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Flexible temperature sensor with high sensitivity ranging from liquid nitrogen temperature to 1200℃

International Journal of Extreme Manufacturing 2023年第1期5卷 355-366页

作者： Zhaojun Liu Bian Tian Zhuangde Jiang Shuimin Li Jiaming Lei Zhongkai Zhang Jiangjiang Liu Peng Shi Qijing Lin State Key Laboratory for Mechanical Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710049People’s Republic of China Department of Electrical&Computer Engineering National University of SingaporeSingapore 117576Singapore

Flexible temperature sensors have been extensively investigated due to their prospect of wide application in various flexible electronic ***,most of the current flexible temperature sensors only work well in a narrow temperature range,with their application at high or low temperatures still being a big *** work proposes a flexible thermocouple temperature sensor based on aerogel blanket substrate,the temperature-sensitive layer of which uses the screen-printing technology to prepare indium oxide and indium tin *** has good temperature sensitivity,with the test sensitivity reaching 226.7μV℃^(−1).Most importantly,it can work in a wide temperature range,from extremely low temperatures down to liquid nitrogen temperature to high temperatures up to 1200℃,which is difficult to be achieved by other existing flexible temperature *** temperature sensor has huge application potential in biomedicine,aerospace and other fields.

关键词： flexible sensor temperature thermocouple

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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Quantum Leaps: Dynamic Event Identification using Phasor Measurement Units in Power systems 9

Quantum Leaps: Dynamic Event Identification using Phasor Mea...

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9th Annual Symposium on Combinatorial Search, SoCS 2016

作者： Jafari, Hossein Aghababa, Hossein Barati, Masoud University of Pittsburgh Swanson School of Engineering Department of Electrical and Computer Engineering and Industrial Engineering PA United States Loyola University in Maryland Department of Engineering MD United States

ISBN: (纸本)9798350312874

Power system dynamics while appearing distinct, all exhibit features that change over time. Within this vast context, deviations from typical time-based patterns can range from being academically intriguing to potentially disastrous. There could be various reasons, including cybersecurity concerns or system abnormalities stemming from both exogenous and endogenous factors. Rapid dynamic phenomena and transients in power system operating parameters, such as voltage, frequency, real power, and reactive power, can lead to system instability and blackouts. Hence, there is a growing need for innovative time series anomaly detection (TAD) algorithms. With the emergence of readily available quantum processing units (QPUs), delving into a quantum-based approach for TAD has become pertinent and is the focus of this research. Our method, termed Quantum Variational Rewinding (QVR), educates a set of tunable unitary time-reversal operators to group standard time series examples within quantum states. New time series are given an anomaly rating based on their proximity to the group's core. If this rating surpasses a specific limit, it indicates unusual activity. After an initial presentation using a straightforward illustrative example, QVR is applied to analyze the genuine challenge of pinpointing irregularities in the Phasor Measurement Unit (PMU) dataset. © 2024 IEEE.

关键词： Phasor measurement units

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Dynamic Network Optimization with Deep Learning and Deployment of Intelligent Reflecting Surfaces 19

Dynamic Network Optimization with Deep Learning and Deployme...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Jang, Nayeon Bae, Jeonghoon Tianyu, Tao Byun, Gyurin Bui, Phuoc-Nguyen Lee, Tae-Jin Choo, Hyunseung Dept. of AI Systems Engineering Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

The proliferation of 5G technologies has presented challenges such as signal attenuation and high energy consumption. This study proposes a novel approach to enhance wireless networks by integrating Intelligent Reflecting Surfaces (IRS) and Artificial Intelligence (AI) to optimize signal propagation and predict the Received Signal Strength Indicator (RSSI). A new framework combines K-means clustering for IRS deployment with a Temporal Convolutional Network-Gated Recurrent Unit (TCN-GRU) model for signal prediction. Using data from 15,000 Wi-Fi access points in Herefordshire, UK, the results demonstrate significant improvements in signal strength, with the proposed model achieving a Mean Squared Error (MSE) of 37% lower than the existing models, indicating higher prediction accuracy. In addition to enhancing signal quality, the framework achieves throughput increases of 21.26% with single-group IRS and 64.91% with multi-group IRS. These results validate the proposed the potential of the framework to improve performance, making it suitable for next-generation wireless networks. © 2025 IEEE.

