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Backscatter t echnologies and the future of Internet of Things:Challenges and opportunities

Intelligent and Converged Networks 2020年第2期1卷 170-180页

作者： Chaochao Yao Yang Liu Xusheng Wei Gongpu Wang Feifei Gao Beijing Key Lab of Transportation Data Analysis and Mining School of Computer and Information TechnologyBeiijing Jiaotong UniversityBeijing 100044China Beijing Institute of Aerospace Control Devices Beijing 100854China VIVO Mobile Company Beijing 100015China Department of Automation Tsinghua UniversityBeijing 100084China

Energy source and circuit cost are two critical challenges for the future development of the Internet of Things(IoT).Backscatter communications offer a potential solution to conveniently obtain power and reduce cost for sensors in IoT,and researchers are paying close attention to the *** technology originated from the Second World War and has been widely applied in the logistics ***,both the academic and industrial worlds are proposing a series of new types of backscatter technologies for communications and *** this paper,we review the history of both IoT and backscatter,describe the new types of backscatter,demonstrate their applications,and discuss the open challenges.

关键词： ambient backscatter backscatter communications battery-less sensor Internet of Things(IoT) Large Intelligent Surface(LIS) Unmanned Aerial Vehicle(UAV)

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Nanopore Sensing for Proteins Involved in Disease

Nanopore Sensing for Proteins Involved in Disease

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E-Health and Bioengineering Conference (EHB)

作者： Iuliana Şoldănescu Andrei Lobiuc Mihai Dimian Integrated Center for Research Development and Innovation for Advanced Materials Nanotechnologies Manufacturing and Control Distributed Systems (MANSiD) Stefan cel Mare University of Suceava Suceava Romania Department of Biomedical Sciences Stefan cel Mare University of Suceava Suceava Romania Department of Computer Electronics and Automation Stefan cel Mare University of Suceava Suceava Romania

ISBN: (数字)9781665485579

ISBN: (纸本)9781665485586

The development of fast, accurate and low-cost methods of analysis is increasingly necessary in the context of species evolution. Techniques based on the use of nanopores for the analysis of various disease-specific biomarkers have received attention from researchers. Studies have developed significantly, from the use of the $\alpha$ - HL protein pore to integrated devices such as the one developed by Oxford Nanopore Technology. The aim is to reach the full potential of using nanopores for clinical analysis, specifically to use them for various molecules regardless of their physicochemical properties (size, electric charge, concentration in solution). In this context, experimental protocols need to be adapted for each molecule to optimize biomarker analysis methods. Early and accurate diagnosis is essential for effective treatment leading to a higher quality of life. Changes at the structural level of proteins involved in various physiological processes cause dysfunctions and implicitly lead to the development of diseases such as Alzheimer's disease which is activated by the degradation of amyloid fibers. The challenge of analyzing proteins with nanopores consists precisely of their large structural size and non-uniform charge loading. The current paper aims to synthesize some of the knowledge in the field to aid in the challenges raised by the experimental part of nanopore sensing for proteins.

关键词： Proteins Sequential analysis Protocols Costs Biomarkers Physiology Sensors

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Distributed Optimization with Gradient Descent and Quantized Communication

arXiv

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arXiv 2022年

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. Division of Decision and Control Systems KTH Royal Institute of Technology SE-100 44 Stockholm Sweden and Digital Futures StockholmSE-100 44 Sweden Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia Cyprus

In this paper, we consider the unconstrained distributed optimization problem, in which the exchange of information in the network is captured by a directed graph topology, thus, nodes can only communicate with their neighbors. Additionally, in our problem, the communication channels among the nodes have limited bandwidth. In order to alleviate this limitation, quantized messages should be exchanged among the nodes. For solving this distributed optimization problem, we combine a gradient descent method with a distributed quantized consensus algorithm (which requires the nodes to exchange quantized messages and converges in a finite number of steps). Specifically, at every optimization step, each node (i) performs a gradient descent step (i.e., subtracts the scaled gradient from its current estimate), and (ii) performs a finite-time calculation of the quantized average of every node’s estimate in the network. As a consequence, this algorithm approximately mimics the centralized gradient descent algorithm. We show that our algorithm asymptotically converges to a neighborhood of the optimal solution with linear convergence rate. The performance of the proposed algorithm is demonstrated via simple illustrative examples. © 2022, CC BY.

