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PZT-based Multi-Mode Cantilever for Viscosity Sensing

PZT-based Multi-Mode Cantilever for Viscosity Sensing

2023 Joint Conference of the European Frequency and Time Forum and IEEE International Frequency Control Symposium, EFTF/IFCS 2023

作者： Javed, N. Singh, Priyanka Tiwari, Sudhanshu Jain, Pavitra Sandeep, Kongbrailatpam Pratap, Rudra Pillai, Gayathri Indian Institute of Science Center for Nanoscience and Engineering Bengaluru India Purdue University The Elmore Family School of Electrical and Computer Engineering United States

ISBN: (纸本)9798350311426

We report a thin film Lead Zirconate Titanate (PZT) on Silicon-on-Insulator (SOI) based microcantilever for fluid dynamic viscosity (ηfluid) sensing using optical and electrical characterization schemes. The viscosity of the medium is modulated by adding glycerol to deionized (DI) water. The measurements are carried over a viscosity range of 0.98-24.24 cP, which covers the operational range for biomedical applications. The fundamental cantilever mode yields a quality factor (Q) of 630 and 15 in air and DI water, respectively, whilst the second mode exhibits a Q of 840 and 30 in air and DI water, respectively. Sensor characterization using Laser Doppler Vibrometer (LDV) shows a power law relation between Q and ηfluid (Q = aηfluidb). A high relative responsivity of 0.38 and 0.41 with respect to ηfluid is observed for the first and second flexural modes of the microcantilever, respectively. These results indicate the development of a good platform for on-chip viscosity measurements for biomedical and industrial applications. © 2023 IEEE.

关键词： MEMS Microcantilever Modes Sensor Viscosity

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RPRB-Net: A Sign Language Recognition Based on Multi-dimension Attentions

RPRB-Net: A Sign Language Recognition Based on Multi-dimensi...

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2023 International Conference on Machine Vision, Image Processing and Imaging Technology, MVIPIT 2023

作者： Yang, Tian Shen, Cong Wang, Xinyue Ma, Xiaoyu Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education School of Computer Science and Engineering Tianjin China

ISBN: (纸本)9798350306545

Sign Language Recognition (SLR) is the process of translating sign language into text or speech, which can also be viewed as a communication and expression tool between deaf individuals and hearing people. SLR using deep neural network has undergone significant development over the last decade. In this study, we design a feature-enhanced SLR technique based on serval spaces. To aid the network learning features from channel and spatial dimensions, we inject various attention mechanisms into the feature extractor backbone. As a result, we named our approach ResNet + Pyramid-Split-Attention + Recurrent-Criss-Cross-Attention + BiLSTM Network (RPRB-Net). Besides, we employ the BiLSTM architecture to encode high-level representations in temporal sequence. The network is ultimately trained using an end-to-end CTC alignment strategy. Extensive trials show that RPRB-Net achieves WERs of 3.5% and 23.6% on the USTC and RWTH-2014 datasets, respectively. Besides, it implies that RPRB-Net significantly surpasses the cutting-edge SLR approaches. Additionally, numerous ablation experiments have confirmed the efficiency of configuration. © 2023 IEEE.

关键词： Audition

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Enhance Planning with Physics-informed Safety Controller for End-to-end Autonomous Driving

Enhance Planning with Physics-informed Safety Controller for...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Zhou, Hang Liu, Haichao Lu, Hongliang Ma, Jun Ji, Yiding Robotics and Autonomous Systems Thrust Systems Hub Guangzhou China School of Engineering The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering SAR Hong Kong Hong Kong Intelligent Transportation Thrust Systems Hub Guangzhou China

ISBN: (纸本)9781665481090

Recent years have seen a growing research interest in applications of Deep Neural Networks (DNN) on autonomous vehicle technology. The trend started with perception and prediction a few years ago and it is gradually being applied to motion planning tasks. Despite the performance of networks improve over time, DNN planners inherit the natural drawbacks of Deep Learning. Learning-based planners have limitations in achieving perfect accuracy on the training dataset and network performance can be affected by out-of-distribution problem. In this paper, we propose FusionAssurance, a novel trajectory-based end-to-end driving fusion framework which combines physics-informed control for safety assurance. By incorporating Potential Field into Model Predictive Control, FusionAssurance is capable of navigating through scenarios that are not included in the training dataset and scenarios where neural network fail to generalize. The effectiveness of the approach is demonstrated by extensive experiments under various scenarios on the CARLA benchmark. © 2024 IEEE.

