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Pothole detection on Roads using CNN for Autonomous vehicles

Pothole detection on Roads using CNN for Autonomous vehicles

引用

Advancements in Electrical, Electronics, Communication, computing and automation (ICAECA), international conference on

作者： K.R.Baskaran G.Shobana V.Vijilesh X. Francis Jency R.Darshan S.Kanagaraj Department of IT Hindusthan College of Engineering Coimbatore Department of IT Kumaraguru College of Technology Coimbatore Department of MCA Kumaraguru College of Technology Coimbatore

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Pothole is a large fissure while the vehicles are on the roadways. The potholes usually lead to underneath caverns and pits in the road to make it difficult to ride. If the vehicles are driven by humans, after seeing them, they move away from that path but when it comes to self-driving or autonomous vehicles, it has to be detected. Autonomous vehicles use a variety of technologies to drive themselves such as "autopilot." In Autopilot mode the driver can specify the initial point and designation point, as it automatically redirects the route for destination location. Additionally, autonomous vehicles help to reduce accidents. Pothole detection is a process that begins with data gathering from roadways. After gathering data from the real world, data preprocessing is done to clean and remove unwanted data. the proposed methods uses convolutional neural networks (CNN) algorithm with various architecture such as alexnet CNN model, Lenet architecture, Manet architecture are used. The comparison of all those models are obtained. To Identify the plain and pothole were implemented using internet of things with accuracy about 87.5% as result.

关键词： Training Surveys Accuracy Roads Computer architecture Autopilot Telecommunication computing Convolutional neural networks Autonomous vehicles Vehicles

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Emerging Trends and Analytical Perspectives in the Development of New Smartphones

Emerging Trends and Analytical Perspectives in the Developme...

引用

Advancements in Electrical, Electronics, Communication, computing and automation (ICAECA), international conference on

作者： M C S Geetha Edric Jeffrey Sam Department of MCA Kumaraguru College of Technology Coimbatore India Department of Data Science Kumaraguru College of Liberal Arts and Science Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

With a brand offering more than 30 models, choosing a smartphone can be overwhelming for consumers. This study introduces a data-driven framework to streamline the decision-making process by showing only the top-performing models based on user-defined criteria such as budget, battery life, and performance. The research employs a comparative approach, analysing raw sales data to identify high-performing models while leveraging visualization techniques for intuitive representation. Predictive algorithms and real-time insights improve the accuracy and personalization of *** findings show that this framework reduces consumer confusion significantly, improves decision satisfaction, and aligns with modern consumer-centric strategies. However, data quality and model scalability are some of the challenges that need to be addressed for wider adoption. This study shows how a focused, data-driven approach can simplify smartphone selection, offering tailored solutions that empower users and improve their experience. Future work involves integrating user feedback and broader datasets to further develop and scale up the framework.

关键词： Visual analytics Scalability Decision making Predictive models Prediction algorithms Real-time systems Data models Batteries Telecommunication computing Performance analysis

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Voice Vision Glass: A Smart Assistive Eyewear for the Blind and Visually Impaired

Voice Vision Glass: A Smart Assistive Eyewear for the Blind ...

引用

Advancements in Electrical, Electronics, Communication, computing and automation (ICAECA), international conference on

作者： Azhaguraja R K S Ashvith Kumar Paranthaman C V Arun Kumar S Dept of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

According to the World Health Organization, there are currently about 45 million blind people worldwide. In addition, 135 million people have low vision. This paper aims to assist blind people and visually impaired people to navigate more independently. This glass uses advanced technologies, including Raspberry Pi 4 and APIs like Gemini, GTTS (Google Text-To-Speech) and a Pre-trained model from Hugging Face to provide assistance and improve the quality of life of visually impaired people. The glass utilizes voice interaction for interactive commands and assistance and then APIs process the data to generate meaningful audio feedback. The Raspberry Pi 4 is the central processing unit, and it combines hardware components and software applications to provide smooth operation. Key elements include a microphone for voice input, speakers/earphones for audio feedback and Raspberry Pi 4 for processing the data received. The device also uses a rechargeable battery for long-term usage. By combining all these, this device can provide a more reliable and efficient way to improve the lives of visually impaired people and help them in different ways from day-to-day activities to activities requiring assistance.

关键词： Accuracy Glass Blindness Software Hardware Text to speech Internet Batteries Telecommunication computing Microphones

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Investigating Topology Optimization Techniques for Enhancing Network Performance in Cloud computing Environments

Investigating Topology Optimization Techniques for Enhancing...

