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Channel Estimation for RIS-aided mmWave Massive MIMO System Using Few-bit ADCs

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arXiv 2023年

作者： Wang, Ruizhe Ren, Hong Pan, Cunhua Fang, Jun Dong, Mianxiong Dobre, Octavia A. National Mobile Communications Research Laboratory Southeast University Nanjing China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu China Department of Sciences and Informatics Muroran Institute of Technology Muroran Japan Department of Electrical and Computer Engineering Memorial University St. John'sNLA1B 3X5 Canada

Millimeter wave (mmWave) massive multiple-input multiple-output (massive MIMO) is one of the most promising technologies for the fifth generation and beyond wireless communication system. However, a large number of antennas incur high power consumption and hardware costs, and high-frequency communications place a heavy burden on the analog-to-digital converters (ADCs) at the base station (BS). Furthermore, it is too costly to equipping each antenna with a high-precision ADC in a large antenna array system. It is promising to adopt low-resolution ADCs to address this problem. In this paper, we investigate the cascaded channel estimation for a mmWave massive MIMO system aided by a reconfigurable intelligent surface (RIS) with the BS equipped with few-bit ADCs. Due to the low-rank property of the cascaded channel, the estimation of the cascaded channel can be formulated as a low-rank matrix completion problem. We introduce a Bayesian optimal estimation framework for estimating the user-RIS-BS cascaded channel to tackle with the information loss caused by quantization. To implement the estimator and achieve the matrix completion, we use efficient bilinear generalized approximate message passing (BiG-AMP) algorithm. Extensive simulation results verify that our proposed method can accurately estimate the cascaded channel for the RIS-aided mmWave massive MIMO system with low-resolution ADCs. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

Multi-Criteria Decision Making for Machine Learning Algorithms Using AHP-VIKOR techniques: Case Study Adult Autism Diagnosis

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Multi-Criteria Decision Making for Machine Learning Algorith...

International Conference on engineering technology and their Applications (IICETA)

作者： Mustafa Mohammed Jassim Manar Hassan Ali Amer S. Elamer Mustafa Musa Jaber Mohammed Q. Mohammed Ahmed Alkhayyat Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Iraqi Commission for Computers and Informatics Informatics Institute for Postgraduate Studies Baghdad Iraq Department of Computer Science Dijlah University Collage Baghdad Department of Information Technology Al-Esraa University College Baghdad College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

autism spectrum disorder is known as a neurodevelopmental disorder with sensory problems. Many infected adults die frequently due to the psychological conditions they live in. Machine learning helps doctors detect cases of autism spectrum disorders in adults. It has proven very successful in this field. In this study, after pre-processing and solving the data class imbalance problem the data we proposed to use MCDM methods to know prioritization and the best method to diagnose autism between four algorithms (SVM, random Forest, Neural Network, and KNN), after obtaining the classification results we utilized AHP method to assign weights to each classification performance metrics based on expert assessment then he used both the obtained weights and the classification results to enter them into the VIKOR method to make a decision based on the weighted table where we would get the ranking of the algorithms. The random Forest method scores the best in this classification for diagnosing autism, with an accuracy rate of 0.9957 and sensitivity 0.9841.

关键词： Support vector machines Autism Machine learning algorithms Sensitivity Neural networks Psychology Medical services

Integer Factorization through Func-QAOA

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arXiv 2023年

作者： Atallah, Mostafa Velmurugan, Haemanth Sharma, Rohan Midha, Siddhant Mamun, Shamim Al Botelho, Ludmila Glos, Adam Salehi, Özlem Department of Physics Faculty of Science Cairo University Giza12613 Egypt Department of Computer Science and Engineering NIT Tiruchirappalli India Department of Applied Geophysics IIT Dhanbad India Department of Electrical Engineering IIT Bombay India Department of Electrical and Electronics Engineering Islamic University of Technology Dhaka Gazipur1704 Bangladesh Joint Doctoral School Silesian University of Technology Akademicka 2A Gliwice Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Poland QWorld Association Tallinn Estonia

Integer factorization is a significant problem, with implications for the security of widely-used cryptographic schemes. No efficient classical algorithm for polynomial-time integer factorization has been found despite extensive research. Although Peter Shor’s breakthrough quantum algorithm offers a viable solution, current limitations of noisy intermediate-scale quantum (NISQ) computers hinder its practical implementation. To address this, researchers have explored alternative methods for factorization suitable for NISQ devices. One such method is the Quantum Approximate Optimization Algorithm, which treats factoring as an optimization problem defined over binary bits, resulting in various problematic aspects. In this paper, we explore the Func-QAOA approach for factorization, which premises overcoming some of the limitations of previous approaches and allows the incorporation of more advanced factorization techniques. After reviewing the most promising quantum implementations for integer arithmetics, we present a few illustrative examples to demonstrate the efficacy of the Func-QAOA approach and discuss methods to reduce the search space to speed up the optimization process. Copyright © 2023, The Authors. All rights reserved.

