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Sub-structure Characteristic Mode Analysis of Microstrip Antennas Using a Global Multi-trace Formulation

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arXiv 2022年

作者： Zhao, Ran Lu, Yuyu Cheng, Guang Shang Zhu, Wei Hu, Jun Bagci, Hakan Chengdu611731 China The Electrical and Computer Engineering Program Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia The Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei230601 China The Anhui Province Key Laboratory of Target Recognition and Feature Extraction Lu’an237010 China Thuwal23955 Saudi Arabia

A characteristic mode (CM) method that relies on a global multi-trace formulation (MTF) of surface integral equations is proposed to compute the modes and the resonance frequencies of microstrip patch antennas with finite dielectric substrates and ground planes. Compared to the coupled formulation of electric field and Poggio-Miller-Chang-Harrington-Wu-Tsai integral equations, global MTF allows for more direct implementation of a sub-structure CM method. This is achieved by representing the coupling of the electromagnetic fields on the substrate and ground plane in the form of a numerical Green function matrix, which yields a more compact generalized eigenvalue equation. The resulting sub-structure CM method avoids the cumbersome computation of the multilayered medium Green function (unlike the CM methods that rely on mixed-potential integral equations) and the volumetric discretization of the substrate (unlike the CM methods that rely on volume-surface integral equations), and numerical results show that it is a reliable and accurate approach to predicting the modal behavior of electromagnetic fields on practical microstrip antennas. Copyright © 2022, The Authors. All rights reserved.

关键词： Microstrip antennas

Pre-training universal language representation

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arXiv 2021年

作者： Li, Yian Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

Despite the well-developed cut-edge representation learning for language, most language representation models usually focus on specific levels of linguistic units. This work introduces universal language representation learning, i.e., embeddings of different levels of linguistic units or text with quite diverse lengths in a uniform vector space. We propose the training objective MiSAD that utilizes meaningful n-grams extracted from large unlabeled corpus by a simple but effective algorithm for pre-trained language models. Then we empirically verify that well designed pre-training scheme may effectively yield universal language representation, which will bring great convenience when handling multiple layers of linguistic objects in a unified way. Especially, our model achieves the highest accuracy on analogy tasks in different language levels and significantly improves the performance on downstream tasks in the GLUE benchmark and a question answering dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Linguistics

e-Inu: Simulating A Quadruped Robot With Emotional Sentience

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arXiv 2023年

作者： Chakravarty, Abhiruph Tripathy, Jatin Karthik Sibi Chakkaravarthy, S. Cherukuri, Aswani Kumar Anitha, S. Kamalov, Firuz Jonnalagadda, Annapurna Cognitive Systems University of Potsdam Germany School of Electronics Engineering VIT-AP University Andhra Pradesh India School of Information Technology Vellore Institute of Technology Vellore India Dept. of Electrical Engineering Canadian University Dubai United Arab Emirates School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Quadruped robots are currently used in industrial robotics as mechanical aid to automate several routine tasks. However, presently, the usage of such a robot in a domestic setting is still very much a part of the research. This paper discusses the understanding and virtual simulation of such a robot capable of detecting and understanding human emotions, generating its gait, and responding via sounds and expression on a screen. To this end, we use a combination of reinforcement learning and software engineering concepts to simulate a quadruped robot that can understand emotions, navigate through various terrains and detect sound sources, and respond to emotions using audio-visual feedback. This paper aims to establish the framework of simulating a quadruped robot that is emotionally intelligent and can primarily respond to audio-visual stimuli using motor or audio response. The emotion detection from the speech was not as performant as ERANNs or Zeta Policy learning, still managing an accuracy of 63.5%. The video emotion detection system produced results that are almost at par with the state of the art, with an accuracy of 99.66%. Due to its "on-policy" learning process, the PPO algorithm was extremely rapid to learn, allowing the simulated dog to demonstrate a remarkably seamless gait across the different cadences and variations. This enabled the quadruped robot to respond to generated stimuli, allowing us to conclude that it functions as predicted and satisfies the aim of this work. © 2023, CC BY-SA.

关键词： Reinforcement learning

A2G Channel Measurement and Characterization via TNN for UAV Multi-Scenario Communications

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A2G Channel Measurement and Characterization via TNN for UAV...

