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Reducing peak to average power ratio in optical NOMA based 5G system using advanced SLM method

Optical and Quantum electronics 2024年第10期56卷 1-13页

作者： Kumar, Arun Chakravarty, Sumit Nanthaamornphong, Aziz Department of Electronics and Communication Engineering New Horizon College of Engineering Bengaluru India Department of Electrical Engineering and Computer Engineering Kennesaw State University KennesawGA United States College of Computing Prince of Songkla University Phuket Thailand

One of the most critical challenges in optical fiber communication systems using non-orthogonal multiple access (NOMA) techniques is reducing the peak-to-average power ratio (PAPR). Optical NOMA often leads to high PAPR, causing non-linear distortion and limiting the system’s gain. The advanced Selected Mapping (SLM) algorithm is introduced in this work to reduce the PAPR of the O-NOMA. In conventional SLM, multiple phase sequences are applied to the input data, which requires numerous Inverse Fast Fourier Transform, increasing computational complexity. The proposed approach simplifies this by selecting a central reference phase sequence, reducing the number of candidate sequences and the number of IFFT operations required. This reduction in complexity does not significantly compromise the PAPR reduction capability, making the method more efficient. The SLM algorithm addresses this issue by applying the phase rotation factor to the transmitted signals, redistributing the power, and reducing the peak levels. This article highlights the benefits and challenges of implementing SLM in optical NOMA systems, including its compatibility with existing NOMA schemes. Matlab 2016 analyzes parameters such as bit error rate (BER), PAPR, and out-of-band radiation. A 66% PAPR reduction can be obtained using the suggested SLM with various phase variables. Furthermore, there is a −2650 reduction in spectrum leakage. The simulation outcomes of the work reveal the potential of SLM to reduce PAPR and enhance the BER and PSD performance of optical NOMA compared with the existing algorithms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： MATLAB

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Adaptive Genghis Khan Shark based convolutional denoising autoencoder for quality enhancement of satellite images

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International Journal of Information Technology (Singapore) 2025年 1-14页

作者： Rajalakshmi, M. Subramanian, R. S. Sankara Tamilselvan, S. Bhuvanesh, A. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Mathematics PSG Institute of Technology and Applied Research Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India

Satellite image processing often enhances feature perception and visibility for exact feature identification. Additionally, the change improves remote sensing data quality and clarity. Due to their long distances, these photos often have weather-related noise and distortion that must be fixed. This paper addresses these challenges with a Deep Learning (DL) satellite image denoising technique. An Adaptive Genghis Khan Shark Algorithm (AGKSA) and a Convolutional Denoised Autoencoder are used to improve satellite images in this study. The CDAE settings are optimized using the AGKSA while keeping important image attributes. AGKSA is used in this hybrid model to optimize CDAE performance under various noise conditions. Experimental results showed that the proposed model outperforms traditional image denoising approaches in satellite imagery feature extraction and visual quality. In very precise image processing applications like environmental monitoring and land use classification, the CDAE-AGKSA works well. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Adaptive Genghis Khan algorithm Convolutional denoised autoencoder Satellite image enhancement Satellite image processing

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Utilising Machine Learning for Precise State-of-Charge Prediction in Li-Ion Batteries 12

Utilising Machine Learning for Precise State-of-Charge Predi...

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12th IEEE International Conference on Intelligent Systems, IS 2024

作者： Khatri, Avinash Lota, Jaswinder Nepal, Prasanna Amirhosseini, Mohammad Hossein School of Architecture Computing and Engineering University of East London London United Kingdom Nepal Engineering College Department of Electrical and Electronics Engineering Bhaktapur Nepal