关键词： Factorization

Enhancing 3D Object Detection in Autonomous Vehicles Based on Synthetic Virtual Environment Analysis

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arXiv 2024年

作者： Li, Vladislav Siniosoglou, Ilias Karamitsou, Thomai Lytos, Anastasios Moscholios, Ioannis D. Goudos, Sotirios K. Banerjee, Jyoti S. Sarigiannidis, Panagiotis Argyriou, Vasileios Kingston University Department of Networks and Digital Media Kingston upon Thames United Kingdom University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece MetaMind Innovations P.C. R"&D Department Kozani Greece Sidroco Holdings Ltd. Nicosia Cyprus University of Peloponnese Department of Informatics and Telecommunications Tripoli Greece Aristotle University of Thessaloniki Physics Department Thessaloniki Greece Bengal Institute of Technology Kolkata India

Autonomous Vehicles (AVs) use natural images and videos as input to understand the real world by overlaying and inferring digital elements, facilitating proactive detection in an effort to assure safety. A crucial aspect of this process is real-time, accurate object recognition through automatic scene analysis. While traditional methods primarily concentrate on 2D object detection, exploring 3D object detection, which involves projecting 3D bounding boxes into the three-dimensional environment, holds significance and can be notably enhanced using the AR ecosystem. This study examines an AI model’s ability to deduce 3D bounding boxes in the context of real-time scene analysis while producing and evaluating the model’s performance and processing time, in the virtual domain, which is then applied to AVs. This work also employs a synthetic dataset that includes artificially generated images mimicking various environmental, lighting, and spatiotemporal states. This evaluation is oriented in handling images featuring objects in diverse weather conditions, captured with varying camera settings. These variations pose more challenging detection and recognition scenarios, which the outcomes of this work can help achieve competitive results under most of the tested conditions. © 2024, CC BY-NC-ND.

关键词： Virtual environments

Few-Shot Medical Image Segmentation with High-Fidelity Prototypes

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arXiv 2024年

作者： Tang, Song Yan, Shaxu Qi, Xiaozhi Gao, Jianxin Ye, Mao Zhang, Jianwei Zhu, Xiatian IMI Group School of Health Sciences and Engineering University of Shanghai for Science and Technology Shanghai China TAMS Group Department of Informatics Universität Hamburg Hamburg Germany School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Surrey Institute for People-Centred Artificial Intelligence Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China

Few-shot Semantic Segmentation (FSS) aims to adapt a pretrained model to new classes with as few as a single labelled training sample per class. Despite the prototype based approaches have achieved substantial success, existing models are limited to the imaging scenarios with considerably distinct objects and not highly complex background, e.g., natural images. This makes such models suboptimal for medical imaging with both conditions invalid. To address this problem, we propose a novel Detail Self-refined Prototype Network (DSPNet) to constructing high-fidelity prototypes representing the object foreground and the background more comprehensively. Specifically, to construct global semantics while maintaining the captured detail semantics, we learn the foreground prototypes by modelling the multi-modal structures with clustering and then fusing each in a channel-wise manner. Considering that the background often has no apparent semantic relation in the spatial dimensions, we integrate channel-specific structural information under sparse channel-aware regulation. Extensive experiments on three challenging medical image benchmarks show the superiority of DSPNet over previous state-of-the-art methods. The code and data are available at https://***/tntek/DSPNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

The maximum number of triangles in Fk-free graphs

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arXiv 2022年

作者： Zhu, Xiutao Chen, Yaojun Gerbner, Dániel Győri, Ervin Karim, Hilal Hama Department of Mathematics Nanjing University Nanjing210093 China Alfréd Rényi Institute of Mathematics Budapest Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

The generalized Turán number ex(n, Ks, H) is the maximum number of complete graph Ks in an H-free graph on n vertices. Let Fk be the friendship graph consisting of k triangles. Erdős and Sós (1976) determined the value of ex(n, K3, F2). Alon and Shikhelman (2016) proved that ex(n, K3, Fk) ≤ (9k−15)(k+ 1)n. In this paper, by using a method developed by Chung and Frankl in hypergraph theory, we determine the exact value of ex(n, K3, Fk) and the extremal graph for any Fk when n ≥ 4k3 Copyright © 2022, The Authors. All rights reserved.

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Deep Neural Network Trained Punjabi Children Speech Recognition System Using Kaldi Toolkit 5

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Deep Neural Network Trained Punjabi Children Speech Recognit...