关键词： Deep neural networks

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CFCS:A Robust and Efficient Collaboration Framework for Automatic Modulation Recognition

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Journal of Communications and Information Networks 2023年第3期8卷 283-294页

作者： Jian Shi Xiaohui Yang Jia Ma Guangxue Yue Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing 314001China School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Research Center of Cyber Science and Technology Hangzhou Innovation InstituteBeihang UniversityHangzhou 311228China College of Information Science and Engineering Jiaxing UniversityJiaxing 314001China

Most of the existing automatic modulation recognition(AMR)studies focus on optimizing the network structure to improve performance,without fully considering cooperation among the basic networks to play their respective *** this paper,we propose a robust and efficient collaboration framework based on the combination scheme(CFCS).This scheme effectively explores the spatial and temporal characteristics of complex signals by associating the advantages of convolutional neural network(CNN)and long and short-term memory(LSTM)*** addition,the robustness of the CFCS is verified by transfer *** demonstrate that the recognition rate of CFCS for highorder modulation signals such as 64QAM,128QAM,and 256QAM is more than 90%at high signal-to-noise ratios(SNRs),and 24 modulation types are effectively ***,CFCS was transferred from RML2018.01a to RML2016.10b using transfer learning,which can still be deployed efficiently while reducing the training time by 20%.The CFCS has strong generalization ability and excellent recognition performance.

关键词： AMR CNN LSTM combination scheme transfer learning

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Research on Entity Recognition Method for Quality Standards of Concrete Prefabricated Components 1

Research on Entity Recognition Method for Quality Standards ...

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1st International Conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Zhang, Tingshu Tu, Ming Wang, Gefan Chen, Bingyong Zhao, Yishi Shang, Jianga School of Computer Science China University of Geosciences Wuhang China Intelligent Construction System Research Institute China Construction Third Engineering Buerau Group Co.L Wuhang China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education School of Computer Science China University of Geosciences Wuhang China

ISBN: (纸本)9781643685779

In recent years, the country has released a large number of standard documents related to prefabricated concrete components. Due to the dispersion and complexity of these standards, it is difficult for industry managers to implement them. Therefore, it is imperative to establish a knowledge graph in the field of quality standards for precast concrete components to achieve knowledge management. The construction of knowledge graph first requires named entity recognition, but due to the specificity of the construction industry, entity recognition of the text in this field still suffers from the problems of low recognition accuracy and high resource consumption. Aiming at the low recognition accuracy and high resource consumption in named entity recognition due to the specificity of the industry, this paper proposes a named entity recognition model ALBERT-BiLSTM-SA-CRF for the field of precast concrete component quality standard, which can capture the full text and local feature information more effectively through the combination of BiLSTM and self-attention mechanism, and accurately recognize entities in the standard. Meanwhile, using ALBERT as the word embedding layer can play the role of reducing resource consumption and improving processing efficiency. © 2025 The Authors.

