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Fractional Fourier Transform-Based OFDM System with low complexity equalizer for Underwater Acoustic Communications 25

Fractional Fourier Transform-Based OFDM System with low comp...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Attar, Hani Ababneh, Jafar Rachini, Ali Bahy, Ramy M. Hafez, Mohamed Solyman, Ahmed Zarqa University Faculty of Engineering Zarqa Jordan Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan Department of Computer Science and Information Technology Jounieh Lebanon Technical Research Center Cairo Egypt Feqs INTI-IU-University Faculty of Engineering Nilai Malaysia Glasgow Caledonian University School of Computing Engineering and Built Environment Glasgow United Kingdom

ISBN: (纸本)9798331540012

Underwater communication systems have garnered significant interest due to the limitations of the wireless spectrum and the growing need for sustained, covert communication capabilities. However, existing systems face significant challenges from doubly dispersive channels that cause time and frequency fading, disrupting the orthogonality of conventional OFDM subcarriers and leading to severe inter-symbol interference (ISI), even with complex equalizers like MMSE. This paper proposes a Fractional Fourier Transform (FrFT)-based OFDM system, offering better adaptability in the time-frequency domain. The FrFT-OFDM system effectively concentrates power around the diagonal of the channel matrix, mitigating Doppler shifts and multipath fading, thus enhancing spectral efficiency and robustness. A low-complexity equalizer is introduced, reducing computational demands while maintaining high performance, ensuring practical real-time application in underwater environments. Simulation results show that the proposed DFrFT-OFDM system with the optimized equalizer surpasses traditional OFDM under severe channel dispersion, demonstrating improved bit error rates and signal-to-noise ratios by about 3 db. This confirms its capability for reliable underwater communication with reduced computational overhead by about 90% comparing the OFDM with MMSA and DFrFT-OFDM with bande MMSA, marking a significant step forward for high-performance data transmission in challenging aquatic environments. © 2024 IEEE.

关键词： Bit error rate

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Real-Time Monitoring and Control Systems Using IoT in Vertical Farming 2nd

Real-Time Monitoring and Control Systems Using IoT in Vertic...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Vijay, Richa Singh, Anupam Kumar, Raj Singh, Ramveer Khan, Rubina Liyakat Ather, Danish Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering Graphic era hill University Dehradun India Faridabad India Department of CSE Galgotias College of Engineering and Technology Greater Noida India The Applied College. Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Amity University in Tashkent Tashkent Uzbekistan

ISBN: (纸本)9789819776023

In the burgeoning field of vertical farming, real-time monitoring and control systems have become pivotal for optimizing growth conditions and resource utilization. This paper details the design, construction, and implementation of a sophisticated real-time control system using the Arduino Uno as the central microcontroller. The system incorporates a Squirrel Cage Blower (12 V) for ventilation, a Submersible Pool Water Pump (240L/H) for water management, and a SparkFun TMP102 digital temperature sensor for accurate climate control. Powering requirements are handled by a 12VDC 2A Wall Adapter, and an N-Channel MOSFET 60 V 30A with a 10 K Ohm Resistor ensures precise voltage regulation. Data visualization is facilitated by a Monochrome 128 × 32 I2C OLED graphic display, while the ESP8266-01 Wifi Module provides remote accessibility to the system. The Bi-Directional Logic Level Converter ensures smooth communication between components operating at different voltage levels. Soil moisture is continually assessed by a soil moisture sensor, allowing for intelligent irrigation. These components are integrated on a BreadBoard using Jumper Wires, Male Headers, and a USB Cable A to B, creating a modular and scalable system. This paper offers an in-depth analysis of the system's functionality, efficacy, and implications for modern agriculture, showcasing a cost-effective and highly adaptable solution for implementing IoT in vertical farming. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Soil moisture

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Heavy-traffic optimal size- and state-aware dispatching 24

Heavy-traffic optimal size- and state-aware dispatching

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2024 ACM SIGMETRICS/IFIP Performance Conference on Measurement and Modeling of computer Systems, SIGMETRICS/PERFORMANCE 2024

作者： Xie, Runhan Grosof, Isaac Scully, Ziv Department of Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA United States Carnegie Mellon University Computer Science Department PittsburghPA United States Georgia Institute of Technology School of Industrial and Systems Engineering AtlantaGA United States School of Operations Research and Information Engineering Cornell University IthacaNY United States

ISBN: (纸本)9798400706240

We study the problem of dispatching jobs to multiple FCFS (First-Come, First-Served) queues. We consider the case where the dispatcher is size-aware, meaning it learns the size (i.e. service time) of each job as it arrives;and state-aware, meaning it always knows the amount of work (i.e. total remaining service time) at each queue. While size- and state-aware dispatching to FCFS queues has been extensively studied, little is known about optimal dispatching for the objective of minimizing mean delay. In this work, we propose the first size- and state-aware dispatching policy, called CARD (Controlled Asymmetry Reduces Delay), that provably minimizes mean delay in heavy traffic. This work summarizes our full paper. © 2024 Copyright held by the owner/author(s).

