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Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

computers, Materials & Continua 2023年第7期76卷 711-729页

作者： Muhammad Irfan Ahmad Shaf Tariq Ali Umar Farooq Saifur Rahman Salim Nasar Faraj Mursal Mohammed Jalalah Samar M.Alqhtani Omar AlShorman Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia College of Engineering Najran UniversityNajran61441Saudi Arabia

A brain tumor is a mass or growth of abnormal cells in the *** children and adults,brain tumor is considered one of the leading causes of *** are several types of brain tumors,including benign(non-cancerous)and malignant(cancerous)*** brain tumors as early as possible is essential,as this can improve the chances of successful treatment and *** this problem,we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models(Resnet50,Vgg16,Vgg19,U-Net)and their integration for computer-aided detection and localization systems in brain *** pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome *** dataset consists of 120 *** pre-trained models have been used to classify tumor or no tumor images,while integrated models are applied to segment the tumor region *** have evaluated their performance in terms of loss,accuracy,intersection over union,Jaccard distance,dice coefficient,and dice coefficient *** pre-trained models,the U-Net model achieves higher performance than other models by obtaining 95%*** contrast,U-Net with ResNet-50 out-performs all other models from integrated pre-trained models and correctly classified and segmented the tumor region.

关键词： Brain tumor deep learning ensemble detection healthcare

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Sample-Efficient Multi-Agent DQNs for Scalable Multi-Domain 5G+ Inter-Slice Orchestration

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 956-977页

作者： Doanis, Pavlos Spyropoulos, Thrasyvoulos Communication Systems Department Eurecom Sophia Antipolis06410 France School of Electrical and Computer Engineering Technical University of Crete Chania731 00 Greece

Data-driven network slicing has been recently explored as a major driver for beyond 5G networks. Nevertheless, we are still a long way before such solutions are practically applicable in real problems. Most solutions addressing the problem of dynamically placing virtual network function chains (‘‘slices’’) on top of a physical topology still face one or more of the following hurdles: (i) they focus on simple slicing setups (e.g. single domain, single slice, simple VNF chains and performance metrics);(ii) solutions based on modern reinforcement learning theory have to deal with astronomically high action spaces, when considering multi-VNF, multi-domain, multi-slice problems;(iii) the training of the algorithms is not particularly data-efficient, which can hinder their practical application given the scarce(r) availability of cellular network related data (as opposed to standard machine learning problems). To this end, we attempt to tackle all the above shortcomings in one common framework. For (i), we propose a generic, queuing network based model that captures the inter-slice orchestration setting, supporting complex VNF chain topologies and end-to-end performance metrics. For (ii), we explore multi-agent DQN algorithms that can reduce action space complexity by orders of magnitude compared to standard DQN. For (iii), we investigate two mechanisms to store to and select from the experience replay buffer, in order to speed up the training of DQN agents. The proposed scheme was validated to outperform both vanilla DQN (by orders of magnitude faster convergence) and static heuristics (3× cost improvement). 2024 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License.

关键词： 5G mobile communication systems

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Design of an RF-CMOS Switched-Capacitor Power Amplifier for NB-IoT RF Transceivers

Design of an RF-CMOS Switched-Capacitor Power Amplifier for ...

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2023 International Young Engineers Forum in electrical and computer engineering, YEF-ECE 2023

作者： Santos, Ana Isabel Pedro Oliveira, Joao NOVA School of Science and Technology Department of Electrical and Computer Engineering Caparica Portugal Centre of Technology and Systems CTS-UNINOVA Department of Electrical and Computer Engineering Caparica Portugal

ISBN: (纸本)9798350300420

The paper presents the implementation of a Switched Capacitor Power Amplifier (SCPA) to be integrated into a Narrowband Internet of Things (NB-IoT) Transceiver. The SCPA is designed to operate at a frequency of 0.9GHz and aiming the maximum output power allowed by the standard of 23dBm. All the blocks within the SCPA were developed using RF components of a standard 130nm CMOS process, with a 1.2V/2.4V supply voltage. Results show that the SCPA is able to produce a maximum output power of 15.61dBm with a maximum Power-Added Efficiency (PAE) of 26.52% and a Total Harmonic Distortion (THD) of 0.68%. The measured HD2 and HD3 are-70.23dBc and-43.41dBc, respectively. Additional Process, Voltage and Temperature (PVT) corners and Monte Carlo simulations indicate the SCPA operates properly when subject to different conditions and mismatches. Besides the SCPA, a 16 QAM modulation stage is designed in VerilogA in order to evaluate the performance of the RF PA when a sequence of symbols is transmitted. © 2023 IEEE.

关键词： Power amplifiers

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Development of a Mobile Monitoring Application for a Message Queuing Telemetry Transport (MQTT)-Based Struvite Fertilizer Production System 10

Development of a Mobile Monitoring Application for a Message...