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Kai Mao Qiuming Zhu Fuqiao Duan Yanheng Qiu Maozhong Song Wei Fan Yang Miao The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China The Radio Systems Faculty of Electrical Engineering Computer Science and Mathematics (EEMCS) University of Twente The Netherlands The Department of Electronic Systems Aalborg University Aalborg Denmark

ISBN: (纸本)9781665435413

Unmanned aerial vehicle (UAV) is considered as an important component for future communication networks. In this paper, an air-to-ground (A2G) channel sounder is designed and implemented for UAV communication channel measurement and characterization. The channel impulse response (CIR) extraction is implemented on a field programmable gate array (FPGA) to improve extraction efficiency. Based on the channel characteristics under measured (or baseline) scenarios, a transfer learning neural network (TNN) framework is also proposed to predict the channel characteristics of other unmeasured (or transferred) scenarios. In the proposed framework, the baseline matrices of neural network parameters are obtained from the measurement data of baseline scenarios. The ray tracing (RT) simulation data is only used to obtain the extrapolation matrices where we utilize imperfect digital map and do not require a highly accurate RT simulation. Then the neural network driven by the baseline and extrapolation matrices is used to predict the channel characteristics of transferred scenarios. To verify the proposed prediction method, the channel characteristics including path loss, K-factor, and root mean square delay spread of a near-urban scenario are firstly measured. Then, the corresponding channel characteristics of a transferred dense-urban scenario are predicted by the proposed TNN method and validated by the measurement data. It is shown that the predicted channel characteristics are well consistent with the measured ones.

关键词： Extrapolation Neural networks Transfer learning Prediction methods Ray tracing Autonomous aerial vehicles Loss measurement

Multi-layered covert QR codes for increased capacity and security

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International Journal of computers and Applications 2015年第1期37卷 17-27页

作者： Meruga, Jeevan M. Fountain, Carly Kellar, Jon Crawford, Grant Baride, Aravind May, P. Stanley Cross, William Hoover, Randy Materials Engineering and Science Program South Dakota School of Mines and Technology 515 E. St. Joseph St Rapid CitySD57701 United States Department of Mathematics and Computer Science South Dakota School of Mines and Technology University of South Dakota United States Department of Electrical and Computer Engineering South Dakota School of Mines and Technology University of South Dakota United States Department of Chemistry University of South Dakota United States

Quick Response (QR) codes, used in marketing, warehouse management, product tracking, and other applications, are usually only comprised of visible black and white modules. The goal of this research is the creation of covert, color QR codes for increased data capacity, and security. This goal was met by layering QR codes, each of a unique color, printing the covert versions of the color QR codes, upconverting the covert codes into visible codes and unlayering them to take advantage of the various color channels present. It was shown that color layering of the QR codes effectively increased the data capacity by three times that of a traditional QR code, while the covert nature of the code provides added security. Finally, it was also demonstrated that a layered QR code with six base colors (using color intensity variations) could further increase QR code data capacity. © 2015 Taylor and Francis.

关键词： Color

Prediction of Post-COVID-19 Pandemic Learning Mode Preferences Using Machine Learning

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Prediction of Post-COVID-19 Pandemic Learning Mode Preferenc...

International Conference on Computing and Networking Technology (ICCNT)

作者： Agus Pratondo Astri Novianty Budi Laksono Putro Siska Komala Sari Department of Multimedia Engineering School of Applied Sciences Telkom University Bandung Indonesia Department of Computer Engineering School of Electrical Engineering Telkom University Bandung Indonesia Department of Computer Science Education Indonesia University of Education Bandung Indonesia Diploma Program of Information System School of Applied System Telkom University Bandung Indonesia

The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective educational methods as institutions prepare for the post-pandemic period, it is essential to understand students' preferences for various learning modes. With the aid of machine learning techniques, this study seeks to forecast preferences for learning styles in post-pandemic higher education. The study made use of a dataset compiled from a wide range of college students, which included information on the institution type (public or private), program type (bachelor's or diploma), academic discipline (science or humanities), gender, year of study, internet use, communication costs, comorbidity status, vaccination history, gadget and laptop ownership, and preferred learning mode (online, in-person, or both). For the aim of prediction, a Support Vector Machine (SVM) was used. Its kernel functions included linear, polynomial, radial basis function (RBF), and sigmoid. Performance criteria like accuracy, precision, recall, and F1 score were used to assess the models after the dataset was randomly partitioned into training and testing sets. The findings showed that for identifying the preferred mode of learning, SVM with the RBF kernel had the highest prediction accuracy (91.67%). Further proving the RBF kernel's efficacy, the confusion matrix analysis showed that high values in the diagonal represented accurate predictions for each of the three classes (both online and onsite). The results imply that machine learning approaches, in particular SVM with the RBF kernel, may accurately forecast students' preferences for learning modes in the post-pandemic situation. In a post-pandemic society, the findings provide insightful information that may be used to build adaptable and inclusive learning models and put them into practice.

