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IEEE Conference on Global Communications (GLOBECOM)

作者： Koosha Pourtahmasi Roshandeh Mostafa Mohammadkarimi Masoud Ardakani Electrical and Computer Engineering Department University of Alberta Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology

The zero padding (ZP) variants of orthogonal frequency-division multiplexing (OFDM) exhibit a lower bit error rate (BER) and higher energy efficiency compared to their cyclic prefix (CP) counterparts. However, the employment of ZP-OFDM demands strict time synchronization, which is challenging in the absence of pilots or CP. Moreover, time synchronization in OFDM systems is even more challenging when impulsive noise is present. It is well known that urban noise, which consists largely of impulsive noise generated by spark plugs used in internal combustion engines, switching and industrial activities, and discharge of high voltage distribution lines, has a strong influence on digital mobile communications. In this paper, we propose a new low-complexity approximate maximum likelihood (A-ML) timing offset (TO) estimator for ZP multiple-input multiple-output (MIMO)-OFDM in impulsive-noise environments. Performance comparison of the A-ML estimator with existing TO estimators demonstrates a superior performance in terms of lock-in probability with similar computational complexity. Also, compared to the optimal ML TO estimator, it offers a significantly lower computational complexity with negligible performance loss. The A-ML estimator can be employed for both frame and symbol synchronization.

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Detection and Classification of Pneumonia, Covid-19 and Tuberculosis Using Deep Learning Methods 4

Detection and Classification of Pneumonia, Covid-19 and Tube...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Gaurkhede, Sanskruti Shendre, Sanika Izankar, Sakshi Burle, Rushikesh Gudadhe, Amit Datta Meghe Institute of Higher Education and Research Computer Science and Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artifical Intelligience & Machine Learning Mahartashtra Wardha442001 India

ISBN: (纸本)9798331523923

The deep learning literature is presented in this publication. in the classification of respiratory diseases, focusing on pneumonia, COVID-19, and tuberculosis. The review explores the potential of various Complex patterns that can be extracted from medical images using advanced neural network designs, like recurrent neural network designs (RNNs) and convolutional neural networks (CNNs). It evaluates the efficacy of these models using a range of datasets and considers limitations like interpretability, class disparity, and data scarcity. The study also discusses integrating multimodal data sources to enhance diagnostic accuracy. A comparative analysis of deep learning models highlights ResNet-18's superior performance in classifying CT scans of tuberculosis, pneumonia, and COVID-COVID-19. The findings suggest that deep learning models can aid radiologists in accurately diagnosing respiratory diseases from CT images, potentially transforming early detection and classification methods. © 2025 IEEE.

关键词： COVID-19

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A Comprehensive Analysis on Machine Learning Based Methods for Lung Cancer Level Classification

SSRN

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SSRN 2025年

作者： Farshchiha, Shayli Asoudeh, Salman Kuhshuri, Maryam Shavali Eisaei, Mehrshad Azadi, Mohamadreza Hesaraki, Saba Department of Computer Science Shamsipour Technical College Tehran Iran Islamic Azad University Science and Research Branch Faculty of Mechanics Electrical Power and Computer Iran University of Birjand Iran Isfahan University of Technology Department of Electrical and Computer Engineering Faculty of Engineering Isfahan Iran Faculty of Computer College of Technical and Engineering University of Velayat Iran University of Isfahan Department of Electrical Engineering Faculty of Engineering Isfahan Iran University of Mazandaran Mazandaran Iran

Lung cancer is a major issue in worldwide public health, requiring early diagnosis using stable techniques. This work begins a thorough investigation of the use of machine learning (ML) methods for precise classification of lung cancer stages. A cautious analysis is performed to overcome overfitting issues in model performance, taking into account minimum child weight and learning rate. A set of machine learning (ML) models including XGBoost (XGB), LGBM, Adaboost, Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), CatBoost, and k-Nearest Neighbor (k-NN) are run methodically and contrasted. Furthermore, the correlation between features and targets is examined using the deep neural network (DNN) model and thus their capability in detecting complex patterns is established. It is argued that several ML models can be capable of classifying lung cancer stages with great accuracy. In spite of the complexity of DNN architectures, traditional ML models like XGBoost, LGBM, and Logistic Regression excel with superior performance. The models perform better than the others in lung cancer prediction on the complete set of comparative metrics like accuracy, precision, recall, and F-1 score. © 2025, The Authors. All rights reserved.

