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Iranian Conference on electrical engineering (ICEE)

作者： Khashayar Dehghan Omid Shoaei Shahin Jafarabadi Ashtiani School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

In this paper, a modified power and data transmitter with high data rate to carrier frequency ratio (DRCF) is proposed for medical implants. The transmitter sends data and power through an inductive link which its primary coil is driven by a class-E power amplifier. The data rate is independent of the elements of the class-E power amplifier (especially RFC and the primary coil). Hence, the data rate can be increased up to the carrier frequency. In the previously proposed transmitter two different power supplies (main and auxiliary) are used. The auxiliary one delivers a negative power which should be transferred to the main power supply so that the saved power is reused. This complexity is solved in the proposed circuit. A proof-of-concept prototype of the proposed transmitter implemented on printed circuit board works at 1 MHz carrier frequency. The data rate is equal to 500 kbps. The power delivered to the load is 53 mW and the power efficiency is close to 58%.

关键词： Coils electrical engineering Power supplies Auxiliary transmitters Printed circuits Prototypes Power amplifiers

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Detecting and counting pistachios based on deep learning

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Iran Journal of computer Science 2022年第1期5卷 69-81页

作者： Rahimzadeh, Mohammad Attar, Abolfazl School of Computer Engineering Iran University of Science and Technology Tehran Iran Department of Electrical Engineering Sharif University of Technology Tehran Iran

Pistachios are nutritious nuts that are sorted based on the shape of their shell into two categories: Open mouth and Closed mouth. The open-mouth pistachios are higher in price, value, and demand than the closed-mouth pistachios. Because of these differences, it is considerable for production companies to precisely count the number of each kind. This paper aims to propose a new system for counting the different types of pistachios with computer vision. We have introduced and shared a new data set of pistachios, including six videos with a total length of 167 s and 3927 labeled pistachios. Unlike many other works, our model counts pistachios in videos, not images. Counting objects in videos need assigning each object between the video frames so that each object be counted once. The main two challenges in our work are the existence of pistachios’ occlusion and deformation of pistachios in different frames, because open-mouth pistachios that move and roll on the transportation line may appear as closed mouth in some frames and open mouth in other frames. Our novel model first is trained on the RetinaNet object detector network using our data set to detect different types of pistachios in video frames. After gathering the detections, we apply them to a new counter algorithm based on a new tracker to assign pistachios in consecutive frames with high accuracy. Our model is able to assign pistachios that turn and change their appearance (e.g., open-mouth pistachios that look closed mouth) to each other so does not count them incorrectly. Our algorithm performs very fast and achieves good counting results. The computed accuracy of our algorithm on six videos (9486 frames) is 94.75%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021.

关键词： Convolutional neural network Deep learning Motile-object counting Multi-object counting Object detection Pistachio counting

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Regional Style and Color Transfer 5

Regional Style and Color Transfer

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5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Ding, Zhicheng Li, Panfeng Yang, Qikai Li, Siyang Gong, Qingtian Columbia University Fu Foundation School of Engineering and Applied Science New York United States University of Michigan Electrical Engineering and Computer Science Ann Arbor United States University of Illinois Urbana-Champaign Department of Computer Science Urbana United States Pace University Lubin School of Business New York United States

ISBN: (纸本)9798350373820

This paper presents a novel contribution to the field of regional style transfer. Existing methods often suffer from the drawback of applying style homogeneously across the entire image, leading to stylistic inconsistencies or foreground object twisted when applied to images with foreground elements such as person figures. To address this limitation, we propose a new approach that leverages a segmentation network to precisely isolate foreground objects within the input image. Subsequently, style transfer is applied exclusively to the background region. The isolated foreground objects are then carefully reintegrated into the style-transferred background. To enhance the visual coherence between foreground and background, a color transfer step is employed on the foreground elements prior to their reincorporation. Finally, we utilize feathering techniques to achieve a seamless amalgamation of foreground and background, resulting in a visually unified and aesthetically pleasing final composition. Extensive evaluations demonstrate that our proposed approach yields significantly more natural stylistic transformations compared to conventional methods. © 2024 IEEE.

