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Exploring the Differences and Applications of Signal Processing Technologies Based on Domestic Large Models 24

Exploring the Differences and Applications of Signal Process...

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Proceedings of the 7th International conference on Information Technologies and Electrical Engineering

作者： Binxiang Wu Jie Li China Satellite Maritime Survey and Control Department Wuxi Jiangsu China

ISBN: (纸本)9798400707087

The large model technology has revolutionized the field of signal processing, providing new ideas and methods for solving complex signal processing problems. This study delves into the performance of large models in typical application scenarios such as speech enhancement, image super-resolution reconstruction, video codec optimization, and industrial IoT anomaly detection. Experimental data shows that the large model approach surpasses traditional methods by an average of 18.7% in key indicators such as processing accuracy, computational efficiency, and system robustness. Notably, in the low-quality speech recognition task with a signal-to-noise ratio of -5dB, the accuracy is improved by 18.6 percentage points. These achievements not only advance signal processing technology but also demonstrate the enormous potential of large models in practical applications. Future research will focus on model structure optimization, generalization ability enhancement, and innovative applications in broader fields.

关键词： image reconstruction

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Hybrid Fusion Approach for Animal Species Identification using Facial images

Hybrid Fusion Approach for Animal Species Identification usi...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International conference on

作者： Rajarajeshwaran. D N. Shanmugapriya Dhilip Prasath. G Marithangam. K Department of Computer Applications(PG) Dr. SNS Rajalakshmi College of Arts and Science Coimbatore

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Species identification is crucial in the conservation of biodiversity, assessment, and monitoring of wildlife and features of an ecosystem. Disadvantages of conventional techniques include low accuracy, variance of environmental factors, and unbalanced data set. It discusses a novel hybrid species identification approach using the ResNet-50 CNN. To improve the quality of inputs, the methodology uses image preprocessing, which encompasses filtering, resizing, and data augmentation. The performance of the model was measured while training and testing it on a diverse group of species captured in various climates. The classifiers for the proposed framework offered a classification accuracy of 94.8%, an average inference per image time of 48 milliseconds, and better precision and recall comparisons to past models. These results highlight the framework's application for real-time wildlife tracking and monitoring. It provides a relevant contribution to environmental issues and provides a practical approach to solving ecological issues.

关键词： YOLO Training Accuracy Biological system modeling Wildlife Time series analysis Real-time systems Convolutional neural networks Environmental management Monitoring

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Virtual Reality-Based Teleoperation system for Robot Forklifts

Virtual Reality-Based Teleoperation System for Robot Forklif...

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IEEE International conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Manuel B. Couto Marcelo R. Petry Abel Mendes Manuel F. Silva CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal FEUP - Faculty of Engineering University of Porto Porto Portugal Flowbotic Coimbra Portugal ISEP - Instituto Superior de Engenharia do Porto Instituto Politécnico do Porto Porto Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

The growing reliance on e-commerce and the demand for efficient intralogistics operations have increased the need for automation, while labour shortages continue to pose significant challenges. When combined with the inherent risks of forklift operation, this circumstance prompted businesses to look for robotic solutions for intralogistics tasks. However, robots are still limited when they come across situations that are outside of their programming scope and often need assistance from humans. To achieve the long-term goal of enhancing intralogistics operation, we propose the development of a virtual reality-based teleoperation system that allows remote operation of robot forklifts with minimal latency. Considering the specificities of the teleoperation process and network dynamics, we conduct detailed modelling to analyse latency factors, optimise system performance, and ensure a seamless user experience. Experimental results on a mobile robot have shown that the proposed teleoperation system achieves an average glass-to-glass latency of 368 ms, with capturing latency contributing to approximately 60% of the total delay. The results also indicate that network os-cillations significantly impact image quality and user experience, emphasising the importance of a stable network infrastructure.

关键词： Radio frequency Visualization 5G mobile communication system performance Robot vision systems Virtual reality Cameras User experience Wireless fidelity Testing

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A Transfer Learning-Based Approach for Brain Tumor Classification with Attention Module

A Transfer Learning-Based Approach for Brain Tumor Classific...

引用

International conference on Electrical, Computer and Communication Engineering (ECCE)

作者： Md. Ariful Haque Khan Md Shakhawat Hossain Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Unusual cell growths in the brain or surrounding tissues are termed brain tumors, which can be benign (noncancerous) or malignant (cancerous). Their prevalence, accounting for approximately 2% of all cancer diagnoses according to the World Health Organization (WHO), raises significant public health concerns. Patients often experience symptoms such as headaches, seizures, memory loss, and other neurological issues, impacting their quality of life depending on the tumor's location and size. This study presents a system for classifying brain tumors using a transfer learning model enhanced by a self-attention mechanism. This approach aims to improve feature representation and emphasize relevant regions in input images. We compare the proposed model's performance against existing classification methods, achieving an impressive accuracy of 98.14%.

