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B-scan-sectioned dynamic micro-optical coherence tomography for bulk-motion suppression

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Chinese Optics Letters 2022年第2期20卷 34-39页

作者： Chaoliang Chen Weisong Shi Zhiyuan Qiu Victor X.D.Yang Wanrong Gao Advanced Photonics Center School of Electronic Science&EngineeringSoutheast UniversityNanjing 210096China Department of Optical Engineering Nanjing University of Science and TechnologyNanjing 210094China Department of Electrical Computerand Biomedical EngineeringRyerson UniversityTorontoOntario M5B 2K3Canada Division of Neurosurgery Sunnybrook Health Sciences CentreTorontoOntario M4N 3M5Canada Division of Neurosurgery Faculty of MedicineUniversity of TorontoTorontoOntario M5T 1P5Canada

In this Letter, we present B-scan-sectioned dynamic micro-optical coherence tomography(BD-MOCT) for high-quality subcellular dynamic contrast imaging. Dynamic micro-optical coherence tomography(D-MOCT) is a functional optical coherence tomography(OCT) technique performed on high-resolution(micron level) OCT systems;hundreds of consecutive B-scans need to be acquired for dynamic signal extraction, which requires relatively long data acquisition time. Bulk motions occurring during data acquisition(even at the micron level) may degrade the quality of the obtained dynamic contrast images. In BD-MOCT, each full B-scan is divided into several sub-B-scans, and each sub-B-scan repeats multiple times before the sample beam moves to the next sub-B-scan. After all of the sub-B-scans for a full B-scan are completely acquired, we stitch all of the sub-B-scans into the same number of full B-scans. In this way, the time interval between two consecutive stitched B-scans could be reduced multiple times for bulk-motion suppression. The performed scanning protocol modulates the scanning sequences of fast scanning and repeat scanning for improving the dynamic contrast image quality, while the total data acquisition time remains almost the same.

关键词： optical coherence tomography high-resolution imaging dynamic imaging bulk-motion suppression

Development of Unmanned Cargo VTOL Aircraft 1

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59th Symposium on engineering Modelling in Mechanics, 2020

作者： Czyba, Roman Gorol, Zbigniew Lemanowicz, Marcin Pawlak, Mariusz Sikora, Adam Faculty of Automatic Control Electronics and Computer Science Department of Automatic Control and Robotics Silesian University of Technology Akademicka Street 2A Gliwice44-100 Poland Faculty of Mechanical Engineering Department of Theoretical and Applied Mechanics Silesian University of Technology Akademicka Street 2A Gliwice44-100 Poland Faculty of Chemistry Department of Chemical Engineering and Process Design Silesian University of Technology Akademicka Street 2A Gliwice44-100 Poland

ISBN: (数字)9783030684556

ISBN: (纸本)9783030684549

The aim of the paper is to present a design cycle regarding to the construction of an unmanned cargo aircraft with own weight up to 25 kg, operating range of up to 100 km and AGL operating ceiling of up to 1 km. The task of the project team was to plan the design cycle, perform analytical calculations and CFD simulations, and finally to build an unmanned aircraft capable of lifting cargo. At the same time, a number of assumptions and technical regulations regarding constructions had to be met, which are specified in legal regulations regarding the given class of unmanned aerial vehicles. In order to achieve optimal UAV aerodynamic properties, including maximization of the payload while minimizing the own weight, aerodynamic calculations and CFD simulations were performed. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Unmanned aerial vehicles (UAV)

Hilbert-Space Ergodicity in Driven Quantum Systems: Obstructions and Designs

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arXiv 2024年

作者： Pilatowsky-Cameo, Saúl Marvian, Iman Choi, Soonwon Ho, Wen Wei Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA02139 United States Departments of Physics & Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Physics National University of Singapore 117551 Singapore Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 117543 Singapore

