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共 4645 条记录，以下是711-720 订阅

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Optimizing Road Extraction in Remote Sensing Imagery: A Lightweight Dilated Inception Deep Residual U-Net with Channel Attention (LDIDRU-CAN) Model and Pruning Strategies

Optimizing Road Extraction in Remote Sensing Imagery: A Ligh...

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International Conference on computing and Networking Technology (ICCNT)

作者： Palvi Sharma Rakesh Kumar Meenu Gupta Ahmed J. Obaid Dept. of Computer Science & Engineering Chandigarh University Punjab India Faculty of Computer Science & Mathematics university of Kufa Najaf Iraq College of Technical Engineering National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the major challenge in Road Extraction is to extract accurate and complete road network and to get high accuracy while minimizing computational power and ensuring faster prediction. It plays important role in urban planning, disaster management, automatic vehicle navigation and GPS navigation. Several earlier studies used complex deep networks that required large amounts of computational power and large storage. In order to address issues such as high storage requirements, computational load, access to memory delays, and low hardware deployment efficiency this research work presents a novel network model called LDIDRU-CAN. the proposed model is fast, compact, and efficient and requires less storage. the objective of this work, is to compress the original model using structured pruning strategy. this strategy simplified the original model and reduced their computation cost as well as storage requirements. For the reduction of the parameters, we focused on the Row Scale Regular Pruning (RSRP) and Low Learning Rate (LLR) retraining. After applying RSRP pruning approach it reduces storage requirements, parameters, and also contributes to a more than 50% reduction in hardware resource consumption and successfully enhances the parallelism of the model. Testing and validations are performed on the Massachusetts Road Dataset. With only 45 M parameters and 180 MB model size, the suggested network achieves remarkable performance with 87.91% precision, 85.51% recall, 86.69% F1-score and 98.91% accuracy.

关键词： Accuracy Navigation Computational modeling Roads Urban planning Memory management Disaster management Parallel processing Computational efficiency Load modeling

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Design and Analysis of a Nano-photonic Processing Unit for Low-Latency Recurrent Neural Network Applications

Design and Analysis of a Nano-photonic Processing Unit for L...

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IEEE International symposium on Embedded Multicore Socs (MCSoC)

作者： Eito Sato Koji Inoue Satoshi Kawakami Kyushu University Fukuoka Japan

ISBN: (纸本)9781665465007

Recurrent neural networks (RNNs) have achieved high performance in inference processing that handles time-series data. Among them, hardware acceleration for fast processing RNNs is helpful for tasks where real-time performance is es-sential, such as speech recognition and stock market prediction. the nano-photonic neural network accelerator is an approach that takes advantage of the high speed, high parallelism, and low power consumption of light to achieve high performance in neural network processing. However, existing methods are inefficient for RNNs due to significant overhead caused by the absence of recursive paths and the immaturity of the model to be designed. therefore, architectural considerations that take advantage of RNN characteristics are essential for low latency. this paper proposes a fast and low-power processing unit for RNNs that introduces activation functions and recursion processing using optical devices. We clarified the impact of noise on the proposed circuit's calculation accuracy and inference accuracy. As a result, the calculation accuracy deteriorated significantly in proportion to the increase in the number of recursions, but the effect on inference accuracy was negligible. We also compared the performance of the proposed circuit to an all-electric design and a hybrid design that processes the vector-matrix product optically and the recursion electrically. As a result, the performance of the proposed circuit improves latency by 467x, reduces power consumption by 93.0% compared with the all-electrical design, improves latency by 7.3x, and reduces power consumption by 58.6% compared with the hybrid design.

关键词： Energy consumption Recurrent neural networks Power demand Optical design Artificial neural networks Optical computing Optical network units

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Forehand to Deep Learning: A CNN-LSTM Framework for Serve Shot Analytics in Table Tennis

Forehand to Deep Learning: A CNN-LSTM Framework for Serve Sh...