关键词： Color

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Prediction of Post-COVID-19 Pandemic Learning Mode Preferences Using Machine Learning 14

Prediction of Post-COVID-19 Pandemic Learning Mode Preferenc...

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14th International Conference on Computing Communication and networking Technologies, ICCCNT 2023

作者： Pratondo, Agus Novianty, Astri Putro, Budi Laksono Komala Sari, Siska Telkom University School of Applied Sciences Department of Multimedia Engineering Bandung Indonesia Telkom University School of Electrical Engineering Department of Computer Engineering Bandung Indonesia Indonesia University of Education Department of Computer Science Education Bandung Indonesia Telkom University School of Applied System Diploma Program of Information System Bandung Indonesia

ISBN: (纸本)9798350335095

The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective educational methods as institutions prepare for the post-pandemic period, it is essential to understand students' preferences for various learning modes. With the aid of machine learning techniques, this study seeks to forecast preferences for learning styles in post-pandemic higher education. The study made use of a dataset compiled from a wide range of college students, which included information on the institution type (public or private), program type (bachelor's or diploma), academic discipline (science or humanities), gender, year of study, internet use, communication costs, comorbidity status, vaccination history, gadget and laptop ownership, and preferred learning mode (online, in-person, or both). For the aim of prediction, a Support Vector Machine (SVM) was used. Its kernel functions included linear, polynomial, radial basis function (RBF), and sigmoid. Performance criteria like accuracy, precision, recall, and F1 score were used to assess the models after the dataset was randomly partitioned into training and testing sets. The findings showed that for identifying the preferred mode of learning, SVM with the RBF kernel had the highest prediction accuracy (91.67%). Further proving the RBF kernel's efficacy, the confusion matrix analysis showed that high values in the diagonal represented accurate predictions for each of the three classes (both online and onsite). The results imply that machine learning approaches, in particular SVM with the RBF kernel, may accurately forecast students' preferences for learning modes in the post-pandemic situation. In a post-pandemic society, the findings provide insightful information that may be used to build adaptable and inclusive learning models and put them into practice. © 2023 IEEE.

关键词： Support vector machines

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Protein-Ligand Interaction Prediction with Effective Method of Graph Attention networks and Long Short-Term Memory 2

Protein-Ligand Interaction Prediction with Effective Method ...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Deepa, N. AlFatlawy, Ramy Shashidhara, K.S. Revathi, R. Reka, M. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Applications Sona College of Arts and Science Salem India

ISBN: (纸本)9798331504960

Nowadays, the drug discovery field of molecules in protein-ligand interactions developing effective therapeutics for identifying potential drug candidates with spatial features to predict binding affinity. However, the existing Deep Learning (DL) based prediction approaches failed to interact and predict the binding affinity of protein-ligand precisely for drug design with binding affinity. To overcome this problem, a Graph Attention networks with Long Short-Term Memory (GATN-LSTM) method is proposed with spatiotemporal forecasting for interaction between protein and ligand in binding affinity prediction for drug design. The proposed GATN-LSTM method is employed on DUD-E dataset consists of enhanced the prediction by capturing relationship (interaction) between protein and ligand in molecules for drug discovery using PubChem for prediction. Then, the preprocessed data of protein-ligand interaction is extracted using (CNN-GNN) for better generalization of both local and global features for predicting drug discovery. Finally, GATN-LSTM is employed for interaction of molecules with binding affinity which enhanced the drug design and also improved the prediction results. Experimental results of the proposed GATN-LSTM method attained accuracy (0.9992), F1-score (0.9985) are higher than existing prediction approaches such as Convolutional Neural network (CNN) and Mutual Information-based Feature Selection (MIFS). © 2024 IEEE.

