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Distributed Weighted Least-squares Estimation for Networked Systems with Edge Measurements

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arXiv 2020年

作者： Yang, Qiqi Zhang, Zhaorong Fu, Minyue School of Control Science and Engineering Shandong University Jinan250061 China School of Electrical Engineering and Computing University of Newcastle NSW2308 Australia School of Automation Guangdong University of Technology Guangzhou510006 China

This paper studies the problem of distributed weighted least-squares (WLS) estimation for an interconnected linear measurement network with additive noise. Two types of measurements are considered: self measurements for individual nodes, and edge measurements for the connecting nodes. Each node in the network carries out distributed estimation by using its own measurement and information transmitted from its neighbours. We study two distributed estimation algorithms: a recently proposed distributed WLS algorithm and the so-called Gaussian Belief Propagation (BP) algorithm. We first establish the equivalence of the two algorithms. We then prove a key result which shows that the information matrix is always generalised diagonally dominant, under some very mild condition. Using these two results and some known convergence properties of the Gaussian BP algorithm, we show that the aforementioned distributed WLS algorithm gives the globally optimal WLS estimate asymptotically. A bound on its convergence rate is also presented. Copyright © 2020, The Authors. All rights reserved.

关键词： Belief propagation

A critical comparizon on attitude estimation: From Gaussian approximate filters to coordinate-free dual optimal control

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arXiv 2021年

作者： Koumpis, Nikolas P. Panagiotou, Panagiotis A. Arvanitakis, Ioannis Faculty of Mechanical Maritime and Materials Engineering Delft Center for Systems and Control TU Delft Mekelweg 2 Delft2628 CD Netherlands Faculty of Electronic and Electrical Engineering University of Sheffield 3 Solly Street SheffieldS1 4DE United Kingdom Faculty of Engineering Environment and Computing Coventry University 3 Gulson Rd CoventryCV1 2JH United Kingdom

This paper conveys attitude and rate estimation without rate sensors by performing a critical comparison, validated by extensive simulations. The two dominant approaches to facilitate attitude estimation are based on stochastic and set-membership reasoning. The first one mostly utilizes the commonly known Gaussian-approximate filters, namely the EKF and UKF. Although more conservative, the latter seems to be more promising as it considers the inherent geometric characteristics of the underline compact state space and accounts -from first principles- for large model errors. We address the set-theoretic approach from a control point of view, and we show that it can overcome reported deficiencies of the Bayesian architectures related to this problem, leading to coordinate-free optimal filters. Lastly, as an example, we derive a modified predictive filter on the tangent bundle of the special orthogonal group TSO(3). Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

Hybrid quantum-classical convolutional neural networks

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Science China(Physics,Mechanics & Astronomy) 2021年第9期64卷 1-8页

作者： Junhua Liu Kwan Hui Lim Kristin LWood Wei Huang Chu Guo He-Liang Huang Information Systems Technology and Design Singapore University of Technology and DesignSingapore 487372Singapore Forth AI Singapore 487372Singapore College of Engineering Design and ComputingUniversity of Colorado DenverColorado 80208USA Guangxi Key Laboratory of Optoelectronic Information Processing Guilin University of Electronic TechnologyGuilin 541004China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and ApplicationsHunan Normal UniversityChangsha 410081China Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of ChinaHefei 230026China Shanghai Branch CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information and Quantum PhysicsUniversity of Science and Technology of ChinaHefei 201315China Henan Key Laboratory of Quantum Information and Cryptography Zhengzhou 450000China

Deep learning has been shown to be able to recognize data patterns better than humans in specific circumstances or *** parallel,quantum computing has demonstrated to be able to output complex wave functions with a few number of gate operations,which could generate distributions that are hard for a classical computer to *** we propose a hybrid quantum-classical convolutional neural network(QCCNN),inspired by convolutional neural networks(CNNs)but adapted to quantum computing to enhance the feature mapping *** is friendly to currently noisy intermediate-scale quantum computers,in terms of both number of qubits as well as circuit’s depths,while retaining important features of classical CNN,such as nonlinearity and *** also present a framework to automatically compute the gradients of hybrid quantum-classical loss functions which could be directly applied to other hybrid quantum-classical *** demonstrate the potential of this architecture by applying it to a Tetris dataset,and show that QCCNN can accomplish classification tasks with learning accuracy surpassing that of classical CNN with the same structure.