关键词： Knowledge graph

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Robust Facial Biometric Authentication System Using Pupillary Light Reflex for Liveness Detection of Facial Images

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computer Modeling in engineering & sciences 2024年第4期139卷 725-739页

作者： Puja S.Prasad Adepu Sree Lakshmi Sandeep Kautish Simar Preet Singh Rajesh Kumar Shrivastava Abdulaziz S.Almazyad Hossam M.Zawbaa Ali Wagdy Mohamed CSE Department Geethanjali College of EngineeringHyderabadTelangana501301India Department of Computer Science LBEF CampusKathmandu44600Nepal School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia CeADAR Ireland’s Centre for AI Technological University DublinDublinD07 EWV4Ireland Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

Pupil dynamics are the important characteristics of face spoofing *** face recognition system is one of the most used biometrics for authenticating individual *** main threats to the facial recognition system are different types of presentation attacks like print attacks,3D mask attacks,replay attacks,*** proposed model uses pupil characteristics for liveness detection during the authentication *** pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light *** proposed framework consists of two-phase *** the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video *** above measurement is converted into feature space using Kohn and Clynes model-defined *** Support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3D mask of the subject in front of the *** the second phase,we perform the facial recognition ***-invariant feature transform(SIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional *** features are scale,rotation,and orientation invariant and are used for recognizing facial *** brute force matching algorithm is used for matching features of two different *** threshold value we considered is 0.08 for good *** analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,Depth).The CASIA-SURF datasets showed an 89.9%Equal Err

关键词： SIFT pupil CASIA-SURF pupillary light reflex replay attack dataset brute force

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GoMatching: A Simple Baseline for Video Text Spotting via Long and Short Term Matching 38

GoMatching: A Simple Baseline for Video Text Spotting via Lo...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： He, Haibin Ye, Maoyuan Zhang, Jing Liu, Juhua Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University China College of Computing & Data Science Nanyang Technological University Singapore

Beyond the text detection and recognition tasks in image text spotting, video text spotting presents an augmented challenge with the inclusion of tracking. While advanced end-to-end trainable methods have shown commendable performance, the pursuit of multi-task optimization may pose the risk of producing sub-optimal outcomes for individual tasks. In this paper, we identify a main bottleneck in the state-of-the-art video text spotter: the limited recognition capability. In response to this issue, we propose to efficiently turn an off-the-shelf query-based image text spotter into a specialist on video and present a simple baseline termed GoMatching, which focuses the training efforts on tracking while maintaining strong recognition performance. To adapt the image text spotter to video datasets, we add a rescoring head to rescore each detected instance's confidence via efficient tuning, leading to a better tracking candidate pool. Additionally, we design a long-short term matching module, termed LST-Matcher, to enhance the spotter's tracking capability by integrating both long- and short-term matching results via Transformer. Based on the above simple designs, GoMatching delivers new records on ICDAR15-video, DSText, BOVText, and our proposed novel test set with arbitrary-shaped text termed ArTVideo, which demonstrates GoMatching's capability to accommodate general, dense, small, arbitrary-shaped, Chinese and English text scenarios while saving considerable training budgets. © 2024 Neural information processing systems foundation. All rights reserved.

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A Scene-Aware Augmented Reality Recommendation System 5

A Scene-Aware Augmented Reality Recommendation System

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5th Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2024

作者： Li, Hua Liu, Xutian Rashidi, Salim Suedi School of Computer Science Changchun University of Science and Technology Changchun130022 China National and Local Joint Engineering Research Center for Special Film Technology and Equipment Changchun130022 China

ISBN: (纸本)9781643685243

Augmented Reality (AR) technology offers users an unparalleled immersive interaction experience. In indoor scenarios devoid of GPS, AR-based recommendation technologies have yet to achieve effective real-time application. This paper introduces a scene-aware AR recommendation system that integrates cutting-edge instance segmentation and feature point matching technologies, alongside a versatile recommendation algorithm, to provide highly personalized AR content recommendations closely linked to the user’s immediate environment. Initially, the system utilizes an enhanced Mask R-CNN model for precise scene segmentation, followed by the extraction of key feature points combined with real-time scene information to devise an efficient recommendation algorithm. Experimental validation in real-world scenarios has demonstrated the system’s effectiveness in improving the accuracy of AR content recommendations and enhancing user satisfaction within indoor environments. Notably, the recommendation accuracy exceeds 80%, signifying a substantial advancement in AR technologies. This research demonstrates potential to profoundly enhance user interaction and satisfaction by providing more accurate and contextually relevant content recommendations in GPS-limited indoor environments. © 2024 The Authors.