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10th International HCI and UX Conference in Indonesia, CHIuXiD 2024

作者： Maulana, Ilham Fitria, Maya Faizah, Cut Nurul Adharani, Daffa Hirta Jinan, Sarah Nada Dawood, Rahmad Universitas Syiah Kuala Department of Electrical and Computer Engineering Banda Aceh23111 Indonesia Universitas Syiah Kuala Interactive Intelligent Systems Reseach Group Department of Electrical and Computer Engineering Banda Aceh23111 Indonesia

ISBN: (纸本)9798350380439

Struvite fertilizer, characterized by its slow-release qualities, is comprised of magnesium, ammonium, and phosphate and is vital for plant growth. The Integrated laboratory Unit of Universitas Syiah Kuala (USK) has developed a prototype for struvite fertilizer production equipment, but the production process requires manual monitoring. This project seeks to create an MQTT (Message Queuing Telemetry Transport)-based mobile application for the real-time monitoring and control of struvite fertilizer production equipment. The application employs the MQTT communication protocol, known for its quick data transmission and minimal bandwidth requirements. This application incorporates sensors on the production machine, which includes a reactor, filter, and dryer, and employs an Amazon Web Service's (AWS) IoT Core as the MQTT broker. Findings from the application's evaluation indicate that the mobile application can efficiently monitor and control several machine components. Data transmission to MQTT topics was successful, facilitating seamless communication. Functional and integration testing demonstrated that the system operates effectively. Usability testing outcomes yielded an average score of 77.75 with a system usability scale (SUS) approach, signifying that the application possesses a satisfactory degree of usability classified within the 'Grade B' category. © 2024 IEEE.

关键词： Telemetering systems

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Deep Learning-Based Estimation of Arterial Stiffness from PPG Spectrograms: A Novel Approach for Non-Invasive Cardiovascular Diagnostics 46

Deep Learning-Based Estimation of Arterial Stiffness from PP...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Abrisham, Kiana Pilevar Alipour, Khalil Tarvirdizadeh, Bahram Ghamari, Mohammad Laboratory Department of Mechatronics Engineering School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology Tehran Iran Kettering University Department of Electrical and Computer Engineering FlintMI United States

ISBN: (纸本)9798350371499

Cardiovascular diseases (CVDs), a leading cause of global mortality, are intricately linked to arterial stiffness, a key factor in cardiovascular health. Non-invasive assessment of arterial stiffness, particularly through Carotid-to-femoral Pulse Wave Velocity (cf-PWV) - the gold standard in this field - is vital for early detection and management of CVDs. This study introduces a novel approach, utilizing photoplethysmogram (PPG) signal spectrograms as inputs for deep learning models to estimate cf-PWV, a significant advancement over traditional methods. Employing a modified ResNet-18 architecture, we analyze PPG signals from digital, radial, and brachial arteries of a simulated dataset of 4374 healthy adults. Our methodology's innovation lies in its direct use of finely tuned spectrogram images, bypassing the complex feature extraction processes. This approach achieved R2 (correlation coefficient) values of up to 0.9902 for the digital artery, 0.9898 for the radial artery, and 0.9825 for the brachial artery, coupled with significantly lower Mean Absolute Percentage Errors (MAPE) of approximately 1.61% for the digital, 1.87% for the radial, and 2.08% for the brachial artery. These findings highlight the efficacy of PPG spectrograms, especially from the digital artery, in providing an accurate, user-friendly, and non-invasive method for cf-PWV estimation, thereby enhancing the capabilities of non-invasive cardiovascular diagnostics. © 2024 IEEE.

关键词： Photoplethysmography

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Receding horizon optimization for distributed control of electric vehicle charging stations

Receding horizon optimization for distributed control of ele...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Malkova, Anna Zepter, Jan Martin Marinelli, Mattia Amezquita, Herbert Morais, Hugo Technical University of Denmark Department of Wind and Energy Systems Roskilde Denmark Department of Electrical and Computer Engineering Lisbon Portugal

ISBN: (纸本)9789531842976

With the continuous increase of electric vehicle (EV) adoption, deploying smart charging techniques offer a practical solution to mitigate the impact of grid overloading caused by simultaneous EV charging. At the same time, smart charging can help to stabilize the fluctuations in the production from local renewable energy sources (RES). This article introduces a receding horizon optimization model for the distributed control of EV chargers at charging stations, focusing on maximizing the profit of the charging station, while enhancing the utilization of local PV generation. The proposed model operates in 5-minute intervals, determining the power reference for the EV cluster at the charging station. Results demonstrate that the proposed model effectively lowers electricity cost for charging stations, while ensuring more than 90% energy delivery for charging EVs. Future research will be focused on integrating wind energy and refining the model in controlled lab tests for practical implementation and validation. © 2024 IEEE.

