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On the Design of a Light-Weight deep learning Framework for Embedding in 5G Software Modem

IEEE ACCESS 2024年 12卷 151008-151018页

作者： Min, Woonggyu Kang, Seungwoo Kim, Juyeop Jo, Ohyun Chungbuk Natl Univ Dept Comp Sci Cheongju 28644 South Korea Sookmyung Womens Univ Dept Elect Engn Seoul 04310 South Korea

In this paper, we present a light-weight deep learning framework specifically designed and implemented for embedding in 5G software modems. The framework is developed completely using the C language to operate in real time by being mounted on a software modem. The framework incorporates an imagification process proposed by the authors which can enhance efficient reference signal classification in constrained environments. Imagification is the proposed technique that converts radio signal data, which is sequence data, into image form, processing the data into a structure similar to the RGB color model used in traditional images. This enables the application of convolutional operations to reduce complexity and training time. The performance of the framework is validated using a spec-compliant 5G software modem testbed developed by the authors, achieving up to 99.7% accuracy even at a relatively low SNR of -2.74 dB. These results demonstrate the feasibility of integrating the deep learning framework into a practical 5G software modem. Additionally, we perform hyperparameter optimization to identify the most suitable learning structure for the system. The developed source code is available at Github for public use.

关键词： 5G deep learning deep learning software modem software modem wireless communication wireless communication wireless communication

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EFFECTIVE FEATURE I NFORMATION IDENTIFICATION OF SUGARCANE BASED ON HYBRID deep learning MODELS

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APPLIED ENGINEERING IN AGRICULTURE 2024年第3期40卷 243-257页

作者： Pan, Mingzhang Gou, Xuanyuan Zeng, Yue Wang, Zongrun Yuan, Leyi Liang, Ke Guangxi Univ State Key Lab Conservat & Utilizat Biol Resources Nanning Guangxi Peoples R China Guangxi Univ Nanning Guangxi Peoples R China

The efficiency of intelligent sugarcane harvesters in harvesting depends on the effectiveness of identifying and locating the sugarcane during the harvesting process. In the actual harvesting process, accurately extracting valid features of sugarcane amidst the dense and interwoven sugarcane becomes a challenging task. To address this issue, we propose a hybrid deep learning approach to extract sugarcane stem contours and internal stem node feature information from sugarcane efficiently in the context of a complex harvest. Firstly, this study combined the MobileNetV3 and U-Net networks to segment overall images that contain information about the external contours of the sugarcane stem. Then, the extracted overall profile images were optimized using a variety of image processing techniques to meet the requirements of harvesting. Lastly, the improved YOLOX model was utilized to identify the internal stem node features of sugarcane from the optimized overall images. The experimental results on a real sugarcane dataset show that the proposed external sugarcane stem segmentation model achieves a high mean intersection over union (MIoU) of 91.68% with an average segmentation time of just 0.025 seconds. Moreover, the proposed model for internal stem node recognition in sugarcane achieves an average precision (AP) of 96.19% with an average detection time of 0.026 seconds. Additionally, this study compares image segmentation models such as PSPNet and deepLabv3+ with target detection models such as YoloV5 and YoloV7. The experimental results show that the sugarcane feature extraction models proposed in this article all exhibit high accuracy and robustness.

关键词： Keywords. deep learning image segmentation Machine vision Sugarcane identification Target detection.

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Identification and characteristic analysis of internal defects in rock-filled concrete based on deep learning method

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MATERIALS TODAY COMMUNICATIONS 2024年 41卷

作者： Ren, Tao Zhang, Yufan Yang, Tao Chen, Qiaolin Yu, Lei Wang, Guoji Song, Meizhou Guizhou Univ Coll Civil Engn Guiyang 550025 Peoples R China Guiyang Shougang Real Estate Dev Co Guiyang 550025 Peoples R China

The distribution characteristics and geometric morphology characteristics of defects within RFC are important factors affecting the strength properties and rupture morphology of RFC. However, the excessive size of commonly used aggregates for RFC leads to difficulties in conducting in-depth experimental studies indoors. Based on the improved U-Net and image processing technology, this research establishes an integrated model for the identification, classification, and extraction of defects inside the RFC, quantitatively counts and analyzes the acquired defect distribution characteristics and geometrical morphology characteristics, and establishes a defect characteristic distribution function that can be used for the numerical reconstruction of defects. In order to realize the acceleration of U-Net training using training weights, use VGG-16 with the fully connected layer removed instead of the Encoder part of the U-Net. The integrated model in this research can realize automatic identification, classification, and extraction of multiple types of defects at the same time, and the established distribution function of defect characteristics provides a data basis and new ideas for the establishment of RFC three-dimensional numerical models containing real defects.