关键词： image segmentation Accuracy Liquids Transfer learning Biopsy Brain tumors Brain modeling Medical diagnostic imaging Diffusion tensor imaging Cancer

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Glaucoma Detection using Golden Jackal Optimization based MobileNet Dual Attention Classification

Glaucoma Detection using Golden Jackal Optimization based Mo...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International conference on

作者： Thirumal P C Aiswarya T Information Technology Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Early identification of glaucoma, world's biggest cause of irreversible blindness, is crucial for preventing vision loss; nevertheless, this is an arduous and time-consuming process when done manually. Using deep learning, this research suggests a way to automatically identify glaucoma in improved retinal scans taken from Kaggle dataset. Algorithm classifies retinal pictures for the purpose of diagnosing glaucoma and creates masks for optic disc and optic cup based on those analyses. Most up-to-date techniques for feature classification and image preprocessing are utilised in this study to provide a thorough approach to glaucoma detection. When preprocessing a dataset, Contrast Limited Adaptive Histogram Equalisation (CLAHE) is used to reduce noise and enhance dataset quality. By combining the best features of many MobileNet designs, the hybrid model boosts classification accuracy. For the purpose of glaucoma image classification, a MobileNet-based Dual Attention Mechanism (MobileNet-DualAttention) classifier has been used. To further enhance the accuracy of the MobileNet-based Dual Attention model, the parameters are hyperparameter tuned using the Golden Jackal Optimisation Algorithm (GJOA). With a test set accuracy of 99.86%, the system proved its better performance during validation on a Kaggle dataset. A generalisable and practical strategy for glaucoma detection, these findings demonstrate efficacy of suggested method.

关键词： Glaucoma Deep learning Histograms Accuracy Retina Optical imaging Classification algorithms Optical noise image preprocessing Optimization

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Enhancement of image Resolution using RDB

Enhancement of Image Resolution using RDB

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Intelligent Control, Computing and Communications (IC3), International conference on

作者： P. Anu N. Kalyani D. Mohanapriya S. Rekha S. Vimala School of Computing Thanjavur India SASTRA Deemed to be University Thanjavur India Department of Computer Science PSG College of Arts & Science Coimbatore India Department of Computer Applications Sri Sarada College for Women(Autonomous) Tirunelveli India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

By using hierarchical features, Convolutional Neural Networks (CNNs) which is a Deep Learning method have shown notable improvements in image restoration (IR) tasks. However, poor performance arises from many existing deep CNN-based IR models' inability to adequately use the hierarchical information included in low-resolution images. To address the problem of image resolution, this study uses the Residual Dense Network (RDN) technology. RDN seeks to improve how well it uses hierarchical features from every convolutional layer. To be more precise, we have extracted localized information using the Residual Dense Block (RDB) and dense convolutional layers. By connecting all the layers of the present RDB twice the state of the preceding RDB, RDB creates a contiguous memory system. We incorporated Local Feature Fusion (LFF) into Residual Dense Blocks to improve the feature extraction from historical and current local data. This improved the stability of training for larger networks. After getting dense local properties, we used Global Feature Fusion (GFF) to extract global hierarchical features. Through the use of RDN in image Super-Resolution (ISR), we have proven its efficacy. Our tests on real datasets show that RDN performs exceptionally well on ISR tasks in visual quality as well as quantitative measures.

关键词： Training Deep learning Visualization Superresolution Neural networks Feature extraction Stability analysis image restoration Convolutional neural networks Intelligent control

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Color and Defect Detection in Industry Automation for Real Time Intelligent Product Sorting

Color and Defect Detection in Industry Automation for Real T...

引用

Device Intelligence, Computing and Communication Technologies, (DICCT), International conference on

作者： Sujatha Rajkumar Shreyas Nirgude Ashwin Anil Kulkarni Sumathi G Hamid Abdi School of Electronics Engineering Vellore Institute of Technology Vellore India Science Engineering and Built Environment Deakin University Waurn Ponds campus Australia

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

This paper presents a real-time object sorting system that classifies objects based on color and detects surface defects in an intelligent manner for multi-purpose application like autonomous vehicles, supply chain, robotics and so on. The proposed system uses state-of-the-art camera module to capture images of objects and processes them through computer vision to classify items based on color and defect status. The captured images are compared against a custom dataset of labeled samples to ensure accuracy. Based on performance metrics, the intelligent CNN algorithm demonstrates superior classification accuracy compared to RFC, especially in complex scenarios. The edge processor module controls sorting mechanism, where servos actuate to direct objects into designated bins based on classification results. The research work illustrates exploring the potential of machine learning algorithm in automated quality assurance, offering scalable, adaptable, and efficient solutions for industrial applications.

关键词： Computer vision Machine learning algorithms Automation Accuracy image color analysis Supply chains Real-time systems Surface treatment Sorting Defect detection

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A 28-GHz 189.2-dBc/Hz FoM 360° Phase-Shifting Quadrature Oscillator Without Phase Ambiguity Achieving 0.13° RMS Phase Error Under 2° Phase Resolution

A 28-GHz 189.2-dBc/Hz FoM 360° Phase-Shifting Quadrature Os...

