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Content based image Retrieval using K-Nearest Neighbours and Convolutional Neural Networks 15

Content based Image Retrieval using K-Nearest Neighbours and...

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15th international conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kumar, C. Siva Bhaskaru, O. Komatlapalli, Bhargavi Areddula, Phani Sai Kumar Maddali, Manikanta Reddy, Busa Rajasekhar Tirupati India Dept. of CSE Gates Institute of Technology Gooty A.P Ananthapur India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331300579

Today, there is a lot of digital image material on the internet. As the need for better image search tools grows, new waysmust be found to solve the problem of telling the difference betweenquestion pictures and received images. This study presents a new technique to enhance the searching of images in retrieval based on image content. The technique involves using several deep neural network methods, such as ResNet50, InceptionResNetV2,VGG19, InceptionV3, DenseNet121, Xception, and Improved CNN. These models make it easier to find interesting photos. The study tested thetechnique on the Dataset and a dataset from a Kaggle competition, using metrics like the confusion matrices, F1 scores, and mAP to assess the model performance. The tests show that the proposed system works very well, taking away some of the problems that come with picture searches being unclear and showing how well it can find appropriate images. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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Grant-Free Random Access for Private 5G Mobile Networks based internet of Energy 8

Grant-Free Random Access for Private 5G Mobile Networks Base...

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8th IEEE international conference on Energy internet, ICEI 2024

作者： Shi, Min-Da Shao, Wei-Ping Zhu, Liang Jiang, Wei Ling, Zhi State Grid Zhejiang Electric Power Co. Ltd. Power Wireless Communication Technology Laboratory Jiaxing Power Supply Company Jiaxing314000 China State Grid Zhejiang Electric Power Co. Ltd Hangzhou310007 China China Electric Power Research Institute Co. Ltd Nanjing210003 China State Grid Zhejiang Electric Power Co. Ltd. Power Wireless Communication Technology Laboratory Hangzhou310007 China

ISBN: (纸本)9798331523558

The internet of Energy (IoE) leveraging private 5G mobile networks represents a novel approach to boosting the overall efficiency of energy infrastructure while minimizing energy waste. As the number of IoE devices grows, grant-free random access (GFRA) has garnered significant attention due to its potential to slash signaling overhead and transmission latency. In our study, we examine GFRA in cell-free massive multiple-input-multiple-output (MIMO) systems within private 5G mobile networks that accommodate mixed-type IoE devices, while also considering the implications of access delay. We introduce a method for identifying colliding devices based on signal-to-noise ratio and calculate the access success probability for each device. Furthermore, we incorporate a backoff mechanism and derive an analytical approximation for the system's average spectral efficiency. By utilizing the gradient descent optimization algorithm, we determine the optimal backoff parameters for each device type. Simulation results indicate a strong correlation among the optimal backoff parameters of diverse device types. Notably, these parameters are significantly influenced by delay constraints, yet their impact diminishes as the devices' delay constraints approach the minimal loose boundary value. © 2024 IEEE.

关键词： signal to noise ratio

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Soil Volumetric Water Content Measurement based on LoRa RSSI and UAV 13

Soil Volumetric Water Content Measurement Based on LoRa RSSI...

引用

13th IEEE international conference on CYBER technology in Automation, Control, and Intelligent systems, CYBER 2023

作者： Zhang, Jie Yang, Jiabao Yang, Yi Wan, Xuefen Jiang, Xueqin College of Information Science and Technology Donghua University Shanghai China College of Computer of North China Institute of Science and Technology Langfang China

ISBN: (纸本)9798350315196

Soil Volumetric Water Content (VWC) is a crucial parameter for agriculture. In recent years, LoRa-based internet of Underground Things (IoUT) has been developed rapidly. In this paper, we propose a method for measuring soil VWC using Received signal Strength Indication (RSSI) of LoRa based IoUT. Low-error LoRa-RSSI data are collected using a hovering planar rotating Unmanned Aerial Vehicle (UAV), and analyzed using Long Short-Term Memory (LSTM) deep learning method. The experimental results show that the soil VWC can be measured more accurately by this method. © 2023 IEEE.