关键词： deep learning improved U -Net image processing technology defect characteristic function rock-filled concrete

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BSMO: Enhancing Multitask learning Through Batch Swapping Optimization

BSMO: Enhancing Multitask Learning Through Batch Swapping Op...

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Conference on real-time image processing and deep learning

作者： Spencer, Zephaniah Schirner, Gunar Tabkhi, Hamed UNC Charlotte 9201 Univ City Blvd Charlotte NC 28223 USA Northeastern Univ 360 Huntington Ave Boston MA 02115 USA

ISBN: (纸本)9781510673878;9781510673861

With the advent of deep learning, there has been an ever-growing list of applications to which deep Convolutional Neural Networks (DCNNs) can be applied. The field of Multi-Task learning (MTL) attempts to provide optimizations to many-task systems, improving performance by optimization algorithms and structural changes to these networks. However, we have found that current MTL optimization algorithms often impose burdensome computation overheads, require meticulously labeled datasets, and do not adapt to tasks with significantly different loss distributions. We propose a new MTL optimization algorithm: Batch Swapping with Multiple Optimizers (BSMO). We utilize single-task labeled data to train on a multi-task hard parameter sharing (HPS) network through swapping tasks at the batch level. This dramatically increases the flexibility and scalability of training on an HPS network by allowing for per-task datasets and augmentation pipelines. We demonstrate the efficacy of BSMO versus current SO TA algorithms by benchmarking across contemporary benchmarks & networks.

关键词： deep learning Multi-Task learning real-time Vision

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Overcoming annotation bottlenecks in underwater fish segmentation: a robust self-supervised learning approach

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SIGNAL image AND VIDEO processing 2025年第4期19卷 1-10页

作者： Saleh, Alzayat Sheaves, Marcus Jerry, Dean Azghadi, Mostafa Rahimi James Cook Univ Coll Sci & Engn Townsville Qld Australia James Cook Univ ARC Res Hub Supercharging Trop Aquaculture Genet S Townsville Qld Australia

Accurate fish segmentation in underwater videos is challenging due to low visibility, variable lighting, and dynamic backgrounds, making fully-supervised methods that require manual annotation impractical for many applications. This paper introduces a novel self-supervised learning approach for fish segmentation using deep learning. Our model, trained without manual annotation, learns robust and generalizable representations by aligning features across augmented views and enforcing spatial-temporal consistency. We demonstrate its effectiveness on three challenging underwater video datasets: deepFish, Seagrass, and YouTube-VOS, surpassing existing self-supervised methods and achieving segmentation accuracy comparable to fully-supervised methods without the need for costly annotations. Trained on deepFish, our model exhibits strong generalization, achieving high segmentation accuracy on the unseen Seagrass and YouTube-VOS datasets. Furthermore, our model is computationally efficient due to its parallel processing and efficient anchor sampling technique, making it suitable for real-time applications and potential deployment on edge devices. We present quantitative results using Jaccard Index and Dice coefficient, as well as qualitative comparisons, showcasing the accuracy, robustness, and efficiency of our approach for advancing underwater video analysis.

关键词： Computer vision Convolutional neural networks Underwater videos deep learning Transformer Self-supervised learning

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TrioConvTomatoNet-BiLSTM: An Efficient Framework for the Classification of Tomato Leaf Diseases in real time Complex Background images

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2025年第1期18卷 1-28页

作者： Ledbin Vini, S. Rathika, P. PSN Coll Engn & Technol ECE Dept Tirunelveli 627152 India

