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eDeepRFID-IPS: Enhanced RFID Indoor Positioning with Deep Learning for internet of Things 1

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37th international conference on Advanced Information Networking and Applications, AINA 2023

作者： Alsinglawi, Belal Rabie, Khaled Western Sydney University Parramatta SydneyNSW Australia Manchester Metropolitan University Manchester United Kingdom

ISBN: (数字)9783031284519

ISBN: (纸本)9783031284502

In smart environments, indoor positioning systems provide several options for smart computing users, businesses, and industries, thereby dramatically enhancing human well-being and productivity. Smart homes and smart indoor environments are prominent emerging technologies in the internet of Things era and future communications, with applications such as providing personalized healthcare to the elderly and those with impairments by connecting them to the world via high-speed wireless communication infrastructure. This study offers a new approach to real-time indoor positioning using passive RFID technology to estimate the real-time location of smart home users based on their movements in smart environment space. An experimental indoor positioning system technique intends to improve assisted living and identify daily activities in a smart environment. To demonstrate this, we conducted a case study on indoor positioning using RFID technology. The experimental investigation is based on a location-based system that leverages the creation of deep learning algorithms in conjunction with radio signal strength indicator (RSSI) measurements of passive RFID-tagged devices. The proposed architecture encourages more precise identification of smart home objects and the ability to precisely locate users in real-time with good measured precision while minimizing technical and technological barriers to the adoption of location-based technologies in the daily lives of smart environment inhabitants. This will eventually facilitate the realization of location-based internet of Things (IoT) systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Indoor positioning systems

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AI Driven IoT Web-based Application for Automatic Segmentation and Reconstruction of Abdominal Organs from Medical images 18

AI Driven IoT Web-Based Application for Automatic Segmentati...

引用

18th Annual international conference on Distributed Computing in Sensor systems (DCOSS)

作者： Villarini, Barbara Asaturyan, Hykoush A. Univ Westminster Sch Comp Sci & Engn London England

ISBN: (纸本)9781665495127

Medical imaging technology has rapidly advanced in the last few decades, providing detailed images of the human body. The accurate analysis of these images and the segmentation of anatomical structures can produce significant morphological information, provide additional guidance toward subject stratification after diagnosis or before a clinical trial, and help predict a medical condition. Usually, medical scans are manually segmented by expert operators, such as radiologists and radiographers, which is complex, time-consuming and prone to inter-observer variability. A system that generates automatic, accurate quantitative organ segmentation on a large scale could deliver a clinical impact, supporting current investigations in subjects with medical conditions and aiding early diagnosis and treatment planning. This paper proposes a web-based application that automatically segments multiple abdominal organs and muscle, produces respective 3D reconstructions and extracts valuable biomarkers using a deep learning backend engine. Furthermore, it is possible to upload image data and access the medical image segmentation tool without installation using any device connected to the internet. The final aim is to deliver a webbased image-processing service that clinical experts, researchers and users can seamlessly access through IoT devices without requiring knowledge of the underpinning technology.

关键词： IoT Web Application Deep Learning 3D Reconstruction Web technology Medical image Computing Organ Segmentation

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Video Steganography in IoT: Information Embedding using OpenCV and 2LSB

Video Steganography in IoT: Information Embedding using Open...

引用

2023 international conference on Sustainable Computing and Smart systems, ICSCSS 2023

作者： Pilania, Urmila Kumar, Manoj Singh, Sanjay Mittal, Vivaan Manav Rachna University Computer Science & Technology Faridabad India