关键词： Long short-term memory

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AI based Forest Fire Detection Using Deep Learning Techniques 2

AI Based Forest Fire Detection Using Deep Learning Technique...

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2nd international conference on Computer Vision and internet of Things, ICCVIoT 2024

作者： Chandramohan, K. Yuvasri, M. Umamaheswari, R. Vijayprabakaran, K. Venkatesan, K. CSE Department Anna University Gnanamani College of Technology Tamilnadu Namakkal India

ISBN: (纸本)9781837242672

Forest fires pose serious risks to ecological components, infrastructure, and human life. It is projected that half of the forest ecosystems would have been completely destroyed by wildfires by 2030. Reducing the damage caused by forest fires can only be accomplished by using fire detection devices. As a result, study centers and universities all around the world are paying close attention to forest-fire detection techniques. There are already several different commercial automated fire sensor systems available, but they are all difficult to use in wide open regions like woodlands because of their poor reaction times, high maintenance needs, and other problems. image processing has been used in this study due to the quick development of digital camera technology, the camera's excellent coverage of wide areas, the faster response time of image processing methods compared to established sensor systems, and the general lower cost of image processing systems compared to sensor systems. False alarm rates may be substantial since certain objects have characteristics with forest fires, which make reliable forest fire detection systems challenging. This study presents a revolutionary four-stage video-based image processing approach for forest fire detection. First, a background-subtraction approach is applied to identify moving zones. Second, potential fire zones are located using RGB colour space. Thirdly, features extraction is used to differentiate between actual fire and fire-like objects since candidate zones may contain moving fire-like objects. Finally, the convolutional neural network method is used to classify the region of interest as either a genuine fire or not. The results of the last experiment show that the recommended strategy finds forest fires with success. © The Institution of Engineering & technology 2024.

关键词： Convolutional neural networks

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Density-based Traffic Control System Using IoT and Machine Learning

Density-Based Traffic Control System Using IoT and Machine L...

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2024 international conference on Knowledge Engineering and Communication systems, ICKECS 2024

作者： Tabassum, Husna Mohammed Ameer, M. Alam, Mohammed Murshid Sharieff, Mohammed Yaseen Ahamed, Naveed Anjum, Ayesha Hkbk College of Engineering Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350359688

Traffic congestion is a critical issue in cities due to the increasing population and automobiles. Traffic jams create delays, tension, increased fuel usage, and air pollution, with megacities particularly affected. Real-time road traffic density estimates are vital for better signal control and traffic management. The current traffic management system lacks emphasis on live scenarios, leading to inefficiencies. Our project, implemented using Python OpenCV and a pre-trained YOLO model, aims to prevent congestion. image processing captures lane footage, and processed data dictates traffic LED commands for signal management and uses sensors and cameras to determine vehicle count and understand the actual crowd of vehicles on each road in real-time. Machine Learning, an intelligent computer system, helps us analyze all this data and make decisions to improve traffic flow based on the current vehicular count. We also provide features for detecting emergency vehicles and giving them more priority over other vehicles to pass through. Additionally, the paper enhances pedestrian safety by incorporating a virtual speed bump when the light is red. It also prioritizes emergency vehicles, improving the efficiency of public services, and introduces a solution called the Density-based Traffic Control System (DBTCS), which combines the power of the internet of Things (IoT) and Machine Learning (ML) to revolutionize how we manage traffic. © 2024 IEEE.

关键词： Traffic congestion

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Pixel super resolution imaging technology based on phase wavefront modulation 6

Pixel super resolution imaging technology based on phase wav...