Despite its long history, a canonical formulation of quantum ergodicity that applies to general classes of quantum dynamics, including driven systems, has not been fully established. Here we introduce and study a notion of quantum ergodicity for closed systems with time-dependent Hamiltonians, defined as statistical randomness exhibited in their longtime dynamics. Concretely, we consider the temporal ensemble of quantum states (time-evolution operators) generated by the evolution, and investigate the conditions necessary for them to be statistically indistinguishable from uniformly random states (operators) in the Hilbert space (space of unitaries). We find that the number of driving frequencies underlying the Hamiltonian needs to be sufficiently large for this to occur. Conversely, we show that statistical pseudo-randomness — indistinguishability up to some large but finite moment, can already be achieved by a quantum system driven with a single frequency, i.e., a Floquet system, as long as the driving period is sufficiently long. Our work relates the complexity of a time-dependent Hamiltonian and that of the resulting quantum dynamics, and offers a fresh perspective to the established topics of quantum ergodicity and chaos from the lens of quantum information. Copyright © 2024, The Authors. All rights reserved.

关键词： Hamiltonians

Image color consistency in datasets: the Smooth-TPS3D method

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arXiv 2024年

作者： Benito-Altamirano, Ismael Martínez-Carpena, David Lizarzaburu-Aguilar, Hanna Ventura, Carles Fàbrega, Cristian Prades, Joan Daniel MIND/IN2UB Department of Electronic and Biomedical Engineering Universitat de Barcelona Carrer de Martí i Franquès 1 Barcelona Barcelona08028 Spain Department of Mathematics and Computer Science Universitat de Barcelona Gran Via de les Corts Catalanes 585 Barcelona Barcelona08007 Spain eHealth Center Faculty of Computer Science Multimedia and Telecommunications Universitat Oberta de Catalunya Rambla del Poblenou 156 Barcelona Barcelona08018 Spain Technische Universität Braunschweig Hans-Sommer Str. 66 BraunschweigD-38106 Germany

Image color consistency is the key problem in digital imaging consistency when creating datasets. Here, we propose an improved 3D Thin-Plate Splines (TPS3D) color correction method to be used, in conjunction with color charts (i.e. Macbeth ColorChecker) or other machine-readable patterns, to achieve image consistency by post-processing. Also, we benchmark our method against its former implementation and the alternative methods reported to date with an augmented dataset based on the Gehler’s ColorChecker dataset. Benchmark includes how corrected images resemble the ground-truth images and how fast these implementations are. Results demonstrate that the TPS3D is the best candidate to achieve image consistency. Furthermore, our Smooth-TPS3D method shows equivalent results compared to the original method and reduced the 11-15% of ill-conditioned scenarios which the previous method failed to less than 1%. Moreover, we demonstrate that the Smooth-TPS method is 20% faster than the original method. Finally, we discuss how different methods offer different compromises between quality, correction accuracy and computational load. © 2024, CC BY-SA.

关键词： Color image processing

LabPET II scanner performances improvement: Thermal stability control based on FPGA

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2023年 6卷

作者： Oukaira, Aziz Said, Dhaou Zbitou, Jamal Fontaine, Réjean Lakhssassi, Ahmed Department of Engineering and Computer Science University of Quebec in Outaouais Gatineau J9A 1L8 QC Canada Department of Electrical and Computer Engineering Sherbrook University Sherbrooke J1K 2R1 QC Canada LABTIC ENSA of Tangier University of Abdelmalek Essaadi Tangier 93000 Morocco ENSA of Tétouan University of Abdelmalek Essaadi Tétouan 93000 Morocco