ISBN: (纸本)9798350350982

As the popularity of electric propulsion using batteries rises alongside the demand for renewable energy, effective battery management and monitoring are crucial for sustain ability and efficiency in electric vehicles (EVs). The battery monitoring system (BMS) employs IoT/sensor networks to estimate crucial battery metrics like remaining useful life (RUL), state-of-health (SoH), and state-of-charge (SoC), using data on current status, temperature, and voltage. Machine learning (ML) and artificial intelligence (AI) are increasingly utilized to enhance BMS accuracy, addressing challenges like real-time data processing and the accuracy of estimations. This paper investigates the effectiveness of Linear Regression and Random Forest models in estimating SoC. During the hyperparameter tuning phase, the models were optimized using the Grid Search method, and their performance was evaluated at various temperatures: 25°C, 10°C, 0°C, and -10°C. The findings indicate that the models' effectiveness enhances as the temperature increases. The Random Forest model exhibited superior performance at 25°C, achieving an R2 score of 0.99646 and an RMSE score of 0.000264. This paper not only contributes to advancing Li-ion battery monitoring system, but also empowers professionals in this field to harness machine learning capabilities effectively. © 2024 IEEE.

关键词： State of charge

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Deep-Dixon: Deep-Learning frameworks for fusion of MR T1 images for fat and water extraction

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Multimedia Tools and Applications 2024年 1-22页

作者： Laddha, Snehal V. Ochawar, Rohini S. Gandhi, Krushna Zhang, Yu-Dong Department of Electronics Engineering School of Electrical and Electronics Engineering Ramdeobaba University Maharashtra Nagpur440013 India Department of Electronics and Communication Engineering School of Electrical and Electronics Engineering Ramdeobaba University Maharashtra Nagpur440013 India M. D. Radiology Gandhi Sonography Clinic Nagpur India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Medical image fusion plays a crucial role in understanding the necessity of medical procedures and it also assists radiologists in decision-making for surgical operations. Dixon has mathematically described a fat suppression technique that differentiates between fat and water signals by utilizing in-phase and out-of-phase MR imaging. The fusion of MR T1 images can be performed by adding or subtracting in-phase and out-phase images, respectively. The dataset used in this study was collected from the CHAOS grand challenge, comprising DICOM data sets from two different MRI sequences (T1 in-phase and out-phase). Our methodology involved training of deep learning models;VGG 19 and RESNET18 to extract features from this dataset to implement the Dixon technique, effectively separating the water and fat components. Using VGG19 and ResNet18 models, we were able to accomplish the image fusion accuracy for water-only images with EN as high as 5.70, 4.72, MI as 2.26, 2.21;SSIM as 0.97, 0.81;Qabf as 0.73, 0.72;Nabf as low as 0.18, 0.19 using VGG19 and ResNet18 models respectively. For fat-only images we have achieved EN as 4.17, 4.06;MI as 0.80, 0.77;SSIM as 0.45, 0.39;Qabf as 0.53, 0.48;Nabf as low as 0.22, 0.27. The experimental findings demonstrated the superior performance of our proposed method in terms of the enhanced accuracy and visual quality of water-only and fat-only images using several quantitative assessment parameters over other models experimented by various researchers. Our models are the stand-alone models for the implementation of the Dixon methodology using deep learning techniques. This model has experienced an improvement of 0.62 in EN, and 0.29 in Qabf compared to existing fusion models for different image modalities. Also, it can better assist radiologists in identifying tissues and blood vessels of abdominal organs that are rich in protein and understanding the fat content in lesions. © The Author(s), under exclusive licence to Springer Science+Business

关键词： Image enhancement

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Satellite Image Classification using CNN with Particle Swarm Optimization Classifier 5

Satellite Image Classification using CNN with Particle Swarm...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Vidhya, S. Balaji, M. Kamaraj, V. Dept. of Computing Technologires School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Dept. of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India

Disaster relief, police work, and environmental monitoring all benefit from satellite images. Objects and infrastructure in the images must be manually identified for these applications. Due to the large areas that need to be searched and the limited number of accessible analysts, automation is essential. However, the accuracy and dependability of existing object recognition and classification algorithms renders them inadequate for the task. One family of machine learning algorithms called "deep learning" has showed immense potential for automating these kinds of jobs. Convolutional neural networks have been successful in the area of image recognition. Here, convolutional neural networks (CNNs) and a particle swarm optimization classifier is utilized to develop efficient algorithms for classifying satellite images. The results of this classifier model are better than those of existing approaches. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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AI based Emotion Aware Music Composition using Spotify App 3

