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Expectation maximization—vector approximate message passing based generalized linear model for channel estimation in intelligent reflecting surface-assisted millimeter multi-user multiple-input multiple-output systems

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PeerJ computer Science 2025年 11卷 e2582-e2582页

作者： Ali, Shoukath K. Philip, Sajan P. Khan, Arfat Ahmad Moses, Leeban Cengiz, Korhan Akleylek, Sedat Ivković, Nikola Department of Electronics and Communication Engineering Presidency University Karnataka Bengaluru India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu Erode India Department of Computer Science College of Computing Khon Kaen University Khon Kaen Thailand Department of Electrical-Electronics Engineering Istinye University Istanbul Turkey Institute of Computer Science University of Tartu Tartu Estonia Faculty of Organization and Informatics University of Zagreb Pavlinska Varaždin Croatia

Channel estimation poses a main challenge in intelligent reflecting surface (IRS)assisted millimeter wave (mmWave) multi-user multiple-input multiple-output (MIMO) systems due to the substantial number of antennas at the base station (BS) and the passive reflective elements within the IRS lacking sufficient signal processing capabilities. This article addresses this challenge by proposing a channel estimation technique for IRS-assisted mmWave MIMO systems. The problem of channel estimation is normally taken as a compressed sensing (CS) problem, typically addressed through algorithms such as Orthogonal Matching Pursuit (OMP), Generalized Approximate Message Passing (GAMP), and Vector Approximate Message Passing with Expectation-Maximization (EM-VAMP). EM-VAMP demonstrates better performance only when a Gaussian mixture (GM) distribution is chosen as the prior for the sparse channel, especially at high signal-to-noise ratios (SNRs). To address this, the article introduces the application of generalized linear models (GLMs), extensions of standard linear models, providing increased flexibility in modeling data that deviates from Gaussian distribution. Numerical results unveil that the proposed Its EM-VAMP-GLM is much more robust to the existing OMP, GAMP and EM-LAMP algorithms. Copyright 2025 K et al. Distributed under Creative Commons CC-BY 4.0

关键词： Gaussian distribution

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Four-dimensional conserved topological charge vectors in plasmonic quasicrystals

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Science (New York, N.Y.) 2025年第6734期387卷 644-648页

作者： Tsesses, Shai Dreher, Pascal Janoschka, David Neuhaus, Alexander Cohen, Kobi Meiler, Tim C. Bucher, Tomer Sapir, Shay Frank, Bettina Davis, Timothy J. Meyer Zu Heringdorf, Frank Giessen, Harald Bartal, Guy rew and Erna Viterbi Department of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa Israel Department of Physics MIT-Harvard Center for Ultracold Atoms and Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge MA United States Faculty of Physics and Center for Nanointegration University of Duisburg-Essen Duisburg-Essen (CENIDE) Duisburg Germany 4th Physics Institute Research Center SCoPE Integrated Quantum Science and Technology Center University of Stuttgart Stuttgart Germany Centre for Disruptive Photonic Technologies and School of Physical and Mathematical Sciences Nanyang Technological University Singapore Faculty of Mathematics and Computer Science Weitzmann Institute of Science Rehovot Israel School of Physics University of Melbourne Parkville VIC Australia

According to Noether's theorem, symmetries in a physical system are intertwined with conserved quantities. These symmetries often determine the system topology, which is made ever more complex with increased dimensionality. Quasicrystals have neither translational nor global rotational symmetry, yet they intrinsically inhabit a higher-dimensional space in which symmetry resurfaces. Here, we discovered topological charge vectors in four dimensions (4D) that govern the real-space topology of 2D quasicrystals and reveal their inherent conservation laws. We demonstrate control over the topology in pentagonal plasmonic quasilattices, mapped by both phase-resolved and time-domain near-field microscopy, showing that their temporal evolution continuously tunes the 2D projections of their distinct 4D topologies. Our work provides a route to experimentally probe the thermodynamic properties of quasicrystals and topological physics in 4D and above.

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A New IoT-Based Smart Greenhouse for Cultivation of Water Convolvulus

A New IoT-Based Smart Greenhouse for Cultivation of Water Co...