关键词： quantum computing quantum machine learning hybrid quantum-classical algorithm convolutional neural network

Hypergraph optimization for multi-structural geometric model fitting

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arXiv 2020年

作者： Lin, Shuyuan Xiao, Guobao Yan, Yan Suter, David Wang, Hanzi Fujian Key Laboratory of Sensing and Computing for Smart City School of Information Science and Engineering Xiamen University China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University China School of Science Edith Cowan University Australia

Recently, some hypergraph-based methods have been proposed to deal with the problem of model fitting in computer vision, mainly due to the superior capability of hypergraph to represent the complex relationship between data points. However, a hypergraph becomes extremely complicated when the input data include a large number of data points (usually contaminated with noises and outliers), which will significantly increase the computational burden. In order to overcome the above problem, we propose a novel hypergraph optimization based model fitting (HOMF) method to construct a simple but effective hypergraph. Specifically, HOMF includes two main parts: an adaptive inlier estimation algorithm for vertex optimization and an iterative hyperedge optimization algorithm for hyperedge optimization. The proposed method is highly efficient, and it can obtain accurate model fitting results within a few iterations. Moreover, HOMF can then directly apply spectral clustering, to achieve good fitting performance. Extensive experimental results show that HOMF outperforms several state-of-the-art model fitting methods on both synthetic data and real images, especially in sampling efficiency and in handling data with severe outliers. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013–2023)

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Computers in Biology and Medicine 2024年 172卷 108207-108207页

作者： Gudigar, Anjan Kadri, Nahrizul Adib Raghavendra, U. Samanth, Jyothi Maithri, M. Inamdar, Mahesh Anil Prabhu, Mukund A. Hegde, Ajay Salvi, Massimo Yeong, Chai Hong Barua, Prabal Datta Molinari, Filippo Acharya, U. Rajendra Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Biomedical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Cardiovascular Technology Manipal College of Health Professions Manipal Academy of Higher Education Manipal576104 India Department of Mechatronics Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Cardiology Kasturba Medical College Manipal Academy of Higher Education Manipal576104 India Manipal Hospitals Karnataka Bengaluru560102 India Biolab PolitoBIOMedLab Department of Electronics and Telecommunications Politecnicodi Torino Turin Italy School of Medicine Faculty of Health and Medical Sciences Taylor's University Subang Jaya47500 Malaysia Cogninet Brain Team Cogninet Australia SydneyNSW2010 Australia Faculty of Business Education Law & Arts University of Southern Queensland ToowoombaQLD4350 Australia Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia School of Mathematics Physics and Computing University of Southern Queensland SpringfieldQLD4300 Australia Centre for Health Research University of Southern Queensland ToowoombaQLD4350 Australia

Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources. © 2024 The Authors

关键词： Medical imaging

Home monitoring of motor fluctuations in Parkinson’s disease patients

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Journal of Reliable Intelligent Environments 2019年第3期5卷 145-162页

作者： Borzì, Luigi Varrecchia, Marilena Olmo, Gabriella Artusi, Carlo Alberto Fabbri, Margherita Rizzone, Mario Giorgio Romagnolo, Alberto Zibetti, Maurizio Lopiano, Leonardo Department of Control and Computing Engineering Politecnico di Torino Turin Italy Department of Neuroscience Rita Levi Montalcini University Hospital Città della Salute e della Scienza Regional Reference Center for Parkinson’s Disease and Movement Disorders Turin Italy

In Parkinson’s disease, motor fluctuations (worsening of tremor, bradykinesia, freezing of gait, postural instability) affect up to 70% of patients within 9 years of L-dopa therapy. Nevertheless, the assessment of motor fluctuations is difficult in a medical office, and is commonly based on poorly reliable self-reports. Hence, the use of wearable sensors is desirable. In this preliminary trial, we have investigated bradykinesia and freezing of gait—FOG—symptoms by means of inertial measurement units. To this purpose, we have employed a single smartphone on the patient’s waist for FOG experiment (38 patients), and on patient thigh for LA (93 subjects). Given the sound performance achieved in this trial (AUC = 0.97 for FOG and AUC = 0.92 for LA), motor fluctuations may be estimated in domestic environments. To this end, we plan to perform measures and data processing on SensorTile, a tiny IoT module including several sensors, a microcontroller, a BlueTooth low-energy interface and microSD card, implementing an electronic diary of motor fluctuations, posture and dyskinesia during activity of daily living. © 2019, The Author(s).

关键词： Wearable sensors

Simulations and Experiments of Time-Varying Takagi Factorization via Zhang Neural Network

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Simulations and Experiments of Time-Varying Takagi Factoriza...

IEEE Advanced Information Technology, Electronic and Automation control Conference (IAEAC)

作者： Zanyu Tang Tianyu Zeng Zhenyu Li Jianrong Chen Yunong Zhang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Modern Control Technology Guangzhou China College of Information Science and Engineering Jishou University Jishou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China School of Public Health Administration Youjiang Medical University for Nationalities Baise China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

The problem of Takagi factorization (TF) for complex time-varying symmetric matrix is proposed in this paper. To solve this difficult and challenging problem, a continuous-time TF (CTTF) model of derivative equation system form is proposed via Zhang neural network (ZNN) method. For the sake of digital hardware implementation, we also build up the corresponding discrete-time (DT) ZNN solution models with different precisions of the derivative equation system discretization. Specifically, an eight-instant DTTF (EIDTTF) model are proposed. We also comparatively use two other multi-instant (e.g., four-instant and six-instant, i.e., FIDTTF and SIDTTF, respectively) models by discretizing the CTTF model. The effective solution step-size intervals of the proposed DTTF models are further discussed lastly. The results of simulations and experiments substantiate the correctness, effectiveness and superiority of the proposed CTTF and DTTF models.