关键词： Lung cancer

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A deep convolutional visual encoding model of neuronal responses in the LGN

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Brain Informatics 2021年第1期8卷 11页

作者： Mounier, Eslam Abdullah, Bassem Mahdi, Hani Eldawlatly, Seif Computer and Systems Engineering Department Faculty of Engineering Ain Shams University 1 El-Sarayat St. Abbassia Cairo Egypt Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt

The Lateral Geniculate Nucleus (LGN) represents one of the major processing sites along the visual pathway. Despite its crucial role in processing visual information and its utility as one target for recently developed visual prostheses, it is much less studied compared to the retina and the visual cortex. In this paper, we introduce a deep learning encoder to predict LGN neuronal firing in response to different visual stimulation patterns. The encoder comprises a deep Convolutional Neural Network (CNN) that incorporates visual stimulus spatiotemporal representation in addition to LGN neuronal firing history to predict the response of LGN neurons. Extracellular activity was recorded in vivo using multi-electrode arrays from single units in the LGN in 12 anesthetized rats with a total neuronal population of 150 units. Neural activity was recorded in response to single-pixel, checkerboard and geometrical shapes visual stimulation patterns. Extracted firing rates and the corresponding stimulation patterns were used to train the model. The performance of the model was assessed using different testing data sets and different firing rate windows. An overall mean correlation coefficient between the actual and the predicted firing rates of 0.57 and 0.7 was achieved for the 10 ms and the 50 ms firing rate windows, respectively. Results demonstrate that the model is robust to variability in the spatiotemporal properties of the recorded neurons outperforming other examined models including the state-of-the-art Generalized Linear Model (GLM). The results indicate the potential of deep convolutional neural networks as viable models of LGN firing. © 2021, The Author(s).

关键词： Neurons

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End-to-End Anti-Backdoor Learning on Images and Time Series

arXiv

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arXiv 2024年

作者： Jiang, Yujing Ma, Xingjun Erfani, Sarah Monazam Li, Yige Bailey, James Faculty of Engineering and Information Technology The University of Melbourne Australia School of Computer Science Fudan University China School of Computer Science and Technology Xidian University China

Backdoor attacks present a substantial security concern for deep learning models, especially those utilized in applications critical to safety and security. These attacks manipulate model behavior by embedding a hidden trigger during the training phase, allowing unauthorized control over the model’s output during inference time. Although numerous defenses exist for image classification models, there is a conspicuous absence of defenses tailored for time series data, as well as an end-to-end solution capable of training clean models on poisoned data. To address this gap, this paper builds upon Anti-Backdoor Learning (ABL) and introduces an innovative method, End-to-End Anti-Backdoor Learning (E2ABL), for robust training against backdoor attacks. Unlike the original ABL, which employs a two-stage training procedure, E2ABL accomplishes end-to-end training through an additional classification head linked to the shallow layers of a Deep Neural Network (DNN). This secondary head actively identifies potential backdoor triggers, allowing the model to dynamically cleanse these samples and their corresponding labels during training. Our experiments reveal that E2ABL significantly improves on existing defenses and is effective against a broad range of backdoor attacks in both image and time series domains. © 2024, CC0.