Tomatoes are the most valuable vegetable worldwide that suffer from leaf diseases, which affect long-term tomato protection. So, to protect the tomato plants from the leaf diseases, it is essential to perform appropriate control measures through early and accurate categorization of leaf diseases. Recently, automated deep learning-based methods, including convolutional neural networks (CNNs), guaranteed accurate and timely classification of tomato leaf diseases. However, CNNs primarily capture local context features within a limited receptive field, making them effective for uniform background images. To handle complex background images, utilizing local and global context features is essential for accurate classification. To do so, it is essential to hybrid CNN architecture with other deep learning modules. This work suggests the TrioConvTomatoNet-BiLSTM framework, a hybridization of CNN architecture named TrioConvTomatoNet with a sequence module named bidirectional long short-term memory (BiLSTM). The proposed framework integrated both local and global context features for the precise classification of images with complex backgrounds. As a result, the proposed framework achieves remarkable accuracy of 99.65%, 98.83%, and 99.20% in classifying tomato leaf disease images with non-uniform, synthetic, and real-time complex backgrounds against the TrioConvTomatoNet and TrioConvTomatoNet-LSTM frameworks. Despite the fact that it requires a lesser number of training parameters and attained maximum accuracy over other existing hybrid approaches, expresses its superiority, robustness, and practical applicability. These features highlight the potential of the proposed framework in the emerging field of smart agriculture by enabling smartphone-based classification of tomato leaf diseases with real-life scenarios.

关键词： image processing Tomato leaf disease classification Smart agriculture deep learning Hybrid model

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Recent Advances in Applying Machine learning and deep learning to Detect Upper Gastrointestinal Tract Lesions

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IEEE ACCESS 2023年 11卷 66544-66567页

作者： Vania, Malinda Tama, Bayu Adhi Maulahela, Hasan Lim, Sunghoon Ulsan Natl Inst Sci & Technol Dept Ind Engn Ulsan 44919 South Korea Ulsan Natl Inst Sci & Technol Ind Intelligentizat Inst Ulsan 44919 South Korea Univ Maryland NSF HDR Inst Harnessing Data & Model Revolut Pola Baltimore MD 21250 USA Univ Indonesia Cipto Mangunkusumo Natl Gen Hosp Div Gastroenterol Dept Internal MedFac Med Jakarta 10430 Indonesia Ulsan Natl Inst Sci & Technol Grad Sch Artificial Intelligence Ulsan 44919 South Korea

The clinical application of a real-time artificial intelligence (AI) image processing system to diagnose upper gastrointestinal (GI) malignancies remains an experimental research and engineering problem. Understanding these commonly used technical techniques is required to appreciate the scientific quality and novelty of AI studies. Clinicians frequently lack this technical background, and AI experts may be unaware of such clinical relevance and implications in daily practice. As a result, there is a growing need for a multidisciplinary, international assessment of how to conduct high-quality AI research in upper GI malignancy detection. This research will help endoscopists build approaches or models to increase diagnosis accuracy for upper GI malignancies despite variances in experience, education, personnel, and resources, as it offers real-time and retrospective chances to improve upper GI malignancy diagnosis and screening. This comprehensive review sheds light on potential enhancements to computer-aided diagnostic (CAD) systems for GI endoscopy. The survey includes 65 studies on automatic upper GI malignancy diagnosis and evaluation, which are compared by endoscopic modalities, image counts, models, validation methods, and results. The main goal of this research is to assess and compare each AI method's current stage and potential improvement to boost performance, maturity, and the possibility to open new research areas for the application of a real-time AI image recognition system that diagnoses upper GI malignancies. The findings of this study suggest that Support Vector Machines (SVM) are frequently utilized in gastrointestinal (GI) image processing within the context of machine learning (ML). Moreover, the analysis reveals that CNN-based supervised learning object detection models are widely employed in GI image analysis within the deep learning (DL) context. The results of this study also suggest that RGB is the most commonly used image modality for GI ana

关键词： Endoscopy upper gastrointestinal tract artificial intelligence machine learning deep learning

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Comparison of deep generative models for real-time generation of synthesized defective wafer maps

Comparison of deep generative models for real-time generatio...

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Conference on real-time image processing and deep learning

作者： Alam, Lamia Kehtarnavaz, Nasser Univ Texas Dallas Dept Elect & Comp Engn Richardson TX 75080 USA

ISBN: (纸本)9781510673878;9781510673861

Modern wafer inspection systems in Integrated Circuit (IC) manufacturing utilize deep neural networks. The training of such networks requires the availability of a very large number of defective or faulty die patterns on a wafer called wafer maps. The number of defective wafer maps on a production line is often limited. In order to have a very large number of defective wafer maps for the training of deep neural networks, generative models can be utilized to generate realistic synthesized defective wafer maps. This paper compares the following three generative models that are commonly used for generating synthesized images: Generative Adversarial Network (GAN), Variational Auto-Encoder (VAE), and CycleGAN which is a variant of GAN. The comparison is carried out based on the public domain wafer map dataset WM-811K. The quality aspect of the generated wafer map images is evaluated by computing the five metrics of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), inception score (IS), Frechet inception distance (FID), and kernel inception distance (KID). Furthermore, the computational efficiency of these generative networks is examined in terms of their deployment in a real-time inspection system.