关键词： 5G mobile communication systems

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FPGA-based UWB Pulse Generator for IR-UWB Chipless RFID Reader 6

FPGA-based UWB Pulse Generator for IR-UWB Chipless RFID Read...

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6th Australian Microwave Symposium, AMS 2025

作者： Lasantha, Likitha Aliasgari, Javad Khan, Shahed I. Ray, Biplob Karmakar, Nemai C. Monash University Department of Electrical and Computer Systems Engineering Clayton Australia Central Queensland University School of Engineering and Technology Melbourne Australia

ISBN: (纸本)9798350379969

Ultra-wideband (UWB) technology has gained traction for applications demanding high bandwidth, precise target localisation, and low power consumption. This paper presents an FPGA-based UWB pulse generator tailored for an Impulse-radio (IR) UWB chipless RFID reader. Using a low-cost FPGA platform, the design generates a square wave signal with a pulse repetition frequency (PRF) of 1-50 MHz. This base signal undergoes pulse sharpening to produce picosecond-scale pulses, achieving the bandwidth exceeding 500 MHz necessary for UWB communication. Such a high-bandwidth signal ensures better range resolution, meeting the requirements of applications of UWB chipless RFID readers. By implementing pulse generation and PRF control on an FPGA, our system combines low cost, flexibility, and scalability, making it suitable for diverse UWB applications. © 2025 IEEE.

关键词： Ultra-wideband (UWB)

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Slow Convergence of Interacting Kalman Filters in Word-of-Mouth Social Learning 60

Slow Convergence of Interacting Kalman Filters in Word-of-Mo...

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60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Krishnamurthy, Vikram Rojas, Cristian R. School of Electrical and Computer Engineering Cornell University United States School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden

ISBN: (纸本)9798331541033

We consider word-of-mouth social learning involving m Kalman filter agents that operate sequentially. The first Kalman filter receives the raw observations, while each subsequent Kalman filter receives a noisy measurement of the conditional mean of the previous Kalman filter. The prior is updated by the m-th Kalman filter. When m=2, and the observations are noisy measurements of a Gaussian random variable, the covariance goes to zero as k-1/3 for 7kappa;observations, instead of O(k-1) in the standard Kalman filter. In this paper we prove that for m agents, the covariance decreases to zero as k-(2m-1), i.e, the learning slows down exponentially with the number of agents. We also show that by artificially weighing the prior at each time, the learning rate can be made optimal as k-1. The implication is that in word-of-mouth social learning, artificially re-weighing the prior can yield the optimal learning rate. © 2024 IEEE.

关键词： Kalman filters

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Leveraging UAV-assisted communications to improve secrecy for URLLC in 6G systems

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Digital Communications and Networks 2023年第6期9卷 1458-1464页

作者： Hemant Kumar Narsani Ali Ranjha Kapal Dev Fida Hussain Memon Nawab Muhammad Faseeh Qureshi Faculty of Engineering and Technology University of SindhPakistan Department of Electrical Engineering Ecole de Technologie Superieure Montreal QCCanada Department of Institute of Intelligent Systems University of JohannesburgSouth Africa Department of Electrical Engineering Sukkur IBA UniversityPakistan Department of Computer Education Sungkyunkwan UniversitySeoulRepublic of Korea

Unmanned Aerial Vehicles(UAVs)will be essential to support mission-critical applications of Ultra Reliable Low Latency Communication(URLLC)in futuristic Sixth-Generation(6G)***,several security vulnerabilities and attacks have plagued previous generations of communication systems;thus,physical layer security,especially against eavesdroppers,is vital,especially for upcoming 6G *** this regard,UAVs have appeared as a winning candidate to mitigate security *** this paper,we leverage UAVs to propose two *** first method utilizes a UAV as Decode-and-Forward(DF)relay,whereas the second method utilizes a UAV as a jammer to mitigate eavesdropping attacks for URLLC between transmitter and receiver ***,we present a low-complexity algorithm that outlines the two aforementioned methods of mitigating interception,*** secrecy rate,and we compare them with the benchmark null method in which there is a direct communication link between transmitter and receiver without the UAV DF ***,simulation results show the effectiveness of such methods by improving the secrecy rate and its dependency on UAV height,blocklength,decoding error probability and transmitter-receiver separation ***,we recommend the best method to enhance the secrecy rate in the presence of an eavesdropper based on our simulations.

