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4‑Terminal Inorganic Perovskite/Organic Tandem Solar Cells Offer 22%Efficiency

Nano-Micro Letters 2023年第2期15卷 172-181页

作者： Ling Liu Hanrui Xiao Ke Jin Zuo Xiao Xiaoyan Du Keyou Yan Feng Hao Qinye Bao Chenyi Yi Fangyang Liu Wentao Wang Chuantian Zuo Liming Ding Center for Excellence in Nanoscience(CAS) Key Laboratory of Nanosystem and Hierarchical Fabrication(CAS)National Center for Nanoscience and TechnologyBeijing 100190People’s Republic of China School of Metallurgy and Environment Central South UniversityChangsha 410083People’s Republic of China School of Physics State Key Laboratory of Crystal MaterialsShandong UniversityJinan 250100People’s Republic of China School of Environment and Energy South China University of TechnologyGuangzhou 510000People’s Republic of China School of Materials and Energy University of Electronic Science and Technology of ChinaChengdu 610054People’s Republic of China School of Physics and Electronic Science East China Normal UniversityShanghai 200241People’s Republic of China State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijing 100084People’s Republic of China School of Materials Science and Engineering Zhejiang Sci-Tech UniversityHangzhou 310018People’s Republic of China Key Laboratory of Semiconductor Materials Science Beijing Key Laboratory of Low Dimensional Semiconductor Materials and DevicesInstitute of SemiconductorsChinese Academy of SciencesBeijing 100083People’s Republic of China

After fast developing of single-junction perovskite solar cells and organic solar cells in the past 10 years,it is becoming harder and harder to improve their power conversion *** solar cells are receiving more and more attention because they have much higher theoretical efficiency than single-junction solar *** device performance has been achieved for perovskite/silicon and perovskite/perovskite tandem solar cells,including 2-terminal and 4-terminal ***,very few studies have been done about 4-terminal inorganic perovskite/organic tandem solar *** this work,semi-transparent inorganic perovskite solar cells and organic solar cells are used to fabricate 4-terminal inorganic perovskite/organic tandem solar cells,achieving a power conversion efficiency of 21.25%for the tandem cells with spin-coated perovskite *** using drop-coating instead of spin-coating to make the inorganic perovskite films,4-terminal tandem cells with an efficiency of 22.34%are *** efficiency is higher than the reported 2-terminal and 4-terminal inorganic perovskite/organic tandem solar *** addition,equivalent 2-terminal tandem solar cells were fabricated by connecting the sub-cells in *** stability of organic solar cells under continuous illumination is improved by using semi-transparent perovskite solar cells as filter.

关键词： 4-Terminal tandem solar cells Inorganic perovskite solar cells Organic solar cells Semitransparent Drop-coating

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Unsupervised Feature Learning for Image Segmentation 15

Unsupervised Feature Learning for Image Segmentation

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Umamaheswari, B. Aggarwal, Diksha Spoorthi, B. Bhoite, Sonali Prashant Hemelatha, S. Pandey, Noopor Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Quantum University Research Center Quantum University India School of Engineering and Technology Electrical and Electronics Engineering Karnataka Bangalore India IT Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India

ISBN: (纸本)9798350370249

Unsupervised function getting to know for image segmentation is a method of utilizing machine learning algorithms to identify the features of an image that can be maximum appropriate for segmentation responsibilities without requiring any previously classified statistics. By using the usage of clustering algorithms together with k-way or Self-Organizing Maps, the set of rules is capable of extracting specific capabilities from the picture and grouping patterns or objects into separate clusters. Those clusters can then be used to identify the specific additives within the picture and reap a segmentation result. This approach has been mainly useful for segmenting herbal pix, where the capabilities of the gadgets aren't without problems manually described because of the complex and varied nature of the records. Furthermore, it's been proven to achieve advanced accuracy in comparison to standard rule-based segmentation strategies. © 2024 IEEE.

