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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Rahaman, Md Anikur Binte Rabbani Nuha, Maksura Tahsin, Md Hossain, Al Islam, Raihan Ul Rifat Ahmmad Rashid, Mohammad Reza, Ahmed Wasif Ripon, Shamim H. East West University Department of Computer Science and Engineering Dhaka Bangladesh International Islamic University Chittagong Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331534400

Cervical cancer is a female-specific malignancy that poses a substantial threat worldwide, especially in less privileged and underdeveloped regions. Despite continuous progress and advances in medical science research, the rate of death from cervical cancer remains strikingly high among women. Therefore, accurate early-stage cervical cancer identification is imperative for effective diagnosis. It facilitates cost-effective and timely treatment to increase the survival rate of patients, leading to an optimal diagnosis improvement in disease classification. Deep learning approaches demonstrate superior accuracy in medical data classification, motivating us to consider these techniques in this research. In our study, we propose DeepCerviNet, a custom CNN model that achieves 99% precision, which exceeds the accuracy of other deep learning models, MobileNetV2 (97%), ResNET34 (95%), and GoogleNet (94%). Furthermore, to assess our model's generalizability, we use another Multicancer dataset where the proposed model classifies various cancers with 99% accuracy. The DeepCerviNet model's strong performance and adaptability can assist in identifying more aggressive or recurrent cervical cancer types, aiding the development of targeted therapeutic strategies. © 2024 IEEE.

关键词： Self-supervised learning

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Resilient control design for EV charging unit with multiple modules consisting of single active bridge (SAB) converters 63

Resilient control design for EV charging unit with multiple ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Manasis, Apostolos S. Papageorgiou, Panos C. Konstantopoulos, George C. University of Patras Department of Electrical and Computer Engineering Rion26504 Greece

ISBN: (纸本)9798350316339

Fast charging stations for electric vehicles (EVs) often consist of charging units with multiple modules connected in parallel to achieve high power ratings and can suffer from cyber-attacks in the modern smart grid architecture or measurement/sensor/communication failure. In this manuscript, an EV charging unit consisting of multiple single active bridge (SAB) converters connected in parallel is examined and a new nonlinear resilient controller is suggested to realize accurate power sharing among the converters with inherent current limitation, while enhancing the entire charging unit resilience under irregular scenarios. By taking into account the dynamic model of the parallel SAB converters and using invariant set and nonlinear ultimate boundedness theory, it is proven that the current injected to the EV battery by each converter is limited below a maximum value at all times. Furthermore, based on the novel dynamic structure of the controller, it is shown that when an unexpected event (e.g. setpoint attack, sensor fault) occurs at one or more converters, the inherent current-limiting property protects the corresponding devices and the remaining non-compromised units automatically share the remaining power to the battery, thus enhancing the EV charger resilience. An EV charging unit with 4 modules is simulated to verify the effectiveness and increased resilience of the proposed control approach under setpoint attacks and sensor/measurement faults. © 2024 IEEE.

关键词： Charging stations

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Learning label-specific features for decomposition-based multi-class classification

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Frontiers of computer science 2023年第6期17卷 101-110页

作者： Bin-Bin JIA Jun-Ying LIU Jun-Yi HANG Min-Ling ZHANG College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China School of Computer Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration(Ministry of Education) Southeast UniversityNanjing 210096China

Multi-class classification can be solved by decomposing it into a set of binary classification problems according to some encoding rules,e.g.,one-vs-one,one-vs-rest,error-correcting output *** works solve these binary classification problems in the original feature space,while it might be suboptimal as different binary classification problems correspond to different positive and negative *** this paper,we propose to learn label-specific features for each decomposed binary classification problem to consider the specific characteristics containing in its positive and negative ***,to generate the label-specific features,clustering analysis is respectively conducted on the positive and negative examples in each decomposed binary data set to discover their inherent information and then label-specific features for one example are obtained by measuring the similarity between it and all cluster *** clearly validate the effectiveness of learning label-specific features for decomposition-based multi-class classification.

