully convolutional network (FCN) architectures, U-Net and ResNet, and fine-tune the fittest to improve segmentation results. …”
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Dental bur detection system based on asymmetric double convolution and adaptive feature fusion by HongLing Hou, Ao Yang, Xiangyao Li, Kangkai Zhu, Yandi Zhao, Zhiqiang Wu

“…A Lightweight Asymmetric Dual Convolution module (LADC) was devised to diminish the detrimental effects of extraneous features on the model’s precision, thereby enhancing the feature extraction network. …”
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Efficient Recognition of the Propagated Orbital Angular Momentum Modes in Turbulences With the Convolutional Neural Network by Zikun Wang, Maxime Irene Dedo, Kai Guo, Keya Zhou, Fei Shen, Yongxuan Sun, Shutian Liu, Zhongyi Guo

“…Generally, atmospheric turbulence can distort the helical phase fronts of OAM beams, which presents a critical challenge to the effective recognition of OAM modes. Recently, convolutional neural network (CNN), as a model of deep learning, has been widely applied to machine vision. …”
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System Development for Liquid Chemicals Point Injection Based on Convolutional Neural Network Models by V. S. Semenyuk, E. A. Nikitin

“…When developing the system, they used the U-net-algorithm of convolutional neural networks, as well as data displaying diseases of winter and spring wheat – brown rust and powdery mildew. …”
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PlantNet: Scalable Convolutional Neural Network for Image-Based Plant Disease Detection by Sinha Anupa, Kumaraswamy Balasubramaniam

“…This research presents PlantNet, a novel Convolutional Neural Network (CNN) architecture tailored for accurate identification of plant diseases from images. …”
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Preprocessing-Free Convolutional Neural Network Model for Arrhythmia Classification Using ECG Images by Chotirose Prathom, Ryuhi Fukuda, Yuto Yokoyanagi, Yoshifumi Okada

“…To address these limitations, this research proposes a convolutional neural network (CNN) model for arrhythmia classification that incorporates two specialized modules. …”
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Automatic Potato Crop Beetle Recognition Method Based on Multiscale Asymmetric Convolution Blocks by Jingjun Cao, Xiaoqing Xian, Minghui Qiu, Xin Li, Yajie Wei, Wanxue Liu, Guifen Zhang, Lihua Jiang

“…Specifically, it comprises several multiscale asymmetric convolution blocks, which are designed to extract features at multiple scales, mainly by integrating different-sized asymmetric convolution kernels in parallel. …”
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Automated skin melanoma diagnostics based on mathematical model of artificial convolutional neural network by D. A. Gavrilov, E. I. Zakirov, E. V. Gameeva, V. Yu. Semenov, O. Yu. Aleksandrova

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Enhanced neurological anomaly detection in MRI images using deep convolutional neural networks by Ahmed Mateen Buttar, Zubair Shaheen, Abdu H. Gumaei, Mogeeb A. A. Mosleh, Mogeeb A. A. Mosleh, Indrajeet Gupta, Samah M. Alzanin, Muhammad Azeem Akbar

“…This study introduces a deep learning framework designed to automate neuro-diagnostics, addressing the limitations of current manual interpretation methods, which are often time-consuming and prone to variability.MethodsWe propose a specialized deep convolutional neural network (DCNN) framework aimed at detecting and classifying neurological anomalies in MRI data. …”
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Recognizing Special Art Pieces Through EEG: A Journey in Neuroaesthetics Classification by Maurizio Palmieri, Marco Avvenuti, Francesco Marcelloni, Alessio Vecchio

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