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Covid Precuationary Alerting Robot 12

Covid Precuationary Alerting Robot

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12th International Conference on computer communication and Informatics, ICCCI 2022

作者： Rooban, S. Manimegalai, R. Eshwar, Idupulapati Venkata Sai Vasista, Ranga P.L.N. Gupta, Puvvada Venkata Madhava Electronics and Communication Engineering Koneru LakshmaiahEducation Foundation Andhra Pradesh Guntur522502 India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering TN Coimbatore District 641062 India

ISBN: (纸本)9781665480352

This paper presents the precautionary robot which can warn and also help the people from affecting with deadly corona virus. We know that the Novel Corona virus had shown a great impact on the world population, many of the people are dying due to this deadly virus because of not following the safety measures and not taking any precautions to overcome the ***-care workersare playing a major role in this pandemic by helping the affected patients to recover from the virus infections. Not only health-care workers but also robots are playing a major part in this pandemic. Robots are being used in laboratories and hospitals for various purposes like disinfection, spraying, manufacturing of medicines/vaccines, surveillance and many more applications. Robots are also equally contributed as healthcare workers around the world to adapt the present needs in order to combat with COVID-19. Mainly in this pandemic robot act as a tool, used in applications which are focused primarily in the area of health and services. The proposed work is to design and implement a robot for the covid applications. This robot is proposed by analyzing the present day's scenario on this pandemic. The proposed robot is user friendly and works particularly for 4 functions like mask detection on people, sanitizer spraying/disinfecting, temperature detection, and finally moving from one place to another place in the hospital or wherever it is required. © 2022 IEEE.

关键词： Robots

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A New Approach to Enhance Learning Data Sets for Object Classification 26

A New Approach to Enhance Learning Data Sets for Object Clas...

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26th International Conference on Circuits, Systems, communications and computers, CSCC 2022

作者： Dziech, Andrzej Wassermann, Jakob Windisch, Michael Baran, Remigiusz Zeja, Andrzej Bogacki, Piotr Institute of Telecommunications Agh University of Science and Technology Kraḱow Poland Department of Electronic Engineering University of Applied Sciences Technikum-Wien Wien Austria Department of Computer Science Electronics and Electrical Engineering Kielce University of Technology Kielce Poland Department of Teleinformatics and Telecommunications University of Computer Engineering Kielce Poland

ISBN: (纸本)9781665481861

In this paper, we propose a novel solution to the problem of insufficient training data in complex image classification tasks. The presented method efficiently recommends new learning samples with the use of contour-based descriptors and Scalable Vocabulary Tree (SVT) technique. The SVT is built from SIFT features and may be used for identification and localization of objects of interest in the analyzed query images. In conditions where the training data set is not large enough, the SVT's classification accuracy is satisfactory and comparable to that achieved by the dedicated CNN network. However, the SVT approach surpasses the classic convnet approaches due to its incremental learning capability. The classification certainty is further increased thanks to contour-based analysis using Point Distance Histogram (PDH) descriptor. At this stage, the PDH representation of the analyzed object is matched with the information stored in the pattern database. This database contains a set of 360 shape outlines for each object that are extracted from its 3D model. This leads to higher classification accuracy of the presented approach. © 2022 IEEE.

关键词： Classification (of information)

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PointBi-FPN: An Extention to Pointpillars for LiDAR 3D Object Detection in Autonomous Vehicles Using Bi-Directional Feature Pyramid Network

PointBi-FPN: An Extention to Pointpillars for LiDAR 3D Objec...

引用

IEEE Region 10 International Conference TENCON

作者： Soumya A Krishna Mohan C Linga Reddy Cenkeramaddi Sobhan Babu Department of Computer Science and Engineering Indian Institute of Technology Hyderabad India Department of Information and Communication Technology University of Agder Grimstad Norway

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

Autonomous vehicles increasingly rely on accurate three-dimensional (3D) object detection for safe navigation. While two-dimensional (2D) methods offer computational efficiency, the shift to 3D detection enhances precision in understanding environments. Point-based and voxel-based approaches are accurate but computationally intensive for onboard deployment. Pillar-based methods like PointPillars provide efficiency but may lack detection accuracy compared to voxel-based approaches. In this paper, we focus on enhancing the performance of PointPillars, a popular pillar-based detector, using LiDAR data. The proposed approach (PointBi-FPN) employs a bi-directional feature pyramid network (Bi-FPN) as a backbone, that aggregates multiscale features in the input data, providing a holistic view of the environment, which is crucial for detecting objects of varying sizes and distances accurately. Bi-FPN improves the model's understanding of complex scenes, making it more robust to occlusions. Through extensive experimentation on the KITTI dataset, the PointBi-FPN approach demonstrates better performance across all three detection benchmarks: BEV (Bird's Eye View), 3D (Three-Dimensional), and AOS (Average Orientation Similarity). Notably, the proposed approach exhibits significant improvements, particularly in accurately detecting objects labeled as “hard” difficulty.

