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Machine Learning to Enhance the V2V Communication-based IoD Using Companding Dataset

Machine Learning to Enhance the V2V Communication-based IoD ...

引用

IEEE SSD international Multi-conference on systems, signals and Devices

作者： O. Daoud Q. Hamarsheh A. Damati S. Al Jaafrah O. Qasem M. Al-Khawaldah Electrical and Computing Engineering School of Engineering and Computing American International University Al-Jahra Kuwait Electrical Engineering Department Faculty of Engineering and Technology Philadelphia University Amman Jordan Researcher

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This paper investigates the improvement of Vehicle-to-Vehicle (V2V) communications in the internet of Drones (IoD) scenario for the reduction of the Peak-to-Average Power Ratio (PAPR) problem in Orthogonal Frequency Division Multiplexing (OFDM) systems. Motivated by the recent research on machine learning and deep neural network, this study will attempt to optimize Quality of Service (QoS) by means of a multiprocessing approach based on Daubechies wavelets. The proposed scheme integrates high-performance PAPR reduction schemes, such as µ-law companding, hybrid precoding-companding, and neural network-based adaptive algorithms. Deep reinforcement learning-driven approach in clipping and filtering is further proposed, which can offer real-time adaptation for the high-demanding communications. Results show a considerable gain in PAPR, over 60% PAPR reduction under high SNR conditions. These findings show that IoD can sustain stable V2V connections in urban use cases, thereby providing a scalable solution for 5G and beyond networks.

关键词： 5G mobile communication Vehicular ad hoc networks Peak to average power ratio Machine learning Quality of service Real-time systems internet Reliability Drones signal to noise ratio

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A Grouping Strategy-based Progressive Fusion Network for Hyperspectral image Super-Resolution

A Grouping Strategy-Based Progressive Fusion Network for Hyp...

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Guohua Lv Baodong Zhang Yongbiao Gao Guixin Zhao Guotao Wang Juncan Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application (Southeast University) Ministry of Education China Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Hyperspectral super-resolution involves combining low-resolution hyperspectral images with high-resolution multispectral images to produce a high-resolution hyperspectral image. Recently, although many methods for hyperspectral image super-resolution have been proposed, they often fail to fully utilize the high similarity among adjacent bands to enhance fusion performance. Therefore, we propose a grouping strategy-based progressive fusion network (GPFNet) for hyperspectral super-resolution. The core of GPFNet is the grouping strategy fusion block (GPF block), in which grouping-based spatial-spectral information fusion and spatial information refinement are performed. We design the spatial-spectral information fusion module (SSIFM) based on grouped convolutions to capture the feature differences from adjacent bands. To refine spatial details, we develop the spatial information enhancement module (SpaEM), which leverages the hierarchical features extracted by the multi-scale feature extraction module (MIEM). Additionally, a progressive fusion strategy, which involves using multiple upsampled hyperspectral images and downsampled multispectral images, further preserves spectral integrity and spatial details. Extensive experiments show that GPFNet outperforms state-of-the-art methods both qualitatively and quantitatively.

关键词： Convolution Superresolution Feature extraction Acoustics Data mining Speech processing Hyperspectral imaging

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Comparative Analysis of Steganography Techniques Using ELSB, FFT and DCT

Comparative Analysis of Steganography Techniques Using ELSB,...

引用

Multi-Agent systems for Collaborative Intelligence (ICMSCI), international conference on

作者： Swati Nitin Shilaskar Viraj Basavaraj Mathapati Sahil Laxmikant Mutekar Mrunal Ganesh Nikam Shripad Bhatlawande Dept of E&TC Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

A comparative analysis of the different steganography techniques such as Enhanced Least Significant Bit (LSB) substitution in the spatial domain, Quantization Index Modulation (QIM) in the frequency domain, and AES encryption for the security of the hidden data. It compares the performance of each of these techniques to assess image quality, data security, and robustness against the commonly expected attacks. Confidentiality of the secret information is provided by encrypting it using AES and placing it inside a cover image. Enhanced LSB embeds the encrypted data into pixel bits, whereas QIM embeds it into the wavelet coefficients of the cover image. The performance metrics include comparisons based on PSNR, SSIM, and attack resistance manifested when an individual manipulates images. Results indicate that AES enhanced techniques take a good middle ground in image quality versus security, but QIM stands out about noise and compression robustness.

