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Efficient Multi-Stage Active Device Identification for Massive Random Access

Efficient Multi-Stage Active Device Identification for Massi...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jyotish Robin Elza Erkip Dept. of Electrical and Computer Engineering New York University Brooklyn NY USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Efficiently identifying active devices with minimal latency is crucial in massive machine-type communication networks characterized by sparse and sporadic device activity. This paper addresses the above challenge by introducing a novel active device identification strategy which employs a multi-stage framework that iteratively refines partial estimates of active devices through feedback and hypothesis testing, leading to an exact recovery. In our proposed method, active devices transmit binary preambles independently in each stage, utilizing feedback signals from the BS. Meanwhile, the BS utilizes non-coherent binary energy detection. In addition to theoretical bounds, practical implementations of our multi-stage active device identification schemes using Belief Propagation (BP) techniques are presented. Our simulation results demonstrate that the multi-stage strategy is superior to the single-stage one introduced in our earlier work and performs close to the theoretical bound, even when considering overhead costs related to feedback.

关键词： Performance evaluation Costs Simulation Numerical simulation Decoding Object recognition Resource management Global communication Communication networks Testing

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Multiclass Categorisation of Respiratory Sound Signals Using Neural Network

Multiclass Categorisation of Respiratory Sound Signals Using...

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2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022

作者： Babu, Naseem Kumari, Jyoti Mathew, Jimson Satija, Udit Mondal, Arijit Indian Institute of Technology Patna Dept. of Computer Science and Engineering Bihar801106 India Indian Institute of Technology Patna Dept. of Electrical Engineering Bihar801106 India

ISBN: (数字)9781665469173

ISBN: (纸本)9781665469173

Respiratory diseases have seriously impacted human life in the last couple of years;as Covid 19 arrived, many lost their beloved ones. Since respiratory diseases directly attack the patient's lungs, it is becoming risky day by day for human life and doctors because a confined number of resources are available in hospitals to detect these respiratory diseases, and detection of these diseases is a difficult job to the doctors. Therefore early-stage diagnosis can help the doctor in saving human lives. Researchers are continuously trying to help doctors by designing efficient and more accurate tools for detecting different types of respiratory diseases. This paper uses a convolution-based deep learning model to classify these respiratory diseases using patient respiratory sound signals with Mel frequency cepstral coefficients (MFFCs) as a feature vector. In this paper, we have tried to keep our neural network model as simple as possible with less trainable parameters and good classification accuracy. The model performance is measured in terms of sensitivity, specificity, average score, and harmonic score. © 2022 IEEE.

关键词： Convolutional neural networks

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Post COVID-19 Vaccination: Infection Rate Analysis Using Time Series Modeling

Post COVID-19 Vaccination: Infection Rate Analysis Using Tim...

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Machine Intelligence and Smart Innovation (ICMISI), International Conference on

作者： Nourin Kadir Rupinder Kaur Tiago Rodrigues Rasha Kashef Dept. of Electrical and Computer Engineering Toronto Metropolitan University (TMU) Ontario Canada

ISBN: (数字)9798350365740

ISBN: (纸本)9798350365757

The COVID-19 pandemic has drastically altered the course of human life and economic stability, with a death toll nearing seven million. Three years in, infections persist, affecting even the vaccinated populace, with no global data to clarify the infection rates post-vaccination or the relationship between vaccine hesitancy and subsequent infection. Addressing this gap, our research introduces a big data-driven analytical model employing TBATS, Prophet, ARIMA, and LSTM forecasting techniques to evaluate vaccination's impact on case trends and potential variant resistance. This paper utilizes WHO-provided datasets of daily cases, deaths, and vaccination rates, including booster shots since 2020, to gauge vaccine efficacy and the possibility of vaccines not entirely preventing new strains. Our analysis aims to shed light on vaccination outcomes and inform public health strategies, offering a clearer understanding of COVID-19's trajectory in the vaccinated demographic.

关键词： COVID-19 Analytical models Technological innovation Time series analysis Data models Stability analysis Vaccines

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Gene Essentiality Mediated Base Substitution in Escherichia coli genome: Machine Learning Analysis

Gene Essentiality Mediated Base Substitution in Escherichia ...