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13th International Electrical engineering Congress, iEECON 2025

作者： Akkaraekthalin, Prayoot Somwong, Tanee Chudpooti, Nonchanutt Hirun, Nuttawut Meesomklin, Suwaluck King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand Rajamangala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Department of Electronic and Telecommunications Engineering Nonthaburi11000 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Industrial Physics and Medical Instrumentation Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok College of Industrial Technology Department of Electronics Engineering Technology Bangkok10800 Thailand

ISBN: (纸本)9798331543952

This paper presents a new IOT system for monitoring and controlling plant cultivation in greenhouse system. The system aims to regulate and monitor the environmental conditions suitable for plant cultivation in real-time. The system utilizes sensors to measure environmental conditions, such as soil moisture sensor, light intensity sensor, temperature sensor, and air humidity sensor. The Node MCU ESP32 is used for processing and controlling these operations. The system is designed to send notifications through Line application and allows farmers to monitor the system's performance and plant growth in real-time via a smartphone application. It can utilize the Blynk IoT application on a smartphone for remotely monitoring and controlling the environmental conditions and provides historical data. This experiment demonstrates that the designed system can successfully cultivate plants and promote better growth compared to traditional cultivation methods with a 13.38% improvement. Additionally, it provides convenience and ease of management for plant cultivation. © 2025 IEEE.

关键词： greenhouse system IOT smart agriculture Smart farming water convolvulus

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An Exploratory Study of Diverse Models and Datasets for Transfer Learning based Image Classification on Sparse Data

An Exploratory Study of Diverse Models and Datasets for Tran...

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Moratuwa engineering Research Conference (MERCon)

作者： Jathurshan Pathmarasa Udaya Wijenayake Ruchire Eranga Wijesinghe Bhagya Nathali Silva Department of Computer Engineering Faculty of Engineering University of Sri Jayewardenepura Nugegoda Sri Lanka Department of Electrical and Electronic Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Excellence in Informatics Electronics & Transmission (CIET) Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331529048

ISBN: (纸本)9798331529055

Transfer learning is a powerful technique for image classification, especially when dealing with limited data. However, selection of the best transfer learning approach and model remains challenging, since the strategy is highly influenced by the scenario. This work conducted a thorough analysis of four transfer learning approaches for binary classification, and multiclass classification using four pretrained models i.e. MobileNetV2, MobileNetV3Large, EfficientNetB0, and EfficientNetB5. The test accuracy was measured with respect to the sample size considering the effects of data augmentation and fine tuning. The key finding of the study revealed that data augmentation has improved the test accuracy by up to $11.8 \%$. Moreover, the lightweight models marked a breakthrough by outperforming large scale models’ accuracy for sparse data settings when fine tuning (FT) and data augmentation (DA) are applied appropriately. Hence, this work provides a comprehensive overview on selecting different transfer learning approaches for sparse data classification by exploring diverse models and datasets, while discussing the consequences and challenges for future real-world applications.

关键词： Knowledge engineering Accuracy Transfer learning Optical wavelength conversion Data augmentation Feature extraction Size measurement Data models Tuning Image classification

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CARA: A Hybrid Framework Integrating Swarm AI Agents and Knowledge Graphs for Advanced LLM Reasoning

CARA: A Hybrid Framework Integrating Swarm AI Agents and Kno...

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Advancements in Computing (ICAC), International Conference on

作者： Nirodya Pussadeniya Ruchira Wijesinghe Udaya Wijenayake Bhagya Silva Department of Computer Engineering Faculty of Engineering University of Sri Jayewardenepura Nugegoda Sri Lanka Department of Electrical and Electronic Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Excellence in Informatics Electronics Transmission (CIET) Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Language models excel in linguistic processing but often face challenges with complex reasoning tasks that require real-world interaction and multi-step logic. This paper presents the Cognitive Adaptive Reasoning Architecture (CARA), a framework that enhances reasoning by integrating a modular swarm architecture with knowledge graphs (KGs) and specialized agents. These agents, equipped with diverse tools, dynamically adapt to various tasks, actively engaging with real-world data. CARA's continuous memory update cycle ensures each reasoning step is based on up-to-date, contextual KG knowledge, enhancing decision-making. Incorporating human insights further aligns outcomes with nuanced cognition. Experimental results demonstrate CARA's superior performance over larger models, advancing language models' reasoning capabilities for more context-aware, informed decision-making in dynamic environments.