关键词： Time series

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Bounded Simultaneous Messages 43

Bounded Simultaneous Messages

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43rd IARCS Annual Conference on Foundations of Software technology and Theoretical computer Science, FSTTCS 2023

作者： Bogdanov, Andrej Dinesh, Krishnamoorthy Filmus, Yuval Ishai, Yuval Kaplan, Avi Sekar, Sruthi School of EECS University of Ottawa Canada Dept. of Computer Science and Engineering Indian Institute of Technology Palakkad India The Henry and Marylin Taub Faculty of Computer Science Technion Haifa Israel University of California BerkeleyCA United States

ISBN: (纸本)9783959773041

We consider the following question of bounded simultaneous messages (BSM) protocols: Can computationally unbounded Alice and Bob evaluate a function f(x, y) of their inputs by sending polynomial-size messages to a computationally bounded Carol? The special case where f is the mod-2 inner-product function and Carol is bounded to AC0 has been studied in previous works. The general question can be broadly motivated by applications in which distributed computation is more costly than local computation. In this work, we initiate a more systematic study of the BSM model, with different functions f and computational bounds on Carol. In particular, we give evidence against the existence of BSM protocols with polynomial-size Carol for naturally distributed variants of NP-complete languages. © 2023 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Automata theory

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Yolov8-Based Deep Learning Model for Improved Drone Intrusion Detection

Yolov8-Based Deep Learning Model for Improved Drone Intrusio...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Mohini Darji Khushi Patel Bansari Patel Krishna Thakkar Mehul Minat Meet K. Patel Computer Science & Engineering Faculty of Technology and Engineering (FTE) Devang Patel Institute of Advance Technology and Research (DEPSTAR) Charotar University of Science and Technology (CHARUSAT) Changa Gujarat India Department of Computer Science & Engineering Student of Bachelor of Technology Faculty of Technology and Engineering (FTE) Devang Patel Institute of Advance Technology and Research (DEPSTAR) Charotar University of Science and Technology (CHARUSAT) Changa Gujarat India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Real-time identification of unmanned aerial vehicle (drone) is a relatively growing nascent research area which leverages on deep learning and computer vision methods. However, the question arises as to possible dangers, and misuse of drones in different circumstances. These concerns are in respect to privacy, safety and security possible violations. Cameras and software are for example bundled in detection systems where visual information is used to facilitate the detection process. Thus, This Study was devoted to examining the object detection feature of the YOLO Only Look Once (YOLOv8) algorithm and its applicability for analyzing visual material captured by drones. One of the challenges in reviewing literature was to look for an online dataset of small drones and make it publicly available. Therefore, a real-world dataset was established accurately in this study and it includes small drones. The outcome presented in the document is expected to help understand the capability of the chosen models when one attempts to recognize drones in complex conditions. Further this shall serve as base on enhancing the development of even better and long-lasting anti-drone detection systems. The mentioned issues were addressed and solved with the help of the YOLOv8 architecture implementation, and the outstanding results were obtained: the mean average precision (mAP) of $\mathbf{9 3. 9 \%}$ , the precision of $\mathbf{9 2. 9 \%}$ , and the recall of $\mathbf{9 0. 3 \%}$ .

关键词： Deep learning YOLO Visualization Software algorithms Software Safety Security Drones Videos Testing

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ZnO Nanorods Coated Glass Substrate Based on Side Coupling Technique for Lactose Sensing Application

ZnO Nanorods Coated Glass Substrate Based on Side Coupling T...

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International Conference on Electrical, Communication, and computer engineering (ICECCE)

作者： Marlina Jahava Mohd Hafiz Jali Md. Ashadi Md Johari Mohd Adili Norashikin Haziezol Helmi Mohd Yusof Aminah Ahmad Faculty of Electrical Technology and Engineering Universiti Teknikal Malaysia Melaka Malacca Malaysia Faculty of Electronics and Computer Technology and Engineering Universiti Teknikal Malaysia Melaka Malacca Malaysia Faculty of Information Technology and Computer Engineering Universiti Teknikal Malaysia Melaka Malacca Malaysia

