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Performance Analysis of ZF and RZF in Low-Resolution ADC/DAC Massive MIMO Systems

China Communications 2024年第8期21卷 115-126页

作者： Talha Younas Shen Jin Muluneh Mekonnen Gao Mingliang Saqib Saleem Sohaib Tahir Mahrukh Liaqat School of Electrical and Electronic Engineering Shandong University of TechnologyZibo 255000China Department of Electrical and Computer Engineering COMSATS University IslamabadSahiwal Campus Department of Electrical and Computer Engineering College of EngineeringDhofar UniversitySalalah 211Oman Department of Electrical and Computer Engineering Addis Ababa Science and Technology UniversityAddis AbabaEthiopia Department of Electrical Engineering College of Electrical and Mechanical EngineeringNUSTIslamabadPakistan

Large number of antennas and higher bandwidth usage in massive multiple-input-multipleoutput(MIMO)systems create immense burden on receiver in terms of higher power *** power consumption at the receiver radio frequency(RF)circuits can be significantly reduced by the application of analog-to-digital converter(ADC)of low *** this paper we investigate bandwidth efficiency(BE)of massive MIMO with perfect channel state information(CSI)by applying low resolution ADCs with Rician *** start our analysis by deriving the additive quantization noise model,which helps to understand the effects of ADC resolution on BE by keeping the power constraint at the receiver in *** also investigate deeply the effects of using higher bit rates and the number of BS antennas on bandwidth efficiency(BE)of the *** emphasize that good bandwidth efficiency can be achieved by even using low resolution ADC by using regularized zero-forcing(RZF)combining *** also provide a generic analysis of energy efficiency(EE)with different options of bits by calculating the energy efficiencies(EE)using the achievable *** emphasize that satisfactory BE can be achieved by even using low-resolution ADC/DAC in massive MIMO.

关键词： low-bit analog-digital converter massive(multiple-input-multiple-output)MIMO minimum mean square error(MMSE) regularized zero forcing zero forcing

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Recent progress of artificial intelligence for liquid-vapor phase change heat transfer

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npj Computational Materials 2024年第1期10卷 2552-2565页

作者： Youngjoon Suh Aparna Chandramowlishwaran Yoonjin Won Department of Mechanical and Aerospace Engineering University of CaliforniaIrvineCAUSA Department of Electrical Engineering and Computer Science University of CaliforniaIrvineCAUSA

Artificial intelligence(AI)is shifting the paradigm of two-phase heat transfer *** innovations in AI and machine learning uniquely offer the potential for collecting new types of physically meaningful features that have not been addressed in the past,for making their insights available to other domains,and for solving for physical quantities based on first principles for phasechange thermofluidic *** review outlines core ideas of current AI technologies connected to thermal energy science to illustrate how they can be used to push the limit of our knowledge boundaries about boiling and condensation *** technologies for meta-analysis,data extraction,and data stream analysis are described with their potential challenges,opportunities,and alternative ***,we offer outlooks and perspectives regarding physics-centered machine learning,sustainable cyberinfrastructures,and multidisciplinary efforts that will help foster the growing trend of AI for phase-change heat and mass transfer.

关键词： transfer meaningful shifting

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Breakdown of effective medium theory:A study of photonic spin Hall effect in deep-subwavelength all-dielectric multilayers

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Science China(Physics,Mechanics & Astronomy) 2024年第4期67卷 173-174页

作者： Yongmin Liu Department of Mechanical and Industrial Engineering Northeastern UniversityBoston 02115USA Department of Electrical and Computer Engineering Northeastern UniversityBoston 02115USA

Effective medium theory(EMT)has been widely applied in material science,electromagnetics and photonics to determine the effective material properties for inhomogeneous composites comprising subwavelength *** versatility of this foundational approach has been established through its application in classical Maxwell-Garnet models,which encompass relatively simple structures。

关键词： composites theory dielectric

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Broadband angular spectrum analog processors based on all-dielectric metasurfaces

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Science China(Physics,Mechanics & Astronomy) 2024年第7期67卷 188-190页

作者： Lin Deng Yongmin Liu Department of Electrical and Computer Engineering Northeastern UniversityBoston 02115USA Department of Mechanical and Industrial Engineering Northeastern UniversityBoston 02115USA

Digital signal processors are extensively used to execute mathematical operations and advanced computational tasks on digital ***,they suffer from several inherent limitations,including low speed,high energy consumption,and large memory requirements,because of the hardware bottleneck and the imperative conversion between digital and analogue signals.

关键词： dielectric spectrum hardware

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Safe prescribed time controller for wheeled mobile robots by using control barrier functions as a safety filter

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ISA Transactions 2025年 162卷 119-133页

作者： Nasab, Ali Alavi Badiei, Roozbeh Asemani, Mohammad Hassan School of Electrical and Computer Engineering Shiraz University Shiraz Iran School of Mechanical Engineering Iran University of Science and Technology Tehran Iran

In this paper, we design a prescribed time controller to achieve a desired trajectory within a user-defined time, ensuring that the error dynamics converge to the equilibrium point at this time. Subsequently, a proportional controller is utilized to maintain the system's errors at the equilibrium point beyond the prescribed time. Additionally, we introduce a control barrier function (CBF) as a safety filter to guarantee that the system remains within a safe zone, avoiding collisions with predefined unsafe areas along the trajectory. To validate the proposed control methodology, we conducted a series of simulations across various scenarios. The results demonstrate the scheme's capability to reach the equilibrium point regardless of initial conditions and user-defined prescribed time, while consistently maintaining safety within the defined set space. © 2025 International Society of Automation

关键词： Proportional control systems

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An Overview of LoRa Localization Technologies

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computers, Materials & Continua 2025年第2期82卷 1645-1680页

作者： Huajiang Ruan Panjun Sun Yuanyuan Dong Hamid Tahaei Zhaoxi Fang Institute of Artificial Intelligence Shaoxing UniversityShaoxing312000China Department of Computer Science and Engineering School of Mechanical and Electrical EngineeringShaoxing UniversityShaoxing312000China

Traditional Global Positioning System(GPS)technology,with its high power consumption and limited perfor-mance in obstructed environments,is unsuitable for many Internet of Things(IoT)*** paper explores LoRa as an alternative localization technology,leveraging its low power consumption,robust indoor penetration,and extensive coverage area,which render it highly suitable for diverse IoT *** comprehensively review several LoRa-based localization techniques,including time of arrival(ToA),time difference of arrival(TDoA),round trip time(RTT),received signal strength indicator(RSSI),and fingerprinting *** this review,we evaluate the strengths and limitations of each technique and investigate hybrid models to potentially improve positioning *** studies in smart cities,agriculture,and logistics exemplify the versatility of LoRa for indoor and outdoor *** findings demonstrate that LoRa technology not only overcomes the limitations of GPS regarding power consumption and coverage but also enhances the scalability and efficiency of IoT deployments in complex environments.

关键词： LoRa localization IoT TDoA RSSI application hybrid models

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Deep Weighted Fusion Learning(DWFL)-based multi-sensor fusion model for accurate building occupancy detection

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Energy and AI 2024年第3期17卷 542-554页

作者： MdRumman Rafi Fei Hu Shuhui Li Aijun Song Xingli Zhang Zheng O’Neill Department of Electrical and Computer Engineering The University of AlabamaTuscaloosaALUSA Mechanical Engineering Texas A&M UniversityTXUSA

With the advancement of artificial intelligence,the dominance of deep learning(DL)models over ordinary machine learning(ML)algorithms has become a reality in recent years due to its capability of handling complex pattern recognition without manual feature *** the growing demands for power savings,building energy loss reduction could benefit from DL *** buildings/rooms with the varying number of occupants,heating,ventilation,and air conditioning(HVAC)systems are often found in operations without much *** reduce the building’s energy loss,accurate occupancy detection/prediction(ODP)results could be used to control the proper operations of ***,ODP is a challenging issue due to multiple reasons,such as improper selection/deployment of sensors,inefficient learning algorithms for pattern recognition,varying room conditions,*** overcome the above challenges,we propose a DL-based framework,i.e.,Deep Weighted Fusion Learning(DWFL),to detect and predict occupancy counts with optimal multi-sensor fusion *** fuses the extracted features from multiple types of sensors with the priority/weight assignment to each *** weight assignment considers different room conditions and the pros/cons of each type of *** evaluate DWFL model in terms of occupancy prediction accuracy,we have set up an experimental testbed with low-cost cameras,carbon dioxide(CO_(2)),and passive infrared(PIR)*** the recently proposed occupancy detection models,DeepFusion utilized deep learning model on heterogeneous sensor data and achieved 88%accuracy in occupancy count estimation(Xue et al.,2019).Another deep learning-based model MI-PIR achieved 91%accuracy on raw analog data from PIR sensors(Andrews et al.,2020).Our research outcome is 94%.Therefore,the experiment results show that our DWFL scheme outperforms the state-of-the-art ODP methods by 3%.

