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Attaining Peak Performance through an Interference-Aware Joint Channel Assignment Scheme for Wireless Mesh Networks 15

Attaining Peak Performance through an Interference-Aware Joi...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Mulla, Tahseen Shikalgar, Anisa Dept. of Computer Engineering Vasantdada Patil Pratishthan's College of Engineering & Visual Arts Sion Mumbai India Dept. of Computer Science & Engineering Walchand College of Engineering Sangli India

ISBN: (纸本)9798331300579

Wireless Mesh Networks (WMNs) have witnessed a remarkable surge in popularity across a diverse spectrum of applications, encompassing military networks and public wireless infrastructures. These networks, emerging as the cornerstone of numerous wireless communication systems, offer expansive wireless connectivity to a substantial user base. Despite these advancements, the inherent challenge of interference among nodes poses a significant hurdle, resulting in diminished performance for WMNs. The prevalence of interference within WMNs is a critical concern, adversely affecting their operational efficiency. The need to address this challenge is underscored by the imperative to optimize WMN performance. A pivotal step in this direction involves the development of channel assignment algorithms that are not only efficient but also effective in mitigating interference issues. Efforts to enhance WMN performance through advanced channel assignment strategies are paramount. By devising algorithms that intelligently allocate channels and manage interference, we can significantly elevate the overall efficiency of WMNs. This imperative undertaking is fueled by the overarching goal of ensuring seamless connectivity and robust performance in the face of escalating demands from various application domains. In summary, the increasing prominence of WMNs in diverse sectors necessitates a proactive approach in overcoming performance limitations. Through the implementation of sophisticated channel assignment algorithms, we can fortify WMNs, ensuring their continued role as a reliable backbone for wireless communication systems. © Grenze Scientific Society, 2024.

关键词： Wireless mesh networks (WMN)

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An Optimized Grid Computing based Feature Selection approaches from Brain Image dataset using Pretrained models 1

An Optimized Grid Computing based Feature Selection approach...

引用

1st IEEE International Conference on Advances in Signal Processing, Power, Communication, and Computing, ASPCC 2024

作者： Nayak, Sradhanjali Biswal, Pradyut Kumar Mishra, Pravakar Pradhan, Sateesh Kumar Niis Institute of Business Administration Dept. of Computer Science Bhubaneswar India Iiit Bhubaneswar Dept. of Etc Bhubaneswar India Niis Institute of Business Administration Dept. of Computer Science and Engineering Bhubaneswar India Utkal University Dept. of Computer Science and Application Bhubaneswar India

ISBN: (纸本)9798350355093

The outcome of simulated experiments expressed the efficiency of grid-based algorithm in the domain of image processing in the context of Brain MRI Images for Tumor Detection. It provides a faster approach in detecting object and achieving higher rate of detection in comparison with existing algorithms for the similar purpose. The foremost goal of this work is improving the rate of detection by using grid based feature fusion approach with Genetic Algorithm(GA), Particle Swarm Optimization(PSO) and Scalp Swarm Optimization Algorithm. This technique produces efficient result due to hybrid fusion of gridded Images and optimization for exhaustive and precise image analysis. In the initial phase Each image is segmented into 4 grids. Pretrained models like Inception-V2, Resnet 50, Mobnetv2, VGG-16 are used to extract features before and after the images are gridded. The extracted features are analysed with classifiers such as Decision tree, Support vector Machine, Multiplayer and Self Attention Transformer that not only chooses the best set of features as well as best feature extractor. Among all these models the VGG-16 gives best result in terms of Accuracy, sensitivity, specificity and F-score. Hence VGG-16 is utilized as evaluation functions in the optimization Algorithms. The performance of the approach is measured by the values produced against the terms as precision, Recall, F-score, and accuracy rate. In the first phase of iteration, features extracted from each grid are assigned with initial weight. Optimization algorithms are used for optimizing the weight values in the combined features to bring best accuracy ratio. Grid based segmentation of Image stabilize the computation time thereby reduces the time complexity. Weighted sum Scalp Swarm Optimisation assigns best weighted values to segmentation parameters to get maximum performance. The experimental outcomes proves the efficiency of the proposed strategy. © 2024 IEEE.

关键词： Genetic algorithms

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Automated Classification of Chest CT Scans for Accurate Diagnosis and Risk Stratification: A Deep Learning Approach

Automated Classification of Chest CT Scans for Accurate Diag...