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6th conference on Frontiers in Optical Imaging and technology: Novel Technologies in Optical systems

作者： Yang, Xu Zhong, Yimeng Xu, Jiaming Wang, Bowen Zuo, Chao School of Electronic and Optical Engineering Nanjing University of Science and Technology Jiangsu Province Nanjing210094 China Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense Nanjing University of Science and Technology Jiangsu Province Nanjing210094 China

ISBN: (数字)9781510679672

ISBN: (纸本)9781510679665

In the field of aperture coding super-resolution imaging, an important reason limiting the image super-resolution effect is that aperture coding greatly reduces the incident light flux of the system. Phase wavefront encoding modulation technology can effectively increase the system light flux and improve image super-resolution results. However, using phase wavefront coding instead of aperture coding will cause complex frequency domain calculations. In this paper, the feasibility of the single-seam phase encoding pattern is proved through the mathematical calculation of phase wavefront coding modulation. In the subsequent reconstruction process, the Fourier ptychographic (FP) imaging algorithm is used for verification. The resolvable line pairs of the USAF are increased from Group0 Number2 to Group1 Number3, means the image resolution is approximately doubled. The optical system built based on this phase mask prove the luminous flux of the acquired down-sampled image is increased about more than 5 times, and the image signal-to-noise ratio (SNR) is increased about more than 7 times. This makes it possible for higher super-resolution computational imaging. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Incident light

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A Few-Shot Modulation Recognition Algorithm based on Enhanced Constellation Diagram 23

A Few-Shot Modulation Recognition Algorithm Based on Enhance...

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23rd IEEE international conference on Communication technology, ICCT 2023

作者： Zha, Yanping Wang, Hongjun Shen, Zhexian College of Electronic Engineering National University of Defense Technology Hefei China

ISBN: (纸本)9798350325959

Few-Shot Learning (FSL) studies how to learn from a few samples, and it has been well applied in image classification. In order to adapt to the scenario where only a small number of signal samples can be obtained in complex electromagnetic environments, this paper designs a few-shot recognition algorithm based on enhanced constellation diagrams for Multiple Phase Shift Keying (MPSK), Multiple Quadrature Amplitude Modulation (MQAM), and Multiple Amplitude Phase Shift Keying (MAPSK) signals. The I/Q sequence signal is firstly converted into a constellation diagram, which a reciprocal attenuation model enhances to improve the ability to express image features. Then an embedding function, designed based on GhostNetV2, can map the signal to a low-dimensional embedding space and achieve modulation recognition by distance measurement in the embedding space. The simulation results show that when the signal-to-noise ratio (SNR) is 30dB and under the 5-way 5-shot condition, the recognition accuracy can reach 84%. Due to the strong generalization and robustness, the algorithm proposed in this paper has a good recognition performance while only requiring a few signal samples. © 2023 IEEE.

关键词： Constellation Diagram Few-Shot Learning Modulation Recognition Reciprocal Attenuation Model

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Transfer Learning based Breast Cancer Detection for Telemedicine systems in Healthare Environment 8

Transfer Learning Based Breast Cancer Detection for Telemedi...

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8th international conference on Smart Cities, internet of Things and Applications, SCIoT 2024

作者： Bhavsar, Ansh Patel, Vansh Patel, Yogi Geddam, Rebakah Gupta, Rajesh Tanwar, Sudeep Mohammad-Hosseini, Melika Shahinzadeh, Hossein Institute of Technology Nirma University Department of Comp. Sci. & Engg. Ahmedabad India Islamic Azad University Najafabad Branch Department of Electrical Engg. Najafabad Iran Amirkabir University of Technology Tehran Polytechnic Department of Electrical Engg. Tehran Iran

ISBN: (纸本)9798350374674

Breast cancer continues to be a serious challenge all over the world with many lives of women affected globally due to changing societal lifestyle dynamics. Timely detection and accurate diagnosis are crucial for easing its impact as well as guiding treatment towards favorable outcomes. This study proposes a framework to meet these necessities i.e. integration of ResNet-101 model with telemedicine technology making use of transfer learning techniques in detecting and classifying breast cancers. The proposed model signifies a change in the way healthcare services are delivered, where telehealth is utilized to overcome geographical barriers and provide access to medical expertise remotely. The aim of this study is to change how breast cancer care works, by enabling timely diagnosis and intervention irrespective of one's physical location. Through robust validation and experimentation, impressive accuracy rate of 95.16% was achieved. Apart from new technology, telemedicine can change special health services, especially in places where there is a lack of doctors or hospitals nearby. This methodology combines the power of ResNet-101 model, transfer learning, and telemedicine that facilitate not only early detection of breast cancer but also creates a path in future of healthcare systems. © 2024 IEEE.

