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IEEE Journal on Flexible Electronics 2024年第6期3卷 234-241页

作者： Ma, Yue Wagner, Sigurd Verma, Naveen Sturm, James C. Princeton University School of Engineering and Applied Science Department of Electrical and Computer Engineering PrincetonNJ08544 United States

Resonant operation, exploiting high quality-factor planar inductors, has recently enabled gigahertz (GHz) applications for large-area electronics (LAE), providing a new technology platform for large-scale and flexible wireless systems. This work first presents the design, analysis, and characterization methodology of flex-compatible large-area planar inductors. Specifically, three distinct radio frequency (RF) inductor characterization methods are experimentally demonstrated and compared, with the most accurate method among them (i.e., S-parameters in a two-port configuration) demonstrating a record-high quality factor of up to 65in the 2.4-GHz frequency band. Enabled by accurate characterization, key inductor design considerations regarding the resistive loss due to inductor's metal traces are then discussed. Finally, a case study of the recently demonstrated LAE resonant switch shows the potential of these high-performance inductors towards large-area and conformal wireless systems for integrated Internet of Things (IoT) and 5G/6G applications. © 2022 IEEE.

关键词： Substrates

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Informative task classification with concatenated embeddings using deep learning on crisisMMD

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International Journal of computers and Applications 2025年第2期47卷 123-140页

作者： Jain, Tarun Gopalani, Dinesh Kumar Meena, Yogesh Department of Computer Science and Engineering Manipal University Jaipur Jaipur India Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur India

Disastrous situations pose a formidable challenge, testing our resilience against nature's fury and the race against time to prevent the loss of human life. It is noted that in such situations that Microblogging platforms like Twitter(now X) have proven to be an invaluable resource for citizens to share the real-time updates in their locality. Building upon this premise, we are proposing a new hybrid robust approach that integrates transfer learning with selective features from Liwc and manually engineered features to achieve state-of-the-art results on the CrisisMMD dataset on the informative task. In our innovative approach, we harnessed the power of Deep learning combined with traditional ML practices to improve the model, we utilized OpenAI embeddings and minute details from the tweets like the sentiment, text polarity, Retweet address, links included etc. We employed a dense-dropout-based concatenate model for further processing. We established baselines of each technique individually as well as commonly used techniques like Tf-Idf, BoW, Glove embeddings. Notably, our proposed model exhibits a significant accuracy improvement, achieving an accuracy of 87.93%, surpassing the highest accuracy of traditional methods of 82.93% obtained with Tf-Idf and ensemble voting. Through this work, we conclude that there is a scope of improvement in disaster-related NLP work with the introduction of transformer-based architecture and Pretrained GPT which include a massive corpus of text improving the semantic coherency which in turn lead to higher evaluation metrics. This work in future can be extended to incorporate different embeddings as well as using techniques like Multi-headed attention as well as RNNs like GST, GRU, etc. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Tweets

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Cocoa-Net: Performance Analysis on Classification of Cocoa Beans Using Structural Image Feature

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第12期48卷 41-54页

作者： Pal, Chandrajit Das, Samikshan Akuli, Amitava Adhikari, Sudip Kumar Dey, Aniruddha Department of Electrical Engineering IIT Hyderabad India Capgemini India Centre for Development of Advanced Computing Kolkata India Department of Computer Science & Engineering CGEC Cooch Behar India Department of Computer Science & Engineering MSIT Kolkata India

The process of cocoa hybridization produces new types that have unique chemical properties impacting the manufacturing of chocolate yet are resistant to a number of plant illnesses. Image analysis is a valuable tool for visually differentiating cocoa beans where deep neural networks (DNN) play a pivotal role in implementing them. In this manuscript, we compare machine learning and deep learning models because it takes into consideration multiple images covering a wide range of agricultural products. Specifically, we extract features from images using a series of image processing techniques, following which we use both traditional machine learning methods (KNN, Decision tree, SVM, and Random Forest) and Convolutional Neural Networks (proposed Cocoa-Net and RESNET 50) to classify the cocoa beans into four categories: large, medium, small, and rejected. Since each methodology offers strong classification performance and has potential for use in the classification of food, they were all chosen. To test these methods, a dataset including 200 samples of fragmented images was utilized. Studies that compare various similar approaches are also carried out. Two optimization techniques: Univariate Selection and Feature Importance have been leveraged to optimize the retrieved features before the learning models are trained. The Adam optimizer is used to optimize the proposed Cocoa-Net model. K-fold cross validation is utilized to assess trained models, and mean cross validation scores are then computed for performance analysis. The empirical result shows that, the proposed Cocoa-Net model predicts with the highest classification accuracy score of 0.85. © 2024 Slovene Society Informatika. All rights reserved.