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2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Upadhyay, Deepak Gupta, Mridul Yadav, Devansh Upadhyay, Abhay Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350388534

The performance assessment metrics of the topic of classification models, such as recall, accuracy, and F1-score were considered in this work. We examined how many types of events our model categorizes well and view the more detailed metrics for every class. A special account of the precision-recall trade-off even shows that there is an undeniable advantage, and there may be a negative side. The outcomes also show how good the model is. For example, a perfect accuracy score in the 2nd Class demonstrates that the algorithm can differentiate occurrences of such a category. The weighted average metrics consider the fundamental dissimilarity in a group better. The model has an average 0.91 F1-score, indicating that it makes a balanced decision between accuracy and recall. While I can be proud of these results, I understand that the presented weaknesses realized that additional study of potential feature engineering and other model architectures remain. This experiment will be valuable for future research and usage in real-life. © 2024 IEEE.

关键词： Contrastive Learning

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Synergizing CXL with Unified Memory for Scalable GPU Memory Expansion

Synergizing CXL with Unified Memory for Scalable GPU Memory ...

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2024 International Conference on Electronics, Information, and Communication, ICEIC 2024

作者： Lee, Junseung Kim, Jungrae Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of Semiconductor Systems Engineering Suwon Korea Republic of

ISBN: (纸本)9798350371888

Heterogeneous computing, which combines the power of CPUs and GPUs, is increasingly important in today's data-centric era. However, it also presents significant challenges, particularly in terms of GPU memory management. Compute eXpress Link (CXL) technology, which is supported by industry leaders, offers enhanced memory expansion capabilities. Alongside this, NVIDIA's Unified Memory (UM) system simplifies the process of sharing data between CPUs and GPUs. We explore the role of CXL memory in various scenarios within the UM framework while simultaneously highlighting issues such as the handling of page faults at the software level. © 2024 IEEE.

关键词： Graphics processing unit

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Implementation of Pruned and Quantized Semantic Segmentation Neural Network Using Cambridge-Driving Labeled Video Database (CamVid) Dataset 2

Implementation of Pruned and Quantized Semantic Segmentation...

引用

2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Upadhyay, Deepak Malhotra, Sonal Gupta, Mridul Mishra, Somesh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350372847

In This research investigates the streamlined implementation of Semantic Segmentation Neural Networks through advanced techniques: pruning and quantization. Leveraging the CamVid dataset, our study achieved remarkable reductions in computational complexity and memory usage. Pruning removed redundant connections, effectively reducing learnable parameters, while quantization significantly minimized memory footprint. Despite these optimizations, the networks maintained high semantic segmentation accuracy. The implications of our findings are pivotal, particularly in resource-constrained applications like autonomous driving and image analysis. By enhancing efficiency without compromising accuracy, this research facilitates the seamless integration of deep learning models into real-time systems. Our study not only advances the field of computer vision but also underscores the practical feasibility of deploying sophisticated neural networks in practical, resource-efficient contexts. © 2024 IEEE.

关键词： Semantic Segmentation

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Joint Power and Channel Allocation in Fading Channels Using Deep Reinforcement Learning 2

Joint Power and Channel Allocation in Fading Channels Using ...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Gupta, Mridul Sharma, Kunj Bihari Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9798331519056

This paper investigates joint power and channel allocation under fading communication channels using Deep Reinforcement Learning (DRL). To this end, we design a DRL to allocate dynamic resources with different network condition properly and achieve the spectral efficiency improvement while reducing latency to meet lower bound in cost function as well keeping target fairness. Results show that our DRL-based approach can achieve better spectrum efficiency and latency than traditional algorithms while at the same time, sustaining a higher value of fairness index. In addition, it is observed that the DRL method also offers both adaptability (with fast adaptation to network changes) and robustness in performance but at a higher computational expense. Our results illustrate that the DRL-based technique may serve as a strong, scalable candidate in future wireless systems up to several orders of magnitude better than existing resource allocation schemes. © 2024 IEEE.

关键词： Deep reinforcement learning

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Few-Shot Learning with Feature Pairing and Mean Discrepancy

Few-Shot Learning with Feature Pairing and Mean Discrepancy

引用

2023 International Conference on Sustainable Computing and Smart systems, ICSCSS 2023

作者： Singh, K.K. Hima Bindu, K. Nit Andhra Pradesh and Rgukt Nuzvid Dept. of Computer Science and Engineering India Nit Dept. of Computer Science and Engineering AP India