关键词： Adaptation models Adaptive systems Decision making Training data Knowledge graphs Linguistics Cognition Artificial intelligence Standards Context modeling

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Securing Data in an Internet of Things Network Using Blockchain technology: Smart Home Case

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SN computer Science 2022年第2期3卷 167页

作者： Tchagna Kouanou, Aurelle Tchito Tchapga, Christian Sone Ekonde, Michael Monthe, Valery Mezatio, Brice Anicet Manga, Josépha Simo, Gael R. Muhozam, Yves Department of Computer Engineering College of Technology University of Buea Buea Cameroon Department of Training Research Development and Innovation InchTech’s Solutions Yaoundé Cameroon Department of Electrical and Electronic Engineering College of Technology University of Buea Buea Cameroon Department of Computer Science Faculty of Sciences University of Yaoundé 1 Yaoundé Cameroon Research Unit Condensed Matter Electronics and Signal Processing Department of Physics Faculty of Science University of Dschang PO Box 67 Dschang Cameroon

Every day, innovative technologies emerge (5G networks, quantum computing) or change to existing technologies (big data, deep learning, blockchain, etc.). These technologies are expected to play an essential role on the Internet of Things (IoT) systems and applications. The IoT architecture generates substantial amounts of diverse types of data at extremely high speeds. All these generated data need to be secure. Scalable and secure data sharing is essential for all technological domains. Taking into consideration of blockchain technology today in data security, we can also think about a method to secure IoT data based on blockchain. In this paper, we proposed a blockchain method to secure data in an IoT architecture. We used EOS Blockchain for its scalability, flexibility and native enhanced throughput. Architecture in a concept of a smart home using blockchain is proposed. We combine Arduino, Raspberry Pi and sensors to create an IoT ecosystem for a smart home. Data are collected and stored in EOS blockchain for better security and integrity. We use Raspberry Pi as a client to get data in our developed blockchain. The real implementation is performed and the obtained results show us that we can send more than 500 data per second in our EOS blockchain. We discuss our results and saw that our blockchain method outperformed those in related works in terms of the number of data stored per millisecond in blockchain and the scalability. The proposed EOS blockchain pipeline for securing Smart Home data is provided at the end of this paper. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022.

关键词： Architecture Blockchain Data security IoT Smart home

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Enhancing Control of a 21-Level Modular Multilevel Converter through Grid-Based Model Predictive Current Control

Enhancing Control of a 21-Level Modular Multilevel Converter...

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2023 IEEE Asia Meeting on Environment and Electrical engineering, EEE-AM 2023

作者： Tran Hung, Cuong Nguyen Nhat, Tung Pham Viet, Phuong Nguyen Xuan, Phuc Nguyen Quang, Thuan Nhat Hai, Nguyen Thuyloi University Faculty of Electrical & Electronics Engineering Ha Noi Viet Nam Ha Noi University of Science and Technology Department of Industrial Automation Ha Noi Viet Nam Key Laboratory for High Voltage Techniques Institute of Energy Ha Noi Viet Nam Hanoi University of Science and Technology Computer Science Department School of Information and Communications Technology Hanoi Viet Nam

ISBN: (纸本)9798350381061

The Modular Multilevel Converter (MMC) has a flexible structure, extending the number of voltage levels on the AC side. This structure has many advantages such as low switching frequency, and a smaller number of transistor valves. Therefore, the MMC converter makes it easy to apply control algorithms and is widely applied to high-voltage power grids. This paper presents the control method of an MMC converter on the AC side, with 10 Sub-modules (SM) in each phase to generate 21 voltage levels. The process is performed by the current control method based on Finite Control Set - Model Predictive Control (FCS-MPC). With the advantage of fast response, optimizing the converter's working process by predicting the working states of the converter, according to the model, the FCS-MPC control algorithm has been used in this model. The results of the proposed method are proven by using Matlab/Simulink software. The results have shown the requirements of the DC/AC converter such as sinusoidal current, and voltage with 21 voltages. © 2023 IEEE.