关键词： image quality Resistance Steganography Visualization Quantization (signal) Noise Modulation Robustness Encryption Wavelet coefficients

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Fruit Quality Monitoring System using CNN

Fruit Quality Monitoring System using CNN

引用

Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Ravikumar B N Vidya M S Naveen Chandra Gowda NarendraBabu C. R Veena H N D Roja Ramani Department of Information Science and Engineering BMS Institute of Technology and Management Bengaluru India Department of Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India School of Computer Science and Engineering REVA University Bengaluru India Department of Computer Science and Engineering SJB Institute of Technology Bengaluru India Department of Computer Science and Engineering New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

The grading of fruits relies on inspections, experiences, and observations, with a proposed system integrating machine learning techniques to assess fruit freshness. By analyzing 2D fruit portrayals based on shape and color, limitations arise as different fruits may share similar color, size, and shape values. To enhance accuracy, the system combines size, shape, and color analysis with Convolutional Neural Network (CNN) technology. Upon capturing the fruit image, properties like size, shape, and color are extracted, followed by CNN training and testing processes. The proposed work have implements UNet and En-UNet chosen over other architectures like ResNet or MobileNet for segmentation. This innovative approach utilizes CNN to extract comprehensive features, yielding promising results in fruit freshness detection with accuracy of 99% and loss rate of 0.0123.

关键词： Training Accuracy Shape image color analysis Computer architecture Feature extraction Convolutional neural networks internet of Things Monitoring Gas detectors

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Dynamic ROI Adaptation for Accurate Non-Contact Heart Rate Estimation Using VGG-13 based Encoder-Decoder Model and Facial Landmarks

Dynamic ROI Adaptation for Accurate Non-Contact Heart Rate E...

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Aravind A Anil Srinivasa Karthik Mohanasankar Sivaprakasam Jayaraj Joseph Department of Electrical Engineering Indian Institute of Technology Madras Chennai India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Heart rate monitoring using cameras is gaining attention for unobtrusive health tracking. This paper proposes an improved method for HR calculation using ROI adaptation tailored to individual facial features. A VGG-13 based encoder-decoder model was used for skin segmentation, classifying image pixels into left cheek, right cheek, and forehead regions, achieving a Dice coefficient of 0.90. ROIs were defined by the exterior coordinates of these skin pixels, adapting naturally to features like forehead size, cheek size, hairstyles, and spectacles. To address the challenge of ROIs with insufficient skin pixel coverage, we integrated Google MediaPipe landmarks to refine ROI selection, ensuring that at least 30 percent of the pixels in each region were skin pixels. The adaptive multi-ROI method was compared with traditional single-forehead and non-adaptive multi-ROI methods using the POS and CHROM rPPG algorithms. The adaptive approach showed better performance, achieving MAE values of 1.51 and 2.15 for POS and CHROM.

关键词： Heart rate Adaptation models Forehead signal processing algorithms signal processing Cameras Skin internet Speech processing Facial features

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Multiplexer SRAM Combination-based Design of Multiple Combinational Gates for In-Memory Computation

Multiplexer SRAM Combination-Based Design of Multiple Combin...

引用

international conference on signal Processing and Communication (ICSC)

作者： Mohit Gupta Ravi S Siddanath Nishant Dwivedi Nitish Kumar Kavindra Kandpal Manish Goswami ECE Department IIIT Allahabad Prayagraj India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Traditional computing systems based on the Von Neumann architecture face challenges with speed and power consumption. To address this, SRAM is used for fast memory access while minimizing power usage. In-memory computation (IMC) leverages SRAM to perform basic computations, such as NAND and NOR, directly within the memory, improving throughput and reducing power consumption. However, IMC adds area overhead due to the integration of computation within the memory. This paper presents a novel design for NAND, NOR, XOR, and Half Adder (HA) for IMC, implemented using 18nm Fin-FET technology, offering better performance with less area overhead than previous methods.

关键词： Multiplexing Power demand image recognition Face recognition Memory management Random access memory Logic gates signal processing Throughput Adders

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IoT-based Real-Time Data Monitoring of a Green House Farm with Offline Monitoring Capability

IoT-Based Real-Time Data Monitoring of a Green House Farm wi...

引用

international conference on Robotics,Electrical and signal Processing Techniques (ICREST)

作者： Tushar Bhadra Mujakkir-Ul-Islam Nowshin Alam Department of EEE Faculty of Engineering American International University-Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

Greenhouses have transformed modern agriculture by precisely managing the environmental conditions of delicate plant species to promote crop growth. A conceptual and practical framework for a low-cost greenhouse farm monitoring system has been proposed in this paper to track real-time data on greenhouse farms with online and offline both monitoring capabilities. The internet of Things (IoT) technology has been used to develop a system prototype, which uses two sensors to collect data on environmental factors, including temperature, humidity, heat index, and soil moisture. An ESP32 microcontroller module collects and sends this sensor data to a Raspberry Pi 4 microcomputer, which displays it on a web application. The application is hosted on a local Raspberry Pi server and built with ***, a JavaScript runtime environment. The communication between ESP32 and Raspberry Pi is established with REST API (Representational State Transfer Application Programming Interface), a way to connect applications and components in microservices architectures. A highly robust PostgreSQL-based database is used to store the data in Raspberry Pi, which allows the collection of farm data locally without having to connect to any web storage. With an internet connection, data can be monitored from anywhere worldwide using a global server made of Vercel, a cloud-based platform that can host websites and web applications. The local Raspberry pi server would be accessible to the internet through SSH (secure shell) tunneling with Ngrok, a cross-platform application.