关键词： machine learning multi-class classification error-correcting output codes label-specific features

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Python-Based Cross-Platform Impedance Benchmarking Tool (CroZBenMa) for IBR's EMT Models in Matlab/Simulink and PSCAD/EMTDC 10

Python-Based Cross-Platform Impedance Benchmarking Tool (Cro...

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10th IEEE International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia

作者： Zhou, Weihua Mohammed, Nabil Bahrani, Behrooz Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

ISBN: (纸本)9798350351330

This paper presents a Python-driven graphical user interface (GUI) explicitly crafted for benchmarking the electromagnetic transient (EMT) models of inverter-based resources (IBRs) across Matlab/Simulink and PSCAD/EMTDC platforms through impedance measurement techniques. The GUI ensures a user-friendly experience, enabling seamless interactions between these platforms while offering advanced visualization of impedance measurement results and associated benchmarking errors. This paper delves into the Python-based implementation that powers these features. Through a series of case studies, the tool's efficacy in benchmarking IBR's EMT models is validated. The benchmarking methodology and corresponding GUI design are extensible to other leading EMT platforms such as DIgSILENT/PowerFactory, PSSE, EMTP-RV, EMTP-ATP, and DSAToolsTM/SSAT, fostering expansive and holistic research in the stability analysis of the IBR-based modern power systems. © 2024 IEEE.

关键词： Benchmarking

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A Low-Power Temperature and Process Insensitive CMOS Power Management Unit

A Low-Power Temperature and Process Insensitive CMOS Power M...

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Eleftheriou, Nikolaos P. Ntasiou, Ourania Alimisis, Vassilis Sotiriadis, Paul P. National Technical University of Athens Department of Electrical and Computer Engineering Greece

ISBN: (纸本)9798350318845

A Power Management Unit aimed for applications requiring low consumption and area efficiency is introduced in this work. Its main goal is to deliver the desired voltage and current outputs to be used from the other circuits of the system. In this particular topology a power consumption of 591 nW is achieved along with PSRR of 25.15 dB in worst case and a dropout voltage of 0.3 V. This work is supposed to serve as a proof of concept for very low consumption design of Power Management Units and may be included in architectures that integrate advanced features, like DC-DC converters, Pulse Width Modulation mode along with complex digital control part. The proposed system is designed and simulated using the Cadence IC Suite in a TSMC 90nm CMOS process. © 2024 IEEE.

关键词： Power management

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SSRepL-ADHD: Adaptive Complex Representation Learning Framework for ADHD Detection from Visual Attention Tasks

SSRepL-ADHD: Adaptive Complex Representation Learning Framew...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Rehman, Abdul Heldal, Ilona Lin, Jerry Chun-Wei Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9798350362480

Self Supervised Representation Learning (SSRepL) can capture meaningful and robust representations of the Attention Deficit Hyperactivity Disorder (ADHD) data and have the potential to improve the model's performance on also downstream different types of Neurodevelopmental disorder (NDD) detection. In this paper, a novel SSRepL and Transfer Learning (TL)-based framework that incorporates a Long Short-Term Memory (LSTM) and a Gated Recurrent Units (GRU) model is proposed to detect children with potential symptoms of ADHD. This model uses Electroencephalogram (EEG) signals extracted during visual attention tasks to accurately detect ADHD by preprocessing EEG signal quality through normalization, filtering, and data balancing. For the experimental analysis, we use three different models: 1) SSRepL and TL-based LSTM-GRU model named as SSRepL-ADHD, which integrates LSTM and GRU layers to capture temporal dependencies in the data, 2) lightweight SSRepL-based DNN model (LSSRepL-DNN), and 3) Random Forest (RF). In the study, these models are thoroughly evaluated using well-known performance metrics (i.e., accuracy, precision, recall, and F1-score). The results show that the proposed SSRepL-ADHD model achieves the maximum accuracy of 81.11% while admitting the difficulties associated with dataset imbalance and feature selection. © 2024 IEEE.