关键词： cocoa beans Cocoa-Net decision tree KNN random Forest SVM

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Fault-tolerant capability analysis of six-phase induction motor with distributed, concentrated, and pseudo-concentrated windings

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Scientia Iranica 2024年第10期31卷 775-789页

作者： Alemi-Rostami, Mehdi Rezazadeh, Ghasem Tahami, Farzad Akbari-Resketi, Hamid Reza Ministry of Science Research and Technology Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Electrical and Computer Engineering Babol Noshirvani University of Technology Babol Iran

Six-phase motors are becoming more popular because of their advantages such as lower torque ripple, better power distribution per phase, higher efficiency, and fault-tolerant capability compared to the three-phase ones. This paper presents the fault-tolerant capability analysis of a symmetrical six-phase induction motor equipped with distributed, conventional concentrated, and pseudo-concentrated windings under open-circuit fault scenarios. For further investigation, different load types such as constant-speed, constant-torque, and constant-power are applied to the motor. Two concepts of magnetic and physical phase separations are introduced as factors affecting the motor reliability. Analytically, these factors give an insight into how the pseudo-concentrated winding could be a fault-tolerant alternative. Moreover, five parameters such as the change of output power, power loss, power factor, efficiency, and expected load loss are considered as the fault-tolerant capability parameters to evaluate the windings reliability. The aforementioned parameters are reported using the finite element analysis for different fault scenarios and different load types. Although the baseline motor dimensions are not optimized for applying the pseudo-concentrated winding, the pseudo-concentrated shows a promising performance with high fault-tolerant capability. © 2024 Sharif University of Technology. All rights reserved.

关键词： Reliability analysis

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Meta-Learning Online Control for Linear Dynamical Systems

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IEEE Transactions on Automatic Control 2025年第6期70卷 4163-4169页

作者： Muthirayan, Deepan Kalathil, Dileep Khargonekar, Pramod P. University of California Irvine Department of Electrical Engineering and Computer Sciences IrvineCA United States Texas A&M University Department of Electrical and Computer Engineering United States

In this paper, we consider the problem of finding a meta-learning online control algorithm that can learn across the tasks when faced with a sequence of N (similar) control tasks. Each task involves controlling a linear dynamical system for a finite horizon of T time steps. The cost function and system noise at each time step are adversarial and unknown to the controller before taking the control action. Meta-learning is a broad approach where the goal is to prescribe an online policy for any new unseen task exploiting the information from other tasks and the similarity between the tasks. We propose a meta-learning online control algorithm for the control setting and characterize its performance by meta-regret, the average cumulative regret across the tasks. We show that when the number of tasks are sufficiently large, our proposed approach achieves a meta-regret that is smaller by a factor D/D∗ compared to an independent-learning online control algorithm which does not perform learning across the tasks, where D is a problem constant and D∗ is a scalar that decreases with increase in the similarity between tasks. Thus, when the sequence of tasks are similar the regret of the proposed meta-learning online control is significantly lower than that of the naive approaches without meta-learning. We also present experiment results to demonstrate the superior performance achieved by our meta-learning algorithm. © 1963-2012 IEEE.

关键词： Cost functions

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Statistical Process Monitoring Based on Ensemble Structure Analysis

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1889-1891页

作者： Likang Shi Chudong Tong Ting Lan Xuhua Shi Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

Dear Editor,This letter presents a novel process monitoring model based on ensemble structure analysis(ESA).The ESA model takes advantage of principal component analysis(PCA),locality preserving projections(LPP),and multi-manifold projections(MMP)models,and then combines the multiple solutions within an ensemble result through Bayesian *** the developed ESA model,different structure features of the given dataset are taken into account simultaneously,the suitability and reliability of the ESA-based monitoring model are then illustrated through ***:The requirement for ensuring safe operation and improving process efficiency has led to increased research activity in the field of process monitoring.

关键词： ensemble preserving letter

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Analysis of the effect of different scattering mechanisms on the excess noise behavior of Sb-based avalanche photodiodes

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Optics Express 2025年第4期33卷 7337-7344页

作者： Karimi, Hannaneh Herrera, Daniel J. Dadey, Adam A. Wei, Dongxia Mcarthur, Andrew Bank, Seth R. Campbell, Joe. C. Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22903 United States Department of Electrical and Computer Engineering University of Texas AustinTX78758 United States

In optical applications where avalanche photodiodes (APDs) provide the benefit of high sensitivity, Sb-based materials systems such as AlInAsSb and AlGaAsSb have shown extremely low excess noise factors. The Monte Carlo (MC) model described in this work reveals the effect of high alloy scattering on the excess noise factor in these material systems. A comparison between the excess noise factor of different combinations of scattering rates is investigated. Furthermore, the ionization path length probability distribution is presented for AlInAsSb APDs with different scattering mechanisms. Good agreement is achieved between simulation results and experimental measurements. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： III-V semiconductors

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Electromagnetic Interference on Unmanned Aerial Vehicles (UAVs): A Case Study of High Power Transmission Line Impacts