关键词： Temperature sensors Temperature measurement Databases Soil moisture Prototypes Greenhouses Real-time systems Sensors Servers Monitoring

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Machine Learning based Leaf Disease Diagnosis System

Machine Learning Based Leaf Disease Diagnosis System

引用

Multi-Agent systems for Collaborative Intelligence (ICMSCI), international conference on

作者： V. Baby Shalini Bittu Kumar P Varun Varma P Vinay Kumar Reddy T Tarun Kumar Department of Information Technology Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

This research work suggests developing a diagnostic tool by using the techniques of machine learning and computer vision for the identification of plant diseases based on leaf images. It incorporates various features such as spots, lesions, abnormal shapes, and signs of insect damage to identify potential health problems in the plants. The tool can use image-processing methods like contour analysis, colour space transformation, and morphological operations to detect and classify the diseases based on risk factors like shape and area of spots, along with the extent of damage on the leaf. The tool is designed to work with uploaded leaf images from Google Drive and then provide an extensive report on plant health indicators such as spots, shape, and insect damage. It's early enough to detect and consequently reduce crop losses. This detection system equally ensures more targeted application of pesticides.

关键词： Plant diseases Shape Insects Crops Machine learning Pesticides internet Medical diagnosis Lesions Morphological operations

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Data-driven Processing using Parametric Neural Network for Improved Bluetooth Channel Sounding Distance Estimation

Data-driven Processing using Parametric Neural Network for I...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Andrii Tsemko Avik Santra Oleg Kapshii Ashutosh Pandey Infineon Technologies Lviv Ukraine Infineon Technologies Irvine CA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurate device-to-device distance estimation is crucial for internet-of-things (IoT) applications. Traditional methods, such as RSSI-based ranging and Time-of-Flight narrowband systems, exhibit limitations. Bluetooth Low Energy (BLE)-based phase ranging, aka Channel Sounding is a preferred technology, but existing approaches can be improved further. This paper proposes a novel approach using a parametric neural network to improve BLE channel sounding performance for device-to-device localization. Our neural network optimizes range estimation by learning from raw channel sounding data, outperforming traditional super-resolution algorithms. We demonstrate significant improvements in accuracy and precision through experimental results, with a reduction in root mean square error of up to 0.4m (indoor) and 0.04m (outdoor) and corresponding standard deviation reductions of up to 0.19m (indoor) and 0.02m (outdoor). This approach enhances device-to-device localization in IoT applications using BLE, enabling a wide range of IoT applications.

关键词： Location awareness Accuracy Neural networks Estimation Channel estimation Feature extraction Distance measurement internet of Things Root mean square Standards

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Research on image brightness enhancement algorithm based on deep learning 24

Research on image brightness enhancement algorithm based on ...

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Proceedings of the 2024 international conference on image Processing, Multimedia technology and Maching Learning

作者： Siqi He Jianping Xing Yu Bai Shuzhen Huang Kaiping Sun Peng Sun Shandong University Jinan Shandong Province China Shandong Vocational and Technical University of Engineering Jinan Shandong Province China

ISBN: (纸本)9798400713880

In view of traditional image improvement technology's limitations in high real-time application environments, a new low-light video image improvement algorithm is proposed in this article. In consideration of traditional improvement algorithms' faults, and under the precondition of studying low-light improvement techniques in image signal processing (ISP) systems, deep learning frameworks are introduced to break through complex structure and long processing pipeline faults in traditional ISP systems. In this article, a lightweight image brightness improvement algorithm with a basis in convolutional neural network (CNN) is proposed. In this algorithm, feature extraction of RGB images is performed through CNN to generate a brightness coefficient matrix, simplifying RGB to RGB direct mapping and reducing model numbers, model parameters, and computational complexity. This technology pays attention to generating coefficients of brightness, can effectively extract changing parts of brightness, maintain detail information, and conduct efficient and natural brightness improvement, and can apply to high real-time application environments such as autonomous driving.

关键词： Convolutional Neural Network

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