关键词： Three-dimensional displays Laser radar Accuracy Two-dimensional displays Urban areas Object detection Bidirectional control Benchmark testing Feature extraction Autonomous vehicles

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An AI-Enhanced IoT Model for Three-Way Authentication and Location Tracking in Secured Jewellery Boxes

An AI-Enhanced IoT Model for Three-Way Authentication and Lo...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： S. Sundararajan P. Ebby Darney K. Palanivel Rajan A. Vegi Fernando J. Nirmal Jothi R. Santhana Krishnan Department of Mechanical Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electrical and Electronics Engineering RajaRajeswari College of Engineering Bangalore India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul India Department of Computer Science and Engineering (AIML) Dayananda Sagar University Bengaluru Karnataka India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India

Securing valuable assets has become imperative in contemporary contexts, with a particular emphasis on safeguarding precious items such as jewellery. This research introduces a comprehensive solution for enhancing the security of jewellery boxes through a novel TripleFuse Recognition Algorithm (TFRA). TFRA integrates Convolutional Neural Networks (CNN) for Fingerprint Recognition, Support Vector Machines (SVM) for Barcode Recognition, and Recurrent Neural Networks (RNN) for OTP Authentication. The algorithm is coupled with an Internet of Things (IoT) model utilizing the Message Queuing Telemetry Transport (MQTT) protocol, facilitating efficient communication and data exchange between the jewellery box and the authentication components. To evaluate the effectiveness of TFRA, simulation analyses are conducted and compared with established algorithms in the field. The simulation metrics include accuracy, precision, recall, and F1-score. TFRA exhibits superior performance in achieving high accuracy rates, ensuring robust Fingerprint Recognition, Barcode Recognition, and OTP Authentication. Furthermore, the proposed model demonstrates enhanced efficiency in communication and data transfer through the MQTT protocol within the IoT framework. Comparative analyses with existing algorithms underscore TFRA's efficacy, particularly in achieving a harmonious synergy between diverse recognition tasks. The results indicate that TFRA not only surpasses its counterparts in terms of authentication accuracy but also offers an optimized solution for real-time location tracking within the secured jewellery box. This research contributes to the burgeoning field of IoT-based security applications, providing a comprehensive solution for safeguarding valuable assets. The proposed TFRA algorithm, integrated into an advanced IoT model, demonstrates promising results in the context of three-way authentication and location tracking for secured jewellery boxes.

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Diagnosis Expert System on Breast Cancer Using Fuzzy Logic and Clustering Technique 5th

Diagnosis Expert System on Breast Cancer Using Fuzzy Logic a...

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5th International Conference on Information Systems and Management Science, ISMS 2022

作者： Awotunde, Joseph Bamidele Sisodia, Dilip Singh Ayodele, Peace Ayomide Ogundokun, Roseline Oluwaseun Chouhan, Virendra Singh Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria National Institute of Technology Raipur Raipur India Department of Multimedia Engineering Kaunas University of Technology Kaunas44249 Lithuania Manipal University Jaipur India

ISBN: (纸本)9783031311529

The disease of breast cancer is now widespread throughout the world. Expert systems are useful tools that have been effective in the diagnosis of various diseases. Therefore, this paper develops an expert system for diagnosis and detection of breast cancer using fuzzy logic enabled with clustering classifier. The fuzzy logic provides a bridge in improving the classification and detection accuracy of breast cancer disease. To balance out the dataset and avoid the costly overhead of collecting and labeling a large set of training tuples or patterns, clustering was utilized for data segmentation. The classifier can then make use of data of each group in terms of how similar they are. These tools have been developed to improve and alter the current expert systems, increasing their usefulness for the reasons for which they were designed. The fuzzy rule-based method includes expert knowledge to address the patient's ailment and render a reliable judgment in accordance with established rules. The proposed model can be applied as a clinical decision support tool to help healthcare professionals. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diseases

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Hierarchical Sugeno-FIS based Multi-hop Routing Protocol for Health Monitoring in WBANs

Hierarchical Sugeno-FIS based Multi-hop Routing Protocol for...