关键词： Graph neural networks

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Towards Deriving Analytical Model for Optimal Cell Overlap to Reduce Handover Signaling 35

Towards Deriving Analytical Model for Optimal Cell Overlap t...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Farooq, Muhammad Umar Bin Zaidi, Syed Muhammad Asad Taufique, Azar Imran, Ali MobileComm Professionals United States Tech Trained United States University of Glasgow James Watt School of Engineering United Kingdom University of Oklahoma AI4Networks Research Center School of Electrical and Computer Engineering United States

ISBN: (纸本)9798350362244

The conventional network dimensioning and optimization approaches prioritize coverage and capacity as the most vital components. However, handover signaling overhead has emerged as a critical concern in the emerging cellular networks. This is particularly evident with the proliferation of network densification leading to a higher number of handovers. Hence, an optimal cell overlap is vital to ensure retainability and service continuity for the ever-growing fraction of mobile users and the expected cell densification. It is also crucial because the unprecedented signaling overhead can clog both the core network and air interface. To address this challenge, this paper presents an analytical model built on the control data separation architecture (CDSA) to quantify the handover signaling overhead as a function of cell overlap, user speed and cell density. We first compute probabilities for handover failures and successes and model the handover signaling overhead as a Markov chain. Numerical results demonstrate that for a given cell density and user velocity, a suitable cell overlap yields substantial reductions in handover signaling by improving handover success rate. The proposed model has the potential to become an integral element in the network planning process for emerging cellular networks. © 2024 IEEE.

关键词： Markov chains

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Machine Learning Based Enhanced Remote Power Analysis Attacks

Machine Learning Based Enhanced Remote Power Analysis Attack...

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Advanced Research in Computing (ICARC), International Conference on

作者： Ishan Kasthuripitiya Jeewantha Ariyawansha Tharindu Ranasinghe Manjula Sandirigama Darshana Jayasinghe Department of Computer Engineering University of Peradeniya Peradeniya Sri Lanka School of Electrical and Computer Engineering The University of Sydney Sydney Australia

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Remote Power Analysis (RPA) attacks have demonstrated the ability to reveal secret keys from cryptographic implementations. RPA attacks have been demonstrated on Advanced Encryption Standard (AES) circuits running on Field Programmable Gate Arrays (FPGAs). Thus far, RPA attacks were conducted using classical attacks such as correlation power analysis attacks (CPA).This paper presents a comprehensive study on the application of machine learning (ML) algorithms to improve the efficacy of RPA attacks against AES. We explore the integration of Convolutional Neural networks (CNNs) and Multi-Layer Perceptrons (MLPs) within the state-of-the-art AISY framework (initially proposed for classical power analysis attacks) to detect and exploit RPA vulnerabilities in AES-encrypted FPGA systems. The proposed methodology involves capturing power traces using a Time-to-Digital Converter (TDC) on-chip sensor during AES operations and then using MLP and CNN models to conduct RPA attacks to evaluate security. We demonstrate that ML-enhanced RPAs can significantly increase attack success rates while requiring fewer data samples. Our results show that CNN-based RPA attacks can reveal the secret within 10,000 traces. The research underscores the need for ongoing innovation in cryptographic defences to counteract the evolving threat landscape.

关键词： Technological innovation Circuits Machine learning Side-channel attacks Stability analysis System-on-chip Convolutional neural networks Integrated circuit modeling Field programmable gate arrays Standards

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Design and implementation of cooperative multi-agent based on MAPPO 5

Design and implementation of cooperative multi-agent based o...

