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Fully tunable Fabry-Pérot cavity based on MEMS Sagnac loop reflector with ultra-low static power consumption

Microsystems & Nanoengineering 2024年第4期10卷 355-365页

作者： Young Jae Park Man Jae Her Youngjae Jeong Dong Ju Choi Dong Uk Kim Min Gi Lim Myung Seok Hong Hyug Su Kwon Kyoungsik Yu Sangyoon Han Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology(DGIST)Daegu 42988Republic of Korea School of Electrical Engineering Korea Advanced Institute of Science and Technology(KAIST)Daejeon 34141Republic of Korea Agency for Defense Development DaejeonRepublic of Korea

The Fabry-Pérot interferometer,a fundamental component in optoelectronic systems,offers interesting applications such as sensors,lasers,and *** this work,we show a tunable Fabry-Pérot cavity consisting of tunable Sagnac loop reflectors(SLRs)and phase shifters based on electrostatic microelectromechanical(MEMS)*** fabrication process of the device is compatible with the standard wafer-level silicon photonics fabrication *** electrostatic actuation mechanism provides well-balanced,scalable pathways for efficient tuning *** extinction ratio of the continuously tunable SLRs’reflectivity is larger than 20 *** 2πphase shifting is achieved,and response times of all the components are less than 25μ*** actuators have extremely low static power,measuring under 20 fW and the energy needed for tuning is both below 20 pJ.

关键词： tunable tuning interferometer

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Versatile Tasks on Integrated Aerial Platforms Using Only Onboard Sensors: Control, Estimation, and Validation

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IEEE Transactions on robotics 2025年 41卷 3518-3538页

作者： Wang, Kaidi Lai, Ganghua Yu, Yushu Du, Jianrui Sun, Jiali Xu, Bin Franchi, Antonio Sun, Fuchun Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Beijing Institute of Technology School of Mechanical Engineering Beijing100081 China University of Twente Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics and Computer Science Enschede7500 AE Netherlands Tsinghua University Department of Computer Science and Technology China

Connecting multiple aerial vehicles to a rigid central platform through passive spherical joints holds the potential to construct a fully-actuated aerial platform. The integration of multiple vehicles enhances efficiency in tasks like mapping and object reconnaissance. This paper proposes a control and state estimation framework for the Integrated Aerial Platform (IAP), enabling it to perform versatile tasks like object reconnaissance and physical interactive tasks with only onboard sensors. In the framework, the 6D motion control serves as the low-level controller, while the high-level controller comprises a 6D admittance filter and a perception-aware attitude correction module. The 6D admittance filter, serving as the interaction controller, is adaptable for aerial interaction tasks. The perception-aware attitude correction algorithm is carefully designed by adopting a geometric Model Predictive Controller (MPC). This algorithm, incorporating both offline and online calculations, proves to be well-suited for the intricate dynamics of an IAP. A 6D direct wrench controller is also developed for the IAP. Notably, both the interaction controller and the direct wrench controller operate without reliance on force/torque sensors. Instead, a wrench observer algorithm is devised, considering external disturbances. Additionally, based on the kinematics constraints of the multiple aerials in the platform, a fusion algorithm for multiple Visual-Inertial Odometry (VIO) and kinematics constraints is developed, providing more accurate localization. A prototype of the IAP is constructed, and its capabilities are demonstrated through experiments including perception-aware object reconnaissance, aerial mapping, aerial peg-in-hole task, and 6D contact wrench generation. All experiments are conducted exclusively with onboard sensors. These tasks exemplify the merits of the proposed IAP and validate the effectiveness of the proposed control framework and fusion algorithm. © 2004-2012

关键词： State estimation

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Reply to comment on “Ferroelectric composite-based piezoelectric energy harvester for self-powered detection of obstructive sleep” by A. Tkach and O. Okhay

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Journal of Materiomics 2024年第5期10卷 1160-1164页

作者： Swati Panda Hyoju Shin Sugato Hajra Yumi Oh Wonjeong Oh Jeonghyeon Lee P.M.Rajaitha Basanta Kumar Panigrahi Jyoti Shukla Alok Kumar Sahu Perumal Alagarsamy Hoe Joon Kim Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology(DGIST)Daegu 42988South Korea Department of Electrical Engineering Siksha‘O’Anusandhan UniversityBhubaneswar 751030India Department of Electrical Engineering Poornima College of EngineeringJaipur 303903India Department of Physics Indian Institute of Technology-GuwahatiAssam 781039India Robotics and Mechatronics Research Center Daegu Gyeongbuk Institute of Science and Technology(DGIST)Daegu 42988South Korea

In this contribution,we reply to comments made by Tkach et *** our publication *** 2023;9:*** main interest of our work is to synthesize a lead-free material,SrTi_(2)O_(5)(STO),and then utilize it in the formation of composites and finally design the piezoelectric nanogenerator(PENG)for self-powered sensor *** authors have observed the presence of piezoelectric voltage and current output from the *** authors humbly indicate that the PENG devices were poled using a DC poling setup as conditions mentioned in *** 2023;9:609(Panda et al.,2023)[1].The doping of STO into the PDMS increased from 2% to 20%(in mass).In this process,the piezoelectric output of the PENG device was observed to be highest for 15%(in mass)STO-PDMS ***,we agree with the comment raised by Tkach et al.,and further we have addressed the issues in a step-by-step response as follows.