关键词： Model predictive control

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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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Fog-Enabled IoT Framework for Heart Disease Diagnosis Systems

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Journal of Mobile Multimedia 2023年第2期19卷 389-418页

作者： Minh, Quang Tran Thai, Do Thanh Phung, Phu H. Huu, Phat Nguyen Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology (HCMUT) 268 Ly Thuong Kiet District 10 Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City (VNU-HCM) Linh Trung Ward Thu DucDistrict Ho Chi Minh City Viet Nam Department of Computer Science University of Dayton Dayton 45469 OH United States School of Electrical and Electronics Engineering Hanoi University of Science and Technology Hanoi Viet Nam

IoT technology has been recently adopted in healthcare systems to quickly detect abnormalities from patients, diagnose diseases and provide supports in time, even remotely. In the field of heart disease, timely diagnosis and prediction help to save people. This paper proposes a fog-based IoT approach to collect and analyze electrocardiogram (ECG) signals from patients to detect abnormalities or heart attacks with a short response time so that appropriate treatments can be provided. Commonly, ECG signals are transmitted to an eco-expert system deployed on the cloud to perform preliminary automatic diagnosis using a knowledge base built from medical experts. Although such an eco-expert system assists patients and supports physicians in performing treatment for their patients, there are several open technical challenges. First, noise in raw ECG signals makes the data imprecise and reduces the prediction accuracy. Second, involving data mining and machine learning on the cloud poses a significant latency since a huge amount of data needs to be transferred in the network. This paper proposes a novel framework that can provide the integrity of the ECG data by removing noise and then extract relevant knowledge for heart disease diagnosis at the network edge based on data mining techniques. Practical experiments demonstrate that the proposed framework not only guarantees the integrity of the data but also enhances the accuracy of the real-time detection compared with previous works. © 2022 River Publishers.

关键词： data mining fog computing heart disease diagnosis IoT

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Low Area PRESENT Cryptography in FPGA Using TRNG-PRNG Key Generation

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computers, Materials & Continua 2021年第8期68卷 1447-1465页

作者： T.Kowsalya R.Ganesh Babu B.D.Parameshachari Anand Nayyar Raja Majid Mehmood Department of Electronic and Communication Engineering Muthayammal Engineering CollegeRasipuramNamakkalTamilnaduIndia Department of Electronics and Communication Engineering SRM TRP Engineering CollegeTiruchirappalliTamilnaduIndia Department of Telecommunication Engineering GSSS Institute of Engineering and Technology for WomenMysuruIndia Faculty of Information Technology Graduate SchoolDuy Tan UniversityDa Nang550000Viet Nam Department of Information and Communication Technology School of Electrical and Computer EngineeringXiamen University MalaysiaSepang43900Malaysia

Lightweight Cryptography(LWC)is widely used to provide integrity,secrecy and authentication for the sensitive ***,the LWC is vulnerable to various constraints such as high-power consumption,time consumption,and hardware utilization and susceptible to the malicious *** order to overcome this,a lightweight block cipher namely PRESENT architecture is proposed to provide the security against malicious *** True Random Number Generator-Pseudo Random Number Generator(TRNG-PRNG)based key generation is proposed to generate the unpredictable keys,being highly difficult to predict by the ***,the hardware utilization of PRESENT architecture is optimized using the Dual port Read Only Memory(DROM).The proposed PRESENT-TRNGPRNG architecture supports the 64-bit input with 80-bit of key *** performance of the PRESENT-TRNG-PRNG architecture is evaluated by means of number of slice registers,flip flops,number of slices Look Up Table(LUT),number of logical elements,slices,bonded input/output block(IOB),frequency,power and *** input retrieval performances analyzed in this PRESENT-TRNG-PRNG architecture are Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Mean-Square Error(MSE).The PRESENT-TRNG-PRNG architecture is compared with three different existing PRESENT architectures such as PRESENT On-TheFly(PERSENT-OTF),PRESENT Self-Test Structure(PRESENT-STS)and PRESENT-Round Keys(PRESENT-RK).The operating frequency of the PRESENT-TRNG-PRNG is 612.208 MHz for Virtex 5,which is high as compared to the PRESENT-RK.

关键词： Dual port read only memory hardware utilization lightweight cryptography malicious attackers present block cipher pseudo random number generator true random number generator

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