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Lukaszewicz-style answer set programming: A preliminary report

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Lukaszewicz-style answer set programming: A preliminary repo...

2nd International Workshop on Answer Set programming Advances in Theory and Implementation, ASP 2003

作者： Delgrande, J.P. Gharib, M. Mercer, R.E. Risch, V. Schaub, T. School of Computing Science Simon Fraser University Burnaby BC V5A 1S6 Canada Institut für Informatik Universität Potsdam D-14415 Potsdam Germany Cognitive Engineering Laboratory Department of Computer Science University of Western Ontario London ON N6A 5B7 Canada InCA Team LSIS - UMR CNRS 6168 Domaine Universitaire de Saint-Jérôme avenue Escadrille Normandie Niemen F-13397 Marseille cédex 20 France

The correspondence between Reiter's default reasoning and logic programming has been exhaustively studied (e.g. [1], [2], [3]). A Contrario the relation with the many variants of the initial theory of Reiter seems far less known. This paper aims to present a preliminary investigation on applying a variant of default reasoning proposed by Witold Lukaszewicz [5] to extended logic programs. We show that the modification made to the notion of extension by Lukaszewicz has its counterpart as a relaxed notion of answer set of an extended logic program. As can be expected from this correspondence: (1) any extended logic program has always at least one relaxed answer set;(2) classical answer sets can be completely characterized among the set of relaxed answer sets of an extended logic program.

关键词： Logic programming

Clarifying Assumptions about Artificial Intelligence before Revolutionising Patent Law

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GRUR International 2022年第4期71卷 295-321页

作者： Kim, Daria Alber, Maximilian Kwok, Man Wai Mitrovic, Jelena Ramirez-Atencia, Cristian Perez, Jesus Alberto Rodriguez Zille, Heiner Max Planck Institute for Innovation and Competition Munich Germany Charite Institute of Pathology Berlin Germany Chinese University of Hong Kong Department of Physics (Formerly) Hong Kong BMBF Program for Women in AI Faculty of Computer Science and Mathematics Chair of Data Science University of Passau Germany Department of Computer Systems Universidad Politecnica de Madrid Spain School of Computing Science College of Science and Engineering University of Glasgow United Kingdom Faculty of Computer Science Otto von Guericke University Magdeburg Germany

This paper examines several widespread assumptions about artificial intelligence, particularly machine learning, that are often taken as factual premises in discussions on the future of patent law in the wake of 'artificial ingenuity'. The objective is to draw a more realistic and nuanced picture of the human-computer interaction in solving technical problems than where 'intelligent' systems autonomously yield inventions. A detailed technical perspective is presented for each assumption, followed by a discussion of pertinent uncertainties for patent law. Overall, it is argued that implications of machine learning for the patent system in its core tenets appear far less revolutionary than is often posited. © 2022 Published by OUP and CH Beck on behalf of GRUR e.V.

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Named entity recognition only from word embeddings

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arXiv 2019年

作者： Luo, Ying Zhao, Hai Zhan, Junlang Department of Computer Science and Engineering Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence Ai Institute Shanghai Jiao Tong University Shanghai China

Deep neural network models have helped named entity (NE) recognition achieve amaz-ing performance without handcrafting features. However, existing systems require large amounts of human annotated training data. Ef-forts have been made to replace human annota-tions with external knowledge (e.g., NE dictio-nary, part-of-speech tags), while it is another challenge to obtain such effective resources. In this work, we propose a fully unsupervised NE recognition model which only needs to take informative clues from pre-trained word embeddings. We first apply Gaussian Hid-den Markov Model and Deep Autoencoding Gaussian Mixture Model on word embeddings for entity span detection and type prediction, and then further design an instance selec-tor based on reinforcement learning to distin-guish positive sentences from noisy sentences and refine these coarse-grained annotations through neural networks. Extensive experiments on CoNLL benchmark datasets demon-strate that our proposed light NE recognition model achieves remarkable performance with-out using any annotated lexicon or corpus. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings