检索结果-内蒙古大学图书馆

Nanodiamond/Ti_(3)C_(2) MXene-coated quartz crystal microbalance humidity sensor with high sensitivity and high quality factor

学校读者我要写书评

暂无评论

Rare Metals 2024年第6期43卷 2719-2729页

作者： Yao Yao Qiao Chen Yan-Qi Li Xian-He Huang Wei-Wei Ling Zhe-MiaoXie Jia-QiWang Chang-Ming Chen College of Communication Engineering Chengdu University of Information TechnologyChengdu 610225China School of Automation Chongqing University of Posts and TelecommunicationsChongqing 400065China School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Advanced Micro-/Nano-Devices Lab Department of Systems Design EngineeringUniversity of WaterlooWaterlooON N2L 3GlCanada

To address the challenge of achieving both high sensitivity and a high quality factor in quartz crystal microbalance(QCM)humidity sensors,a nanodiamond(ND)/Ti_(3)C_(2)MXene composite-coated QCM humidity sensor was *** material characteristics of ND,Ti_(3)C_(2)MXene,and ND/Ti_(3)C_(2)MXene composite were analyzed by transmission electron microscopy(TEM)and Fourier transform infrared(FTIR)*** experimental results demonstrated that the hydrophilic ND nanoparticles coated on Ti_(3)C_(2) MXene nanosheet prevented the self-stacking of Ti_(3)C_(2)MXene and enhanced the sensitivity of Ti_(3)C_(2) MXene-based QCM humidity ***,the high mechanical modulus of Ti_(3)C_(2) MXene material helped ND/Ti_(3)C_(2)MXene composite-coated QCM humidity sensor to achieve a high quality factor(>20,000).ND/Ti_(3)C_(2)MXene compositecoated QCM humidity sensor exhibited a sensitivity of 82.45 Hz/%RH,a humidity hysteresis of 1.1%RH,fast response/recovery times,acceptable repeatability,and good stability from 11.3%RH to 97.3%*** response mechanism of ND/Ti_(3)C_(2) MXene composite-coated QCM humidity sensor was analyzed in combination with a bi-exponential kinetic adsorption ***,the potential application of ND/Ti_(3)C_(2)MXene composite-coated QCM humidity sensor was demonstrated through its frequency response to wooden blocks with different moisture contents.

关键词： Humidity sensor Quartz crystal microbalance Nanodiamond Ti_(3)C_(2)MXene High quality factor

Autocalibration for Creating 2D/3D Displays on Complex Geometric Surfaces

学校读者我要写书评

暂无评论

Information Display 2022年第6期38卷 31-37页

作者： Arnold, Katherine Fieguth, Paul Dennison, Andrew Vision and Image Processing Lab University of Waterloo Canada Systems Design Engineering Department University of Waterloo Canada Software Solutions Development Department Christie Digital Systems

Geometric calibration methods tend to rely on precalibrated cameras or real calibration targets. Here we explore the chal-lenge of achieving a sufficiently accurate projector system calibration for complex projection mapping tasks that can rely on neither precalibrated cameras nor calibration targets. Virtual scene models can take the place of a real calibration target for the purpose of providing scene information that is used to refine geometric estimates. Refinement of the display parameters aims to reduce the reprojection error, or the error between the theoretical coordinate (generated from the projection of the scene estimate coordinate by the estimated parameters) and the actual coordinate (obtained from pixel cor-respondences). For Christie installations, the final reprojection error of each pixel should be less than 1 pixel. Mystique was not always so capable. Christie’s research department, Christie labs, has a history of creating solutions for new projection concepts. Part of that strategy has been a long partnership with the university of waterloo, particularly the Vision and Image Processing (VIP) lab in systems design engineering. Graduate students study computer vision and machine learning—topics related to problems often at the heart of techniques—which can be used to bring new ideas, meth-ods, and solutions to Mystique and, by extension, to Christie’s customers. Years of investment in research and development, including partnership with the VIP lab and others, have enabled Mystique to handle challenges beyond any other projec-tor-alignment solution. ID. © 2022, John Wiley and Sons Inc. All rights reserved.

关键词： Pixels

Particle-Filtering-based Latent Diffusion for Inverse Problems

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Nazemi, Amir Sepanj, Mohammad Hadi Pellegrino, Nicholas Czarnecki, Chris Fieguth, Paul Vision & Image Processing Lab Department of Systems Design Engineering University of Waterloo WaterlooON Canada

Current strategies for solving image-based inverse problems apply latent diffusion models to perform posterior sampling. However, almost all approaches make no explicit attempt to explore the solution space, instead drawing only a single sample from a Gaussian distribution from which to generate their solution. In this paper, we introduce a particle-filtering-based framework for a nonlinear exploration of the solution space in the initial stages of reverse SDE methods. Our proposed particle-filtering-based latent diffusion (PFLD) method and proposed problem formulation and framework can be applied to any diffusion-based solution for linear or nonlinear inverse problems. Our experimental results show that PFLD outperforms the SoTA solver PSLD on the FFHQ-1K and ImageNet-1K datasets on inverse problem tasks of super resolution, Gaussian debluring and inpainting. © 2024, CC BY-NC-ND.

关键词： Wiener filtering

Explore the Future Earth with Wander 2.0: AI Chatbot Driven By Knowledge-base Story Generation and Text-to-image Model

学校读者我要写书评

暂无评论

Explore the Future Earth with Wander 2.0: AI Chatbot Driven ...

Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing systems, CHI EA 2023

作者： Sun, Yuqian Xu, Ying Cheng, Chenhang Li, Yihua Lee, Chang Hee Asadipour, Ali Computer Science Research Centre Royal College of Art London United Kingdom Department of Industrial Design School of Art and Design Wuhan University of Technology Wuhan China Shanghai China Department of Product Design College of Fashion and Design Donghua University Shanghai China Affective Systems and Cognition Lab Industrial Design Department College of Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)9781450394222

People always envision the future of earth through science fiction (Sci-fi), so can we create a unique experience of "visiting the future earth"through the lens of artificial intelligence (AI)? We introduce Wander 2.0, an AI chatbot that co-creates sci-fi stories through knowledge-based story generation on daily communication platforms like WeChat and Discord. Using location information from Google Maps, Wander generates narrative travelogues about specific locations (e.g. Paris) through a large-scale language model (LLM). Additionally, using the large-scale text-to-image model (LTGM) Stable Diffusion, Wander transfers future scenes that match both the text description and location photo, facilitating future imagination. The project also includes a real-time visualization of the human-AI collaborations on a future map. Through journeys with visitors from all over the world, Wander demonstrates how AI can serve as a subjective interface linking fiction and reality. Our research shows that multi-modal AI systems have the potential to extend the artistic experience and creative world-building through adaptive and unique content generation for different people. Wander 2.0 is available at http://***/ © 2023 Owner/Author.

关键词： Knowledge based systems

Memory-Efficient Continual Learning Object Segmentation for Long Videos

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Nazemi, Amir Shafiee, Mohammad Javad Gharaee, Zahra Fieguth, Paul Vision & Image Processing Lab Department of Systems Design Engineering University of Waterloo WaterlooON Canada

Recent state-of-the-art semi-supervised Video Object Segmentation (VOS) methods have shown significant improvements in target object segmentation accuracy when information from preceding frames is used in segmenting the current frame. In particular, such memory-based approaches can help a model to more effectively handle appearance changes (representation drift) or occlusions. Ideally, for maximum performance, Online VOS methods would need all or most of the preceding frames (or their extracted information) to be stored in memory and be used for online learning in later frames. Such a solution is not feasible for long videos, as the required memory size grows without bound, and such methods can fail when memory is limited and a target object experiences repeated representation drifts throughout a video. We propose two novel techniques to reduce the memory requirement of Online VOS methods while improving modeling accuracy and generalization on long videos. Motivated by the success of continual learning techniques in preserving previously-learned knowledge, here we propose Gated-Regularizer Continual Learning (GRCL), which improves the performance of any Online VOS subject to limited memory, and a Reconstruction-based Memory Selection Continual Learning (RMSCL), which empowers Online VOS methods to efficiently benefit from stored information in memory. We also analyze the performance of a hybrid combination of the two proposed methods. Experimental results show that the proposed methods are able to improve the performance of Online VOS models by more than 8%, with improved robustness on long-video datasets while maintaining comparable performance on short-video datasets such as DAVIS16, DAVIS17, and YouTube-VOS18. © 2023, CC BY-NC-SA.

关键词： Image segmentation

waterloo building dataset: a city-scale vector building dataset for mapping building footprints using aerial orthoimagery

学校读者我要写书评

暂无评论

Geomatica 2022年第3期75卷 99-115页

作者： He, Hongjie Jiang, Zijian Gao, Kyle Fatholahi, Sarah Narges Tan, Weikai Hu, Bingxu Xu, Hongzhang Chapman, Michael A. Li, Jonathan Geospatial Sensing and Data Intelligence Lab Department of Geography and Environmental Management University of Waterloo WaterlooONN2L 3G1 Canada Department of Civil Engineering Ryerson University TorontoONM5B 2K3 Canada Department of Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Automated building footprint extraction is an important area of research in remote sensing with numerous civil and environmental applications. In recent years, deep learning methods have far surpassed classical algorithms when trained on appropriate datasets. In this paper, we present the waterloo building dataset for building footprint extraction from very high spatial resolution aerial orthoimagery. Our dataset covers the Kitchener–waterloo area in Ontario, Canada, contains 117 000 manually labelled buildings, and extends over an area of 205.8 km2. At a spatial resolution of 12 cm, it is the highest-resolution publicly available building footprint extraction dataset in North America. We provide extensive benchmarks for commonly used deep learning architectures trained on our dataset, which can be used as a baseline for future models. We also identified a key challenge in aerial orthoimagery building footprint extraction, which we hope can be addressed in future research. © Canadian Science Publishing. All rights reserved.