关键词： Synthesized Wafer Maps Generative Adversarial Network Variational Autoencoder CycleGAN real-time image Synthesis

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Improving Biomedical image Pattern Identification by deep B4-GraftingNet: Application to Pneumonia Detection

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IET image processing 2025年第1期19卷

作者： Shah, Syed Adil Hussain Shah, Syed Taimoor Hussain Fayyaz, Abdul Muiz Shah, Syed Baqir Hussain Yasmin, Mussarat Raza, Mudassar Di Terlizzi, Angelo Deriu, Marco Agostino Politecn Torino PolitoBIOMed Lab Dept Mech & Aerosp Engn Turin Italy GPI SpA Dept Res & Dev R&D Trento Italy Univ Teknol PETRONAS Comp & Informat Sci Dept Seri Iskandar Perak Malaysia COMSATS Univ Islamabad CUI Dept Comp Sci Wah Pakistan Univ Taxila HITEC Dept Comp Sci Taxila Pakistan Namal Univ Mianwali Dept Comp Sci Mianwali Pakistan

VGG-16 and Inception are widely used CNN architectures for image classification, but they face challenges in target categorization. This study introduces B4-GraftingNet, a novel deep learning model that integrates VGG-16's hierarchical feature extraction with Inception's diversified receptive field strategy. The model is trained on the OCT-CXR dataset and evaluated on the NIH-CXR dataset to ensure robust generalization. Unlike conventional approaches, B4-GraftingNet incorporates binary particle swarm optimization (BPSO) for feature selection and grad-CAM for interpretability. Additionally, deep feature extraction is performed, and multiple machine learning classifiers (SVM, KNN, random forest, na & iuml;ve Bayes) are evaluated to determine the optimal feature representation. The model achieves 94.01% accuracy, 94.22% sensitivity, 93.36% specificity, and 95.18% F1-score on OCT-CXR and maintains 87.34% accuracy on NIH-CXR despite not being trained on it. These results highlight the model's superior classification performance, feature adaptability, and potential for real-world deployment in both medical and general image classification tasks.

关键词： biomedical imaging convolutional neural nets decision making feature extraction feature selection image classification nearest neighbour methods pattern classification supervised learning X-ray imaging

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An image-Based Transfer learning Approach for Using In Situ processing Data to Predict Laser Powder Bed Fusion Additively Manufactured Ti-6Al-4V Mechanical Properties

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3D PRINTING AND ADDITIVE MANUFACTURING 2025年第1期12卷 48-60页

作者： Luo, Qixiang Shimanek, John D. Simpson, Timothy W. Beese, Allison M. Penn State Univ Dept Mat Sci & Engn University Pk PA USA Penn State Univ Dept Ind & Mfg Engn University Pk PA USA Penn State Univ Dept Mech Engn University Pk PA USA Penn State Univ Dept Mat Sci & Engn University Pk PA 16802 USA

The mitigation of material defects from additive manufacturing (AM) processes is critical to reliability in their fabricated parts and is enabled by modeling the complex relations between available build monitoring signals and final mechanical performance. To this end, the present study investigates a machine learning approach for predicting mechanical properties for Ti-6Al-4V fabricated through laser powder bed fusion (PBF-LB) AM using in situ photodiode processing signals. Samples were fabricated under different processing parameters, varying laser powers and scan speeds for the purpose of probing a wide range of microstructure and property variations. Photodiode data were collected during fabrication, later to be arranged in image format and extracted to information-dense vectors by the transferal of deep convolutional neural network (DCNN) structures and weights pretrained on a large computer vision benchmark image database. The extracted features were then used to train and test a newly designed regression model for mechanical properties. Average cross-validation accuracies were found to be 98.7% (r(2) value of 0.89) for the prediction of ultimate tensile strength, which ranged from 900 to 1150 MPa in the samples studied, and 93.1% (r(2 )value of 0.96) for the prediction of elongation to fracture, which ranged from 0 to 17%. Thus, with high accuracy and hardware-accelerated inference speeds, we demonstrate that a transfer learning framework can be used to predict strength and ductility of metal AM components based on processing signals in PBF-LB, illustrating a potential route toward real-time closed-loop control and process optimization of PBF-LB in industrial applications.

关键词： machine learning computer vision deep convolution neural network laser powder bed fusion in situ processing monitoring

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