ISBN: (纸本)9798350333602

Few-Shot Learning (FSL) is a sub-area of machine learning which mainly deals with data where there is a scarcity of training supervised samples. Few shot learning (FSL) more closely resembles the human brain in comparing new concepts to others based on prior experience rather than identifying it exactly. FSL aims to generalize the model across the tasks (in meta learning) opposed to the classical supervised learning which generalizes across the data points. In general the FSL models may suffer from underfitting because of scarcity of supervised samples and at the same time it causes overfitting as it is likely to memorize task specific features of the training set. This work aims to reduce such problems and is presented as a metric based model 'Few Shot Learning with Feature Pairing and Mean Discrepancy' (FL-FPMD). As the title suggests, feature pairing is one among various data augmentations. It is observed that flip augmentation is more suitable in the context of pairing the features within the given task. Memorizing task specific features is reduced by incorporating the discrepancy of mean distributions of the query and the support embedding in the loss function. The training and the evaluation is performed at the miniImageNet dataset and the results indicate that the proposed model outperforms the state-of-the-art models of similar complexity. © 2023 IEEE.

关键词： Embeddings

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An Approach of Fog Computing and Edge Computing for Computing Resources Optimization Strategies 1

An Approach of Fog Computing and Edge Computing for Computin...

引用

1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Gupta, Mridul Sharma, Kunj Bihari Yadav, Devansh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9798350365016

This paper investigates optimization strategies for fog and edge computing systems, focusing on the key challenges of resource allocation, load balancing, latency minimization, and power efficiency. We analyze the results of the simulation study to determine the impact and potential of our strategies and find that the improvements achieved are small to moderate, varying between 1.75% and 2.95%. This knowledge demonstrates that system optimization requires small, gradual increments and that fog and edge computing are dynamic systems. Our results suggest that optimization based on integration and adaptability can produce a significant difference in the performance and reliability of distributed systems. © 2024 IEEE.

关键词： Fog computing

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Detecting Helmets Of The Bike Riders Using Deep Learning Algorithms 14

Detecting Helmets Of The Bike Riders Using Deep Learning Alg...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Mia, Yousuf Rahman, Md. Solaimanur Basak, Animesh Hossain, Sheikh Mufrad Zulfiker, Md. Sabab Sumaia, Merina Akter Daffodil International University Dept. of Computer Science & Engineering Dhaka Bangladesh Model Institute of Science & Technology Dept. of Computer Science & Engineering Gazipur Bangladesh

ISBN: (纸本)9798350335095

We propose real-time bike helmet detection in our study. A lot of people ride bikes in our nation. Motorbikes are more popular than vehicles because they are cheaper to maintain, take up less parking space, and provide more mobility and adaptability in urban situations. Bike riding is entertaining yet risky. Bicyclist safety is the planned system's main purpose. Many drivers don't wear helmets even though they're mandated by law. In emerging countries, mortality has been growing steadily. A helmet detection technology that identifies drivers without helmets is needed to safeguard the public. We employ a real-time 3202 dataset for this approach. We gather wearing helmet 1911 and no helmet 1291 data and utilize algorithms like VGG16, Resnet50, MobileNet v.02, Inception V3, EfficientNet, and CNN. The EfficientNet achieved 98% accuracy. Each person's comparison statement techniques are in the implementation section. To construct the optimum model for the conditions, this inquiry uses model validation approaches. © 2023 IEEE.

关键词： Deep learning

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Alpha-x Framework for Assessing Network Robustness in Nakagami-m Fading and Co-Channel Interference Environments 3

Alpha-x Framework for Assessing Network Robustness in Nakaga...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Upadhyay, Deepak Siwach, Karan Dhondiyal, Shiv Ashish Upadhyay, Abhay Gupta, Mridul Sharma, Kunj Bihari Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India G.L. Bajaj Institute of Technology and Management Dept. of Computer Science and Engineering Greater Noida India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9798331518592

This article explores how the Alpha-x framework can improve network robustness under Nakagami-m fading and co-channel interference (CI), The study evaluates network performance using key metrics such as outage probability, spectral efficiency, and capacity degradation. The simulations show a 50% reduction in outage probability, a 20% boost in spectral efficiency, and a 33.3% lower capacity degradation compared to traditional models. These results demonstrate how the Alpha-x framework enhances network performance by adapting to various fading conditions and interference levels in wireless communication systems. The framework not only improves reliability and efficiency but also shows practical relevance in network planning and management. This can potentially lead to better user experiences and increased operational resilience. Future research could focus on optimizing the underlying algorithms and testing their performance on a larger scale in real-world environments. As telecommunications systems evolve, frameworks like Alpha-x could play a crucial role in ensuring reliable and efficient wireless networks. © 2024 IEEE.

关键词： Cochannel interference

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