AI based Emotion Aware Music Composition using Spotify App

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Subbulakshmi, N. Begum, S. Ariffa Gari Archana, Bayapureddy Lakshmireddy Chandru, R. School of Computing Kalsalingam Academy of Research and Education Virudhunagar India Sri Ramakrishna Engineering College Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350396157

Users' emotions and moods can be inferred from their facial expressions. The live video from the system camera reveals these expressions. Many researches have been carried out in the fields of machine learning (ML) and computer vision to teach robots to identify various human emotions or moods. Numerous techniques for identifying human emotions are provided by machine learning. Our proposed method is a music player which works the human mood-based and it provides song recommendations in terms of the real-Time mood detection. This is now a capability that is linked with the standard music player application that come preinstalled on our phones. The objectives of this method are to study the user's look, forecast their facial expression, and suggest music based on mood. The discipline of music creation has changed dramatically as a result of technical improvements, providing new avenues for artists and composers to create captivating and emotionally stirring musical works. The concept of 'Emotional-Aware Music Composition,' a cutting-edge technique that employs artificial intelligence and machine learning to give compositions a profound emotional authenticity, is explored in this abstract. Emotionally aware music creation allows composers to produce music that not only transfers emotion but also deeply engages listeners by bridging the gap between human creativity and technological prowess. This multidisciplinary field uses components from psychology, music theory, and machine learning to achieve this. © 2024 IEEE.

关键词： Machine learning

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Effortless learning: leveraging summarization and tabulation for automated note making

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Multimedia Tools and Applications 2024年 1-26页

作者： Arivarasi, A. Srinivasan, R. Rajeswari, D. Govindasamy, Alagiri School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Future Connect Technologies Pvt. Ltd Tamil Nadu Kanchipuram600034 India

In this fast-paced Digital Age, Natural Language Processing (NLP) can prove beneficial in consuming quality information efficiently. With the ever-growing number of learning resources, it is becoming onerous for students to choose the best-suited learning resource. Furthermore, many students find it difficult to understand and retain highly factual information. To overcome these issues, this paper proposes a novel Automated TExt Summarization of scholastic text in Table format (Auto-TEST) to provide information at a glance by mimicking the student’s style of note-making. The Auto-TEST framework is achieved through two steps which is Summarization and Tabulation. Initially, the Extractive Single Document Text Summarization (ESDTS) method is adopted to identify the salient information from the non-fictional (scholastic) text which is extracted and grouped together to form a concise summary. A Named Entity Recognition (NER) method is implemented to extract noun chunks and then arrange them in a tabulation format that aids in memorizing essential facts and figures by enabling effortless retrieval. The performance of the Auto-TEST framework is gauged by using scholastic domain-specific dataset using ROUGE as an evaluation metric. The extracted entities gives an average Recall of 71.40%, an average Precision score of 75.80% and an average F-score of 73.54% for the dataset respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Students

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Internet of things enabled parking management system using long range wide area network for smart city

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 82-98页

作者： Jabbar, Waheb A. Tiew, Lu Yi Ali Shah, Nadiah Y. College of Engineering Faculty of Computing Engineering and the Built Environment Birmingham City University BirminghamB4 7XG United Kingdom Faculty of Electrical and Electronics Engineering Technology Universiti Malaysia Pahang Pahang Pekan26600 Malaysia