ISBN: (纸本)9798350333602

After a review of many research study it is been concluded that various security techniques are used for data transmission in internet of Things (IoT) devices. Considerable progress has been made in enhancing data transmission security, but still, some gaps exist. Existing steganography techniques exhibit gaps such as limited concealing capacity, substandard quality of stego files, and reduced resilience when faced with image processing attacks. To overcome some of the existing gaps proposed work utilizes video steganography. In this work, a video steganography technique for securing data transmission in IoT devices has been proposed using OpenCV and Least Significant Bit (LSB). Extraction of video frames for hiding secret data has been done using OpenCV and then concealing the secret message is employed by the 2 LSB technique. The two Least Significant Bits are modified to hide the secret message. OpenCV facilitates the identification of suitable regions of interest for concealing secret data, leading to enhanced robustness in hiding information. Videos offer a wide range of possibilities for embedding secret information, resulting in a significant capacity for concealment and imperceptibility. For measuring the performance of research work Mean Square Error (MSE), Peak signal to Noise Ratio (PSNR), Entropy, and Histogram analysis are done. © 2023 IEEE.

关键词： internet of things

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A multi-level and multi-agent collaborative control platform for city-scale virtual power plants based on a cloud-pipe-edge-end fusion architecture 4

A multi-level and multi-agent collaborative control platform...

引用

4th international signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Baorong, Zhou Jiangnan, Li Wenmeng, Zhao Tao, Wang Biao, Zhao Wenyuan, Zheng Guanglei, Huang Mingyu, Ou Electric Power Research Institute China Southern Grid Guangzhou510530 China Shenzhen Power Supply Bureau Co. Ltd. Shenzhen518000 China Tsinghua University Shenzhen International Graduate School Shenzhen518000 China

ISBN: (纸本)9798331528676

With the continuous expansion and complexity of urban power grids, traditional power control methods are difficult to meet the needs of multi-agent, multi-energy and multi-level in modern power grids. To address this problem, this paper proposes a multi-level and multi-agent collaborative control platform for urban-scale virtual power plants based on a cloud-pipe-edge-end fusion architecture. By introducing technologies such as cloud computing, edge computing and the internet of Things, the platform designs a multi-level control architecture that includes functional modules such as physical information modeling, resource aggregation optimization, automatic power control and instruction decomposition. The innovation of the platform lies in its ability to enable the full process of adjustable resources on the load side of the virtual power plant to participate in the automatic generation control (AGC) and automatic voltage control (AVC) of the power grid, and integrates intelligent monitoring and traceability technology of electric carbon, providing real-time and efficient power control and carbon emission management. Through actual case analysis and verification, the platform is significantly superior to traditional systems in terms of supply and demand balance, response speed, energy efficiency optimization and carbon emission management, which improves the overall operation efficiency and stability of the power grid and provides strong technical support for the intelligent management of urban power grids. ©2024 IEEE.

关键词： Cloud platforms

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15th EAI international conference on Bio-inspired Information and Communications Technologies, BICT 2024

15th EAI International Conference on Bio-inspired Informatio...

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15th EAI international conference on Bio-inspired Information and Communications Technologies, BICT 2024

ISBN: (纸本)9783031815980

The proceedings contain 16 papers. The special focus in this conference is on Bio-inspired Information and Communication Technologies. The topics include: Optimizing Drug Delivery Strategies by Pathway Analysis for Waveform Modulation-based Molecular Communication;Enhancing DNA-based IoBNT Throughput and Reducing Congestion with Yin-Yang Coding;a Molecular Communication Model Driven by Magnetic Field Force;CDM based on Izhikevich Neuron Model;multi-user Diffusive Molecular Communication systems with A Passive Relay Node: Receiver Design and Performance Analysis;machine Learning-based Detection Time Estimation for Molecular Communication;taming signal-Dependent Counting Noise with Machine Learning for Molecular Communication;energy-Efficient Transmitter Creation in Molecular Communication;a Method for Determining Relay Node Location in Molecular Communication;glycemic Oscillation Decomposition-based Personalized Blood Glucose Prediction with Continuous Glucose Monitoring;A Compact Bio-inspired Microstrip Antenna (Bi-MPA) for Medical Microwave Imaging Applications;image Encryption and Decryption Algorithm based on DNA Sequence: Performance Analysis of Channel Fusion Processing;Optimizing the Classification of SSVEP signals in Brain-Computer Interfaces: A Novel Sliding Window Data Segmentation Method based on Weighted Voting Mechanism;spontaneous Motion of Nanorobots Inspired Computational technology for Tumor Boundary Exploration.