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7501-7513页

作者： Salehi Rad, Shervin Zheng, Zilong Kheirollahi, Reza Mostafa, Amr Zhao, Shuyan Wang, Yao Chevinly, Javad Nadi, Elias Bensala, Tarik Zhang, Hua Friedman, Gary Lu, Fei Drexel University Department of Electrical and Computer Engineering PhiladelphiaPA19104 United States Lehigh University Department of Electrical and Computer Engineering BethlehemPA18015 United States

Nowadays, unmanned aerial vehicles (UAVs) play a significant role in transmission line inspection. This article focuses on the inertial measurement unit (IMU) as a critical component to study the impacts of electromagnetic interference. A case study is conducted by selecting 500-kV/25-kA 60-Hz transmission lines as sources of strong electromagnetic fields and investigates their influences on an IMU unit and operation of an UAV. An experimental testing methodology is proposed to explore the impacts of magnetic and electric fields separately. A high-power inverter-based testing platform is implemented to excite a 2.2-kA planar coil and a 7.5-kA solenoid coil to generate magnetic fields and emulate high current impacts from transmission lines. Meanwhile, a pair of metal plates are adopted to generate a 2-kV/m electric field and study the high-voltage impacts. Multiple long-time continuous testings of the DJI Marvic 2 drone have been conducted in the generated magnetic and electric fields. It validates that the onboard magnetometer is sensitive to magnetic fields, while other sensors remain immune to both magnetic and electric fields, at least up to 3100 μT and 2 kV/m, respectively. © 2015 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Developing a Comprehensive NLP Framework for Indigenous Dialect Documentation and Revitalization

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International Journal of Advanced computer science and Applications 2025年第4期16卷 815-823页

作者： Fakhreldin, Mohammed Department of Computer Science-College of Engineering and Computer Science Jazan University Jazan45142 Saudi Arabia

The disappearance of Indigenous languages results in a decrease in cultural diversity, hence making the preservation of these languages extremely important. Conventional methods of documentation are lengthy, and the present AI solutions somehow do not deliver due to data scarcity, dialectal variation, and poor adaptability to low-resource languages. A novel NLP framework is being proposed to solve the existing problems. This framework intermixes Meta-Learning and Contrastive Learning to counter these problems. Thus, adaptation to low-resourced languages becomes rapid via meta-learning (MAML), while dialect differentiation is enhanced through contrastive learning. The model training is carried out on Tatoeba (text) and Mozilla Common Voice (speech) datasets to ensure robust performance in both text and phonetic tasks. The results indicate that there is a reduction of 15% in Word Error Rate (WER), an 18% improvement in BLEU score corresponding to translation, and a 12% improvement in F1-score related to dialect classification. The testing was also done with native speakers to assess its practical viability. It is a real-time translation, transcription, and language documentation system deployed via a cloud-based platform, thereby reaching out to Indigenous communities globally. This dual-learning framework represents a scalable, adaptive, and cost-efficient solution for the revitalization of languages. The models proposed have been a game changer for language preservation, have set new standards for low-resource NLP, and have made some tangible contributions towards the digital sustainability of endangered dialects. © (2025), (science and Information Organization). All Rights Reserved.

关键词： Natural language processing systems

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Cavity-agnostic acoustofluidic manipulations enabled by guided flexural waves on a membrane acoustic waveguide actuator

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Microsystems & Nanoengineering 2024年第2期10卷 153-171页

作者： Philippe Vachon Srinivas Merugu Jaibir Sharma Amit Lal Eldwin J.Ng Yul Koh Joshua E.-Y.Lee Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore Institute of Microelectronics Agency for ScienceTechnology and Research(A*STAR)SingaporeSingapore SonicMEMS Laboratory School of Electrical and Computer EngineeringCornell UniversityIthacaNYUSA School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves(GFWs)generated by a membrane acoustic waveguide actuator(MAWA).By harnessing the potential of GFWs,cavity-agnostic advanced particle manipulation functions are achieved,unlocking new avenues for microfluidic systems and lab-on-a-chip *** localized acoustofluidic effects of GFWs arising from the evanescent nature of the acoustic fields they induce inside a liquid medium are numerically investigated to highlight their unique and promising *** traditional acoustofluidic technologies,the GFWs propagating on the MAWA’s membrane waveguide allow for cavity-agnostic particle manipulation,irrespective of the resonant properties of the fluidic ***,the acoustofluidic functions enabled by the device depend on the flexural mode populating the active region of the membrane *** demonstrations using two types of particles include in-sessile-droplet particle transport,mixing,and spatial separation based on particle diameter,along with streaming-induced counter-flow virtual channel generation in microfluidic PDMS *** experiments emphasize the versatility and potential applications of the MAWA as a microfluidic platform targeted at lab-on-a-chip development and showcase the MAWA’s compatibility with existing microfluidic systems.

关键词： waveguide fluid cavity

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