5th IEEE International Conference on Computing Communication and Automation, ICCCA 2020

作者： Taniya Bhardwaj, Vivek Kadyan, Virender Chitkara University Institute of Engineering and Technology Chitkara University Punjab India University of Petroleum and Energy Studies School of Computer Science Department of Informatics Dehradun India

ISBN: (纸本)9781728163246

Despite the number of developed Automatic Speech Recognition (ASR) systems for different languages, still no work has been done on children's speech of Punjabi language. Due to the unavailability of children's speech corpus for Punjabi Language, it is a challenging task to collect speech data. In our current work, efforts have been made to collect Punjabi children's speech corpus and build Children ASR system for Indian regional Punjabi language. The recognition rate of ASR systems is observed to be improved drastically by the emergence of Deep Neural Networks (DNN). In our work, the DNN acoustic model has been implemented by varying number of hidden layers. Approximately four hours of Punjabi children's speech corpus has been collected and several experiments have been performed using the DNN modeling technique. Experimental results have revealed that the system has attained 87% accuracy. © 2020 IEEE.

关键词： Acoustic Modeling

Performance Analysis for Molecular Communication Under Feedback Channel Using Multipath and Single Path Technique

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SSRN

SSRN 2022年

作者： Ningthoujam, Sanjit Chingkheinganba, Tekcham Chakraborty, Swarnendu Kumar Elngar, Ahmed A. Chakrabarti, Prasun Chakrabarti, Tulika Praveen, S. Phani Gupta, Amit Margala, Martin School of Computer Science and Engineering VIT University Andhra Pradesh 522237 India BSNL Dimapur797112 India Dept. of Computer Science and Engineering National Institute of Technology Arunachal Pradesh 791112 India Faculty of Computers and Information Techno India NJR Institute of Technology India Department of Chemistry Sir Padampat Singhania University Rajasthan Udaipur313601 India Prasad V Potluri Siddhartha Institute of Technology Andhra Pradesh 520007 India Department of ECE Narasaraopeta Engineering College Andhra Pradesh Narasaraopeta522601 India Computer Science School of Computing and Informatics University of Louisiana United States

Nanomachines that can run tasks and molecular communication (MC) for interconnecting nanomachines have been made possible by advances in nanotechnology. Different techniques of Automatic Repeat Request (ARQ) protocols have been devised in MC for acquiring the necessary molecules, although the retransmission of duplicate copies requires special attention. This work presents a diffusion-based MC with a feedback loop to improve MC performance. Based on the received ACK(s) or NACK(s), the Channel state determines if the state is good (G) or bad (B), and then single path or multipath procedures are used to the ‘G' and ‘B' states. The results of the MATLAB simulation illustrate how the systems compare in terms of communication rate, the possibility of complete transmission of the desired molecules, and energy consumption. When the channel state is bad, the multipath approach performs better, but when the channel state is perfect in MC, the single path strategy performs better. © 2022, The Authors. All rights reserved.

关键词： Molecules

Predictive Analytics for Hospital Readmissions Using Logistic Regression and IoT Sensor Data

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Predictive Analytics for Hospital Readmissions Using Logisti...

International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Kiruthiga B Arshadh Ariff Mohamed Abuthahir Riazulhameed rajapriya. M R. Sarasu N. Mohankumar A. Kasthuri Department of Management Studies SRM Valliammai Engineering College Chennai Tamil Nadu India Blackstraw Technologies Pvt Ltd Chennai Tamil Nadu India Department of Management Studies Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Spatial Informatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed University) Nagpur Maharashtra India Department of Computer Science and Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Inayaka Mission's Research Foundation (DU) Salem Tamil Nadu India

ISBN: (数字)9798350379297

ISBN: (纸本)9798350379303

Hospital readmissions affect healthcare systems globally, increasing costs and morbidity. Readmissions must be predicted and prevented to improve patient outcomes and save healthcare costs. This research leverages the Internet of Things (IoT) sensor data and Logistic Regression (LR) to reduce hospital readmissions using predictive analytics. Patient health data from IoT devices during and after hospitalization is used in the paper. Heart rate, blood pressure, and oxygen saturation are constantly monitored. LR is used to examine this dataset and create a prediction model. The LR algorithm trains us to recognize IoT sensor data patterns and correlations that indicate readmission risks. Our methodology provides developed risk estimates incorporating patient-specific parameters, including age, multiple illnesses, and medical history. The temporal data permits dynamic predictions, considering patient health changes over time. This research detected hospital readmission risk with promising accuracy. These predictions allow healthcare practitioners to proactively intervene and deliver targeted treatments, including prescription modifications, lifestyle advice, and follow-up consultations to decrease readmission. This proactive strategy may improve patient outcomes and save healthcare expenses.

关键词： Logistic regression Costs Hospitals Transforms Predictive models Internet of Things Predictive analytics