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Comparison of Handover Latency Using Mobility Protocols for Wi-MAX Networks 15

Comparison of Handover Latency Using Mobility Protocols for ...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Deshpande, Vivek Ravivarman, G. Reddy, B. Karthika, K. Tusha Vinay Kumar, S.B. Department of Computer Engineering Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140417 India Department of Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Quantum University Research Center Quantum University India Karnataka Bangalore India

ISBN: (纸本)9798350370249

Due to the growing call for for wireless facts offerings, handover latency should be minimized a good way to ensure consumer revel in. so as to reduce handover latency, one-of-a-kind mobility protocols are to be had for We-MAX networks, including fast Base Station Switching (FBSS), Dynamic pleasant of carrier (Duos), and greater dispensed Channel get admission to (EDCA). this paper offers a comparative study of handover latencies for these three mobility protocols. the research was carried out the usage of a Web-MAX network simulator to simulate numerous scenarios. results show that EDCA was discovered to be the first-class in phrases of handover latency, with an average latency of 1.18 ms, at the same time as fast Base Station Switching and Duos had a latency of 6.02 ms and 13.43 ms respectively. Additionally, the effects showed that EDCA decreased the handover latency by using 14.4%, as compared to FBSS and 39.2% in comparison to Duos. the look at additionally suggests that EDCA is extra suitable for handover in We-MAX networks, due to its low latency and high performance. ©2024 IEEE.

关键词： Wimax

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A Novel Approach for Coverless Information Hiding with Encrypted Images 15

A Novel Approach for Coverless Information Hiding with Encry...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Gautam, Sangeeta Agarwal, Ruchi Kumar, Manoj Babasaheb Bhimrao Ambedkar University Department of Computer Science U.P. Lucknow India Pranveer Singh Institute of Technology MCA Department U.P. Kanpur India

ISBN: (纸本)9798350370249

the current coverless information hiding methods face challenges such as the requirement for large image datasets and complex adversarial networks. In order to address these issues, this paper introduces a new approach by employing the mapping rule and generating multiple hash sequences within single cover image. these hash sequences are used to hide secret information within the cover image which maps with the segments of the secret data. the sender encrypts the cover image after hiding the secret message, which the recipient decrypts to extract the hidden information. Image encryption is achieved through bitwise XOR with a pseudo-random number matrix generated using a seed value, ensuring consistency in random number generation during decryption. the proposed method achieves a high embedding capacity with low computation and encrypts the image to prevent image attacks. Further ensuring that the transmitted information remains confidential and secure. After decryption with the key and mapping information, hidden information can be retrieved along with the recovery of original images. the proposed approach enhances the hiding capacity and provides image encryption, hence increasing security against image processing attacks while maintaining low computational demands. Moreover, in terms of hiding capacity, the proposed method demonstrates superior performance compared to existing coverless information-hiding techniques. © 2024 IEEE.

关键词： Coverless information hiding Eigen vector Hash sequence Image Encryption Image steganography Security

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Metaversal 6G: Deciphering Complex Requirements and Multivariate KPIs in high-performance computing 15

Metaversal 6G: Deciphering Complex Requirements and Multivar...

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15th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / the 7th International Conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Kumar, Sudhakar Singh, Sunil K. Ayushi Chilkoti, Vanshika Arya, Varsha Chui, Kwok Tai Gupta, Brij B. CSE Chandigarh College of Engineering and Technology Sector 26 Chandigarh India Department of Business Administration Asia University Taiwan Hong Kong Hong Kong Asia University Taiwan

As the next frontier in wireless technology, 6G represents the sixth generation of mobile communication. Expected to surpass the capabilities of 5G, 6G aims to provide unprecedented data speeds, ultra-low latency, and enhanced connectivity, unlocking new possibilities for advanced technologies like augmented reality, artificial intelligence, and the Internet of things. It intertwines seamlessly with the Metaverse, a digital universe where users can interact with computer-generated environments, objects, and other users in real-time high-performance computing. this research attempts to explore the transformative domain of Metaverse integration in 6G Networks, delving into the potential landscape with a keen focus on emerging prerequisites, key performance indicators (KPIs), and future opportunities in the HPC environment. the integration of 6G and the metaverse signifies a fundamental shift in connectivity and user experiences, offering assurances of heightened data speeds, decreased latency, and inventive applications. Within this framework, the article introduces the all-encompassing structure of 6G Cloudification and Cloud Native Technologies, highlighting the critical role played by sophisticated cloud technologies in shaping the evolution of the metaverse ensuring the scalability, flexibility, and efficiency required to support the dynamic nature of immersive digital experiences. © 2024 Elsevier B.V.. All rights reserved.