关键词： Ferroelectric Lead-free Piezoelectric Polycrystalline

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Ferroelectric composite-based piezoelectric energy harvester for selfpowered detection of obstructive sleep

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Journal of Materiomics 2023年第4期9卷 609-617页

作者： Swati Panda Hyoju Shin Sugato Hajra Yumi Oh Wonjeong Oh Jeonghyeon Lee P.M.Rajaitha Basanta Kumar Panigrahi Jyoti Shukla Alok Kumar Sahu Perumal Alagarsamy Hoe Joon Kim Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology(DGIST)Daegu42988South Korea Department of Electrical Engineering Siksha‘O’Anusandhan UniversityBhubaneswar751030India Department of Electrical Engineering Poornima College of EngineeringJaipur303903India Department of Physics Indian Institute of Technology-GuwahatiAssam781039India Robotics and Mechatronics Research Center Daegu Gyeongbuk Institute of Science and Technology(DGIST)Daegu42988South Korea

Lead-free piezoelectric ceramic is a promising material for energy harvesters,as they have superior electromechanical,ferroelectric,and piezoelectric *** addition,piezoelectric ceramics can be blended with polymer to achieve high-flexibility polymer-ceramic composites,providing mechanical robustness and *** this context,a new lead-free ferroelectric material,having the chemical formula SrTi_(2)O_(5)(STO),was synthesized using a high-temperature solid-state *** analyses of the structural,morphological,and electrical properties of the synthesized material were *** crystallizes with orthorhombic symmetry and space group of *** frequency and temperature-dependent dielectric parameters were evaluated,and impedance spectroscopy shed light on the charge *** PDMS-STO composites at different mass fraction of the STO were prepared using a solvent casting route,and a corresponding piezoelectric nanogenerator(PENG)was *** electrical output of the different PENG by varying massfractions of STO in PDMS and varying force were *** 15%(in mass)PENG device delivered the highest peak-to-peak voltage,current,and power density of 25 V,92 nA,and 0.64 mW@500 MU,*** biomechanical energy harvesting using the PENG device by daily human motions,bending of the device,and attaching the device to laboratory equipment was *** the PENG device was attached to the human throat region,and snoring signals were recorded.A classification model was designed employing the convolutional neural network(CNN)*** have been laid to differentiate between normal and abnormal snores,which could help the patient with screening and early disease detection,contributing to self-powered healthcare applications.

关键词： Lead-free Ferroelectric Nanogenerator Sleepdisorder

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Strain sensor on a chip for quantifying the magnitudes of tensile stress on cells

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Microsystems & Nanoengineering 2024年第3期10卷 305-315页

作者： Yuyin Zhang Yue Wang Hongze Yin Jiahao Wang Na Liu Songyi Zhong Long Li Quan Zhang Tao Yue School of Mechatronics Engineering and Automation Shanghai UniversityShanghaiChina School of Future Technology Shanghai UniversityShanghaiChina Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical EngineeringZhejiang UniversityHangzhouChina Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai UniversityShanghaiChina Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghaiChina

During cardiac development,mechanotransduction from the in vivo microenvironment modulates cardiomyocyte growth in terms of the number,area,and arrangement ***,the response of cells to different degrees of mechanical stimuli is ***-on-a-chip,as a platform for investigating mechanical stress stimuli in cellular mimicry of the in vivo microenvironment,is limited by the lack of ability to accurately quantify externally induced ***,previous technology lacks the integration of external stimuli and feedback sensors in microfluidic platforms to obtain and apply precise amounts of external ***,we designed a cell stretching platform with an in-situ *** in-situ liquid metal sensors can accurately measure the mechanical stimulation caused by the deformation of the vacuum cavity exerted on *** platform was applied to human cardiomyocytes(AC16)under cyclic strain(5%,10%,15%,20 and 25%),and we found that cyclic strain promoted cell growth induced the arrangement of cells on the membrane to gradually unify,and stabilized the cells at 15%amplitude,which was even more effective after 3 days of *** platform’s precise control and measurement of mechanical forces can be used to establish more accurate in vitro microenvironmental models for disease modeling and therapeutic research.