关键词： Directed graphs

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Food Classification of Indian Cuisines Using Handcrafted Features and Vision Transformer Network

SSRN

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SSRN 2022年

作者： Nijhawan, Rahul Batra, Ashita Loyola-González, Octavio Kumar, Manoj Jain, Deepak Kumar Department of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Department of Computer Science IIT Guwahati Assam Guwahati India Altair Management Consultants Calle de José Ortega y Gasset 22-24 5th floor Madrid28006 Spain School of Computer Science Key Laboratory of Intelligent Air Ground Cooperative Control for Universities in Chongqing College of Automation Chongqing University of Posts and Telecommunications Chongqing China

To harmonize food classification through food composition databases, allowing better consumption of food and its nutrient levels. To bring innovative ideas for food identification, the work carried out within this paper established a combination of Vision Transformer ensemble architecture with the hand-crafted features, thereby making a hybrid Vision Transformer food classification system. Recently, Vision transformers have been introduced as an alternative means of classification to convolutional neural networks. It performs classification without using convolutions and interprets an image as a sequence of patches. The amalgamation of Vision Transformer and hand-crafted features like GIST, HoG (Histogram of Oriented Gradients), and LBP (Local Binary Pattern) were employed on the dataset. The dataset was specifically created (for this work) from the public logging system. It consisted of 13 food categorieswith 400 images of Indian food items like Ghevar, Idli, Dosa, and much more. It helped embrace a variety of images from every domain and culture. It was judged on their applicability for making food consumption data comparable for different ensemble combinations at the food level. It culminated that the hybrid system can perform better than either of the models alone in enhancing the performance and accuracy of the system as a whole. This work incorporated only some of the common and readily available food items, which can further be increased by adding on the specialties (dishes) from different regions for increasing the peculiarity in the results. Various experiments were performed on CNN with various classifiers like Random forest, SVM. Further, we compared our proposed approach with several ensemble of CNN architectures with multiple feature extraction techniques. The experiments proved that our proposed approach outperformed the state-of-the-art ensemble CNN architectures for classifying food cuisines. The proposed hybrid approachachieved an accuracy of 94.63%

关键词： Feature extraction

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Introduction to MATLAB

Advanced Textbooks in Control and Signal Processing

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Advanced Textbooks in control and Signal Processing 2019年 1-27页

作者： Keviczky, László Bars, Ruth Hetthéssy, Jenő Bányász, Csilla Institute for Computer Science and Control Hungarian Academy of Sciences Budapest Hungary Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest Hungary

MATLAB™ is an interactive environment for scientific and engineering calculations, simulations, and data visualization. MATLAB™ provides a powerful platform to solve mathematical and engineering problems related to matrix algebra, differential equations, etc. MATLAB is especially effective in solving control problems. SIMULINK provides block-oriented simulation. The chapter gives a short introduction to MATLAB and SIMULINK. © Springer Nature Singapore Pte Ltd. 2019.

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Fusionplanner: A Multi-Task Motion Planner for Mining Trucks Using Multisensor Fusion Method

SSRN

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SSRN 2023年

作者： Teng, Siyu Chen, Long Li, Luxi Li, Yuchen Hu, Xuein Li, Lingxi Ai, Yunfeng Department of Computer Science Hong Kong Baptist University Hong Kong The School of Artificial Intelligence Hubei University Wuhan China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Waytous Inc. Haidian Distinct Beijing China Indianapolis United States School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

In recent years, significant achievements have been made in motion planning for intelligent vehicles. However, as a typical unstructured environment, open-pit mining attracts limited attention due to its complex operational conditions and adverse environmental factors. A comprehensive paradigm for unmanned transportation in open-pit mines is proposed in this research, including a simulation platform, a testing benchmark, and a trustworthy and robust motion planner. Firstly, we introduce the Parallel Mining Simulator (PMS), a high-fidelity simulator specifically designed for open-pit mining scenarios. PMS enables to management and control of open-pit mine transportation from both the single-truck control and multi-truck scheduling perspectives. Then, we develop a novel benchmark called MiningNav, which offers three validation approaches suitable for transportation roads. The purpose of MiningNav is to evaluate the trustworthiness and robustness of algorithms in open-pit mines. Finally, we propose the first multi-task motion planning algorithm, called FusionPlanner, for the mining truck by the multisensor fusion method to adapt both lateral and longitudinal control tasks for unmanned transportation. We validate the performance of our planner using MiningNav in PMS, and empirical results demonstrate a significant reduction in the number of collisions and takeovers of FusionPlanner, thereby improving overall trustworthiness and robustness. © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Multimodal Information-Based Broad and Deep Learning Model for Emotion Understanding

Multimodal Information-Based Broad and Deep Learning Model f...

