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VANET Congestion Control and Stability performance Analysis using Seagull Optimization Deep Learning 15

VANET Congestion Control and Stability Performance Analysis ...

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

作者： Niaz Ahamed, V.M. Sivaraman, K. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Tamil nadu Chennai73 India

ISBN: (纸本)9798350370249

Significant matters in VANET environment include high vehicle versatility, changing vehicle thickness and dynamics between vehicle separating. therefore, improved routing protocol enhances overall performance of VANET through intermittent service availability. In this regard, a group-based AI technique is implemented to predict VANET mobility. Effective routing based on a hybrid metaheuristic algorithm combined with Ensemble Learning produces significantly improved results. Vehicle Ad Hoc Networks (VANETs) are currently used in various applications such as transportation using vehicle-to-vehicle multi hop wireless communication since they offer scalable and cost-effective solutions for it. On the other side, multi-hop communication development is regarded as a challenging task in VANET conditions due to rapid alterations of periodic and topological network breakdowns. In general, mobile ad hoc routing protocols, this leads to failure of routing or its invalidation. In order to solve the problem of routing and get multi-constrained QoS (quality of service) measures in VANETs, numerous research works have been conducted on it. Another aspect for application is traffic collision control. Unfortunately, there are hardly any sensors anywhere which make it basically impossible to obtain continuous information about traffic states. Accurate predictions about traffic flow are difficult because road accidents and public events cannot be predicted in advance and both these can essentially affect the traffic flow. this article reports the framework of Seagull Optimization Deep Learning for predicting accuracy and experiments various nodes. this framework exhibition focuses on different vehicle flows, position rates, and road organization typologies. Our framework effectively reduces the general waiting time of vehicles during aggregation and shortens the vehicle travel season even when the location rate is low. © 2024 IEEE.

关键词： Congestion control (communication)

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Not All SWAPs Have the Same Cost: A Case for Optimization-Aware Qubit Routing 28

Not All SWAPs Have the Same Cost: A Case for Optimization-Aw...

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

作者： Liu, Ji Li, Peiyi Zhou, Huiyang North Carolina State Univ Raleigh NC 27695 USA

ISBN: (纸本)9781665420273

Despite rapid advances in quantum computing technologies, the qubit connectivity limitation remains to be a critical challenge. Both near-term NISQ quantum computers and relatively long-term scalable quantum architectures do not offer full connectivity. As a result, quantum circuits may not be directly executed on quantum hardware, and a quantum compiler needs to perform qubit routing to make the circuit compatible with the device layout. During the qubit routing step, the compiler inserts SWAP gates and performs circuit transformations. Given the connectivity topology of the target hardware, there are typically multiple qubit routing candidates. the state-of-the-art compilers use a cost function to evaluate the number of SWAP gates for different routes and then select the one with the minimum number of SWAP gates. After qubit routing, the quantum compiler performs gate optimizations upon the circuit with the newly inserted SWAP gates. In this paper, we observe that the aforementioned qubit routing is not optimal, and qubit routing should not be independent on subsequent gate optimizations. We find that with the consideration of gate optimizations, not all of the SWAP gates have the same basis-gate cost. these insights lead to the development of our qubit routing algorithm, NASSC (Not All Swaps have the Same Cost). NASSC is the first algorithm that considers the subsequent optimizations during the routing step. Our optimization-aware qubit routing leads to better routing decisions and benefits subsequent optimizations. We also propose a new optimization-aware decomposition for the inserted SWAP gates. Our experiments show that the routing overhead compiled with our routing algorithm is reduced by up to 69.30% (21.30% on average) in the number of CNOT gates and up to 43.50% (7.61% on average) in the circuit depth compared with the state-of-the-art scheme, SABRE.

关键词： quantum computing compiler optimization qubit routing

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An Unified Approach for Traffic Sign Classificationand Flow Prediction using Deep Learning Technique 15

An Unified Approach for Traffic Sign Classificationand Flow ...