关键词： Unsupervised learning

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A GNN-Based Learning Approach for Energy Optimization in Relay-Assisted IoT Networks

A GNN-Based Learning Approach for Energy Optimization in Rel...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Yang, Huapeng Ji, Han Huang, Zhangqin Wu, Xiping College of Computer Science Beijing University of Technology Beijing Engineering Research Center for IoT Software and Systems Beijing100124 China School of Electrical and Electronic Engineering University College Dublin DublinD04 V1W8 Ireland School of Information Science and Engineering Southeast University The National Mobile Communications Research Laboratory Nanjing210096 China

ISBN: (纸本)9798350368369

Minimizing energy consumption is critical for the long-range (LoRa) Internet of Things (IoT) networks, to extend the battery lifetime of end devices (EDs) while reducing the maintenance cost. To overcome the excessive computational complexity required by the traditional optimization methods, learning approaches such as deep neural network (DNN) and reinforcement learning (RL) have been researched in wireless networks, where star topologies are widely employed. In contrast, LoRa usually deploys relays to assist the connection between the EDs and the gateway (GW), leading to a much more complex network topology. Consequently, DNN and RL would become less effective in LoRa, since those methods are difficult to learn the complex network topology constructed by LoRa. In this paper, we propose a learning method based on graph neural network (GNN), which is known for its prominent ability to capture and represent intricate graph-structural dependencies, to tackle the energy optimization problem for relay-assisted LoRa. Specifically, the multi-hop LoRa network is modeled as a directed graph, with channel state information (CSI) defined as node features, while the spreading factor and transmission power are deemed labels. A hierarchical message aggregation mechanism is proposed to effectively capture the multi-hop structural dependencies, followed by the process of inductive learning. Results show that against conventional optimization algorithms, the proposed method can achieve near-optimal energy consumption with a gap of 10% to 16%, while reducing the runtime by about six orders of magnitude. Compared to DNN, the GNN-based model can provide an energy saving of up to 32%, at a similar level of inference time. © 2025 IEEE.

关键词： Reinforcement learning

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Insect Identification in Pulsed Lidar Images Using Changepoint Detection Algorithms

Insect Identification in Pulsed Lidar Images Using Changepoi...

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2023 Annual Intermountain engineering, technology and Computing, IETC 2023

作者： Sweeney, Nathaniel Xu, Caroline Shaw, Joseph A. Hocking, Toby D. Whitaker, Bradley M. Montana State University Electrical and Computer Engineering Department United States University of Michigan Electrical Engineering and Computer Science Department United States Montana State University Optical Technology Center United States School of Informatics Computing and Cyber Systems Northern Arizona University United States

ISBN: (纸本)9798350335903

Noninvasive entomological insect monitoring often utilizes a variety of tools such as LiDAR to gather information without interfering with the insects in their habitat. These collection methods often result in large amounts of data that can be te-dious and lengthy to interpret and analyze. Machine learning has been previously used in the past in order to analyze Li-DAR images to detect insects, but often suffers from pitfalls such as long training times and large computational power requirements. In an attempt to offer an alternative that takes little to no training on the data and much less computational power, this paper looks at the use of changepoint detection algorithms to analyze LiDAR images containing insects. By analyzing the rows or columns of a LiDAR image, the algorithms should be able to detect abrupt changes in the image that would represent the insects. While not as accurate, the changepoint detection algorithms give comparable results to a machine learning algorithm tested on the same dataset without the need for supervised training. © 2023 IEEE.