关键词： Symmetric matrices Conferences Neural networks Hardware Numerical models Mathematical model Information technology

The development of pineapple leaf diseases classification using convolutionary neural network for mobile apps

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AIP Conference Proceedings 2024年第1期3135卷

作者： Muhammad Najmi Hasnol Mizam Sulaiman Mahzan Siti Fairuz Nurr Sadikan Mohd Ab Malek Md Shah Mohd Aliff Afira Sani College of Computing Informatics & Mathematic Universiti Teknologi MARA Melaka Malaysia. Faculty of Plantation & Agrotechnology Universiti Teknologi MARA Melaka Malaysia. Department of Law Universiti Teknologi MARA Melaka Malaysia. Department of Instrumentation and Control Engineering Universiti Kuala Lumpur Johore Malaysia.

Pineapple diseases are linked to worms, viruses, bacteria, and fungi. Insects including ants, scales, mealybugs, and souring beetles are some of the most prevalent pests that can harm the pineapple industry over time if early pineapple leaf disease detection is not made There are many smallholders especially in the rural area who lack of latest technology, knowledge, and experience about pineapple disease treatment and management. Thus, lack of a precise diagnosis method prevents them from understanding the disease and creates an effective control for its spread. This issue further impacts the effectiveness of pineapple production, subsequently affecting their income and the country’s, as well as jeopardizing national food security. This study presents an innovative approach aimed at enhancing early detection and management of pineapple leaf diseases by integrating a Convolutional Neural Network (CNN) algorithm and import it using TensorFlow Lite into an android mobile. The CNN model achieved an impressive total accuracy of 98% in precisely classifying three types of pineapple leaf diseases: Leaf Spot, Mealybug Wilt, and Pink Disease. When implemented in the mobile application, the system attained an overall confidence of 83.33% by leveraging both camera-captured and gallery images. Although the accuracy gained is impressive, additional research and adjustments can be conducted to improve the system’s performance and achieve even higher levels of accuracy.

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Cavity-Quantum Electrodynamics with Moiré Flatband Photonic Crystals

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arXiv 2024年

作者： Wang, Yu-Tong Ye, Qi-Hang Yan, Jun-Yong Qiao, Yufei Chen, Chen Cheng, Xiao-Tian Li, Chen-Hui Zhang, Zi-Jian Huang, Cheng-Nian Meng, Yun Zou, Kai Zhan, Wen-Kang Zhao, Chao Hu, Xiaolong Tee, Clarence Augustine T.H. Sha, Wei E.I. Huang, Zhixiang Liu, Huiyun Jin, Chao-Yuan Ying, Lei Liu, Feng State Key Laboratory of Extreme Photonics and Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Physics Zhejiang Key Laboratory of Micro-nano Quantum Chips and Quantum Control Zhejiang University Hangzhou310027 China School of Precision Instrument and Optoelectronic Engineering Tianjin University Tianjin300072 China Key Laboratory of Optoelectronic Information Science and Technology Ministry of Education Tianjin300072 China Laboratory of Solid State Optoelectronics Information Technology Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Science Beijing101804 China College of Physics and Electrical Information Engineering Zhejiang Normal University Hangzhou310058 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom International Joint Innovation Center Zhejiang University Haining314400 China

Quantum emitters are a key component in photonic quantum technologies. Enhancing their single-photon emission by engineering the photonic environment using cavities can improve the overall efficiency in quantum information processing. However, this enhancement is often limited by the need for precise nanoscale control over emitter’s position within micro/nano-cavities. Inspired by the fascinated physics of moiré pattern, we present an approach to strongly modify the spontaneous emission rate of a quantum emitter using fine-designed multilayer moiré photonic crystal with a robust isolate-flatband dispersion. Theoretical analysis reveals that due to its nearly infinite photonic density of states, the moiré cavity can simultaneously possess high Purcell factor and large tolerance over emitter’s position. We experimentally demonstrate the coupling between this moiré cavity and a quantum dot via the cavity-determined polarization of the dot’s emission. The radiative lifetime of the quantum dot can be tuned by a factor of 40 from 42 ps to 1692 ps, attributed to strong Purcell enhancement and Purcell inhibition effects. Our findings pave the way for moiré flatband cavity-enhanced quantum light sources, quantum optical switches and quantum nodes for quantum internet. Copyright © 2024, The Authors. All rights reserved.

关键词： Particle beams