关键词： stress deformation arrangement

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Analytical and Deep Neural Network Based Hybrid Modelling of Single-Segment Continuum Robot

Analytical and Deep Neural Network Based Hybrid Modelling of...

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International Conference on Industrial Instrumentation and Control,ICI2C 2021

作者： Bhattacherjee, Saptak Chattopadhyay, Sudipta Sengupta, Aparajita Bhaumik, Subhasis School of Mechatronics and Robotics Indian Institute of Engineering Science and Technology Howrah Shibpur India Department of Electrical Engineering Indian Institute of Engineering Science and Technology Howrah Shibpur India

ISBN: (纸本)9789811670107

Hyper-redundancy and ability to propagate through complex curvilinear pathways have made the continuum robots immensely popular in different fields of application. Precise control of continuum robot position relies on the accurate knowledge of the manipulator kinematics. However, the inverse kinematic model of continuum robots based on analytical modelling fails to include the unmodeled dynamics and effect of disturbances due to gravity and friction resulting in low accuracy in modelling and control. On the other hand, purely data driven continuum models require huge amount of data and time for training and suffer from high inaccuracy in modelling as well. Hybrid modelling in this scenario has been perceived to achieve better accuracy by combining the advantages of these two modelling approaches. Therefore, in the present work, we focus on the inverse kinematics of the single-segment continuum robot by using the analytical and deep neural network based hybrid modelling approach. Experimental results indicate that the proposed method increases the modelling accuracy and also at the same time it reduces the network training time significantly. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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A deep learning-based model for automated STN localization using local field potentials in Parkinson’s disease

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Soft Computing 2025年第4期29卷 2343-2362页

作者： Hosny, Mohamed Naeem, Mohamed A. Zhu, Minwei Gao, Wenpeng Elshenhab, Ahmed M. Fu, Yili Department of Electrical Engineering Benha Faculty of Engineering Benha University Benha Egypt State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150080 China Department of Mechatronics and Robotics Engineering Faculty of Engineering South Valley University Qena Egypt First Affiliated Hospital Harbin Medical University Harbin150001 China School of Life Science and Technology Harbin Institute of Technology Harbin150080 China Department of Mathematics Faculty of Science Mansoura University Mansoura35516 Egypt

Accurate identification of the subthalamic nucleus (STN) borders is time-consuming, relying heavily on the neurosurgeon expertise in manually interpreting the electrophysiological signals. Local field potentials (LFPs) have garnered imperative attention due to their strong correlation with the STN. However, existing detection models often face challenges with high computational complexity, hyperparameter optimization and lack of explainability, making them unreliable for clinicians. Therefore, this study introduces an explanatory framework using convolutional neural networks (CNN) for detecting the STN region from LFPs. Continuous wavelet transform is employed to convert LFPs signals into scalogram images, which are then processed by sixteen CNN models. We evaluated our framework by examining the impact of various limiting factors on the classification performance, including model size, learning rate (LR), optimizers and data scaling. Deep features are extracted from the top-performing CNN architectures to capture rich representations of the scalograms. These features are then fused and classified using k-nearest neighbour algorithm. Gradient-weighted class activation mapping is used to explain the decisions made by the proposed model. Our approach achieved an accuracy of 99.61%, outperforming individual CNN models for STN localization. The experimental results revealed that CNN models, embedded with additional hyperparameters and layers, generally outperformed smaller models. Besides, low LR significantly enhanced the performance compared to high LR. Moreover, features extracted from untuned networks produced lower performance than tuned networks. The proposed system could revolutionize deep brain stimulation surgery by increasing efficiency and reducing reliance on clinician expertise for STN detection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Wavelet transforms

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Micro-Seismic Event Detection using statistical feature extraction and machine learning techniques 7

Micro-Seismic Event Detection using statistical feature extr...

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7th IEEE International conference for Convergence in technology, I2CT 2022

作者： Chakraborty, Mainak Das, Manisha Aruchamy, Srinivasan Indian Institute of Engineering Science and Technology School of Mechatronics and Robotics Shibpur India CSIR- Cmeri Robotics and Automation Group Durgapur India

ISBN: (纸本)9781665421683

In this paper, we propose a novel methodology for micro-seismic event detection using multiple statistical feature extraction and machine learning techniques. The method is based on amplitude thresholding and is grounded on the idea, that each relevant seismic event crosses a particular threshold value. We have created a unique dataset of movement, hammering and no-movement using off-the-shelve geophones and 16-bit analog to digital converter. The data is sampled at 10000Hz. Each recorded dataset is of duration 2 min. The data recorded is passed through amplitude thresholding method. Multiple statistical features like root mean square, standard deviation, variance, minimum value and maximum value are extracted from the dataset in both time and frequency domain. k-fold cross validation is done to obtain validation accuracy. We have explored various machine learning algorithms and the results are discussed here. Among all classifiers, Random Forest algorithm achieves the overall highest accuracy of 92.76%. © 2022 IEEE.