关键词： Anomaly detection

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Multimedia Classification via Tensor Linear Discriminant Analysis

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IEEE Transactions on Broadcasting 2024年第4期70卷 1139-1152页

作者： Chang, Shih-Yu Wu, Hsiao-Chun Yan, Kun Chih-Hao Huang, Scott Wu, Yiyan San Jose State University Department Of Applied Data Science San JoseCA95192 United States Louisiana State University School Of Electrical Engineering And Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center For Ai Applications Taoyuan32003 Taiwan Guilin University Of Electronic Technology Guangxi Key Laboratory Of Wireless Wideband Communication And Signal Processing Department Of Information And Telecommunication Guilin541004 China National Tsing Hua University Department Of Electrical Engineering Hsinchu300 Taiwan Western University Department Of Electrical And Computer Engineering LondonONN6A 3K7 Canada

Linear discriminant analysis (LDA) is a well-known feature-extraction technique for data analytic and pattern classification. As the dimensionality of multimedia data has increased in this big era, it is often to characterize data by tensors. Over the past two decades, researchers have thus explored to extend LDA to the general tensor space, especially in two common ways: LDA of tensors using tensor decomposition methods (by conversion of tensors to matrices) and LDA of tensors built upon the T-product. However, both of the aforementioned approaches have restrictions thereby. A critical problem about how to carry out LDA of arbitrary scatter tensors based on the Einstein product still remains unsolved by the existing methods. Therefore, we propose a novel tensor LDA (a.k.a. TLDA) approach, which can carry out the LDA of arbitrary-dimensional scatter-tensors without any need of tensor decomposition. Besides, for reducing the computation time, we also design a parallel paradigm to execute our proposed TLDA in this work. Numerical experiments conducted over real multimedia data demonstrate the efficacy of our proposed new TLDA in terms of classification accuracy. Moreover, the comparison of the classification accuracies, computational-complexities, and memory-complexities of our proposed novel TLDA scheme and other existing tensor-based LDA methods is made. By leveraging TLDA for high-dimensional feature extraction, segmentation, and user-item interaction data processing, future multimedia recommendation systems can facilitate more accurate, engaging, and satisfactory user experience over the Internet. © 1963-12012 IEEE.

关键词： Tensors

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Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations 39

Cross-Modal Few-Shot Learning with Second-Order Neural Ordin...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Yi Cheng, Chun-Wun He, Junyi He, Zhihai Schönlieb, Carola-Bibiane Chen, Yuyan Aviles-Rivero, Angelica I. Harbin Institute of Technology China Department of Electrical and Electronic Engineering Southern University of Science and Technology China Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Pengcheng Laboratory China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Yau Mathematical Sciences Center Tsinghua University China

ISBN: (纸本)157735897X

We introduce SONO, a novel method leveraging Second-Order Neural Ordinary Differential Equations (Second-Order NODEs) to enhance cross-modal few-shot learning. By employing a simple yet effective architecture consisting of a Second-Order NODEs model paired with a cross-modal classifier, SONO addresses the significant challenge of overfitting, which is common in few-shot scenarios due to limited training examples. Our second-order approach can approximate a broader class of functions, enhancing the model's expressive power and feature generalization capabilities. We initialize our cross-modal classifier with text embeddings derived from class-relevant prompts, streamlining training efficiency by avoiding the need for frequent text encoder processing. Additionally, we utilize text-based image augmentation, exploiting CLIP's robust image-text correlation to enrich training data significantly. Extensive experiments across multiple datasets demonstrate that SONO outperforms existing state-of-the-art methods in few-shot learning performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Differential equations

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Measurement of peripheral nerve magnetostimulation thresholds of a head solenoid coil between 200 Hz and 88.1 kHz

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IEEE Journal of Translational engineering in Health and Medicine 2025年 13卷 275-285页

作者： Barksdale, Alex C. Ferris, Natalie G. Mattingly, Eli Sliwiak, Monika Guerin, Bastien Wald, Lawrence L. Davids, Mathias Klein, Valerie MIT Department of Electrical Engineering and Computer Science CambridgeMA United States Martinos Center for Biomedical Imaging CharlestownMA United States Harvard University Harvard Graduate Program in Biophysics CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology BostonMA United States Harvard Medical School BostonMA United States