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Revolutionizing Travel Planning: An image based Destination Recognition and Recommendation System 1

Revolutionizing Travel Planning: An Image Based Destination ...

引用

1st international conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Gupta, Megha Goyal, Vanshika Rathi, Vanshika Jss Academy of Technical Education Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350367720

Due to the abundance of information available online and the wide range of possibilities available, our preparation has become more difficult and time-consuming. This is especially true given that every visitor has different interests and preferred destinations in addition to tour-specific limitations like vaccination records and travel restrictions due to pandemics. A number of travel agencies and organizations have sprung up with more advanced internet services in an attempt to take advantage of the growing global travel market by employing technology to provide travelers with relevant recommendations. However, these systems often use a destination-based recommendation strategy and are sold in packages with little room for modification to account for the tastes of individual travelers. In order to overcome these restrictions. With the use of artificial intelligence-powered picture recognition, our TravelGaze system can now identify destinations from user-uploadedimages, marking a significant advancement in travel technology. In addition to recognition, the system offers customized trip suggestions that include neighboring lodging options, eateries, art galleries, and current weather information. The solution improves destination recognition accuracy and revolutionizes travel planning by utilizing cutting-edge AI algorithms. This paper offers insights for developers and stakeholders looking to reinvent how consumers explore and plan their travels, adding to the ongoing conversation on intelligent travel solutions. © 2024 IEEE.

关键词： Tourism

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18th international KES conference on Agents and Multi-Agent systems: Technologies and Applications, KES-AMSTA 2024

18th International KES Conference on Agents and Multi-Agent ...

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18th international KES conference on Agents and Multi-Agent systems: Technologies and Applications, KES-AMSTA 2024

ISBN: (纸本)9789819764686

The proceedings contain 34 papers. The special focus in this conference is on Agents and Multi-Agent systems: Technologies and Applications. The topics include: Fair Path Generation for Formation Control Combining Ant Colony and Particle Swarm Optimizations;preference Definition in a Multi-agent System Approach for User-Centric internet of Things Smart Environments;AI-Driven Nitrogen Stress Management in Cereal Crops via Drone technology;agent-based Simulation for Withdrawal of Energy-Inefficient Housing;resource Limitations of Business Process Simulations: Literature Study;data Augmentation for Business Process Alignment: Proof of Concept and Experimental Design;smart Scheduling in Urban Traffic: A Multi-Agent System for Dynamic Vanpooling;analysis of the Impact on Area Prices Using Propensity Score Matching, Considering the Proportions of Photovoltaic and Thermal Power Generation Simultaneously;consideration of Building a Simulator to Evaluate Methods to Maximize the Effectiveness of Advertising to Households;Human-AI-Collaboration SECI Model: The Knowledge Management Model of the Experts’ Tacit Knowledges with Augmented LLM-based AI;modeling Agent Awareness After Hierarchical Managerial Training;development of An Application for Extracting Needs Related to Actual Gas Use Using Text Mining;Impact of Intelligent systems in the Home Office on Employee Satisfaction—Lessons Learned from the COVID-19 Pandemic;agile Methodologies in Software Development: A Study of Effective Management Strategies for Companies in the Digital Era;predictive Analysis of Cyclical Sales Data;EMG signal Classification Using Machine Learning for Augmentative Assistance in Cerebral Palsy;wellnessWave—Platform for the Digitization of the Romanian Public Medical System;TRIAGE Predictive Model based on Artificial Neural Networks and Deep Learning;platform for Automated Assessment of Obstetric Ultrasounds, Using Machine Learning;identification of Fonts in Unstructured Environments Through Deep Lea

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Insulator Location and Segmentation Method based on Improved S2ANet 8

Insulator Location and Segmentation Method Based on Improved...