The LabPET II detection module serves as the fundamental component within Positron Emission Tomography (PET) scanners designed for achieving ultra-high-resolution imaging of small to medium-sized animals as well as the human brain. To ensure peak performance, it is imperative that this module operates consistently at a stable temperature. In this paper, we present an automatic and real-time method to improve the thermal control of the scanner LabPET II by evacuating the heat to reduce the overall temperature and maintain the thermal stability of the system. Based on the Thermo-Electric Cooler (TEC) process, our method applies two numerical techniques, Computational Fluid Dynamics (CFD) and Heat Transfer Analysis (HTA). The Dirichlet Boundary Conditions (DBC) method is applied for the Application-Specific Integrated Circuit (ASIC) at 25^∘ C. The real measurements confirm and validate the simulation results with the Finite Element Method (FEM) based on the software COMSOL Multiphysics™. Field-Programmable Gate Array (FPGA) implementation on the Development and Education (DE1) board is performed with comparative results study. The proposed method is both simple and practical, and ensures thermal stability at 35^∘C for the duration of operation of the scanner, which has a complex structure. © 2023 The Author(s)

关键词： Application-specific integrated circuit Computational fluid dynamics (CFD) Field-programmable gate array (FPGA) Finite element method (FEM) Positron emission tomography (PET) Thermo-electric cooler (TEC)

Head2Head++: Deep Facial Attributes Re-Targeting

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IEEE Transactions on Biometrics, Behavior, and Identity Scie...

IEEE Transactions on Biometrics, Behavior, and Identity science 2021年第1期3卷 31-43页

作者： Doukas, Michail Christos Koujan, Mohammad Rami Sharmanska, Viktoriia Roussos, Anastasios Zafeiriou, Stefanos Department of Computing Imperial College London LondonN1C 4AG United Kingdom College of Engineering Mathematics and Physical Sciences University of Exeter ExeterEX4 4PY United Kingdom Department of Computing Imperial College London LondonSW7 2BU United Kingdom Institute of Computer Science Heraklion700 13 Greece

Facial video re-targeting is a challenging problem aiming to modify the facial attributes of a target subject in a seamless manner by a driving monocular sequence. We leverage the 3D geometry of faces and Generative Adversarial Networks (GANs) to design a novel deep learning architecture for the task of facial and head reenactment. Our method is different to purely 3D model-based approaches, or recent image-based methods that use Deep Convolutional Neural Networks (DCNNs) to generate individual frames. We manage to capture the complex non-rigid facial motion from the driving monocular performances and synthesise temporally consistent videos, with the aid of a sequential Generator and an ad-hoc Dynamics Discriminator network. We conduct a comprehensive set of quantitative and qualitative tests and demonstrate experimentally that our proposed method can successfully transfer facial expressions, head pose and eye gaze from a source video to a target subject, in a photo-realistic and faithful fashion, better than other state-of-the-art methods. Most importantly, our system performs end-to-end reenactment in nearly real-time speed (18 fps). © 2019 IEEE.

关键词： Generative adversarial networks

SGNet: A super-class guided network for image classification and object detection

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arXiv 2021年

作者： Li, Kaidong Wang, Nina Y. Yang, Yiju Wang, Guanghui Department of Electrical Engineering and Computer Science University of Kansas Lawrence KS66045 United States Department of Computer Science University of Toronto TorontoONM5S 1A1 Canada Department of Computer Science Ryerson University TorontoONM5B 2K3 Canada

Most classification models treat different object classes in parallel and the misclassifications between any two classes are treated equally. In contrast, human beings can exploit high-level information in making a prediction of an unknown object. Inspired by this observation, the paper proposes a super-class guided network (SGNet) to integrate the high-level semantic information into the network so as to increase its performance in inference. SGNet takes two-level class annotations that contain both super-class and finer class labels. The super-classes are higher-level semantic categories that consist of a certain amount of finer classes. A super-class branch (SCB), trained on super-class labels, is introduced to guide finer class prediction. At the inference time, we adopt two different strategies: Two-step inference (TSI) and direct inference (DI). TSI first predicts the super-class and then makes predictions of the corresponding finer class. On the other hand, DI directly generates predictions from the finer class branch (FCB). Extensive experiments have been performed on CIFAR-100 and MS COCO datasets. The experimental results validate the proposed approach and demonstrate its superior performance on image classification and object detection. Copyright © 2021, The Authors. All rights reserved.

