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PhytoNodes for Environmental Monitoring: Stimulus Classification based on Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System 22

PhytoNodes for Environmental Monitoring: Stimulus Classifica...

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2nd ACM Conference on Information Technology for Social Good, GoodIT 2022

作者： Buss, Eduard Rabbel, Tim-Lucas Horvat, Viktor Krizmancic, Marko Bogdan, Stjepan Wahby, Mostafa Hamann, Heiko Institute of Computer Engineering University of Lübeck Lübeck Germany Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (纸本)9781450392846

Cities worldwide are growing, putting bigger populations at risk due to urban pollution. Environmental monitoring is essential and requires a major paradigm shift. We need green and inexpensive means of measuring at high sensor densities and with high user acceptance. We propose using phytosensing: using natural living plants as sensors. In plant experiments, we gather electrophysiological data with sensor nodes. We expose the plant Zamioculcas zamiifolia to five different stimuli: wind, temperature, blue light, red light, or no stimulus. Using that data, we train ten different types of artificial neural networks to classify measured time series according to the respective stimulus. We achieve good accuracy and succeed in running trained classifying artificial neural networks online on the microcontroller of our small energy-efficient sensor node. To indicate later possible use cases, we showcase the system by sending a notification to a smartphone application once our continuous signal analysis detects a given stimulus. © 2022 Owner/Author.

关键词： Sensor nodes

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Rural Ecosystem Monitoring in Food Security Analysis Based on Sustainable Agriculture: Artificial Intelligence Application

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Remote Sensing in Earth Systems Sciences 2024年第1期8卷 56-64页

作者： M, Mohideen AbdulKader Kumaran, M. Senthil Keerthika, Vijay Reddy, Polu Srinivasa Rajendra, Alla R, Subbulakshmi Department of Computing Technologies School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai India Department of Artificial Intelligence and Machine Learning (AI & amp ML) MLR Institute of Technology Dundigal Hyderabad 500043 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Telangana Hyderabad 500043 India Department of Civil Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering Karpagam Institute of Technology Coimbatore 641105 India

The ecological security patterns (ESPs) in the Qinling Mountains are facing significant challenges due to the worldwide issues of harsh climate and urbanization. Achieving sustainable development in China requires an understanding of the features of ESPs in the Qinling Mountains, an important ecological barrier. With a Yangxian focus, this work makes use of techniques like geographic information systems (GISs), remote sensing, and machine learning (ML). This research proposed a novel technique in rural region ecosystem monitoring-based food security based on sustainable agriculture using machine learning in artificial intelligence application. Here, input is collected as food security analysis for rural region ecosystem monitoring as well as processed for noise removal and normalization. Then, this data features are extracted and classified using reinforcement radial Gaussian encoder with adversarial Boltzmann temporal neural networks. Experimental analysis is carried out for parameters like training accuracy, random precision, sensitivity, AUC, and F-1 score. The proposed method obtained 98% of training accuracy, 96% of sensitivity, 95% of F-1 score, 97% of AUC, and 93% of random precision. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Artificial intelligence application Ecosystem monitoring Food security Machine learning Rural region Sustainable agriculture

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Attention-Guided Deep Learning Framework For Movement Quality Assessment 48

Attention-Guided Deep Learning Framework For Movement Qualit...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Kanade, Aditya Sharma, Mansi Muniyandi, Manivannan Indian Institute of Technology Madras Department of Electrical Engineering India Amrita Vishwa Vidyapeetham Amrita School of Computing Coimbatore Department of Computer Science and Engineering India Indian Institute of Technology Madras Department of Applied Mechanics India

ISBN: (纸本)9781728163277

Physical rehabilitation programs frequently begin with a brief stay in the hospital and continue with home-based rehabilitation. Lack of feedback on exercise correctness is a significant issue in home-based rehabilitation. Deep learning-based movement quality assessment (MQA) can assist with home-based rehabilitation by providing the necessary quantitative feedback. However, systems developed for home-based settings must be fast and offer interpretability of the generated assessment. In this paper, we explore an attention-guided transformer-based architecture for MQA. A comparative analysis against the current state-of-the-art methods is undertaken to establish the validity of the proposed model. Further, we show that the proposed model offers significant performance improvement in training and inference time, which is pivotal for any real-time system. Finally, we show that analysis of the attention maps of the proposed model can give critical insights into the decision-making process of the deep-learning model, thus improving the overall interpretability of predicted assessment scores. © 2023 IEEE.