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2022 International Interdisciplinary Conference on Mathematics, engineering and science, MESIICON 2022

作者： Kurmi, Annushree Das, Debashis Sen, Piyali Ray, Suvendra Kumar Satapathy, Siddhartha Sankar Tezpur University Dept. of Computer Science and Engineering Tezpur India Tezpur University Dept. of Molecular Biology and Biotechnology Tezpur India

ISBN: (纸本)9781665470728

Several selection and mutation factors are known to influence evolution in the nucleotide sequence in the genome of organisms. Using the machine learning approach, we considered gene essentiality as a selection factor, and we investigated base substitution among essential and remaining gene sets in the Escherichia coli genome. RF and SVM classifiers achieved an AUC score of 75% each. The importance of the base substitution-based features differs in predicting essentiality. Features such as G to A substitutions, mutations within the amino group, and transversion mutations were found to be the most important features. In contrast, G to C, G to T, and T to C substitutions were among the least ones. This result indicates that the base substitution patterns in essential genes might differ from that in non-essential genes. The result of this manuscript will be helpful for understanding evolution at the molecular level. © 2022 IEEE.

关键词： Genes

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An Overview of Protocols-Based Security Threats and Countermeasures in WLAN 4

An Overview of Protocols-Based Security Threats and Counterm...

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4th International Conference for Emerging Technology, INCET 2023

作者： Ojha, Nitish Kumar Baray, Elyas Indian Institute of Information Technology Dept. of Information Technology Allahabad211012 India Sharda University Knowledge Park - III Dept. of Computer Science and Engineering Greater Noida201310 India

ISBN: (纸本)9798350335750

WLAN security protocols are the most important section for wireless networks which controls all the security-related issues by securing the network with some pre-defined rules made by wireless-protected access organizations, further which makes it the main target of hacking and threats by interested parties and to be safe from such threats. There should be countermeasures for such threats where there will be a solution for any threat encountered and to mention. WIFI or wireless local area Network is a cost-effective easy deployment Network that makes users take advantage of vast data shared on the internet and by the network without the need for any cable for connections. Here in this overview paper by reviewing many researchers' techniques and works on the WLAN security topic, there are shared threats to WLAN and the ways of taking precautions against those threats as countermeasures and for which the tables are used. For comparison of techniques used by authors and some famous and highly dangerous threats are also defined further. WPA3's new security protocol is also defined with its newly found vulnerabilities and countermeasures except there is a conclusion on the downgrade attack problem with WPA3. Further, there are also countermeasures for that as well, nowadays people think the network is secured with standards defined by the IEEE organization but some know this is not the case so it makes it a more important area for research. © 2023 IEEE.

关键词： Wireless local area networks (WLAN)

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Multi-Target Integrated Sensing and Communication with MIMO In-Band Full-Duplex Systems 58

Multi-Target Integrated Sensing and Communication with MIMO ...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Chih, Yung-Chih Shen, Chung-An Celik, Abdulkadir Fouda, Mohammed E. Eltawil, Ahmed National Taiwan University of Science and Technology Electronic and Computer Engineering Dept. Taipei Taiwan King Abdullah University of Science and Technology Thuwal Saudi Arabia Rain Neuromorphics Inc. San FranciscoCA United States

ISBN: (纸本)9798350354058

In recent years, the integration of Multi-Input Multi-Output (MIMO) technology with In-Band Full-Duplex (IBFD) systems has emerged as a promising approach for multi-targets Integrated Sensing and Communication (ISAC), particularly in the context of 6G and vehicle-to-everything (V2X) communications. However, the simultaneous transmission and reception of signals in IBFD systems face significant challenges, mainly due to self-interference (SI), which severely impacts system performance. This paper presents a novel framework that combines self-interference cancellation (SIC) techniques with the MIMO-IBFD-ISAC system to enhance multi-target sensing capabilities. The framework utilizes an independent component analysis (ICA) algorithm to effectively separate self-interference from the desired signals. Additionally, a specialized multi-targets detection method is employed to recover radar signals reflected from targets accurately. Simulation results demonstrate that the proposed approach improves target detection accuracy and communication efficiency, making it a viable solution for next-generation ISAC systems. © 2024 IEEE.

关键词： Radar interference

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Improved Performance Two-Dimensional Direction of Arrival Estimation Algorithm with Unknown Number of Noncoherent Sources 14

Improved Performance Two-Dimensional Direction of Arrival Es...

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14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

作者： Tayem, Nizar Hussain, Ahmed A Moinuddeen, AbuMuhammad Radaydeh, Redha M. Alghazo, Jaafar M. Texas A & M Univ. Commerce Dept. of Engineering & Technology United States Prince Mohammad Univ. Dept. of Electrical Engineering Saudi Arabia Texas A & M Univ. Commerce Dept. of Computer Science United States Virginia Military Institute Electrical & Computer Engg. Dept. United States

ISBN: (纸本)9781665487719

Two-dimensional direction of arrival estimation is a computationally complex problem that has been the focus of research in the area of array signal processing for several decades now. This paper proposes a novel2D DOA azimuth and elevation angle estimation algorithm for multiple RF noncoherent sources using an L-shaped array antenna configuration. The proposed method employs a MUSIC-like estimation algorithm in conjunction with QR decomposition to improve the accuracy of 2D DOA estimation and provide much lower computational complexity when compared with existing methods such as the Capon method. It does not require knowing the number of sources in advance and pairs the estimated azimuth and elevation angles automatically for multiple sources. Simulation results are presented to validate the efficacy of the proposed method and its performance is compared with the Capon method. © 2022 IEEE.