As the Internet of Things (IoT) evolves, it paves the way for vital smart city applications, with the Smart Parking Management System (SPMS) standing as a prime example. This research introduces a novel IoT-driven SPMS that leverages Long Range Wide Area Network (LoRaWAN) technology, termed as IoT-SPMS-LoRaWAN, to surmount typical restrictions related to communication range, energy usage, and implementation cost seen in traditional systems. IoT-SPMS-LoRaWAN features intelligent sensing nodes that incorporate an Arduino UNO microcontroller and two sensors—a triaxial magnetic sensor and a waterproof ultrasonic sensor. These components collaboratively detect vehicle occupancy and transmit this data to the server via a LoRaWAN gateway. Notably, the integration of LoRa technology enables extensive network coverage and energy efficiency. Users are provided with real-time updates on parking availability via the accessible AllThingsTalk Maker graphical user interface. Additionally, the system operates independently, sustained by a solar-powered rechargeable battery. Practical testing of IoT-SPMS-LoRaWAN under various scenarios validates its merits in terms of functionality, ease of use, reliable data transmission, and precision. Its urban implementation is expected to alleviate traffic congestion, optimize parking utilization, and elevate awareness about available parking spaces among users. Primarily, this study enriches the realm of smart city solutions by enhancing the efficiency of parking management and user experience via IoT. © 2023 The Authors

关键词： Internet of things

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Video Compression through Advanced Video Saliency Aware Spatial-Temporal Integration and Attention Mechanisms

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SN Computer Science 2024年第7期5卷 926页

作者： Ravishankar, H. AnithaKumari, R.D. Sarvamangala, D.R. Rashmi, C. Deepa, K.R. School of Computing and Information Technology REVA University Karnataka Bangalore India School of Electronics and Communication Engineering REVA University Karnataka Bangalore India School of Electrical and Electronics Engineering REVA University Karnataka Bangalore India

In the field of computer vision, video saliency detection is a critical task that involves identifying and highlighting areas within video frames that are most likely to attract human attention. Despite its importance, this task poses significant challenges due to the dynamic nature of video content, which encompasses varying spatial and temporal features. Addressing these challenges, the Dynamic Attentive Integration for Spatial-Temporal Saliency (DAISTS), a novel methodology that significantly enhances the precision of video saliency detection is designed. DAISTS introduces a dual-path spatial-temporal feature hierarchy, effectively merging deep and shallow learning attributes to improve saliency map accuracy. Additionally, spatial and channel attention aided integration is designed, which adaptively refine feature fusion based on scene context and training data. Moreover, DAISTS incorporates a frame-based attention model, a breakthrough in temporal analysis that selectively prioritizes frames for improved saliency prediction. The methodology is further supported by a comprehensive loss function that integrates Relative Entropy, linear correlation coefficient, and normalized scan-way saliency metrics, ensuring a balanced and effective approach to training. This encapsulates the approach to advancing video saliency detection, highlighting DAISTS as a robust, efficient, and adaptable solution, to make a significant impact in the realm of computer vision. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computer vision DAISTS Relative entropy Temporal analysis

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NLP-Based SMS Spam Detection Using Ensemble Learning 4

NLP-Based SMS Spam Detection Using Ensemble Learning

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Rajalakshmi, M. Rengaraj, R. Karthikeyan, M. Aruna, S. Srm Institute of Science and Technology School of Computing Dept of Computing Technologies KTR Campus Chennai India Sri Sivasubramaniya Nadar College of Engineering Dept of Electrical and Electronics Engineering Kalavakkam Chennai India Srm Insitute of Science & Technology Dept of Electrical and Electronics Engineering Ramapuram Chennai India

ISBN: (纸本)9798331508845

In today's environment, spam text messages are a common issue and a source of frustration for users. This study suggests a novel method for identifying SMS spam that makes use of natural language processing (NLP), multiple model creation, and ensemble learning. The suggested approach entails pre-processing the data using natural language processing (NLP) techniques including lemmatizing, stemming, and stopword removal in order to extract pertinent features from the text data and convert it into numerical representations. The suggested approach's effectiveness was assessed using an actual dataset and contrasted with conventional machine learning algorithms like SVM and Naïve *** altered data is used to train the numerous classifiers, and an ensemble learning technique is then used to combine their predictions and produce a more accurate outcome. More precision was provided by the final model than by conventional models. In order to lessen the quantity of spam messages that users get, the research's conclusions may be helpful in the development of an efficient spam SMS detector. Furthermore, the model was hosted using the streamlit web app framework on the cloud-based platform Railway, making it instantly accessible to users without the need to install any software. © 2024 IEEE.

关键词： Contrastive Learning

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