关键词： attention mechanism deep learning Movement Quality Assessment performance score generation transformer

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Distributed Connectivity Control in Bio-Hybrid Wireless Sensor Networks 22

Distributed Connectivity Control in Bio-Hybrid Wireless Sens...

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2nd ACM Conference on Information Technology for Social Good, GoodIT 2022

作者： Križmančić, Marko Rabbel, Tim-Lucas Buss, Eduard Wahby, Mostafa Hamann, Heiko Bogdan, Stjepan University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia Institute of Computer Engineering University of Lübeck Lübeck Germany

ISBN: (纸本)9781450392846

In the WatchPlant project, we aim to develop a bio-hybrid wireless sensor network (WSN) that uses natural plants as sensors for a variety of environmental conditions. One of the major challenges in such a system is managing connectivity while balancing higher data throughput with energy conservation. In this work, connectivity is controlled through the so-called Fiedler value, the second smallest eigenvalue of the Laplacian of the communication graph. We propose a distributed connectivity control method that relies on consensus-based topology estimation, and in which links in the graph are added or removed according to the values of the Fiedler eigenvector obtained from the estimated adjacency matrix. The proposed strategy for estimating and maintaining connectivity was validated in a simulation and in a physical proof-of-concept WSN consisting of several single-board mini-computers. © 2022 Owner/Author.

关键词： Network topology

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Artificial Magnetic Conductor as Planar Antenna for 5G Evolution

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computers, Materials & Continua 2023年第1期74卷 503-522页

作者： Komsan Kanjanasit Pracha Osklang Terapass Jariyanorawiss Akkarat Boonpoonga Chuwong Phongcharoenpanich College of Computing Prince of Songkla UniversityPhuket Campus83120Thailand Faculty of Technical Education Rajamangala University of Technology IsanKhonkaen Campus40000Thailand Department of Electrical Engineering Faculty of EngineeringKasetsart University10900Thailand Research Center of Innovation Digital and Electromagnetic Technology Department of Electrical and Computer EngineeringFaculty of EngineeringKing Mongkut’s University of Technology North BangkokBangkok10800Thailand School of Engineering King Mongkut’s Institute of Technology LadkrabangBangkok10520Thailand

A 5G wireless system requests a high-performance compact antenna *** research work aims to report the characterization and verification of the artificial magnetic conductor(AMC)metamaterial for a high-gain planar *** configuration is formed by a double-side structure on an intrinsic dielectric *** 2-D periodic pattern as an impedance surface is mounted on the top surface,whereas at the bottom surface the ground plane with an inductive narrow aperture source is *** characteristic of the resonant transmission is illustrated based on the electromagnetic virtual object of the AMC resonant structure to reveal the unique property of a magnetic material *** characteristics of the AMC metamaterial and the planar antenna synthesis are investigated and verified by experiment using a low-cost FR4 dielectric *** directional antenna gain is obviously enhanced by guiding a primary field *** loss effect in a dielectric slab is essentially studied having an influence on antenna *** verification shows a peak of the antenna gain around 9.7 dB at broadside which is improved by 6.2 dB in comparison with the primary aperture antenna without the AMC *** thin antenna profile ofλ/37.5 is achieved at 10GHz for *** emission property in an AMCstructure herein contributes to the development of a lowprofile and high-gain planar antenna for a compact wireless component.

关键词： AMC metamaterial planar antenna high-gain antenna

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DETECTION OF AUTHENTICITY – OF CONTENT FOR FORENSICS USING FORENSHIELD 11

DETECTION OF AUTHENTICITY – OF CONTENT FOR FORENSICS USING ...

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11th IEEE International Conference on Cloud computing in Emerging Markets, CCEM 2023

作者： Govindarajan, Priya Surendran, Aswin P. Kumar, Sanjeev S. Tesfahun, Abebe Department of Computer Science Amrita School of Computing Amrita Vishwa Vidyapeetham Mysuru Campus India School of Electrical & Computer Engineering Scientific Director Debre Markos Institute of Technology Debre Markos University Ethiopia

ISBN: (纸本)9798350360059

The rampant spread of deepfake videos, a significant threat to media integrity, occurs widely on social media and news platforms. Detecting Deepfakes is a formidable challenge. The proposed study involves a novel algorithm to detect the deepfake. The model comprises of various pre-existing classifiers and a novel algorithm named ForenShield – which detects the deepfakes with an accuracy of 97%. The proposed novel technique works on voting strategy and it outperforms all the pre-existing classifiers, exhibiting the models excellence through the same. ©2023 IEEE.