关键词： Computational complexity

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Machine Learning Approaches for Survival Prediction in Covid-19 Patients: A Comparative Analysis

Machine Learning Approaches for Survival Prediction in Covid...

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Advances in Computing,Communication, electrical, and Smart Systems (iCACCESS), International Conference on

作者： Md. Muhiminul Islam M. I. R. Shuvo Dept. of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh

The COVID-19 pandemic has underscored the significance of accurately predicting patient survival in order to promptly administer efficient medical care. The augmentation of biological and healthcare service data volume has been found to enhance the precision of disease prognosis, survival prediction, and other clinical assessments. Numerous biological characteristics serve as the underlying factors contributing to the etiology of numerous diseases. Therefore, it is imperative to have precise medical data that possesses appropriate characteristics in order to facilitate an analysis that exhibits exceptional clinical accuracy. In order to effectively analyze data, it is imperative to employ a machine learning model that is both exact and accurate in predicting sickness or survival outcomes. An expeditious and accurate assessment of the disease's magnitude is crucial during a particular phase of a pandemic, such as the Covid-19 outbreak. The primary aim of this research is to employ machine learning methodologies in order to forecast the survival outcomes of individuals diagnosed with COVID-19. This will be accomplished by using a publicly available dataset comprising various medical attributes pertaining to 383,499 COVID-19 patients, which was collected and made accessible by the Directorate General of Epidemiology, Secretariat of Health in Mexico. Various machine learning techniques, including Regression methods, Artificial Neural Networks, Random Forest Classifier, Support Vector Machine, AdaBoost, and XGBoost, are employed on the dataset that has undergone diverse preprocessing procedures. The experimental findings demonstrated that the system yielded numerous advantages in comparison to previous efforts in the same field.

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De-Centralized Voting System using Blockchain 1

De-Centralized Voting System using Blockchain

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1st IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2022

作者： Jangada, Atharva Dadlani, Nimish Raina, Sanchit Sooraj, V.S. Buchade, A.R. Pune Institute of Computer Technology Savitribai Phule Pune University Dept. of Computer Engineering Pune India Mit School of Engineering Mit Art Design and Technology University Dept. of Comp Science and Engineering Pune India

ISBN: (数字)9781665428323

ISBN: (纸本)9781665428323

The revolutionary concept of blockchain, the technology behind the popular cryptocurrency Bitcoin and its successors, has ushered in a new era in the Internet and online services. While most people focus only on cryptocurrencies when they think about Blockchain, in fact many administrative activities and day-to-day services are managed offline and/or privately are now being securely transferred to the Internet as online services. Electronic Voting is one of the trending and important aspect of online services. Traditional voting systems in our country include the use of ballots at polling stations or EVM's (Electronic Voting Machine). The ballot system is prone to tampering, whereas EVM's being a centralised system, are prone to hacking. We aim to offer a more secure, transparent, safer, cheaper and easier to use alternative to the above methods of voting, by making use of the revolutionary Blockchain technology. Smart contracts are meaningful pieces of code that are incorporated into the Blockchain and implemented on schedule at each stage of the Blockchain update. Ethereum and its network is one of the most convenient due to its stability, wide usability and smart contract logic. In this work, we will be implementing and testing a prototype of an E-Voting application using Ethereum Wallet and Solidity language as a smart contract for the Ethereum network. © 2022 IEEE.

关键词： Smart contract

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Transformed Deep Spatio Temporal-Features with Fused Distance for Efficient Video Retrieval 4

Transformed Deep Spatio Temporal-Features with Fused Distanc...

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4th International Conference on Artificial Intelligence and Speech Technology, AIST 2022

作者： Banerjee, Alina Kumar, Ela Ravinder, M. Indira Gandhi Delhi Technical University for Women Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665499026

For the goal of video retrieval, this research proposes wavelet transformations on deep spatiotemporal characteristics. The component-wise similarities between the query video feature and prototype video feature are calculated because level 1 wavelets extract two components from any signal or feature vector. The ultimate dissimilarity for determining the top 1 and top 5 accuracy is created by fusing these differences. The outcomes demonstrate that the suggested technique performs better than a baseline strategy. The following strategy for improvement can be investigated further by employing fast learning networks that are trained on the training sets of both data sets to provide better classification of the query as well as the prototype feature vectors, which would enhance the retrieval accuracy. © 2022 IEEE.

关键词： Wavelet transforms

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