关键词： Queueing theory

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An Advanced and Ideal Method for Tumor Detection and Classification from MRI Image Using Gaussian mixture and RCNN 1

An Advanced and Ideal Method for Tumor Detection and Classif...

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1st International Conference on Computational Intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Ganesh Karthikeyan, V. Saravanakumar, S. Tamboli, Asif Lipi, Kumari Anthoniraj, S. Rajendran, D. Vijayalaxmi Sastra Deemed University School of Computing Thanjavur India School of Computer Science and Engineering Ramanagara India Krishna Institute of Medical Sciences Krishna Vishwa Vidyapeeth Deemed to Be University Department of Radiology Maharashtra Karad India Akhildev Ipr and Research Services UP Greater Noida India Christ University School of Business and Management Delhi-NCR India

ISBN: (纸本)9798350365405

Extensive research and methodological improvements over time attest to the necessity of brain tumour detection and analysis for any indication system. Accurate tumour detection is essential for this study;hence an efficient automated method must be implemented Numerous segmentation algorithms have been developed to classify brain tumors with greater precision. Medical image processing experts agree that brain picture segmentation presents unique difficulties. A novel automatic detection and classification system is proposed in this work. The suggested method is broken down into several stages, including preprocessing of MRI images, segmentation of those images, extraction of features, and classification of those features. The noise in the MRI picture is reduced during the preprocessing stage by applying an adaptive filter. The grey level co-occurrence matrix (GLCM) is employed for feature extraction, while the improved Means clustering (IMC) technique is used for image segmentation. To categorize MRI scans, we employed a deep learning algorithm scan after features were extracted from the scans them as benign lesions, gliomas, meningiomas, or pituitary tumours. In order to accomplish the classification task, recurrent convolutional neural networks (RCNN) were utilised. For a given input dataset containing photographs of the brain, the proposed technique yields superior classification results. The investigations made use of MRI pictures from Kaggle's data sets, which included 464 testing sets and 3264 training sets. The outcomes show that the proposed strategy outperforms its predecessors. The suggested RCNN method is then compared to BP, VG-Net, and RCNN, three of the most popular classification techniques currently in use. The suggested classifier successfully identified 96.21 percent of brain tumour tissues in magnetic resonance imaging scans. © 2024 IEEE.

关键词： Positron emission tomography

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Integrating the Bigdata and Deep Learning Analysis Human Movement to Improve the Sports

Integrating the Bigdata and Deep Learning Analysis Human Mov...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： R Usha G Vasundhara Devi Basi Reddy.A B. Divya R. Senthamil Selvan Department of computer science and engineering MITS Madanapalle Madanapalle India Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati India Department of computer science and engineering Gurunanak institute of technical campus Ibrahimpatnam India Department of Electronics and communication engineering Annamacharya Institute of Technology and Sciences Tirupati India

Key technological tools driving the fast growth of integrated manufacturing may be found in information technologies including big data, deep learning, cloud computing, and the Iot. Recent years have seen significant progress in the application of deep learning to the analysis of large datasets. This work focuses on the automated comprehension of human motions in free gymnastics movies as part of the research of a high-precision classification model for sports footage. This work uses deep learning and large-data-based computer imagery to identify and represent human motions in videos. Sports footage is classified using an independent narrative based on long- and short-term memory associations. Short-term and long-term memory networks learn the map relationship between word sequences and video frame sequences in S2VT. We recommend stressing freestyle gymnastics' building blocks. This study creates a competitive freestyle gymnastics routine database. Using predicted sampling and data and custom-built dataset evaluation, we can narrow training decoder-prediction decoder disparities. This research found rethinking sports footage categorization better. Deep learning video categorization with big data enhances user experience by classifying videos more correctly. Feature extraction networks automate free gymnastics movement descriptions and compare feature extraction results to classify lifting sports models.