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第40届中国控制会议

作者： Min Li Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

Multimodal information-based broad and deep learning model（MIBDL） for emotion understanding is proposed, in which facial expression and body gesture are used to achieve emotional states recognition for emotion understanding. It aims to understand coexistence multimodal information in human-robot interaction by using different processing methods of deep network and broad network, which obtains the features of depth and width dimensions. Moreover, random mapping in the initial broad learning network could cause information loss and its shallow layer network is difficult to cope with complex tasks. To address this problem, we use principal component analysis to generate the nodes of the broad learning, and the stacked broad learning network is adapted to make it easier for the existing broad learning networks to cope with complex tasks by creating deep variations of the existing network. To verify the effectiveness of the proposal, experiments completed on benchmark database of spontaneous emotion expressions are developed, and experimental results show that the proposal outperforms the state-of-theart methods. According to the simulation experiments on the FABO database, by using the proposed method, the multimodal recognition rate is 17,54%, 1.24%, and 0.23% higher than those of the temporal normalized motion and appearance features（TN）,the multi-channel CNN（MCCNN）, and the hierarchical classification fusion strategy（HCFS）, respectively.

关键词： Convolution neural network broad learning emotion understanding facial expression body gesture

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End-to-End Streaming Video Temporal Action Segmentation with Reinforcement Learning

arXiv

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arXiv 2023年

作者： Zhang, Jinrong Wen, Wujun Liu, Shenglan Li, Yunheng Li, Qifeng Feng, Lin Huang, Gao The School of Control Science and Engineering Dalian University of Technology Dalian116024 China The School of Computer Science and Technology Dalian University of Technology Dalian116024 China The School of Innovation and Entrepreneurship Dalian University of Technology Dalian116024 China The Department of Automation Tsinghua University Beijing100084 China The School of Information and Communication Engineering Dalian Minzu University 116600 Liaoning Dalian116600 China

The streaming temporal action segmentation (STAS) task, a supplementary task of temporal action segmentation (TAS), has not received adequate attention in the field of video understanding. Existing TAS methods are constrained to offline scenarios due to their heavy reliance on multimodal features and complete contextual information. The STAS task requires the model to classify each frame of the entire untrimmed video sequence clip by clip in time, thereby extending the applicability of TAS methods to online scenarios. However, directly applying existing TAS methods to SATS tasks results in significantly poor segmentation outcomes. In this paper, we thoroughly analyze the fundamental differences between STAS tasks and TAS tasks, attributing the severe performance degradation when transferring models to model bias and optimization dilemmas. We introduce an end-to-end streaming video temporal action segmentation model with reinforcement learning (SVTAS-RL). The end-to-end modeling method mitigates the modeling bias introduced by the change in task nature and enhances the feasibility of online solutions. Reinforcement learning is utilized to alleviate the optimization dilemma. Through extensive experiments, the SVTAS-RL model significantly outperforms existing STAS models and achieves competitive performance to the state-of-the-art TAS model on multiple datasets under the same evaluation criteria, demonstrating notable advantages on the ultra-long video dataset EGTEA. Code is available at https://***/Thinksky5124/SVTAS. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ruijie Xu Shichao Chen Wenqiao Sun Yisheng Lv Jialiang Luo Ying Tang The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology Beijing China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems The Center of National Railway Intelligent Transportation System Engineering and Technology Institute of Automation Chinese Academy of Sciences China Academy of Railway Sciences Corporation Limited Beijing China China Academy of Railway Sciences Corporation Limited Transportation and Economics Research Institute and the Center of National Railway Intelligent Transportation System Engineering and Technology Beijing China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Information Engineering China University of Geosciences Beijing Beijing China Department of Electrical and Computer Engineering Rowan University Glassboro USA

Global Navigation Satellite Systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation System (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance.

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Design and Implementation of a Two-Limbed 3T1R Haptic Device

Design and Implementation of a Two-Limbed 3T1R Haptic Device

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Long Kang Yang Yang Byung-Ju Yi PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Xi'an Modern Control Technology Research Institute Xi'an China School of Automation Nanjing University of Information Science and Technology Nanjing China Department of the Electronic Systems Engineering Hanyang University Ansan South Korea

This paper presents a haptic device with a simple architecture of only two limbs that can provide translational motion in three degrees of freedom (DOF) and one-DOF rotational motion. Actuation redundancy eliminates all forward-kinematic singularities and improves the motion-force transmission property. Thanks to the special structure of the kinematic chains, all actuators are close to the base and full gravity compensation is achieved passively by using springs. Force producibility analysis shows that this haptic device is able to produce long-term continuous force feedback of 15–30 N in each direction. By developing a prototype of the haptic device and a virtual three-dimensional simulator, a preliminary performance evaluation of the haptic device was conducted. In addition, a torque distribution algorithm considering a relaxed form of actuator-torque saturation was experimentally evaluated, and a comparison with other algorithms reveals that this algorithm offers several advantages.

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