关键词： Augmented reality

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Tunable Multicolor Fluorescence of Perovskite-Based Composites for Optical Steganography and Light-Emitting Devices

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Research 2023年第1期2022卷 185-195页

作者： Kewei Ma Qingfeng Gui Cihui Liu Yunyi Yang Fangjian Xing Yunsong Di Xiaoming Wen Baohua Jia Zhixing Gan Center for Future Optoelectronic Functional Materials School of Computer and Electronic Information/School of Artificial IntelligenceNanjing Normal UniversityNanjing 210023China College of Naval Architecture and Ocean Engineering Jiangsu Maritime InstituteNanjing 211170China Centrefor Translational Atomaterials Schoolof ScienceSwinburne University of TechnologyJohn Street Hawthon VIC3122Australia School of Science RMIT UniversityMelbourne3000 VICAustralia College of Materials Science and Engineering Qingdao University of Science and TechnologyQingdao 266042China

Multicolor fluorescence of mixed halide perovskites enormously enables their applications in photonics and ***,it remains an arduous task to obtain multicolor emissions from perovskites containing single halogen to avoid phase ***,a fluorescent composite containing Eu-based metal-organic frameworks(MOFs),OD Cs PbBre,and 3D CsPbBrs is *** excitations at 365 nm and 254nm,the pristine composite emits blue(B)and red(R)fluorescence,which are ascribed to radiative defects within CsPbBr and 5Do→7F,transitions of Eu^(3+),***,after light soaking in the ambient environment,the blue fluorescence gradually converts into green(G)emission due to the defect repairing and 0D-3D phase *** permanent and unique photochromic effect enables anticounterfeiting and microsteganography with increased security through a micropatterning ***,the RGB luminescence is highly stable after encapsulation by a transparent polymer ***,trichromatic light-emitting modules are fabricated by using the fluorescent composites as color-converting layers,which almost fully cover the standard color ***,this work innovates a strategy for construction of tunable multicolor luminescence by manipulating the radiative defects and structural dimensionality.

关键词： composite composites perovskite

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Lithographic printing inspired in-situ transfer of MXene-based films with localized topo-electro tunability for high-performance flexible pressure sensors

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Nano Research 2023年第11期16卷 12670-12679页

作者： Qiuyang Yan Yi Zhou Yin Cheng Liangjing Shi Ranran Wang Lian Gao Jing Sun State Key Laboratory of High Performance Ceramics and Superfine Microstructure Shanghai Institute of CeramicsChinese Academy of ScienceShanghai 200050China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore School of Chemistry and Materials Science Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

MXene-based films have been intensively explored for construction of piezoresistive flexible pressure sensors owing to their excellent mechanical and electrical *** pressure sensitivity relies on pre-molding a flexible substrate,or regulating the micromorphology of MXene sheets,to obtain a micro-structured ***,the two avenues usually require complicated and time-consuming microfabrication or wet chemical processing,and are limited to non-adjustable topographicelectrical(topo-electro)***,we propose a lithographic printing inspired in-situ transfer(LIPIT)strategy to fabricate MXene-ink films(MIFs).In LIPIT,MIFs not only inherit ridge-and-valley microstructure from paper substrate,but also achieve localized topo-electro tunability by programming ink-writing patterns and *** MIF-based flexible pressure sensor with periodical topo-electro gradient exhibits remarkably boosted sensitivity in a wide sensing range(low detection limit of 0.29 Pa and working range of 100 kPa).The MIF sensor demonstrates versatile applicability in both subtle and vigorous pressuresensing fields,ranging from pulse wave extraction and machine learning-assisted surface texture recognition to piano-training glove(PT-glove)for piano *** LIPIT is quick,low-cost,and compatible with free ink/substrate combinations,which promises a versatile toolbox for designing functional MXene films with tailored morphological-mechanical-electrical properties for extended application scenarios.

关键词： MXene film transfer printing surface tunability flexible pressure sensor

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