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International Conference on computing and Networking Technology (ICCNT)

作者： Vikrant Sharma Shiva Mehta Computer Science and Engineering Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

It introduces a novel method for detecting and recognizing the table tennis serve-shot motions based on the advanced convolutional and Long Short-Term Memory models. the model is designed to classify serve shots into five categories: side-spin, backspin, shots with no spin, slicing, and illegal service. It consists of CNNs for precision image classification and the LSTM network for considering the time movement of people through a sequence of shots. Experimental data proved the viability of a model, so the overall accuracy was achieved at ${9 0. 3 9 \%}$. the accuracy metrics for the varied classes can stretch from 84.6% to ${9 4. 2 2 \%.}$ Another key indicator is the high recall rates for Serve shots. this table has an astonishing rate of ${9 7. 0 3 \%}$ for class 3, demonstrating how the model can effectively recognize serve shots. the high F1 scores illustrate the balance between the Precision and the Recall, the values of which exceed 87% for all instances. the model’s performance is guaranteed as performance evaluations are based mainly on macro, weighted, and micro averages. We usually have 90-95 percent accuracy, recall, and F1-Score scores. the experiment proves that the classifier finds the answer to the issue, which is indeed a significant achievement considering the job’s toughness and numerous data. the CNN-LSTM model’s fantastic accuracy and dependability are a reference tool for coaches and athletes they can rely upon. While some argue that it can help trainers and give a macro view into the course, others cannot stop them for fear that this will create doubts about the authenticity of the competition.

关键词： Training performance evaluation Analytical models Accuracy Computational modeling Interference Data models Convolutional neural networks Long short term memory Sports

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Interference-Limits Aware Power and Receiver Optimization for 5G NR-U and Wi-Fi in the 6 GHz Shared-Spectrum

Interference-Limits Aware Power and Receiver Optimization fo...

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IEEE Annual Ubiquitous computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Rasika Nilaweera Kalahe-Wattege Fernando Beltran Jairo Gutierrez Information Systems The University of Auckland Auckland New Zealand Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

Adjacent Channel Interference (ACI) presents a significant concern for densely deployed Wi-Fi networks in the $\mathbf{6 G H z}$ spectrum. Coexisting Wi-Fi and 5G may be exposed to ACI due to the limitations in the devices’ receiver filtering capabilities or nearby user devices’ high transmit power. Since users of 5G and Wi-Fi networks operate in close proximity in frequency, space, and time, harmful ACI between 5 G and $\mathrm{Wi}-\mathrm{Fi}$ is unavoidable. this study investigates interference-limits aware approaches for enhancing throughput in distributed 5G NR-U-enabled femtocells and interference mitigation in Wi-Fi6E/7 network. Users act rationally and non-cooperatively to optimize their own utility. We have developed a non-cooperative Stackelberg game model to maximize throughput for 5G and mitigate ACI in $\mathrm{Wi-Fi}$ user devices. Our analysis explores the conditions necessary for the existence of a Stackelberg equilibrium within this context. the effectiveness of the proposed model is further demonstrated through simulation results.

关键词： performance evaluation 5G mobile communication Radio transmitters Receivers Games throughput Resource management Femtocells Wireless fidelity Optimization

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Seamless Video Quality Assessment Using Image Fusion

Seamless Video Quality Assessment Using Image Fusion

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International Conference on computing and Networking Technology (ICCNT)

作者： Vivek Deshpande V.J. Vijayalakshmi Hivam Khurana S Hemalatha Gunveen Ahluwalia Roopa Traisa Department of Computer Engineering Vishwakarma Institute of Technology Pune Maharashtra India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - UG JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Seamless video first-class evaluation is a critical challenge in multimedia communique structures. Photograph fusion is an effective method used to evaluate the video’s high quality by means of combining visible statistics from more than one asset. Image fusion can be used to hit upon variations in video frames and may be used to perform class and anomaly detection tasks. In this work, a photograph fusion method is proposed for seamless video exceptional evaluation. the proposed technique makes use of a fused photo from two videos with special resolutions as enter, which is then processed via a deep convolutional neural community for great assessment. the performance of the proposed method is evaluated on several public video satisfactory datasets. Effects display that it achieves aggressive outcomes compared to present techniques.