关键词： Buildings

Wireless Federated Learning With Hybrid Local and Centralized Training: A Latency Minimization design

学校读者我要写书评

暂无评论

IEEE Journal on Selected Topics in Signal Processing 2023年第1期17卷 248-263页

作者： Huang, Ning Dai, Minghui Wu, Yuan Quek, Tony Q. S. Shen, Xuemin State Key Lab of Internet of Things for Smart City China University of Macau Department of Computer and Information Science China University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science China Zhuhai Um Science and Technology Research Institute Zhuhai519031 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore National Cheng Kung University Taiwan University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Wireless federated learning (FL) is a collaborative machine learning (ML) framework in which wireless client-devices independently train their ML models and send the locally trained models to the FL server for aggregation. In this paper, we consider the coexistence of privacy-sensitive client-devices and privacy-insensitive yet computing-resource constrained client-devices, and propose an FL framework with a hybrid centralized training and local training. Specifically, the privacy-sensitive client-devices perform local ML model training and send their local models to the FL server. Each privacy-insensitive client-device can have two options, i.e., (i) conducting a local training and then sending its local model to the FL server, and (ii) directly sending its local data to the FL server for the centralized training. The FL server, after collecting the data from the privacy-insensitive client-devices (which choose to upload the local data), conducts a centralized training with the received datasets. The global model is then generated by aggregating (i) the local models uploaded by the client-devices and (ii) the model trained by the FL server centrally. Focusing on this hybrid FL framework, we firstly analyze its convergence feature with respect to the client-devices' selections of local training or centralized training. We then formulate a joint optimization of client-devices' selections of the local training or centralized training, the FL training configuration (i.e., the number of the local iterations and the number of the global iterations), and the bandwidth allocations to the client-devices, with the objective of minimizing the overall latency for reaching the FL convergence. Despite the non-convexity of the joint optimization problem, we identify its layered structure and propose an efficient algorithm to solve it. Numerical results demonstrate the advantage of our proposed FL framework with the hybrid local and centralized training as well as our proposed alg

关键词： Information management

REGION-LEVEL labELS IN ICE CHARTS CAN PRODUCE PIXEL-LEVEL SEGMENTATION FOR SEA ICE TYPES

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Patel, Muhammed Chen, Xinwei Xu, Linlin Chen, Yuhao Scott, K. Andrea Clausi, David A. Vision and Image Processing Lab Systems Design Engineering University of Waterloo Canada School of Marine Science and Engineering South China University of Technology China Department of Mechanical and Mechatronics Engineering University of Waterloo Canada

Fully supervised deep learning approaches have demonstrated impressive accuracy in sea ice classification, but their dependence on high-resolution labels presents a significant challenge due to the difficulty of obtaining such data. In response, our weakly supervised learning method provides a compelling alternative by utilizing lower-resolution regional labels from expert-annotated ice charts. This approach achieves exceptional pixel-level classification performance by introducing regional loss representations during training to measure the disparity between predicted and ice chart-derived sea ice type distributions. Leveraging the AI4Arctic Sea Ice Challenge Dataset, our method outperforms the fully supervised U-Net benchmark, the top solution of the AutoIce challenge, in both mapping resolution and class-wise accuracy, marking a significant advancement in automated operational sea ice mapping. © 2024, CC BY.

关键词： Sea ice

Open access dataset for electromyography-based multi-code biometric authentication

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Pradhan, Ashirbad He, Jiayuan Jiang, Ning Engineering Bionics Lab Department of Systems Design Engineering Faculty of Engineering University of Waterloo Waterloo Canada

Recently, electromyogram (EMG) has been proposed as a novel biometrics for addressing some key limitations of current biometrics. EMG signals are inherently different for individuals (biometrics), and they can realize multi-length codes or passwords (for example, by performing different gestures). However, current EMG-based biometric research has two critical limitations: 1) a small subject pool, compared to other more established biometric traits, and 2) limited to single-session data sets. In this study, forearm and wrist EMG data were collected from 43 participants over three different days with long separation (Day 1, 8, and 29) while they performed static hand/wrist gestures. The multi-day biometric authentication resulted in a median EER of 0.017 and 0.025 for the forearm and wrist setup, respectively. These results demonstrated the potential of EMG-based biometrics in practical applications. The large-sample multi-day data set would facilitate further research on EMG-based biometrics and other EMG gesture recognition-based applications. © 2022, CC BY.

关键词： Biometrics