关键词： Artificial intelligence

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Hybrid Deep Learning Framework for Automated Classification of Wildlife Camera Trap Images 15

Hybrid Deep Learning Framework for Automated Classification ...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sudeepa, P. Seema, P.D. Kumawat, Shubham Sah, Mohan Choudhary, Rahul C. M S Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370249

Camera traps play a crucial role in wildlife research and conservation, as they provide invaluable insights into animal behavior and population dynamics. However, the manual analysis of the extensive image data generated by these traps presents a significant challenge. Deep-learning models offer a promising solution for automating image classification and facilitating efficient wildlife monitoring. In this paper, a novel hybrid object classification model for wildlife image classification in camera trap data has been proposed. this model combines the strengths of two well-established deep learning architectures, ResNet-50 and DenseNet-121, with an ensemble-learning approach using logistic regression. the data was acquired from camera traps deployed in wildlife habitats and pre-processed it to enhance image quality and facilitate model training. the pretrained ResNet-50 and DenseNet-121 models on the wildlife image dataset were fine-tuned and the ensemble approach combined the feature vectors extracted by both models and uses logistic regression for final classification. the performance of the model was evaluated using metrics such as log loss and accuracy. the results show that the proposed hybrid object classification model achieves high accuracy of 89% in classifying wildife species from camera trap images. th advances automated wildlife image classification and supports conservation efforts by enabling efficient species identification and population monitoring © 2024 IEEE.

关键词： Camera Trap Image Analysis Deep Learning Ensemble Learning for Species Recognition Hybrid Deep Learning Models Wildlife Image Classification

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OpenMDS: An Open-Source Shell Generation Framework for high-performance Design on Multi-Die FPGAs 30

OpenMDS: An Open-Source Shell Generation Framework for High-...

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IEEE 30th International symposium on Field-Programmable Custom computing Machines (FCCM)

作者： Shin, Gyeongcheol Kim, Junsoo Kim, Joo-Young Korea Adv Inst Sci & Technol Sch Elect Engn Daegeon South Korea

ISBN: (纸本)9781665483322

FPGA is a promising platform in designing a hardware accelerator due to its design flexibility and fast development cycle, despite the device's limited hardware resources. To address this, latest FPGAs have adopted a multi-die architecture providing abundant hardware resources with high yield and cost-benefit. However, the multi-die architecture causes critical timing issues when signal paths cross the die-to-die boundaries, adding another design challenge in using FPGA. We propose OpenMDS, an open-source shell generation framework for high-performance design on multi-die FPGAs. Based on the user's design requirements, it generates an optimized shell for the target FPGA via automated bus pipelining, customized floorplanning, and scalable clocking scheme.

关键词： computer architecture Timing Field programmable gate arrays Open source software Hardware acceleration Pipeline processing

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Machine Intelligence at the Crease: A Quintuple-Class CNN-LSTM Model for Umpire Gesture Recognition 15

Machine Intelligence at the Crease: A Quintuple-Class CNN-LS...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Vats, Satvik Mehta, Shiva Graphic Era Hill University Computer Science and Engineering Dehradun248002 India Graphic Era Deemed to be University Dehradun248002 India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact & Outcome Punjab India