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

作者： Kavitha, D. Lekhana, Kommareddy Rajeshri, Vigrahala Rushitha, Chennareddygari School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331300579

Efficient and safe traffic management is an important concern in modern urban environments. To address this challenge, we propose an integrated deep learning based solution that unifies traffic sign classification and traffic flow prediction using the LeNet architecture. the seamless fusion of these two critical tasks enables a holistic approach to traffic management, benefiting autonomous vehicles, traffic control systems, and road safety. In this unified approach, LeNet, a seminal convolutional neural network (CNN), serves as the backbone for traffic sign classification. Lenet Network is a very famous kind of configuration of convolutional neural networks that can be used to classify *** this paper, we have used Lenet to classify traffic signs, mainly used for selfdriving cars. Leveraging LeNet's capability to recognize traffic signs with high accuracy, we train it on a comprehensive dataset comprising 43 different classes of traffic signs. this dataset encompasses a wide range of shapes, colors, and conditions, allowing the LeNetbased classifier not only to identify individual traffic signs but also to provide valuable context for downstream traffic flow prediction. Extensive experiments on diverse datasets validate the effectiveness of our unified approach. We demonstrate superior traffic sign classification accuracy using LeNet, surpassing previous state-of- art methods. Additionally, our traffic flow prediction capabilities exhibit impressive accuracy and robustness across various traffic scenarios. Traffic flow prediction helps people for effective route planning so that the people can chose their routes to save and fuel and it helps to reduce traffic congestion. this research represents a significant step toward enhancing traffic management systems efficiency and safety by leveraging deep learning techniques. Our unified approach, combining LeNet- based traffic sign classification with traffic flow prediction, holds great promise for smarter and more r

关键词： Convolutional neural networks

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Byways: high-performance, Isolated Network Functions for Multi-Tenant Cloud Servers 15

Byways: High-Performance, Isolated Network Functions for Mul...

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15th Annual ACM symposium on Cloud computing, SoCC 2024

作者： Han, Xinyu Gao, Yuan Parmer, Gabriel Wood, Timothy The George Washington University WashingtonDC United States

ISBN: (纸本)9798400712869

Network functions (NFs) have become pervasive in data centers as a means to monitor and transform traffic as it flows between services. Softwarization of the network has further added to the diversity of functions that can be deployed, yet managing the performance, efficiency, tenant-customizability, and security of these functions remains a major challenge. We present Byways, an abstraction that provides facilitates to safely deploy NFs alongside end-host VMs in a multi-tenant cloud environment. Byways guarantee strict isolation between the host system, the network functions, and VM-based cloud applications, while still maintaining high performance. A Byway manages a specific set of services, and an associated NF only processes flows associated with those services, using per-byway resources (e.g., processing time). this separation of end-host traffic across Byways provides strong fault isolation - a failing NF does not impact other services. Byways augment this isolation with per-Byway access rights that restrict a NFs access (e.g., read, write, drop) to the flow, limiting the impact of a faulty NF on even its own services. We have implemented BywayOS, a μ-kernel instantiation of Byways, and evaluated its performance, efficiency, and isolation properties compared to state of the art virtual machine networking technologies. A Byway processing memcached traic through an isolated NF demonstrates throughput and latency competitive with, and often better than, Linux host performance (i.e., without a NF nor a VM), and throughput 1.25x-6.43x higher than other host NF+VM technologies, while offering stronger isolation and a trusted computing base (TCB) more than 20x smaller. © 2024 ACM.

关键词： Network function virtualization

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DCT Implementation on a Custom FPGA RISC-V Processor 65

DCT Implementation on a Custom FPGA RISC-V Processor

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65th International symposium ELMAR, ELMAR 2023

作者： Harmina, Tomislav Hofman, Daniel Benjak, Jakov University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (纸本)9798350325126

this paper presents our implementation of an Out-of-Order RISC-V core design. the increasing relevance of open-standard computer architectures and corresponding tools makes the development of custom hardware an increasingly attractive alternative to off-the-shelf components. RISC-V is such an open Instruction Set architecture (ISA) that has become widely used in recent years, especially in low-cost microcontrollers, but also in high-performance computing in recent years. Here we showcase a custom-developed core capable of executing the Discrete Cosine Transform algorithm which gives the core potential uses in image and video processing. Problems with developing such a core are presented and future possibilities and improvements are discussed. © 2023 IEEE.

关键词： computer architecture

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QULATIS: A Quantum Error Correction Methodology toward Lattice Surgery 28

QULATIS: A Quantum Error Correction Methodology toward Latti...