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5th International Conference on Telecommunications, Optics, and computer Science, TOCS 2024

作者： Wang, Haolong Fei, Rong Chen, Ruizhu School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shannxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China

ISBN: (纸本)9781510691667

In the past few years, with the continuous development of deep reinforcement learning, the application of deep reinforcement has been set off within the realms of self-driving vehicle technology, game games, and natural language processing, and the proximal policy optimization algorithm has emerged as a focal point in the sphere of deep reinforcement learning research. In this paper, the Proximal Policy Optimization (PPO) algorithm and the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm are studied, and a cooperative multi-agent system is designed and implemented based on the MAPPO algorithm in the field of StarCraft game, which is compared with the popular Q-Value Mixing (QMIX) reinforcement learning algorithm in recent years, and proposes a direction for subsequent system improvement. In the proximal policy optimization algorithm, the traditional policy gradient algorithm, natural policy gradient algorithm, trust domain policy optimization algorithm, proximal policy optimization algorithm, and independent proximal policy optimization algorithm are studied. In the multi-agent proximal policy optimization algorithm, the centralized training decentralized execution architecture, athlete critic method, value standardization, experience playback, and GAE (GAE) adopted by the MAPPO algorithm are studied. Designing and implementing a cooperative multi-agent system, introducing the StarCraft environmental campaign. Regarding the specifics of the training process, the policy gradient algorithm is used to update the policy network, the time series difference algorithm is improved, and the generalized advantage estimation is used to update the value network. Compared with the QMIX learning algorithm, the model trained by the MAPPO algorithm has the advantages of faster convergence and better stability. © 2025 SPIE.

关键词： Deep reinforcement learning

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A Multi-Source Retrieval Question Answering Framework Based on RAG

A Multi-Source Retrieval Question Answering Framework Based ...

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Ridong Wu Shuhong Chen Xiangbiao Su Yuankai Zhu Yifei Liao Jianming Wu School of Computer Science and Network Engineering Guangzhou University Guangzhou China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

With the rapid development of large-scale language models, Retrieval-Augmented Generation (RAG) has been widely adopted. However, existing RAG paradigms are inevitably influenced by erroneous retrieval information, thereby reducing the reliability and correctness of generated results. Therefore, to improve the relevance of retrieval information, this study proposes a method that replaces traditional retrievers with GPT-3.5, leveraging its vast corpus knowledge to generate retrieval information. We also propose a web retrieval based method to implement fine-grained knowledge retrieval, Utilizing the powerful reasoning capability of GPT-3.5 to realize semantic partitioning of problem. In order to mitigate the illusion of GPT retrieval and reduce noise in Web retrieval, we proposes a multi-source retrieval framework, named MSRAG, which combines GPT retrieval with web retrieval. Experiments on multiple knowledge-intensive QA datasets demonstrate that the proposed framework in this study performs better than existing RAG framework in enhancing the overall efficiency and accuracy of QA systems.

关键词： Knowledge engineering Information science Runtime Costs Semantic segmentation Noise Semantics

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Secure health data transmission on IOT 2

Secure health data transmission on IOT

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and networking Technologies, ViTECoN 2023

作者： Dhivya, R. Kumar, Manoj S. Mathesh, J. Thanikaiselvan, V. Mahalingam, Hemalatha Amirtharajan, Rengarajan Mepco Schlenk Engineering College Department of Electronics and Communication Engineering Sivakasi India School of Electronics Engineering Vellore Institute of Technology Vellore India King Abdulaziz University Department of Electrical and Computer Engineering Jeddah22254 Saudi Arabia School of Electrical and Electronics Engineering SASTRA Deemed University Thanjavur613401 India

ISBN: (纸本)9798350347982

Data security is essential to the e-healthcare industry for protecting patients' confidential information. It is necessary to continuously monitor the patient's health to give them the proper medical care. The proposed work develops a Raspberry Pi cryptosystem by sending real-time patient health data to the physician via the Thing Speak cloud environment. In order to create the highly random keys for encrypting the color medical images and sensor data (temperature, heartbeat, medical images), this research uses numerous chaotic maps in a single procedure. Firstly, the shuffling algorithm based on a logistic map is designed to shuffle the image pixels randomly. Then, the diffusion algorithm based on circle map is developed to diffuse the shuffled image pixels. The effectiveness of the proposed algorithm is analysed based on MSE (Mean Squared Error), PSNR (Peak Signal-to-Noise), Entropy, Histogram, and correlation is calculated analyses. © 2023 IEEE.

关键词： Cryptography

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