ISBN: (纸本)9798350370249

the model is advanced and state-of-the-art. How successful can a methodology be assessed in identifying essential performance practicalities and applying them to all five umpire actions accomplished? the precisions, recall, and F1 scores have been measured correctly;this allows us to quantify the model's prediction accuracy. For Class 1, which is facile to detect, the precision record was higher than 87.3%, with a recall of 9 7. 3 % and an F 1 score of 92.1%. A fantastic level of accuracy characterizes class 2, as it reported a high precision of above 87% and a recall of above 94%, with F1-score precision above 9 0 %. the Grade 3 model displays an accuracy of 86.94%, a recall of 84.12%, and an F1-score of 85.50. the precision for Class 4 is 89%, with a sensitivity of 81% and an F 1 score of 85%. Its accuracy is 90.43%, and its recall is 87.93%, so the F1 score is also 89.16%. Among the averaging techniques, macro, micro, and weighted averages incorporate class-wise errors and summarise the entire model performance. the macro average indicates that the accuracy, number of articles returned, and F1 score are 88.27 %, 89.02 %, and 88.51%, respectively. the weighted average is concerned with the weighted class distribution, which provides accuracy, recall, and F1 of 88.38%, 88.32%, and 88.23%, respectively. the micro average results from all courses' total contributions show a global performance of 88.32% in accuracy, recall, and F1 score. Moreover, the accuracy rate of the proposed model will astonish anyone at 88.3%. When we add it up, it is evident that the CNN-LSTM model, which we use to recognize umpire actions automatically, is an accurate statement. this model displays the possibility of a dramatic increase in the accuracy and dependability of the cricket referee. © 2024 IEEE.

关键词： Convolutional neural networks

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Examining the Potential of Machine Learning in Reducing Prescription Drug Costs 15

Examining the Potential of Machine Learning in Reducing Pres...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Patil, Sachin C. Madasu, Sairam Rolla, Krishna J Gupta, Ketan Yuvaraj, N. United States Microsoft CharlotteNC United States University of The Cumberlands Department of Information Technology WilliamsburgKY United States Sri Shakthi Institute of Engineering and Technology Computer Science and Engineering Tamil Nadu Coimbatore641062 India

ISBN: (纸本)9798350370249

the ability of the system to gain knowledge for lowering prescription drug prices has been increasingly studied in recent years. Gadget-gaining knowledge is a branch of synthetic intelligence that enables machines and PC systems to enhance their overall performance on specific obligations by means of mastering beyond facts. Within the context of prescription drug costs, gadget learning may be used to find patterns and developments in drug pricing and usage, decide disparities in getting admission to and usage, and have a look at pricing strategies hired by pharmaceutical businesses. the use of gadgets to gain knowledge can help to discover elements using prescription drug costs, inclusive of the pricing and utilization of individual capsules or the relationships among drug costs and effects. Moreover, AI-primarily based algorithms may be used to use massive datasets in an effort to apprehend the role that healthcare vendors, insurers, and pharmaceutical organizations have on prescription drug ***, AI-based total fashions can also be used to become aware of and degree cost elements associated with specific drug combinations. Sooner or later, machine studying may be leveraged to examine the effects of policy changes and healthcare reforms on prescription drug costs and to become aware of goal populations with high prescription drug prices. By combining gadget studying with conventional healthcare price analysis gear, researchers can gain insights into the drivers of prescription drug charges and make public coverage decisions. In sum, a device gaining knowledge can be a powerful tool. © 2024 IEEE.

关键词： Combinations Knowledge performance Pharmaceutical Prescription

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Enabling high-Quality Uncertainty Quantification in a PIM Designed for Bayesian Neural Network 28

Enabling High-Quality Uncertainty Quantification in a PIM De...

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28th Annual IEEE International symposium on high-performance computer architecture (HPCA)

作者： Li, Xingchen Wu, Bingzhe Sun, Guangyu Zhang, Zhe Yuan, Zhihang Wang, Runsheng Huang, Ru Niu, Dimin Zheng, Hongzhong Lu, Zhichao Zhao, Liang Chang, Meng-Fan Marvin Guan, Tianchan Si, Xin Peking Univ Beijing Peoples R China Alibaba Grp Inc Hangzhou Peoples R China Hefei Reliance Memory Ltd Hefei Peoples R China Natl Tsing Hua Univ Hsinchu Taiwan Southeast Univ Nanjing Peoples R China Adv Inst Informat Technol Beijing Peoples R China

ISBN: (纸本)9781665420273

Uncertainty quantification measures the prediction uncertainty of a neural network facing out-of-training-distribution samples. Bayesian Neural Networks (BNNs) can provide high-quality uncertainty quantification by introducing specific noise to the weights during inference. To accelerate BNN inference, ReRAM processing-in-memory (PIM) architecture is a competitive solution to provide both high-efficient computing and in-situ noise generation at the same time. However, there normally exists a huge gap between the generated noise in PIM hardware and that required by a BNN model. We demonstrate that the quality of uncertainty quantification is substantially degraded due to this gap. To solve this problem, we propose a holistic framework called W2W-PIM. We first introduce an efficient method to generate noise in ReRAM PIM design according to the demand of a BNN model. In addition, the PIM architecture is carefully modified to enable the noise generation and evaluate uncertainty quality. Moreover, a calibration unit is further introduced to reduce the noise gap caused by imperfection of the noise model. Comprehensive evaluation results demonstrate that W2W-PIM framework can achieve high-quality uncertainty quantification and high energy-efficiency at the same time.