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Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： Vishnu Priya Thotakura Subba Reddy Chavva Krishna Santosh Naidana Department of Electronics and Communication Engineering SRK Institute of Technology Vijayawada India Department of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

WBANs, or wireless body area networks, have recently gained attention as a potentially useful tool in the field of health monitoring. However, maximizing energy efficiency in WBANs is challenging due to the nodes’ limited battery life. In this research, we propose a Hierarchical Sugeno Fuzzy Inference System (FIS) based routing for health monitoring that minimizes energy consumption while maintaining a high level of accuracy. The protocol also utilizes multi-hop routing to increase network coverage and maintain network connectivity over longer periods of time. The simulation results demonstrate that the proposed method is more efficient and has a longer lifetime for the network than the state-of-the-art protocols. The protocol also utilizes multi-hop routing to increase network coverage and maintain network connectivity over longer periods and stability. The proposed method is more efficient than the existing method based on simulation results and has a longer lifetime for the network than the state-of-the-art protocols.

关键词： Wireless communication Wireless sensor networks Simulation Spread spectrum communication Routing Routing protocols Energy efficiency

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Model Predictive Control Method for Nonisolated Universal Battery Charger 32

Model Predictive Control Method for Nonisolated Universal Ba...

引用

32nd IEEE International Symposium on Industrial electronics, ISIE 2023

作者： Guler, Naki Fesli, Ugur Komurcugil, Hasan Bayhan, Sertac Gazi University Technology Faculty Department of Electrical-Electronic Engineering Ankara Turkey Qatar Environment and Energy Research Institute Doha Qatar Gazi University Technical Science Vocational School Electrical and Energy Department Ankara Turkey Eastern Mediterranean University Famagusta Department of Computer Engineering Via Mersin 10 Turkey

ISBN: (纸本)9798350399714

This paper proposes a Model Predictive Control (MPC) method for a universal bridgeless non-isolated wide-output voltage range onboard battery charger. The onboard battery charger has two different (boost and buck) operation modes in one cycle of the grid voltage. While the boost mode occurs twice in each half cycle, the buck mode takes place once in every half cycle. Contrary to the existing control methods, the proposed MPC method is based on grid current control and does not need a mode detection algorithm. Aside from this feature, the proposed method offers easy implementation, fast dynamic response, and no need for complex modulation schemes. The grid current reference is generated by a proportional-integral (PI) controller depending on the battery's current or voltage, since the battery should be charged in constant current (CC) and constant voltage (CV) modes. Simulation studies are conducted to investigate the effectiveness of the proposed control strategy for non-isolated wide-output voltage range bridgeless onboard battery chargers. Simulation studies are conducted using two different battery models having nominal voltages of 48V and 350V. The results reveal that the proposed method successfully charges the two batteries in both CV and CC modes. Moreover, the grid current is maintained at unity power factor and total harmonic distortion (THD) is smaller than the limits defined in international standards. © 2023 IEEE.

关键词： Model predictive control

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Automated Exoskeleton Prosthetic Suit With Monitoring System

Automated Exoskeleton Prosthetic Suit With Monitoring System

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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： C Jeeva Lalit Kumar Sagar Mudit P Srivastava Abinaya R Narmatha M Shanmuga Priya M Department of Electrical and Electronic Engineering Sri Sairam Engineering College Tamil Nadu India Department of Computer Science Engineering SRM Institute of Science and Technology Delhi India Department of Physics SRM Institute of Science and Technology Delhi India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

The Automated Exoskeleton Prosthetic Suit with Monitoring System aims to develop a fully functional prosthetic suit with coordinated strength and response time. The suit is ameliorative and can be worn or removed as needed. Designed as a wearable inner suit, it operates autonomously, incorporating sensors and navigation systems to reduce inequality. In regions where the suit is worn, it analyzes blood flow using ultrasound Doppler principles and aids in improving circulation. The exoskeletons support or replace impaired body parts, allowing users to function properly. Electrical motors and precise position control form the core of its most common design. This suit explores the entire body using various actuators, tasks, and degrees of freedom. The product can be worn as an inner suit, featuring an autonomous sensor and navigation system to eliminate inequality. It measures blood flow in the regions covered by the suit, enhancing circulation. The primary goal of the product is to detect even small objects and prevent the wearer from falling.