关键词： Classifiers Deepfake ForenShield Voting Strategy

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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

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Science China(Information Sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU Shuguang CUI Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Yoga in digital Age: A Hybrid Approach using ML and computer vision 11

Yoga in digital Age: A Hybrid Approach using ML and Computer...

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11th IEEE International Conference on Cloud computing in Emerging Markets, CCEM 2023

作者： Govindarajan, Priya Achaiah, K.K. Pramod, Arjun S. Tesfahun, Abebe Department of Computer Science Amrita School Of Computing Amrita Vishwa Vidyapeetham Mysuru campus India School of Electrical & Computer Engineering Scientific Director Debre Markos Institute of Technology Debre Markos University Ethiopia

ISBN: (纸本)9798350360059

—In today’s fast-paced world, stress and pressure affect people of all ages, both physically and mentally. Yoga has emerged as a highly sought-after solution due to its numerous benefits. In the present technology-dependent era of digital age, it’s only natural that we turn to technology for assistance in their yoga journey. This is where the proposed article comes into play, serving as one’s digital Yoga Guru. In this paper, seven classifiers, out of which six are Machine Learning classifiers namely Support Vector Machine, Naïve Bayes, Random Forest, K-Nearest Neighbor, Logistic Regression, and Ensembler are employed to classify the dataset. These classifiers exhibit a promising average accuracy exceeding 90%. Additionally, the proposed method incorporates a Deep Learning model, specifically the Multi-Perceptron Neural Network, which has been trained to predict outputs with an impressive accuracy of 92%. This Paper utilizes the power of MediaPipe, a cutting-edge technology that plays a pivotal role in this innovative approach to yoga. MediaPipe is capable of identifying individuals on the screen and provides 33 key-points, including critical markers like facial key-points, and main keypoints of the body such as shoulders, elbows, knees, feet, wrists, and etc. These key points are essential for pose recognition, and assessing the correctness of yoga postures. What sets the proposed method apart is its real-time, live tracing feature, boasting an impressive accuracy rate of 99%. As one engages in yoga practice, this technology tracks their movements, and compares them to the ideal yoga postures. This immediate feedback ensures that one performs each pose correctly, promoting better results and minimizing the risk of injury. In essence, the proposed method bridges the gap between ancient yoga wisdom and modern technology, offering a digital Yoga Guru that guides one to achieve physical and mental well-being with precision and confidence. ©2023 IEEE.

关键词： Decision trees

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Field-based formalism for calculating multiqubit exchange-coupling rates for transmon qubits

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Physical Review Applied 2024年第6期22卷 064084-064084页

作者： Ghazi Khan Thomas E. Roth Elmore Family School of Electrical and Computer Engineering Purdue University Indiana 47906 USA Purdue Quantum Science and Engineering Institute Purdue University Indiana 47907 USA

Superconducting qubits are one of the most mature platforms for achieving practical quantum computers, but significant performance improvements are still needed. To improve the engineering of these systems, three-dimensional (3D) full-wave computational electromagnetics analyses are increasingly being turned to. Unfortunately, existing analysis approaches often rely on full-wave simulations using eigenmode solvers that are typically cumbersome, not robust, and computationally prohibitive if devices with more than a few qubits are to be analyzed. To improve the characterization of superconducting circuits while circumventing these drawbacks, this work begins the development of an alternative modeling framework that we illustrate in the context of evaluating the qubit-qubit exchange coupling rate between transmon qubits. This quantity is a key design parameter that determines the entanglement rate for fast multiqubit gate performance and also affects decoherence sources like qubit crosstalk. Our modeling framework uses a field-based formalism in the context of macroscopic quantum electrodynamics, which we use to show that the qubit-qubit exchange coupling rate can be related to the electromagnetic dyadic Green’s function linking the qubits together. We further show how the needed quantity involving the dyadic Green’s function can be related to the impedance response of the system that can be easily and efficiently computed with classical computational electromagnetics tools. We demonstrate the validity and efficacy of this approach by simulating four practical multiqubit superconducting circuits and evaluating their qubit-qubit exchange coupling rates. We validate our results against a 3D numerical diagonalization method and against experimental data where available. We also demonstrate the impact of the qubit-qubit exchange coupling rate on qubit crosstalk by simulating a multicoupler device and identifying operating points where the qubit crosstalk becomes zero.

关键词： Qubits

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