关键词： Self-supervised learning

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Blockchain-Based Knowledge Repository for Training Artificial Intelligence Models: Bridging AIML with Decentralized Data

Blockchain-Based Knowledge Repository for Training Artificia...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Das, Amit Kumar Tonoy, Md. Thouhedul Alam Hossain, Meherab Chandigarh University Department of Computer Engineering Mohali India United International University Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350364866

This paper presents a Blockchain-Based Knowledge Repository (BBKR) tailored for the secure storage and management of training data crucial for Artificial Intelligence and Machine Learning (AIML) models. Leveraging Ethereum blockchain technology and smart contracts, the BBKR ensures data integrity, enhances security measures, and promotes decentralization principles. The proposed mechanism includes a detailed explanation of data validation processes, decentralized access control, and scalable transaction mechanisms. Our empirical evaluations validate the efficacy of BBKR in upholding data integrity, enhancing scalability, and fortifying security provisions. The results show significant improvements in data management performance compared to traditional centralized repositories. This research underscores the transformative potential of BBKR in revolutionizing AIML data management paradigms and offers a robust foundation for further advancements in the field. Future work will explore real-world applications and potential enhancements using advanced cryptographic techniques. © 2024 IEEE.

关键词： Artificial Intelligence and Machine Learning (AIML) Blockchain blockhain-Based Knowledge Repository (BBKR) Data integrity Decentralization Ethereum Security Smart Contracts Training data

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Enhancing Image Quality of Aging Smartphones Using Multi-scale Selective Kernel Feature Fusion Network 3rd

Enhancing Image Quality of Aging Smartphones Using Multi-sca...

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3rd International Conference on Intelligent Systems and Pattern Recognition, ISPR 2023

作者： Hossain, Md. Yearat Rakib, Md. Mahbub Hasan Nijhum, Ifran Rahman Rahman, Tanzilur Department of Electrical and Computer Engineering North South University Dhaka Bangladesh

ISBN: (纸本)9783031463341

Smartphones are now the most popular medium for photography. Smartphones can capture better images than the hardware they use thanks to Machine Learning-based computational photography. Even though modern phones have sophisticated image enhancement applications, older and aged phones lag behind for a variety of reasons. Lens quality degradation, which results in washed out, soft-looking images, is one of the most common issues seen in older devices. We propose a method for improving such images by using an attention-based multi-scale residual neural network trained on a synthetic dataset. We chose two smartphones: an old device that captures degraded images and a modern flagship that provides reference enhanced images. Then we used the bloom filter, contrast, and highlight adjustment to make the reference images appear degraded. Later, we trained the model using synthetically degraded images and tested it on a variety of older devices. We achieved a maximum Peak Signal to Noise Ratio (PSNR) score of 74 throughout our experiments. To evaluate the model's images, we used Blind Image Quality Assessment (BIQA) methods such as HyperIQA. Aside from correcting the lens issue, the model achieves comparatively better sharpness, contrast, and color processing. Our proposed method generalizes well and achieves up to 11.45% improvement on novel devices by utilizing a very limited amount of data. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smartphones

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Single-mode waveguides in barium titanate-on-insulator platform fabricated by wet etching

Single-mode waveguides in barium titanate-on-insulator platf...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Lin, Hong-Lin Aashna, Pragati Cao, Yu Danner, Aaron Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore

We present, for the first time, single-mode single-crystal barium titanate waveguides on insulator, achieved through a wet-etching method. This etching approach opens new possibilities for the development of barium-ti... 详细信息

关键词： Barium titanate

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Resist Denial-of-service Attack and Enhance Network Security with Protocol-independent Packet Processor-Based Two-Layer Firewall 4

Resist Denial-of-service Attack and Enhance Network Security...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Chou, Pin-Lin Wei, Hsin-Wen Lee, Wei-Tsong Tamkang University Department of Electrical and Computer Engineering New Taipei Taiwan

ISBN: (纸本)9798350360721

We introduced a Protocol-independent Packet Processor (P4)-based two-layer firewall with a detection system for identifying denial-of-service (DDoS) attacks. P4-based switch enables real-time packet inspection in the Data Plane by reducing latency associated with packet filtering. The system enhances network security through dynamic traffic-blocking rules trained by machine learning modeling. Using Software-Defined Networking (SDN), the system seamlessly operates with or without current SDN protocols and dynamically adapts to changing threats. The simulation results showed that the proposed approach enhanced network security in the continuously evolving internet environment. © 2024 IEEE.

关键词： Software defined networking

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