关键词： Object detection

Cooperative Resource Management in Quantum Key Distribution (QKD) Networks for Semantic Communication

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arXiv 2022年

作者： Kaewpuang, Rakpong Xu, Minrui Lim, Wei Yang Bryan Niyato, Dusit Yu, Han Kang, Jiawen Shen, Xuemin Sherman The School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Alibaba Group and Alibaba NTU Joint Research Institute Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Increasing privacy and security concerns in intelligence-native 6G networks require quantum key distribution-secured semantic information communication (QKD-SIC). In QKD-SIC systems, edge devices connected via quantum channels can efficiently encrypt semantic information from the semantic source, and securely transmit the encrypted semantic information to the semantic destination. In this paper, we consider an efficient resource (i.e., QKD and KM wavelengths) sharing problem to support QKD-SIC systems under the uncertainty of semantic information generated by edge devices. In such a system, QKD service providers offer QKD services with different subscription options to the edge devices. The QKD services are envisioned to follow cloud computing that has the subscription in the reservation and on-demand options, i.e., for long and short (immediate) terms, respectively. As such, to reduce the cost for the edge device users, we propose a QKD resource management framework for the edge devices communicating semantic information. The framework is based on a two-stage stochastic optimization model to achieve optimal QKD deployment. Moreover, to reduce the deployment cost of QKD service providers, QKD resources in the proposed framework can be utilized based on efficient QKD-SIC resource management, including semantic information transmission among edge devices, secret-key provisioning, and cooperation formation among QKD service providers. In detail, the formulated two-stage stochastic optimization model can achieve the optimal QKD-SIC resource deployment while meeting the secret-key requirements for semantic information transmission of edge devices. Moreover, to share the cost of the QKD resource pool among cooperative QKD service providers forming a coalition in a fair and interpretable manner, the proposed framework leverages the concept of Shapley value from cooperative game theory as a solution. Experimental results demonstrate that the proposed framework can reduce th

关键词： Game theory

R-Bench: Are your Large Multimodal Model Robust to Real-world Corruptions?

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IEEE Journal on Selected Topics in Signal Processing 2025年

作者： Li, Chunyi Zhang, Jianbo Zhang, Zicheng Wu, Haoning Tian, Yuan Sun, Wei Lu, Guo Min, Xiongkuo Liu, Xiaohong Lin, Weisi Zhang, Xiao-Ping Zhai, Guangtao Shanghai Jiao Tong University Institute of Image Communication and Network Engineering Shanghai200240 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Shanghai AI Lab Shanghai200232 China East China Normal University School of Communication and Electronic Engineering Shanghai200240 China Shanghai Jiao Tong University John Hopcroft Center Shanghai200240 China Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering TorontoONM5B 2K3 Canada

The outstanding performance of Large Multimodal Models (LMMs) has made them widely applied in vision-related tasks. However, various corruptions in the real world mean that images will not be as ideal as in simulations, presenting significant challenges for the practical application of LMMs. To address this issue, we introduce R-Bench, a benchmark focused on the Real-world Robustness of LMMs. Specifically, we: (a) model the complete link from user capture to LMMs reception, comprising 33 corruption dimensions, including 7 steps according to the corruption sequence, and 7 groups based on low-level attributes;(b) collect reference/distorted image dataset before/after corruption, including 2,970 question-answer pairs with human labeling;(c) propose comprehensive evaluation for absolute/relative robustness and benchmark 20 mainstream LMMs. Results show that while LMMs can correctly handle the original reference images, their performance is not stable when faced with distorted images, and there is a significant gap in robustness compared to the human visual system. We hope that R-Bench will inspire improving the robustness of LMMs, extending them from experimental simulations to the real-world application. © 2007-2012 IEEE.

关键词： AI-Generated Content Image Compression Perceptual Quality Ultra-Low Bitrate