关键词： URLLC UAV-Assisted communications 6G systems Secrecy rate

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Area-Oriented Resubstitution For Networks of Look-Up Tables

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2025年第7期44卷 2571-2584页

作者： Costamagna, Andrea Calvino, Alessandro Tempia Mishchenko, Alan Micheli, Giovanni De Swiss Federal Institute of Technology Lausanne Integrated Systems Laboratory Lausanne1015 Switzerland University of California Department of Electrical Engineering and Computer Sciences Berkeley United States

This paper addresses the challenge of reducing the number of nodes in Look-Up Table (LUT) networks with two significant applications. First, Field-Programmable Gate Arrays (FPGAs) can be modelled as networks of LUTs, and minimizing the node count is imperative to meet resource constraints. Second, in area-oriented design space exploration for standard-cell designs, collapsing a circuit into a LUT network, restructuring it, and later remapping to the original representation helps escape local minima. Thus, the development of algorithms for optimizing and restructuring LUT networks holds considerable promise for area-oriented optimization. Substitution (also called resubstitution) is a powerful logic minimization method that can identify non-local logic dependencies and exploit them for logic minimization. State-of-the-art substitution algorithms for LUT networks rely heavily on SAT solving, limiting the number of optimization attempts and the size of the substitution sub-networks to one node mishchenko2011scalable. Conversely, our method relies on circuit simulation to increase the number of substitution candidates and enables substitutions with more than one node. The experimental results show that the proposed method identifies optimization opportunities overlooked by other methods, improving 11 out of 23 best-known results in the EPFL synthesis competition and yielding a 3.46% area reduction compared to the state-of-the-art. © 1982-2012 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems

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Digital Communications and Networks 2023年第5期9卷 1113-1122页

作者： Tejasvi Alladi Varun Kohli Vinay Chamola F.Richard Yu Department of Computer Science and Information Systems BITS-PilaniPilani Campus333031India Department of Electrical and Electronics Engineering&APPCAIR BITS-PilaniPilani Campus333031India Department of Systems and Computer Engineering Carleton UniversityOttawaONK1S 5B6Canada

With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation systems(C-ITSs)have become an important area of *** the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also *** addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be ***,there is a need to augment them with intelligent network intrusion detection *** machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent ***,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection *** learning solutions are lucrative options as they remove the necessity for feature ***,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more *** work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge *** data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this *** proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing *** running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of

关键词： Vehicular Ad-hoc Networks(VANETs) Intelligent Transportation systems(ITS) Artificial Intelligence(AI) Deep Learning Internet of Things(IoT)

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Machine Learning Techniques in Additive Manufacturing: A Review

Machine Learning Techniques in Additive Manufacturing: A Rev...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Alsalem, Ahmed Zohdy, Mohamed Oakland University Department of Industrial and Systems Engineering RochesterMI United States Oakland University Department of Electrical and Computer Engineering RochesterMI United States

ISBN: (纸本)9798350322347

Additive manufacturing (AM) is a 3D printing process widely used in industries such as automotive, healthcare, aerospace, and consumer goods. It creates different 3D objects by adding different layers over one another. AM manufacturing processes are capable enough to produce complex parts, consisting of intricate designs and geometries, characterized by well-established grain structure and properties without expensive tooling in reduced lead time and at low cost. It is a multi-physical process, including various parameters that impact the final product's quality. Moreover, Machine Learning (ML) techniques are suitable approaches aimed at understanding and predicting the complex processes of AM. In this study, an overview of AM and the challenges in the AM is presented, as well as the ML approaches to address the challenges in AM processes are also discussed. The recently introduced ML techniques in AM processes are evaluated explicitly. To be more precise, limited training data, material choice, and standardization regarding data pose major challenges that need to be addressed when applying ML techniques to AM methods. © 2023 IEEE.

关键词： 3D printing

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