引用

8th international conference on Intelligent Computing and signal Processing, ICSP 2023

作者： Deng, Zhixiang Zhu, Aili Cai, Changchun Hohai University Key Laboratory of Power Transmission & Distribution Equipment Technology Changzhou China Hohai University College of Internet of Things Engineering Changzhou China

ISBN: (纸本)9798350302455

Insulator is one of the important components of power system, which greatly affects the safe operation of transmission line. Aiming at the problem that insulator in aerial images has arbitrary rotation angle which leads to inaccurate location of insulator region and contains redundant background information, S2ANet model based on PAN is proposed to locate insulator region in aerial images. Firstly, rotation, cutting and noise were used to enhance the insulator data set and expand the number of samples. Secondly, S2ANet model based on PAN is used to locate the insulator and enhance the feature extraction ability of insulator edge. Finally, the insulator region is segmented accurately based on the image outspread clipping method. Experiments show that AP of improved S2ANEet model on the test data set is 92.6%, and outspread clipping method can completely segment the insulator region. The method proposed for insulator location and segmentation has good engineering application value. © 2023 IEEE.

关键词： Location

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Enhancing Grayscale image Clarity Through Blind Deconvolution and PSF Estimation With Total Variation Regularization 3rd

Enhancing Grayscale Image Clarity Through Blind Deconvolutio...

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3rd international conference on Intelligent systems and Sustainable Computing, ICISSC 2023

作者： Narasimharao, Jonnadula Kumar, Voruganti Naresh Kirankumar, Adepu Pooja, Bejjanki Rambabu, D. Joshi, Ganpat Department of CSE CMR Technical Campus Telangana Hyderabad India Sreenidhi Institute of Science and Technology Telangana Hyderabad India Department of Computer Science and Engineering Madhav University Rajasthan India

ISBN: (纸本)9789819783540

Nowadays, the usage of digital cameras in daily life has increased, leading to a growing demand for enhancing the quality of captured images. One of the most challenging problems in image processing is the restoration of blurred or out-of-focus images. This paper presents Enhancing grayscale image clarity through blind deconvolution and PSF estimation with total variation regularization. This blind deconvolution approach is used to handle the problem of removing blur from acquired photos, making it easier to restore defocused grayscale photographs. Determining the ideal picture and the point spread function (PSF) may be challenging when using blind deconvolution, which aims to recreate a clear ideal image from a single fuzzy image. As a result, this work also introduces a unique PSF estimate technique that makes use of an adaptive filter and total variation regularization. Finally, the results obtained after implementation are demonstrated based on the blur type and performance metrics, which include the mean square error and the peak signal-to-noise ratio. The experimental outcomes indicate that our method is the best among various other methods in terms of deblurring performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adaptive filtering

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Retrievable image Encryption based on Adaptive Block Compressed Sensing

Retrievable Image Encryption Based on Adaptive Block Compres...

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international conference on image, Vision and Intelligent systems, ICIVIS 2023

作者： Fang, Jiuchuan Zhao, Hui Zheng, Mingwen Wang, Zijian Niu, Sijie Gao, Xizhan Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information Science and Engineering University of Jinan Jinan250022 China School of Mathematics and Statistics Shandong University of Technology Zibo255000 China

ISBN: (纸本)9789819708543

The increasing popularity of the internet and mobile devices, cloud storage has emerged as a popular storage method. However, because data carries a lot of information, untrustworthy servers can access users’ private information. This paper introduces an approach based on adaptive block compressed sensing and hash retrieval. The objective of this paper is to reduce the storage space required by encryption algorithms while simultaneously improving interaction efficiency. Firstly, compress images using block compressed sensing, and use BCS-SPL algorithm to reconstruct. And curves are fitted based on the gradient features and root mean square values of the blocks. Then, the sub-rates are allocated based on the gradient characteristics of the image using a fitting curve, and then the image is compressed. Next, encrypt the image and save it. The features of the queried image are used for retrieval, the results are decrypted, and then reconstructed. Finally, through a series of security analyses, the method is effective. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud storage

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