关键词： Charging stations

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Diversified and compatible web APIs recommendation based on game theory in IoT

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Digital Communications and Networks 2024年第4期10卷 1198-1209页

作者： Wenwen Gong Huiping Wu Xiaokang Wang Xuyun Zhang Yawei Wang Yifei Chen Mohammad R.Khosravi College of Information and Electrical Engineering China Agricultural UniversityBeijing100000China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguang262700China Department of Computer Science St.Francis Xavier UniversityAntigonishNSCanada Department of Computing Macquarie UniversitySydneyNSW2122Australia Department of Computer Engineering Persian Gulf UniversityBushehr***Iran Department of Electrical and Electronic Engineering Shiraz University of TechnologyShiraz71557-13876Iran

With the ever-increasing popularity of Internet of Things(IoT),massive enterprises are attempting to encapsulate their developed outcomes into various lightweight Web Application Programming Interfaces(APIs)that can be accessible *** this context,finding and writing a list of existing Web APIs that can collectively meet the functional needs of software developers has become a promising approach to economically and easily develop successful mobile ***,the number and diversity of candidate IoT Web APIs places an additional burden on application developers’Web API selection decisions,as it is often a challenging task to simultaneously ensure the diversity and compatibility of the final set of Web APIs *** this challenge and latest successful applications of game theory in IoT,a Diversified and Compatible Web APIs Recommendation approach,namely DivCAR,is put forward in this *** of all,to achieve API diversity,DivCAR employs random walk sampling technique on a pre-built“API-API”correlation graph to generate diverse“API-API”correlation ***,with the diverse“API-API”correlation subgraphs,the compatible Web APIs recommendation problem is modeled as a minimum group Steiner tree search problem.A sorted set of multiple compatible and diverse Web APIs are returned to the application developer by solving the minimum group Steiner tree search *** last,a set of experiments are designed and implemented on a real dataset crawled from *** results validate the effectiveness and efficiency of our proposed DivCAR approach in balancing the Web APIs recommendation diversity and compatibility.

关键词： Internet of things Web APIs recommendation Game theory Diversity and compatibility

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Topological Photonic Molecule with Time-Varying Modulations

Topological Photonic Molecule with Time-Varying Modulations

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2023 Photonic Networks and Devices, Networks 2023 in Advanced Photonics Congress - Part of Advanced Photonics Congress 2023

作者： Suh, Joseph Kim, Gyunghun Park, Hyungchul Yu, Sunkyu Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

We propose dynamic modulations of a photonic molecule to achieve topological properties of light. We investigate the Hall transport in synthetic dimensions and the system modulation strategy to demonstrate the pumping... 详细信息

ISBN: (纸本)9781957171265

关键词： Molecules

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Deep Learning for Fault Detection of Digital VLSI Circuits 21

Deep Learning for Fault Detection of Digital VLSI Circuits

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21st International Learning and Technology Conference, L and T 2024

作者： Gaber, Lamya Hussein, Aziza I. Minia University Computers and Systems Engineering Department Minia Egypt Effat University Electrical and Computer Engineering Department Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

With the increasing complexity and scale of digital VLSI designs, ensuring reliability in IC design necessitates effective fault detection processes during the pre-silicon stage. Many fault detection algorithms lead to significant computational time due to the problem of search space explosion. To handle the ever-growing volume of data, deep learning algorithms, a subset of machine learning techniques, can be employed. In this paper, we propose two novel approaches for fault detection (FD) of digital VLSI circuits, specifically targeting stuck-at faults. The first proposed model is semi-supervised FD model that aims to mitigate the search space explosion issue by leveraging both unsupervised and supervised learning processes. The second presented model is based on an optimizer for finding the appropriate configurations for detecting stuck-at faults in digital circuits. The initial proposed model achieves maximum validation accuracy of approximately 98% applied to circuits from ISCAS'85. This model yields a higher accuracy compared to the second approach that achieves maximum accuracy of around 95% when applied to the same circuits from the ISCAS'85 benchmark. © 2024 IEEE.

关键词： Fault detection

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Combination of Interleaved Single-input Multiple-output DC-DC Converters

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CSEE Journal of Power and Energy systems 2022年第1期8卷 132-142页

作者： Eladio Dur´an Aranda Salvador P´erez Litr´an Mar´ıa Bella Ferrera Prieto Electronic Engineering Computer Systems and Automation DepartmentUniversity of HuelvaHuelvaSpain Electrical and Thermal Engineering Design and Projects DepartmentUniversity of HuelvaHuelvaSpain

This paper analyses,simulates and verifies an experimental prototype of a four-phase interleaved DC-DC *** is based on a SEPIC-Cuk *** developed prototype has been used in single-input multiple-output(SIMO)*** combined converter allows obtaining dual output voltages of the same value,from a single input DC voltage and with only a power *** interleaved DC-DC converters achieve a better dynamic response and low ripple,maintaining their *** converter is connected in parallel,thereby managing their losses by distributing them between more components,which facilitates the thermal management of the multiphase converter and allows handling high power values in small sizes with respect to solutions for a single *** control strategies were applied:synchronous operation mode(SOM)and interleaved operation mode(IOM).The simulation results allow the comparison of both operational modes,verifying that the IOM presents advantages with respect to the ripple at the input and output *** experimental prototype was designed for a distributed power architecture and bipolar DC microgrid(MG).

关键词： Bipolar DC DC-DC converter interleaved

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