A study has been conducted to investigate the sensing performance of zinc oxide (ZnO) nanorods coated glass substrate using the side coupling approach for lactose sensing application. The concentration of lactose solution was varied from 0% to 100% in order to examine the performance of the proposed sensor, which is influenced by the scattering and absorption of light. Coated glass substrates with optical side coupling could solve the lower output coupling voltage existed in the recent approach. Experimental results show that the proposed approach improved by a factor of 2.5 as compared to the uncoated glass substrate when exposed to variation concentration of lactose solution. The average sensitivity of the sensor was observed to be 0.0201 V/%Concentration throughout the tested %Concentration levels. The utilization of affordable and uncomplicated sensors allows for the precise identification of alterations in the refractive index solution, hence presenting potential applications in the domains of environmental and optical sensing.

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Triboinformatic Approaches for Composite Coatings on Titanium Alloys 1

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11th International Conference on Industrial Tribology, IndiaTrib-2022

作者： Naveen Kumar, K. Barman, Utpal Masset, Patrick J. Rao, T.V.V.L.N. Department of Metallurgical and Materials Engineering Indian Institute of Technology Madras Chennai600036 India Faculty of Computer Technology Assam down town University Gandhi Nagar SankarMadhab Path Assam Panikhaiti Guwahati781026 India Lukasiewicz Research Network Krakow Institute of Technology ul. Zakopiańska 73 Kraków30-418 Poland Faculty of Engineering Assam down town University Gandhi Nagar SankarMadhab Path Assam Panikhaiti Guwahati781026 India

ISBN: (数字)9789819992645

ISBN: (纸本)9789819992638

Ti-6Al-4 V is an (α + β) titanium alloy that has been most widely used in automotive, aerospace, and biomedical applications due to the extensive material properties of high strength, toughness, high strength-to-weight ratio, and biocompatibility. Machine learning (ML) algorithms of data-driven methods provide a better understanding of the correlation between material properties and tribological properties. Correlations of tribological test variables (sliding speed, sliding distance, and normal load) with the tribological properties (coefficient of friction and wear rate) were studied using machine learning algorithms. A total of 41 data points based on the Ti-6Al-4 V alloy coating were divided into training and testing sets in the ratio of 80:20. The ML-based algorithms, which include Decision Tree (DT) and Random Forest (RF) algorithms, have been studied to predict the wear rates and coefficient of friction. The evaluation metrics like MAE, MSE, and RMSE are used to find the best suitable algorithm for the predictions. Using the data analysis, the coefficient of friction and wear rates have been satisfactorily predicted from the considered data sets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tribology

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A Hybrid Three-Port Topology for Urban Charging Stations

A Hybrid Three-Port Topology for Urban Charging Stations

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Mohammadreza Khodaparast Klidbari Naser Souri Zahra Sadat Habibolahi Hamid Montazeri Hedeshi Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran The Bradley Department of Electrical and Computer Engineering Virginia Tech United States Department of Electrical Engineering Iran University of Science and Technology Tehran Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

Electric vehicles are rapidly gaining popularity as a sustainable alternative to conventional gasoline. In urban areas, chargers with different ratings can accommodate the diverse needs of electric vehicles. However, the available multiport topologies have variable switching frequencies. This paper introduces a hybrid multiport isolated DC-DC converter for urban charging stations, incorporating fast and slow charging ports with a fixed switching frequency. It provides isolation and enables soft switching on the primary side of the converter without circulating current on its secondary side. The primary side does not need feedback, which reduces complexity. The second stage generates a wide output voltage range to charge the electric vehicle battery by employing a switch. In addition, the proposed topology offers reduced component count and simple control with fixed-frequency operation. This paper provides the concept and the operation modes. Experimental results are provided to validate its features. The prototype converter achieves 96% peak efficiency.

关键词： Switching frequency Soft switching Prototypes Switches DC-DC power converters Zero voltage switching Charging stations Electric vehicle charging Topology Complexity theory

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