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

作者： Ueno, Yosuke Kondo, Masaaki Tanaka, Masamitsu Suzuki, Yasunari Tabuchi, Yutaka Univ Tokyo Grad Sch Informat Sci & Technol Tokyo Japan Keio Univ Fac Sci & Technol Tokyo Japan RIKEN Ctr Quantum Comp Tokyo Japan Nagoya Univ Grad Sch Engn Nagoya Aichi Japan NTT Comp & Data Sci Labs Tokyo Japan JST PRESTO Tokyo Japan

ISBN: (纸本)9781665420273

Due to the high error rate of a qubit, detecting and correcting errors on it is essential for fault-tolerant quantum computing (FTQC). Surface code (SC) associated with its decoding algorithm is one of the most promising quantum error correction (QEC) methods because it has high fidelity and requires only nearest neighbor qubits connectivity. To realize FTQC, we need a decoder circuit capable of not only QEC in a 3-D lattice to deal with errors in measurement on ancillary qubits but also quantum operations on logically constructed qubits. Whereas several methods to perform logical operations on SC, such as lattice surgery (LS), are known, no practical decoders supporting them have been proposed yet. One of the most promising QC implementations today is made up of superconducting qubits that are located in a cryogenic environment. To reduce the hardware complexity of QC and latency of QEC, we are supposed to perform QEC in a cryogenic environment. Hence a power-efficient decoder is required due to the limited power budget inside a dilution refrigerator. In this paper, we propose an online-QEC algorithm that supports LS with a practical decoder circuit, as well as a new FTQC architecture. We design a key building block of the proposed architecture with a hybrid of SFQ- and Cryo-CMOS-based digital circuits and evaluate it with a SPICE-level simulation. Each logic element includes about 2400 Josephson junctions, and power consumption is estimated to be 2.07 mu W when operating with a 2 GHz clock frequency. We evaluate the decoder performance by a quantum error simulator for an essential operation of LS with code distances up to 11, and it achieves a 0.6% accuracy threshold. In an LS-based architecture further supporting a magic-state distillation protocol, which is expected to run for near-term universal quantum computing, we evaluate the QEC performance and power consumption of the architecture and show that it is practical to be operated in 4-K temperature region of a

关键词： Quantum computing Quantum Error Correction Single flux quantum (SFQ)

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Overview of QuantumCLEF 2024: the Quantum computing Challenge for Information Retrieval and Recommender Systems at CLEF 15th

Overview of QuantumCLEF 2024: The Quantum Computing Challeng...

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15th International Conference of the Cross-Language-Evaluation-Forum-Association (CLEF)

作者： Pasin, Andrea Dacrema, Maurizio Ferrari Cremonesi, Paolo Ferro, Nicola Univ Padua Padua Italy Politecn Milan Milan Italy

ISBN: (纸本)9783031719073;9783031719080

Quantum computing (QC) is an innovative research field that has gathered the interest of many researchers in the last few years. In fact, it is believed that QC could potentially revolutionize the way we solve very complex problems by dramatically decreasing the time required to solve them. Even though QC is still in its early stages of development, it is already possible to tackle some problems by means of quantum computers and to start catching a glimpse of its potential. therefore, the aim of the QuantumCLEF lab is to raise awareness about QC and to develop and evaluate new QC algorithms to solve challenges that can be encountered when implementing Information Retrieval (IR) and Recommender Systems (RS) systems. Furthermore, this lab represents a good opportunity to engage with QC technologies which are typically not easily accessible. In this work, we present an overview of the first edition of QuantumCLEF, a lab that focuses on the application of Quantum Annealing (QA), a specific QC paradigm, to solve two tasks: Feature Selection for IR and RS systems, and Clustering for IR systems. there have been a total of 26 teams who registered for this lab and eventually 7 teams managed to successfully submit their runs following the lab guidelines. Due to the novelty of the topics, participants have been provided with many examples and comprehensive materials that allowed them to understand how QA works and how to program quantum annealers.

关键词： Quantum computers

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ArcvaVX: OpenVX Framework for Adaptive Reconfigurable computer Vision architectures 19th

ArcvaVX: OpenVX Framework for Adaptive Reconfigurable Comput...