关键词： Decision trees

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Lower Limb Joint Torque Estimation via Bayesian Regularized Backpropagation Neural Networks 2

Lower Limb Joint Torque Estimation via Bayesian Regularized ...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Narayan, Jyotindra Gritli, Hassène Dwivedy, Santosha K. Indian Institute of Technology Guwahati Mechatronics and Robotics Laboratory Department of Mechanical Engineering Assam Guwahati781039 India National Engineering School of Tunis Le Belvédère Tunis1002 Tunisia

ISBN: (纸本)9798350360523

Prior gait analysis has played a crucial role in assessing overall health and well-being, particularly in individuals of different age groups. Nevertheless, acquiring gait data has necessitated the in-person presence of participants, incurring potential expenses. We introduce a deep learning technique called Bayesian regularized backpropagation neural network (BR-BPNN) to estimate joint torques in lower-limb motion based on anthropometric data and joint movements. The study collects joint movement data from 40 healthy participants (25 males and 15 females) spanning ages 7 to 65, utilizing an established motion-capture system. The input data encompassed various parameters such as age, gender, total body mass, height, thigh length, calf length, ankle-foot length, thigh mass, calf mass, ankle mass, and joint angles for the hip, knee, and ankle. The output data included joint torques calculated for a simplified leg model using the inverse dynamics method derived from the Euler-Lagrangian principle. We presented the conceptual details of the proposed BR-BPNN model and contrast model named Levenberg-Marquardt-based BPNN (LM-BPNN). Performing a comparative analysis, the results revealed that the BR-BPNN model (Rtrain = 0.985, Rtest = 0.955) excelled in accurately estimating joint torques compared to the LM-BPNN model (Rtrain = 0.973, Rtest = 0.924). Furthermore, the BR-BPNN model is found to be effective by 87.19%, 77.87%, and 72.73% in estimating hip, knee, and ankle joint torques for a 51-year-old male subject from the testing dataset. © 2024 IEEE.

关键词： Neural network models

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Unleashing the potential of morphotropic phase boundary based hybrid triboelectric–piezoelectric nanogenerator

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Journal of Materiomics 2024年第4期10卷 792-802页

作者： Sugato Hajra Aneeta Manjari Padhan Basanta Kumar Panigrahi Phakkhananan Pakawanit Zvonko Jagličić Naratip Vittayakorn Yogendra Kumar Mishra Sanghoon Lee Hoe Joon Kim Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and TechnologyDaegu42988Republic of Korea Department of Electrical Engineering Siksha O Anusandhan UniversityBhubaneswar751030India Synchrotron Research and Applications Division Synchrotron Light Research InstituteNakhon Ratchasima30000Thailand Institute of Mathematics Physics and Mechanics&Faculty of Civil and Geodetic EngineeringUniversity of LjubljanaJadranska191000LjubljanaSlovenia Advanced Materials Research Unit Department of ChemistrySchool of ScienceKing Mongkut's Institute of Technology LadkrabangBangkok10520Thailand Mads Clausen Institute NanoSYDUniversity of Southern DenmarkAlsion 26400SønderborgDenmark Robotics and Mechatronics Research Center Daegu Gyeongbuk Institute of Science and Technology(DGIST)Daegu42988Republic of Korea

Morphotropic phase boundary(MPB)-based ceramics are excellent for energy harvesting due to their enhanced physical properties at phase boundaries,broad operating temperature range,and ability to customize properties for efficient conversion of mechanical energy into electrical *** this work,Bi_(1–x)Na_(x)Fe_(1–x)Nb_(x)O_(3)(x=0.20,0.30,0.32 and 0.40,BNFNO abbreviation)based ceramics were synthesized using a solid-state route and blended with Polydimethylsiloxane(PDMS)to achieve flexible *** material characterization and energy harvesting were performed by designing a hybrid piezoelectric(PENG)-triboelectric(TENG)*** voltage and current of PENG,TENG,and hybrid bearing same device area(2 cm×2 cm)were recorded as 11 V/0.3μA;60 V/0.7μA;110 V/2.2μ*** strategies for enhancing the output performance of the hybrid device were evaluated,such as increased surface area(creating micro-roughness and porous morphology)and increasing electrode size and multi-layer hybrid device *** self-powered acceleration monitoring was demonstrated using the hybrid ***,the low-frequency-based wave energy is converted into electrical energy,confirming the usage of hybrid PENG-TENG devices as a base for battery-free sensors and blue energy harvesting.

关键词： Lead-free Acceleration Wave energy Morphotropic

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