Objective: Magnetic fields switching at kilohertz frequencies induce electric fields in the body, which can cause peripheral nerve stimulation (PNS). Although magnetostimulation has been extensively studied below 10 kHz, the behavior of PNS at higher frequencies remains poorly understood. This study aims to investigate PNS thresholds at frequencies up to 88.1 kHz and to explore deviations from the widely accepted hyperbolic strength-duration curve (SDC). Methods and procedures: PNS thresholds were measured in the head of 8 human volunteers using a solenoidal coil at 16 distinct frequencies, ranging from 200 Hz to 88.1 kHz. A hyperbolic SDC was used as a reference to compare the frequency-dependent behavior of PNS thresholds. Results: Contrary to the predictions of the hyperbolic SDC, PNS thresholds did not decrease monotonically with frequency. Instead, thresholds reached a minimum near 25 kHz, after which they increased by an average of 39% from 25 kHz to 88.1 kHz across subjects. This pattern indicates a significant deviation from previously observed behavior at lower frequencies. Conclusion: Our results suggest that PNS thresholds exhibit a non-monotonic frequency dependence at higher frequencies, diverging from the traditional hyperbolic SDC. These findings offer critical data for refining neurodynamic models and provide insights for setting PNS safety limits in applications like MRI gradient coils and magnetic particle imaging (MPI). Further investigation is needed to understand the biological mechanisms driving these deviations beyond 25 kHz. Clinical impact: These findings call for further basic research into biological mechanisms underlying high frequency PNS threshold trends, and supports refinement of safety guidelines for MRI and MPI systems for clinical implementation. © 2013 IEEE.

关键词： Neuroimaging

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A linear partitioning diversity metric for evaluation of permutation-based metaheuristic algorithms

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Evolutionary Intelligence 2025年第4期18卷 1-67页

作者： Shahbazi, Majid Safi-Esfahani, Faramarz Shahbazi, Sara Mirjalili, Seyedali Faculty of Computer Engineering Najafabad Branch Islamic Azad University Najafabad Iran Big Data Research Center Najafabad Branch Islamic Azad University Najafabad Iran School of Information Systems and Modelling Faculty of Engineering and IT University of Technology Sydney SydneyNSW2007 Australia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia University Research and Innovation Center Obuda University Budapest Hungary Faculty of Electrical Engineering and Computer Science VSB - Technical University of Ostrava Ostrava Czech Republic

Metaheuristic algorithms, implicitly or explicitly, typically aim to achieve a delicate balance between exploration and exploitation when improving a population of solutions for a given optimization problem. Diversity metrics are crucial indicators for both exploration and exploitation, which serves as a valuable tool for assessing the performance of metaheuristic algorithms. Although several metrics for diversity have been proposed, the majority of them are only suitable for problems with continuous space. However, many optimization problems are defined/modeled based on discrete variables, and permutation representation is widely used for modeling real-world problems. Many existing metrics are not applicable to permutation-based problems. Applying other existing metrics to such problems frequently encounters two primary drawbacks: computational cost and effectiveness to represent the actual diversity of the population. To address these drawbacks, a new exclusive diversity metric is proposed in this work for optimization problems modeled by permutations. This method calculates diversity by linearizing and partitioning the problem space based on the population distribution in the space and counting the number of individuals in the partitions. Compared to the eleven available metrics (including both permutation-based and usable non-permutation-based metrics) in the literature, the proposed method demonstrates superior performance, exhibiting acceptable accuracy, stability, sensitivity, and robustness, particularly in the presence of outliers and requiring minimal computational cost. The accuracy of the results of the method is, on average, 35% better than baseline metrics. The computational cost of the method is also nearly 50% better than the fastest available method. These characteristics make the metric a valuable mean for analyzing existing algorithms’ behavior or designing new ones. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of

关键词： Linear programming

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An Efficient Authenticated Elliptic Curve Cryptography Scheme for Multicore Wireless Sensor Networks

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IEEE Access 2023年 11卷 1306-1323页