关键词： Building energy saving Convolutional neural network(CNN) Occupancy detection Deep learning Multi-sensor fusion Wavelet transform

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Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection

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computer Modeling in engineering & Sciences 2025年第2期142卷 1667-1695页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saifur Rahman Saad Arif Salim Nasar Faraj Mursal Muhammad Irfan Haris Aziz Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl Ahsa31982Saudi Arabia Department of Mechanical Industrial and Energy System EngineeringUniversity of SargodhaSargodha40100Pakistan

Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection *** common challenges in ransomware detection,particularly dataset class imbalance,the synthetic minority oversampling technique(SMOTE)is employed to generate synthetic samples for minority class,thereby improving detection *** integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware *** on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional *** approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity *** results demonstrate the model’s potential as an effective tool for proactive ransomware detection,especially in environments where evolving threats require adaptable and high-accuracy solutions.

关键词： Ransomware attacks cybersecurity vision transformer convolutional neural network feature fusion encryption threat detection

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Koopman-based Deep Learning for Nonlinear System Estimation 63

Koopman-based Deep Learning for Nonlinear System Estimation

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Sun, Zexin Chen, Mingyu Baillieul, John Boston University Division of Systems Engineering United States Boston University Department of Electrical and Computer Engineering United States Boston University Division of Systems Engineering Departments of Mechanical Engineering Electrical and Computer Engineering BostonMA02215 United States

ISBN: (纸本)9798350316339

Nonlinear differential equations are encountered as models of fluid flow, spiking neurons, and many other systems of interest in the real world. Common features of these systems are that their behaviors are difficult to describe exactly and invariably unmodeled dynamics present challenges in making precise predictions. In this paper, we present a novel data-driven linear estimator based on Koopman operator theory to extract meaningful finite-dimensional representations of complex non-linear systems. The Koopman model is used together with deep reinforcement networks that learn the optimal stepwise actions to predict future states of nonlinear systems. Our estimator is also adaptive to a diffeomorphic transformation of the estimated nonlinear system, which enables it to compute optimal state estimates without re-learning. © 2024 IEEE.

关键词： Deep reinforcement learning

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Rotor Eccentricity Impact on Electromagnetic Behavior in Combined Winding Bearingless Motors Towards Displacement Self-Sensing

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IEEE Transactions on Energy Conversion 2025年第1期40卷 323-336页

作者： Petersen, Nathan P. Severson, Eric L. Department of Electrical Computer Engineering MadisonWI53706 United States University of Minnesota Department of Mechanical Engineering MinneapolisMN55455 United States

Levitation control of bearingless motors requires rotor displacement sensors. Recent research has explored self-sensing (sensorless) control which enables removing these sensors, but only for separated windings. Combined windings - where each coil can create torque and force - have been proven to be a critical component for realizing high performance bearingless motors for industrial applications. These applications are often cost-sensitive and require bearingless solutions with minimal overhead compared to traditional motors. Self-sensing control with combined windings removes barriers for wider adoption of bearingless technology - this paper forms the analytic basis for showing this is possible. The primary contribution is a unified modeling framework which encompasses several popular combined winding types found in the literature. For each winding type, winding functions are defined - each type with differing winding coil circuit arrangements and drive interfaces. The terminal inductance matrix and back-EMF is computed to include the effects of a non-centered rotor, and then transformed into decoupled torque and suspension force components. Through the presented transformations, all combined winding types support radial self-sensing, albeit with additional control complexity. Finite element simulations and hardware experiments validate the modeling approach. Future self-sensing research can build upon these models to design control algorithms and machines which work for any combined winding type. © 1986-2012 IEEE.

关键词： Winding

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