关键词： ReRAM Bayesian Neural Network Analog computing Noise

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A high-performance Hardware architecture for ECC Point Multiplication over Curve25519 30

A High-Performance Hardware Architecture for ECC Point Multi...

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IEEE 30th International symposium on Field-Programmable Custom computing Machines (FCCM)

作者： Wu, Guiming He, Qianwen Jiang, Jiali Zhang, Zhenxiang Long, Xin Zhao, Yuan Zou, Yinchao Alibaba Grp Hangzhou Peoples R China Ant Grp Hangzhou Peoples R China

ISBN: (纸本)9781665483322

As one of the most secure ECC curves, Curve25519 is employed by some secure protocols, such as TLS 1.3, IRTF's RFC7748, Diffie-Hellman Private Set Intersection (DH-PSI) protocol, etc. high performance implementation of ECC is required, especially for the DH-PSI protocol. Point multiplication, the chief cryptographic primitive in ECC, is computationally expensive. To improve the performance of DH-PSI protocol, we propose a novel and high-performance hardware architecture for point multiplication over Curve25519. the proposed architecture features a pipelined Finite-field Arithmetic Unit (FAU) and a simple and highly efficient instruction set architecture (ISA). Compared to the best existing work on Xilinx Zynq 7000 series FPGA, our implementation with one Processing Element (PE) can achieve 3.14x speedup on the same device. To the best of our knowledge, our implementation appears to be the fastest among the state-of-the-art works. We also have implemented our proposed architecture consisting of 4 Compute Groups (CGs), each with 16 PEs, on an Intel Agilex AGF027 FPGA. the experimental results show the peak performance of 4.52 Mops/s (million point multiplication operations per seconds) can be achieved. Moreover, the measured performance of 4.48 Mops/s is achieved, with the PE utilization of 99% and at the cost of 86 Watts power, which is the record-setting performance for point multiplication over Curve25519 on FPGAs.

关键词： performance evaluation Protocols Power measurement Costs Instruction sets computer architecture Elliptic curve cryptography

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A Big Data Optimization Comparison using Apache Spark and Apache Airflow 15

A Big Data Optimization Comparison using Apache Spark and Ap...

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15th IEEE Annual computing and Communication Workshop and Conference, CCWC 2025

作者： Hartman, Taylor Poudel, Samir Upadhya, Jiblal Hasan, Md Nahid Upadhyay, Kritagya Poudel, Khem Middle Tennessee State University Department of Computer Science MurfreesboroTN37132 United States Middle Tennessee State University MurfreesboroTN37132 United States

ISBN: (纸本)9798331507695

While comparisons between Apache Hadoop and Apache Spark are well-documented, there has been limited research comparing Apache Spark with Apache Airflow, especially in terms of speed and memory usage. With Apache Airflow's recent introduction of dynamic task mapping, which performs similar functions to Apache Spark's map operation, a detailed comparison between the two tools has become increasingly relevant. A comparison in these areas would provide valuable insights for the Big Data Science community, helping determine which methods are better suited for tasks requiring high speed and efficient memory usage. this study focuses on comparing the Apache Spark Map function and Apache Airflow Dynamic Task Mapping function on two key metrics: memory utilization and computation speed. Specifically, we evaluate their performance in sorting formatted electrocardiogram sensory data. We hypoth-esize that Apache Spark will demonstrate faster processing times due to its advanced in-memory processing and sorting algorithms. However, this speed advantage is expected to come with higher memory usage compared to Apache Airflow. Our findings provide actionable insights into the strengths and limitations of these tools, guiding data scientists and engineers in choosing the most suitable framework for specific big data processing tasks. these results are particularly relevant for large-scale data sorting and transformation operations, contributing to informed decision-making in the Big Data Science community. © 2025 IEEE.

关键词： Mapping

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