作者： Oladipupo, Esau Taiwo Abikoye, Oluwakemi Christiana Imoize, Agbotiname Lucky Awotunde, Joseph Bamidele Chang, Ting-Yi Lee, Cheng-Chi Do, Dinh-Thuan The Federal Polytechnic Bida Department of Computer Science Bida912211 Nigeria University of Ilorin Faculty of Information and Communication Sciences Department of Computer Science Ilorin240003 Nigeria University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Akoka Lagos100213 Nigeria Institute of Digital Communication Ruhr University Department of Electrical Engineering and Information Technology Bochum44801 Germany National Changhua University of Education Department of Industrial Education and Technology Changhua City500 Taiwan Fu Jen Catholic University Research and Development Center for Physical Education Health and Information Technology Department of Library and Information Science New Taipei City24205 Taiwan Asia University Department of Computer Science and Information Engineering Taichung City41354 Taiwan University of Mount Union School of Engineering AllianceOH44601 United States

The need to ensure the longevity of Wireless Sensor Networks (WSNs) and secure their communication has spurred various researchers to come up with various WSN models. Prime among the methods for extending the life span of WSNs is the clustering of Wireless Sensors (WS), which reduces the workload of WS and thereby reduces its power consumption. However, a drastic reduction in the power consumption of the sensors when multicore sensors are used in combination with sensors clustering has not been well explored. Therefore, this work proposes a WSN model that employs clustering of multicore WS. The existing Elliptic Curve Cryptographic (ECC) algorithm is optimized for parallel execution of the encryption/decryption processes and security against primitive attacks. The Elliptic Curve Diffie-Helman (ECDH) was used for the key exchange algorithm, and the Elliptic Curve Digital Signature Algorithm (ECDSA) was used to authenticate the communicating nodes. Security analysis of the model and comparative performance analysis with the existing ones were demonstrated. The security analysis results reveal that the proposed model meets the security requirements and resists various security attacks. Additionally, the projected model is scalable, energy-conservative, and supports data freshness. The results of comparative performance analysis show that the proposed WSN model can efficiently leverage multiprocessors and/or many cores for quicker execution and conserves power usage. © 2013 IEEE.

关键词： Electric power utilization

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Transductive - Long Short-Term Memory Network for the Fake News Detection 3

Transductive - Long Short-Term Memory Network for the Fake N...

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3rd IEEE International Conference on Distributed Computing and electrical Circuits and Electronics, ICDCECE 2024

作者： Pillai, Sanjaikanth E Vadakkethil Somanathan Avacharmal, Rajiv Reddy, R. Archana Pareek, Piyush Kumar Zanke, Pankaj School Of Electrical Engineering And Computer Science University Of North Dakota Grand Forks United States Center For Data Science And Artificial Intelligence New York Life Insurance Company Farmington HillsMI United States School Of Computer Science And Engineering Sr University Telangana Warangal India Nitte Meenakshi Institute Of Technology Department Of Aiml And Ipr Cell Bengaluru India Sapiens International Corporation United States

ISBN: (纸本)9798350318609

Recently, fake news and rumors are distributing majorly and rapidly in all over world. That circumstance causes the production and propagation of incorrect news articles. As well as mis data and fake news are maximized through user without correct verification. In this manuscript, proposed a Transductive - Long Short-Term Memory (T-LSTM) method for detecting fake news. The Buzzfeed and ISOT datasets are used in this manuscript for fake news detection and it is pre-processed by tokenization, removal of stop word and stemming. Then, the reduction of feature is performed by using Probability Principal Component Analysis (PPCA) and it is classified by using T-LSTM method. The proposed T-LSTM method attained accuracy 96.51 %, precision 94.64 %, recall 92.33 % and f1-score 93.57% on Buzzfeed dataset. The proposed T-LSTM method attained accuracy 98.43%, precision 97.76%, recall 97.02% and f1-score 97.35% on ISOT dataset. The proposed T-LSTM method performed well than other existing methods like LSTM -Levy Flight (LSTM-LF) and OptNet. © 2024 IEEE.

关键词： Long short-term memory

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