关键词： image classification

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Machine Learning for Sensor-based Handwritten Character Recognition: A Brief Survey 21st

Machine Learning for Sensor-Based Handwritten Character Reco...

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21st international conference on Distributed Computing and Intelligent technology

作者： Singh, Shashank Kumar Chaturvedi, Amrita IIT BHU Dept Comp Sci & Engn Varanasi 221005 Uttar Pradesh India

ISBN: (纸本)9783031814037;9783031814044

Handwriting recognition technologies have evolved significantly, with recent advancements incorporating wearable devices such as smartpens, smartwatches, and smartphones to capture motion and gesture data. This paper reviews various efficient handwriting recognition models that utilize inertial sensors, visual sensors, EMG sensors, and acoustic and Wi-Fi signals. Inertial sensor-based approaches often use accelerometers and gyroscopes to collect three-dimensional data during handwriting, with models achieving high accuracy in recognizing digits and alphabets. Visual sensor-based techniques, leveraging cameras and image processing, have been fundamental in developing Optical Character Recognition (OCR), further enhanced by deep learning algorithms. EMG sensors have been explored for their potential to capture muscle activity for gesture recognition. At the same time, acoustic and WiFi signal-based methods offer innovative ways to recognize handwriting through sound and signal variation. Despite the progress, challenges such as sensor drift, data generalization, and computational requirements persist. Integrating multi-modal sensors and advanced machine learning techniques holds promise for overcoming these limitations and improving the accuracy and usability of handwriting recognition systems across diverse applications.

关键词： Inertial Sensor Machine Learning Visual Sensors

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Fusion-based Digital Twin Framework with Convolutional Neural Networks (CNN) for Real-Time Threat Detection and image Enhancement in Low-Light IoT Environments 2

Fusion-based Digital Twin Framework with Convolutional Neura...

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2nd IEEE international conference on Integrated Intelligence and Communication systems, ICIICS 2024

作者： Kumar, K. Ashok Barmola, Pareshwar Prasad Velmurugan, S.P. Manjunath, A.C. Soni, Umang Maranan, Ramya Department of Physics B V Raju Institute of Technology Medak Narsapur India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishankoil India Department of Mechanical Engineering Brindavan college of Engineering Bangalore India Faculty of Engineering and Technology Parul institute of Engineering and Technology Vadodara India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9798331504960

image acquisition in low-light environments often suffers from poor contrast, high noise, and loss of fine details, making effective enhancement challenging. Conventional enhancement methods frequently fail to balance detail preservation and noise suppression. This paper introduces a novel fusion-based technique for enhancing images captured in low-light settings. The proposed method integrates multi-scale decomposition, adaptive histogram equalization, and wavelet transform to improve image quality. Multi-scale decomposition captures critical structural information, adaptive histogram equalization adjusts intensity levels for enhanced contrast, and wavelet transform reduces noise while refining image details. Evaluation on standard low-light image datasets demonstrates that this method outperforms current state-of-the-art techniques. The enhanced images show a peak signal-to-noise ratio (PSNR) of 32.45 dB and a structural similarity index (SSIM) of 0.87, reflecting an 18% improvement in PSNR and a 12% gain in SSIM over traditional methods. Additionally, the technique reduces noise variance by 20% and enhances visual clarity, making it suitable for applications such as surveillance, medical imaging, and autonomous driving. In conclusion, this fusion-based approach provides an effective solution for low-light image enhancement, maintaining critical details and achieving improved visual quality through balanced contrast enhancement and noise reduction. © 2024 IEEE.

关键词： Wavelet decomposition

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