引用

automation and Computation (AUTOCOM), international conference on

作者： Manish Nandy Ahilya Dubey Department of CS & IT Kalinga University Raipur India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

In this paper, a literature review of network optimization of cloud services and the improvement achieved in terms of performance is presented. The measures that are used in this regard were latency, throughput, utilization, packet loss, energy consumption, fault tolerance and the time the network took to recover. Optimization strategies enhance the general latency; on average from 120 ms to 85 ms = 29.17% and the throughput, increasing from 7.5 Gbps to 9.3 Gbps = 24%. A variation in the bandwidth usage from 65% to 85% was noticed (an enhancement of 30.77 % ) and a decrease in percentage of packets lost from (3.2% from 56.25 % ) to 1.4%. As with energy efficiency, it rose from 0.0028J/ bit to 0.0019J/ bit, altogether an improvement of 32.14% while the fault tolerance also improved from 85% to 92%, an improvement of 8.24%. However, the enhancement of the overall network recoveries within this project layout saw the utilisation of the slope of the recovery time within the network recovery be reduced by 46.67%, and this knocked off 8 seconds from the previous 15 seconds used in this process. The findings outlined and discussed here therefore point to the fact that while there is need to advance targeted optimization strategies for network performance, reliability, and efficiency in cloud computing settings and use contexts as above, in terms of utilization and utilization experience.

关键词： Cloud computing Fault tolerance Network topology Fault tolerant systems Packet loss Throughput Energy efficiency Topology Network function virtualization Optimization

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Real-Time Network-Aware Roadside LiDAR Data Compression 11

Real-Time Network-Aware Roadside LiDAR Data Compression

引用

11th international conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2025

作者： Mollah, Md Parvez Sankaradas, Murugan Rajendran, Ravi K. Chakradhar, Srimat T. Department of Computer Science The University of New Mexico AlbuquerqueNM United States NEC Laboratories America Inc. PrincetonNJ United States

ISBN: (纸本)9789897587450

LiDAR technology has emerged as a pivotal tool in Intelligent Transportation Systems (ITS), providing unique capabilities that have significantly transformed roadside traffic applications. However, this transformation comes with a distinct challenge: the immense volume of data generated by LiDAR sensors. These sensors produce vast amounts of data every second, which can overwhelm both private and public 5G networks that are used to connect intersections. This data volume makes it challenging to stream raw sensor data across multiple intersections effectively. This paper proposes an efficient real-time compression method for roadside LiDAR data. Our approach exploits a special characteristic of roadside LiDAR data: the background points are consistent across all frames. We detect these background points and send them to edge servers only once. For each subsequent frame, we filter out the background points and compress only the remaining data. This process achieves significant temporal compression by eliminating redundant background data and substantial spatial compression by focusing only on the filtered points. Our method is sensor-agnostic, exceptionally fast, memory-efficient, and adaptable to varying network conditions. It offers a 2.5x increase in compression rates and improves application-level accuracy by 40% compared to current state-of-the-art methods. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Edge computing

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An Autonomous Charging System for A Two Wheeled Movable Robot

An Autonomous Charging System for A Two Wheeled Movable Robo...

引用

Sensors and Information Technology (ICSI), international conference on

作者： Jiamei Wang Guangliang Liu Quancheng Lin Lin Zhu Yali Wang Institute of Automation Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Robot and Manufacturing Automation Technology Jinan China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

This paper designs an autonomous charging system for a two-wheeled mobile robot. The system utilizes LiDAR to identify a charging station with a specific structure, collects and processes data to determine the target point, and employs a feedback control law to achieve smooth motion of the robot. Ultimately, the robot precisely docks with the charging station and total the charging job.

关键词： Smart agriculture Laser radar Robot kinematics process control Charging stations Robot sensing systems Sensor systems Safety Mobile robots Intelligent sensors

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Leveraging Machine Learning for Dynamic Resource Allocation in Cloud automation

Leveraging Machine Learning for Dynamic Resource Allocation ...

引用

automation and Computation (AUTOCOM), international conference on

作者： Gunveen Ahluwalia Vaibhav Goutham Suresh M D Anto Praveena Kalyan Acharjya Trushna Paridaname Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Cloud automation enables the dynamic management and allocation of resources in a computing environment and is one of the most common uses available. Yet despite their power when cloud systems were simple and small, traditional approaches to allocating resources have proven far less feasible lately. This scenario has also created the need for new evolution methods that will adapt to dynamic cloud environments and improve the system's overall performance. A solution to this challenge comes from machine learning, which has been increasingly used recently. ML algorithms can process vast amounts of data and identify patterns and trends to predict resource use and user needs. Deep learning helps cloud-enabled automation systems adapt in real-time to changing conditions, distribute resources efficiently and improve user experience. For example, reinforcement learning and deep learning are ML techniques that can be applied to resource allocation algorithms or demand prediction models to increase accuracy. Integrating cloud automation and machine learning brings effectiveness in dynamic resource allocation, boosting the overall performance of a system, which leads to savings, better experience, and greater flexibility in using cloud resources. Reinforcement and deep learning are machine learning techniques to optimize order allocation algorithms and enhance demand estimation models.