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19th International symposium on Applied Reconfigurable computing (ARC) - architectures, Tools, and Applications

作者： Kalms, Lester Nickel, Matthias Gohringer, Diana Tech Univ Dresden Dresden Germany ScaDS AI Ctr Scalable Data Analyt & Artificial Intelligenc Dresden Germany ScaDS AI Ctr Scalable Data Analyt & Artificial Intelligenc Leipzig Germany

ISBN: (纸本)9783031429200;9783031429217

the field of computer vision is steadily growing in its complexity and application areas. FPGAs have shown that they can meet the growing demands for performance and energy efficiency. However, their programmability is a major challenge for software programmers. With OpenVX a standard for cross platform acceleration of computer vision applications exists. Existing OpenVX FPGA frameworks often contain non-standard constructs and consider either fixed processor architectures or specialized non-adaptive accelerators. therefore, we propose ArcvaVX, a framework that generates a runtime-adaptive vision architecture from OpenVX applications, which is performant and flexible. It (1) verifies the user implemented OpenVX applications, partitions them into task graphs and creates their meta-data (2) maps these tasks to physical nodes, creates a schedule, and clusters and places the nodes within a partition-based topology (3) creates the hardware architecture, including additional components required to generate a runtime-adaptive system. these components contain runtime configurable network adapters that can prevent deadlocks, a controller for direct memory access, and a manager that configures both and maintains the schedule. the architecture is designed for applications with high data rates and low synchronization overhead. the evaluation shows a low latency overhead of 0.006% added by the architecture, while resource consumption is more than halved compared to a design consisting only of accelerators.

关键词： OpenVX computer Vision FPGA HLS Framework

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Advanced Skin Disease Classification with VGG19, YOLOv3, and MobileNet 15

Advanced Skin Disease Classification with VGG19, YOLOv3, and...

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

作者： Bhatia, Himani Vats, Satvik Sharma, Rishabh Lovely Professional University Punjab India 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 Punjab Rajpura India

ISBN: (纸本)9798350370249

Skin diseases are classified into various types hence proper classification of the disease is very important because its determination of the type of treatment to offer the patients will highly determine the results of the patients. this research paper presents an advanced approach to skin disease classification by employing three state-of-the-art deep learning models: VGG19 - a convolutional model that is 19 layers deep, YOLOv3 - an improved version of the YOLO algorithm, MobileNet - a lightweight neural network designed for mobile devices. the performance of these models will be assessed and compared to identify the most effective way of categorizing numerous skin diseases using a large data set. ResNet and VGG19 are employed based on their resilience in feature extraction, and despite the model's complexity, it is rather accurate. When it comes to the detection of skin abnormalities, object localization is critical, for which YOLOv3 is used because of its high accuracy. MobileNet is evaluated for its applicability in real-time classification for mobile and integrated embedded device applications. the dataset is composed of skin lesion images and has high resolutions and dermatoscopic images, which means that the models had a diverse set of skin conditions on which to train. Metrics including accuracy, precision, recall, and F1-score are used to assess and rank the efficiency of one specific model with another. In highlighting the strengths and weaknesses of each model, the findings help in informing studies applying such frameworks in real-world practice contexts. this present comparative study intends to Provide an Improved understanding in the field of dermatology With Better Machine Learning Algorithms to Automate skin Disease Identification, with an impressive patient care impact. © 2024 IEEE.

关键词： deep learning MobileNet Skin disease VGG19 YOLOv3

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A Novel Framework for Fake News Detection Using LDA and QDA 15

A Novel Framework for Fake News Detection Using LDA and QDA

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

作者： Srinivas, Challamalla Satya Devanandhan, S.P. Manoj, Venigalla Venkata Abhishek, S. Anjali, T. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India Rolls-Royce Inc Banglore India

ISBN: (纸本)9798350370249

In today's digital age, the rise of online media has made information readily accessible, but it has also facilitated the rapid spread of fake news. this phenomenon undermines trust in reliable news sources and fosters confusion among the public. Consequently, individuals must exercise caution when sharing information, verifying its credibility to prevent the spread of false information and uphold the trustworthiness of reputable channels. A highly accurate system has been designed to detect fake news efficiently and with a high level of accuracy. In this paper, an enhanced approach is suggested for classification based on the Linear Discriminant Analysis and Quadratic Discriminant Analysis models, some advanced methods that help fight against fake media stories. A number of natural language processing techniques, including tokenization, stop word removal, and URL and HTML tag removal, were applied to a comprehensive dataset of fake news collections. For the purpose of classification, two models-Linear Discriminant Analysis and Quadratic Discriminant Analysis models-were applied for the correct detection of fake news. In order to evaluate effectiveness, performance measures such as accuracy, precision, recall, F1 score, confusion matrix, and ROC curve were critically analyzed. the accuracy of LDA is 97%, while QDA achieves 67%. this paper attempts to provide a step toward efforts engaged in fighting misinformation in the digital age and trying to keep reliable news sources intact. © 2024 IEEE.

关键词： Data accuracy

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