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MassSpecGym: a benchmark for the discovery and identification of molecules 24

MassSpecGym: a benchmark for the discovery and identificatio...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Roman Bushuiev Anton Bushuiev Niek F. de Jonge Adamo Young Fleming Kretschmer Raman Samusevich Janne Heirman Fei Wang Luke Zhang Kai Dührkop Marcus Ludwig Nils A. Haupt Apurva Kalia Corinna Brungs Robin Schmid Russell Greiner Bo Wang David S. Wishart Li-Ping Liu Juho Rousu Wout Bittremieux Hannes Rost Tytus D. Mak Soha Hassoun Florian Huber Justin J.J. van der Hooft Michael A. Stravs Sebastian Böcker Josef Sivic Tomáš Pluskal Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Bioinformatics Group Wageningen University & Research Department of Computer Science University of Toronto Chair for Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Department of Computer Science University of Antwerp Department of computing science University of Alberta and Alberta Machine Intelligence Institute Department of Molecular Genetics University of Toronto Bright Giant GmbH Department of Computer Science Tufts University Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Department of computing science and Department of Biological Sciences University of Alberta Department of Computer Science Aalto University Mass Spectrometry Data Center National Institute of Standards and Technology Department of Computer Science and Department of Chemical and Biological Engineering Tufts University Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Bioinformatics Group Wageningen University & Research and Department of Biochemistry University of Johannesburg Eawag: Swiss Federal Institute of Aquatic Science and Technology

ISBN: (纸本)9798331314385

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym.

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Navigating the Future of Healthcare: AI-Powered Solutions, Personalized Treatment Plans, and Emerging Trends in 2023 1

Navigating the Future of Healthcare: AI-Powered Solutions, P...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Arshi, Oroos Chaudhary, Aryan Singh, Rashmi School of Computer Science University of Petroleum and Energy Studies Department of Cyber Security and Forensics Dehradun India Bio Tech Sphere Research India Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798350330915

Artificial intelligence (AI) technology is advancing rapidly, changing how patients are diagnosed and treated and how the healthcare system functions. This paper investigates the rapidly developing area of artificial intelligence applications in healthcare, emphasizing personalized medicine and treatment plans. This research aims to examine artificial intelligence's (AI) substantial impact on addressing the crises facing the healthcare industry. A background on these concerns is provided contextually in the introduction. The body of literature is thoroughly reviewed, offering an understanding of the current knowledge on the topic and laying the groundwork for future discussions. The paper discusses the healthcare industry's challenges and the need for innovative solutions, focusing on the role of AI technology in treatment planning and personalized medicine, highlighting the intricacies of AI-driven decision-making and personalized patient guidance. To provide insights into the evolving landscape and its implications for healthcare professionals and patients, the research also looks at the more significant effects of AI on the advancements in healthcare today. One way to look forward is to look at the top AI trends predicted to impact healthcare in 2023. Summarising the key findings of the research, the conclusion emphasizes how AI may change how healthcare is delivered, enhance patient outcomes, and accelerate the sector's ongoing transformation. © 2023 IEEE.

关键词： AI innovations & technologies artificial intelligence machine learning personalized medicine

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A Comparative Analysis for Android Malware Detection Using Machine Learning Models

A Comparative Analysis for Android Malware Detection Using M...

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

作者： Harsh Shah Vidit Shah Nirmay Soni Vaishali Vadhavana Krishna Patel Department of Computer Science & Engineering Devang Patel Institute of Advance Technology & Research (DEPSTAR) Charotar University of science and Technology(CHARUSAT) Anand India Department of Computer Science & Engineering Parul Intitute of Engineering and Technology (PIET) Parul University Waghodia Gujarat India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

This research study explores the usability of machine learning algorithms in classifying whether Android applications are excellent or malicious. The data used here for the study is obtained from Kaggle and named “TUANDROMD.” Both kinds of application samples are present in the data. From application displays and runtime behaviour, pertinent features have been retrieved using methods including K-Nearest Neighbor(KNN), Support Vector Machine(SVM), and Decision Tree classifiers. With regard to accuracy, precision, recall, F1-score, and ROC AUC metrics, every result is thoroughly examined. The provided SVM model achieved the highest possible accuracy and reliability in malware identification. Thus, the research demonstrates how to combine static and dynamic analysis as a fundamental method for detecting malware and future directions regarding improvements in mobile Security, including deep learning and real-time detection systems. The classification accuracy of the classifier model KNN, SVM, and Decision Tree classify a 97.65%, 98.62%, and 99.10% accuracy rate, respectively.