关键词： Deep learning Cloud computing automation Machine learning algorithms Heuristic algorithms Estimation Dynamic scheduling Prediction algorithms Real-time systems Resource management

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Smart Insulin Patch With AI Optimized Delivery

Smart Insulin Patch With AI Optimized Delivery

引用

Advancements in Electrical, Electronics, Communication, computing and automation (ICAECA), international conference on

作者： Manimekalai V Varshika K Nalen G J Sri Sharen C K Electronics and Instrumentation Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Effective diabetes management requires an accurate and personalized delivery of insulin to maintain optimal blood glucose levels. Blood glucose levels are monitored regularly, and insulin is manually adjusted, which can result in inconsistent control and raise the risk of high or low blood sugar. These fluctuations may compromise patient health and quality of life, stressing a great need for more advanced solutions. The smart insulin patch is an innovative solution designed to address gaps in diabetes management, utilizing continuous, non-invasive blood glucose monitoring with an AI-driven linear regression model running on an STM32 microcontroller. With the operation of the smart insulin patch, a patient is given real-time and continuous delivery of matching insulin in accordance with his or her arm glucose levels for dynamic, personalized glucose management thereby favouring meter readings. The system can thus react faster to ascending and descending changes in glucose, providing steady and precise regulation according to the metabolic requirement of individual patients. Continuous monitoring and automated delivery are an integrated system which brings advances into management of diabetes because it provides better glucose control so that rate of risk of complications associated with the traditional manual methods is greatly diminished. Besides, it provides patient comfort because it lessens finger-prick tests and other manual methods of control which patients are subjected to. Finally, the smart insulin patch ultimately aims at improving diabetes patients' quality of life by being a safer, more effective, and easier way to manage diabetes.

关键词： Manuals Regulation Glucose Diabetes Telecommunication computing Insulin Biomedical monitoring Artificial intelligence Monitoring Blood

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Advancing IoT process Modeling: A Comparative Evaluation of BPMNE4IoT and Traditional BPMN on User-Friendliness, Effectiveness, and Workload 25th

Advancing IoT Process Modeling: A Comparative Evaluation of...

引用

25th international conference on Internet computing and IoT, ICOMP 2024, and 22nd international conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied computing, CSCE 2024

作者： Winter, Michael Kirikkayis, Yusuf Pryss, Rüdiger Reichert, Manfred Institute of Clinical Epidemiology and Biometry University of Würzburg Würzburg Germany Institute of Medical Data Science University Hospital of Würzburg Würzburg Germany Institute of Databases and Information Systems Ulm University Ulm Germany

ISBN: (纸本)9783031859229

In the evolving Internet of Things (IoT) landscape, integrating advanced technologies into business processes has become crucial for enhancing efficiency and automation. Accurate representation of IoT within business processes is a prerequisite for leveraging automation and monitoring benefits, underscoring the limitations of Business process Model and Notation (BPMN) 2.0 in effectively capturing IoT-specific behaviors. For this reason, this study investigated the enhancement of IoT business process modeling through BPMNE4IoT, an extension of the standard BPMN 2.0, focusing on its user-friendliness, effectiveness, and workload compared to traditional BPMN. The comparative user study with 30 participants demonstrated that BPMNE4IoT significantly improves the modeling experience, reducing mental and physical strain and increasing satisfaction. These findings support the importance of user-centered design in IoT modeling tools to enhance productivity and foster adoption, highlighting the need for further investigation into the impact of features and scalability within complex IoT environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： User centered design

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Research on Singular Avoidance Algorithm for Redundant Manipulators Based on Screw Theory

Research on Singular Avoidance Algorithm for Redundant Manip...

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Robotics, control and automation (ICRCA), international conference on

作者： Hongwu Wang Yifan Liu Lei Lv Yunji Li Xiangkun Zhang Robotics Engineering Center The 21st Research Institute of CETC Shanghai China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

The singularity avoidance problem of redundant DOF manipulator is studied in this paper. Firstly, a kinematic model of a 7-DOF manipulator was established based on the screw theory. Then, the singularity of the manipulator was analyzed, and the velocity level inverse kinematics algorithm for singularity avoidance was derived based on the minimum singular value as the singularity optimization index. Finally, a path planning experiment for singularity avoidance was conducted based on the velocity level inverse kinematics algorithm. The experimental results showed that the inverse kinematics algorithm has good accuracy, which verifies the effectiveness of the singularity avoidance algorithm and lays the foundation for the motion planning of redundant manipulators.

关键词： Redundancy process control Kinematics Fasteners Manipulators Path planning Mathematical models Planning Indexes Optimization

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