关键词： Measurement Image resolution Streaming media Quality assessment Reliability Image fusion Video recording Anomaly detection

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Directed Adversarial Training for Comprehensive Robustification of CNNs

Directed Adversarial Training for Comprehensive Robustificat...

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International Conference on computing and Networking Technology (ICCNT)

作者： Bhuvi Ghosh Harsh Shetye Atharv Salian Nilesh Marathe Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Deep convolutional neural networks (CNNs), renowned for their high accuracy on clean datasets, remain notably vulnerable to adversarial attacks. this study assesses how the VGG16 and ResNet50 models withstand various white-box attacks when used for traffic sign classification. Despite their impressive standard accuracy, our findings reveal that these accuracies do not predict robustness against attacks. Techniques such as the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) substantially lowered model accuracy, while DeepFool and Carlini & Wagner (C&W) attacks were even more disruptive. In particular, ResNet50 was more prone to these attacks, with its accuracy dropping to as low as $\mathbf{1 2. 8 5 \%}$ under DeepFool and $\mathbf{1 7. 4 2 \%}$ under C&W at specific confidence levels. Retraining models with adversarial examples did enhance their resilience to certain attacks but at the expense of reduced effectiveness on clean data. these results highlight the critical need for developing more robust defense mechanisms that protect CNNs against a wide array of adversarial threats without compromising performance on standard inputs.

关键词： Training Accuracy Noise Robustness Data models Iterative methods Standards Residual neural networks Glass box Resilience

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YOLOv8-AF: A Novel Customized YOLOv8-Accurate Fast Deep Learning Model for Enhancing Border Security in Various Global Regions

YOLOv8-AF: A Novel Customized YOLOv8-Accurate Fast Deep Lear...

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International Conference on computing and Networking Technology (ICCNT)

作者： Debendra Muduli Aman Ujwal Toppo Vikrant Singh Mahendra Singh Divy Prakash Tiwari Dept. of Computer Science Engineering C. V. Raman Global University Bhubaneswar Odisha India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the proliferation of ground-pit tunnels along international borders poses significant security threats, including illegal immigration, smuggling, human trafficking, and potential terrorist activities. To address these challenges, this paper presents an innovative surveillance system that integrates drones equipped with high-resolution cameras and a deep learning-based object detection model, specifically the YOLOv8-AF (You Only Look Once v8-Accurate Fast) variant. the proposed model has been designed to autonomously identify ground pits from aerial imagery, providing real-time alerts to security authorities. the YOLOv8-AF model, trained on a publicly available comprehensive Ground Pit Image Dataset (GPID), demonstrates superior performance with over $95 \%$ accuracy and a mean average precision (mAP) of 0.895, outperforming other models like Faster-RCNN, Fast-RCNN, and SSD. the proposed model ability to process 116.28 frames per second makes it highly efficient for real-time applications, offering a cost-effective and robust solution for enhancing border security in various global regions.

关键词： Deep learning YOLO Accuracy Terrorism Surveillance Neural networks Predictive models Feature extraction Real-time systems Drones

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Ensuring Traceability and Orchestration in the Food Supply Chain 15th

Ensuring Traceability and Orchestration in the Food Supply C...

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15th Multidisciplinary International Congress on Science and Technology, CIT 2020

作者： Espinoza-Mejía, Mauricio Saquicela, Víctor Abril-Ulloa, Victoria Computer Science Department University of Cuenca Cuenca Ecuador Faculty of Medical Sciences University of Cuenca Cuenca Ecuador

ISBN: (纸本)9783030680794

Ensure traceability and orchestration of the participants in the food supply chain can help improve the food production and reduce the distribution of unsafe or low-quality products. this article provides some insights about the design of a high-level architecture to support a semantic blockchain platform that ensures traceability and orchestration of the food systems. the design involves two layers that cover: (a) the decentralized orchestration of the participants, (b) the semantic modeling of the data and processes involved, and (c) the storage and integrity of the data. To deal with the platform design, it is analyzed the operation and attributes of the food supply chain management and it is discussed how the combination of Semantic and Blockchain technologies can address the platform features. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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AI Dermatologist: Skin Cancer Detection with Deep Learning Technique

AI Dermatologist: Skin Cancer Detection with Deep Learning T...