引用

Custom Integrated Circuits conference (CICC)

作者： Hongkun Li Yiyang Shu Xun Luo University of Electronic Science and Technology of China

ISBN: (数字)9798331517458

ISBN: (纸本)9798331517465

Advanced communication systems, including 5G NR mm-wave applications, have increasing requirements for multiphase low phase noise local oscillators (LOs) to support the complex modulation with image rejection. Meanwhile, for massive MIMO/phased array systems, the LO phase-shifting scheme guarantees the resolution and gain independence of the beam steering with reduced system-level cost and complexity. However, the beam-forming quality of the signal is still limited by the amplitude-phase characteristics of LO phase shifter. Therefore, multi phase oscillator with integrated phase-shifting function is attractive for high-performance array system. Traditional methods for quadrature LO generation include frequency division, polyphase filters, and ring oscillators. However, each of these methods has considerable tradeoffs among the key performances. The ring-type multi phase oscillator based on LC resonator offers low phase noise in the mm-wave domain [1]. However, this method has the issue of phase-mode ambiguity, making it unsuitable for strict phase sequence requirements. The coupled three-stage ring oscillator in [2] generates a 6-phase signal with a phase shift in the single-GHz band. However, the 120° fixed phase difference in such a scheme makes it suffer the severe deterioration of phase noise. The in-phase injection coupled quadrature oscillator [3] deals with the issue of phase noise deterioration with fixed phase, which still meets phase-mode ambiguity.

关键词： Phase noise Ring oscillators Phased arrays Local oscillators image resolution Filters Phase modulation Phase shifters Frequency conversion Signal resolution

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G-Litter Marine Litter Dataset Augmentation with Diffusion Models and Large Language Models on GPU Acceleration

G-Litter Marine Litter Dataset Augmentation with Diffusion M...

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Euromicro conference on Parallel, Distributed and Network-Based Processing

作者： Gennaro Mellone Ciro Giuseppe De Vita Emanuel Di Nardo Giuseppe Coviello Diana Di Luccio Pietro Patrizio Ciro Aucelli Angelo Ciaramella Raffaele Montella Department of Science and Technology University of Napoli Parthenope Napoli Italy Dep. of Science and Technologies University of Naples Parthenope Naples Italy Integrated System NEC Laboratories America Inc Princeton NJ USA

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Marine litter detection is crucial for environmental monitoring, yet the imbalance in existing datasets limits model performance in identifying various types of waste accurately. This paper presents an efficient data augmentation pipeline that combines generative diffusion models (e.g., Stable Diffusion) and Large Language Models (LLMs) to expand the G-Litter dataset, a marine litter dataset designed for autonomous detection in heterogeneous environments. Leveraging scalable diffusion models for image generation and Alpaca LLMs for diverse prompt generation, our approach augments underrepresented classes by generating over 200 additional images per class, significantly improving the dataset’s balance. Training G-Litter augmented dataset using YOLOv8 for object detection demonstrated an increase in detection performance, improving recall by 7.82% and mAP50 by 3.87% (compared with baseline results). This study emphasizes the potential for combining generative AI with HPC resources to automate data augmentation on large-scale, unstructured datasets, particularly in edge computing contexts for real-time marine monitoring. The models were tested on real videos captured during simulated missions, demonstrating a superior ability to detect submerged objects in dynamic scenarios. These results highlight the potential of generative AI techniques to improve dataset quality and detection model performance, laying the foundation for further expansion in real-time marine monitoring.

关键词： Training Accuracy Computational modeling Large language models Object detection quality control Diffusion models Data augmentation Real-time systems Videos

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Optimized Almond Damage Detection with UNet and Xception-based Segmentation and Classification

Optimized Almond Damage Detection with UNet and Xception-bas...

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Electronics and Renewable systems (ICEARS), International conference on

作者： Ajay Kumar Raj Gaurang Tiwari Naresh Kumar Trivedi Vinay Gautam Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

To ensure crop quality and productivity, almond damage detection requires efficient and reliable diagnostic techniques. This research presents an automated almond damage detection method using segmentation and deep learning-based classification. UNet is used to identify almonds in the input image properly. Deep learning architectures like VGG16, VGG19, Xception, InceptionV3, Densenet201, and NASNetMobile are used to categorize damage after segmentation. Using the cross-entropy loss function, Dice coefficient, Jaccard index, and area-under-the-curve (AUC), the performance of the proposed system is evaluated. For robust training, the segmentation models were trained on a large dataset of almond images, including damaged and undamaged examples. Evaluation metrics showed segmentation and classification accuracy and reliability. UNet+Xception leads with 98.23% accuracy. Because of its precision, deep learning can automate almond crop damage detection. With a 95.67% Dice coefficient and 92.45% Jaccard index, UNet can accurately identify damage. Precision agriculture is using more AI, enabling crop health monitoring and management research. In order to improve almond yield management and sustainability, agricultural methods must employ advanced machine-learning algorithms.

关键词： Precision agriculture Deep learning Training image segmentation Accuracy Robustness Classification algorithms Indexes Sustainable development Stress

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