关键词： Ultrasonic imaging Navigation Exoskeletons Sensor systems Time factors Spinal cord injury Monitoring Wearable sensors Prosthetics Blood flow

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Development and Evaluation of Sequential Batch Reactor with Natural Coagulants for Dairy Waste Water Treatment

Development and Evaluation of Sequential Batch Reactor with ...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： R. Sheela Daniel S. Sundararajan C. Antony Vasantha Kumar J. Nirmal Jothi R. Santhana Krishnan C. Ramasamy Sankar Ram Department of Civil Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Mechanical Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Electrical and Communication Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Computer Science and Engineering University College of Engineering Anna University Tiruchirappalli India

India is often heralded as the "Cynosure" of the global dairy industry, boasting a staggering valuation of INR 14,899.8 billion in 2022. This industry has exhibited remarkable and sustained growth in milk product availability since the year 1998. However, it's imperative to concede that the dairy sector concurrently bears the dubious distinction of being one of the most ecologically burdensome industries, primarily due to its substantial effluent output. Approximately 50% of the total dairy wastewater emanates from the rinsing of processing units intricately involved in the production of dairy commodities. The sources of this wastewater are multifarious, encompassing spills and seepage from pipelines, suboptimal tanker drainage, foaming incidents, tank spillages, cleaning operations, and liquid losses. This wastewater comprises milk solids, detergents, sanitizers, milk remnants, and cleaning water. It is typified by elevated concentrations of nutrients, both organic and inorganic, and may also encompass acids, alkalis, active agents, disinfectants, in conjunction with a substantial microbiological load, inclusive of pathogenic viruses and bacteria. In recognition of the imperative need to reclaim valuable milk solids to reinvigorate agriculture, particularly in regions grappling with water scarcity, a comprehensive wastewater treatment strategy was proffered. This strategic framework encompassed the meticulous scrutiny of pivotal parameters such as pH, BOD (Biological Oxygen Demand), and turbidity during the treatment regimen. Moreover, an ongoing discourse pertains to the utilization of natural coagulants for the efficacious treatment of dairy wastewater. Diverse ratios of these coagulants were judiciously introduced to the wastewater under treatment, and the levels of pH, BOD, and turbidity were assiduously assessed upon the culmination of each processing stage. The determination of the treatment process's effectiveness hinged upon the discerning comparison of out

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Smart Antennas and Machine Learning: Enhancing Beamforming in Wireless communications

Smart Antennas and Machine Learning: Enhancing Beamforming i...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： A V V Sudhakar Upasana Sharma K. Visala K. Suresh Kumar Chirag Arora Kuldeep Chouhan Department of EEE School of Engineering SR University Warangal Telangana India Department of Electronics and Communication Engineering ABES Engineering College Ghaziabad UP Computer Science Engineering Vignan's Institute of Information Technology Vishakapatnam Andhra Pradesh MBA Department Panimalar Engineering College Varadarajapuram Chennai Department of Electronics and Communication Engineering KIET Group of Institutions Ghaziabad U.P Department of Computer Science and Applications Sharda School of Engineering and Technology Sharda University Greater Noida India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

One of the most effective and widely used technological advancements for increasing capacity, enhancing quality, and expanding coverage in wireless communications systems is the smart antenna. Smart antennas are essential for increasing the effectiveness and dependability of wireless communication systems because they make use of cutting-edge signal processing techniques. Smart antennas' beamforming capability, which concentrates signal transmission and reception in particular directions, increases signal strength, lowers interference, and expands network capacity. As wireless networks become more complicated, machine learning (ML) has become a potent tool for beamforming optimisation. ML algorithms can proactively adjust antenna characteristics by using real-time data, learning from fluctuations in the environment to optimise performance. In order to improve beamforming capabilities, this research investigates the incorporation of ML with smart antenna systems. We go over several ML techniques, such as supervised and reinforcement learning, for resource allocation, user localisation, and signal-to-noise ratio (SNR) optimisation. Additionally, we draw attention to how deep learning might enhance spatial filtering, facilitating more effective communication in 5G and beyond. The potential direction for next-generation wireless communication networks is presented by the convergence of ML and smart antennas.

关键词： Wireless communication Array signal processing OFDM Transmitting antennas Signal processing algorithms Interference Resource management Antennas Optimization Signal to noise ratio

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