关键词： Support vector machines Deep learning Accuracy Runtime Operating systems Computational modeling Malware Real-time systems Decision trees Security

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Chaotic Particle Swarm Optimization Empowered Deep Convolutional Neural Network for Prostate Cancer Diagnosis

Chaotic Particle Swarm Optimization Empowered Deep Convoluti...

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International Conference on Advances in Information Technology (ICAIT)

作者： Gokulnath C N. Kalaiarasi Yeshaswini R P. Brinda Jeyakarthika K S.T. Gopukumar Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Kattankulathur Chengalpattu Department of ECE R.M.K college of engineering and technology Chennai Department of computer science Rajarajeshwari college of engineering Kumbalgodu Bangalore Department of Computer science and Engineering Veltech High tech Dr rangarajan Dr sakunthala engineering college Avadi Computer science and Engineering Ramco Institute of Technology Virudhunagar District Tamil Nadu Department of Research Activities and Biostatistics Sarada Krishna Homoeopathic Medical College Kanniyakumari District Tamilnadu India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Prostate cancer (PCa) Classification on magnetic resonance imaging (MRI) is an innovative use of medical imaging and machine learning (ML) models that objects to precisely recognize and classify PCa lesions in MRI scans. This advanced technique permits healthcare experts to analyze PCa more efficiently, define its level and severity, and improve modified treatment tactics. By analyzing subtle differences in tissue faces and recognizing cancerous areas, this methodology provides enhanced early recognition which is vital for patient results. It allows oncologists and radiologists to make knowledgeable verdicts, improving accuracy and efficacy of PCa diagnosis and successive treatment plans. This manuscript provides a proposal for Chaotic Particle Swarm Optimization Empowered Deep Convolutional Neural Network (CPSO-DCNN) technique for PCa Diagnosis. The developed CPSO-DCNN architecture incorporates a multi-faceted approach, starting with Wiener filtering (WF) for sound reduction, improving excellence and clarity of medical images. Feature extraction accomplished by employing SqueezeNet which is a great deep learning (DL) design expert at taking complex image patterns. This method further influences CPSO for hyperparameter tuning, enhancing its performance in order to support exact dataset characteristics. For critical task of PCa diagnosis, classification performed via bi-directional gated recurrent unit (BiGRU) networks and well-known for their capability to seize temporal needs within data. The simulation outcomes highlighted that CPSO-DCNN technique characterizes a substantial development in field of medical diagnostics. It provides a strong and exact solution for early recognition and classification of PCa.

关键词： Image recognition Magnetic resonance imaging Feature extraction Proposals Medical diagnosis Prostate cancer Particle swarm optimization Tuning Medical diagnostic imaging Principal component analysis

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CNN-Based Pneumonia Detection and Severity Analysis in Chest X-Ray Images for Enhanced Diagnostic Support

CNN-Based Pneumonia Detection and Severity Analysis in Chest...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： D. Surendiran Muthukumar Bommineni Sreekar Chowdary D. Senthil Kumar P. Jagadeesan B. Mythili B. Swapna Department of computer science and Engineering Kalasalingam Academy of Research and Education Srivilliputhur Tamilnadu India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering R.M.D. Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Vel tech Multi tech Dr.Rangarajan Dr. Sakunthala Engineering college Chennai Tamil Nadu India Department of Electronics and communication engineering Dr.M.G.R Educational and Research Institute Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Pneumonia continues to pose a substantial global health challenge, necessitating prompt and precise diagnosis to enhance patient outcomes. In order to detect pneumonia and classify its severity into mild, moderate, and severe phases, the web-based program uses deep learning algorithms to analyse chest X-ray pictures. A Convolutional Neural Network (CNN) model that has been trained on publicly accessible datasets and achieved high accuracy is integrated into the application. Furthermore, it provides personalized health recommendation, downloadable PDF reports, and a local hospital database to facilitate users in locating nearby treatment facilities. The system utilizes Fast API for backend processing and user-friendly interfaces to enhance accessibility and efficiency. This solution underscores the potential of artificial intelligence in supporting early diagnosis and effective pneumonia management.

关键词： Deep learning Pneumonia Accuracy Hospitals Databases Classification algorithms Reliability Convolutional neural networks Artificial intelligence X-ray imaging

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