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International Conference on computing and Networking Technology (ICCNT)

作者： Afraz Ul Haque Rupak Sadman Sadik Khan Alaya Parvin Alo Rupa Ghosh Md Fatin Ishrak Md Sanowar Hossain Sabuj Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Khulna University Khulna Bangladesh Department of Electrical and Electronic Engineering North South University Dhaka Bangladesh College of Business Administration International University of Business Agriculture and Technology Dhaka Bangladesh

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

this paper explores the application of deep learning technologies in the detection and diagnosis of skin cancer, leveraging advanced convolutional neural networks (CNNs) such as VGG-16, ResNet50, and MobileNetV2. Given the increasing incidence of skin cancer worldwide, there is a critical need for more accurate and efficient diagnostic methods. Our study employs transfer learning to enhance the capability of these networks, enabling them to detect skin cancer from dermoscopic images with a high degree of accuracy, often surpassing human expert performance. We detail the process of data collection, preprocessing, model training, and evaluation, emphasizing the adaptation of these models to skin cancer detection. the results demonstrate significant improvements in diagnostic accuracy, with MobileNetV2 showing notable efficiency in processing and resource utilization—qualities that are ideal for real-time diagnostic applications. In MobileNetV2 we achieved 97% accuracy. Furthermore, the study addresses the challenges of adversarial attacks and the integration of non-image data to refine diagnostic procedures. Our findings highlight the transformative potential of AI in medical diagnostics, suggesting that deep learning could become a cornerstone in the early detection and treatment of skin cancer, thereby improving patient outcomes substantially.

关键词： Deep learning Training Adaptation models Accuracy Transfer learning Real-time systems Convolutional neural networks Artificial intelligence Skin cancer Residual neural networks

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CryoCore: A Fast and Dense Processor architecture for Cryogenic computing 20

CryoCore: A Fast and Dense Processor Architecture for Cryoge...

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47th ACM/IEEE Annual International symposium on computer architecture (ISCA)

作者： Byun, Ilkwon Min, Dongmoon Lee, Gyu-hyeon Na, Seongmin Kim, Jangwoo Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea

ISBN: (纸本)9781728146614

Cryogenic computing can achieve high performance and power efficiency by dramatically reducing the device's leakage power and wire resistance at low temperatures. Recent advances towards cryogenic computing focus on developing cryogenic-optimal cache and memory devices to overcome memory capacity, latency, and power walls. However, little research has been conducted to develop a cryogenic-optimal core architecture despite its high potentials in performance, power, and area efficiency. Once a cryogenic-optimal core becomes available, it will also take full advantage of the cryogenic-optimal cache and memory devices, which leads to a cryogenic-optimal computer. In this paper, we first develop CryoCore-Model (CC-Model), a cryogenic processor modeling framework which can accurately estimate the maximum clock frequency of processor models running at 77K. Next, driven by the modeling tool, we design CryoCore, a 77K-optimal core microarchitecture to maximize the core's performance and area efficiency while minimizing the cooling cost. the key idea of CryoCore is to architect a core in a way to reduce the size and number of cooling-unfriendly microarchitecture units and maximize the potential of a voltage and frequency scaling at 77K. Finally, we propose two halfsized, but differently voltage-scaled CryoCore designs aiming for either the maximum performance or power efficiency. With both conventional and our design integrated with cryogenic memories, our high-performance CryoCore design achieves 41% higher single-thread performance for the same power budget and 2x higher multi-thread performance for the same die area. Our lowpower CryoCore design reduces the power cost by 38% without sacrificing the single-thread performance.

关键词： Cryogenic computing Cryogenic processor Modeling Simulation

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