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نتیجه جستجو - measurement

تعداد مقالات یافته شده: 725
ردیف عنوان نوع
1 Moving towards intelligent telemedicine: Computer vision measurement of human movement
حرکت به سمت پزشکی از راه دور هوشمند: اندازه گیری بینایی کامپیوتری حرکت انسان-2022
Background: Telemedicine video consultations are rapidly increasing globally, accelerated by the COVID- 19 pandemic. This presents opportunities to use computer vision technologies to augment clinician visual judgement because video cameras are so ubiquitous in personal devices and new techniques, such as DeepLabCut (DLC) can precisely measure human movement from smartphone videos. However, the accuracy of DLC to track human movements in videos obtained from laptop cameras, which have a much lower FPS, has never been investigated; this is a critical gap because patients use laptops for most telemedicine consultations. Objectives: To determine the validity and reliability of DLC applied to laptop videos to measure finger tapping, a validated test of human movement. Method: Sixteen adults completed finger-tapping tests at 0.5 Hz, 1 Hz, 2 Hz, 3 Hz and at maximal speed. Hand movements were recorded simultaneously by a laptop camera at 30 frames per second (FPS) and by Optotrak, a 3D motion analysis system at 250 FPS. Eight DLC neural network architectures (ResNet50, ResNet101, ResNet152, MobileNetV1, MobileNetV2, EfficientNetB0, EfficientNetB3, EfficientNetB6) were applied to the laptop video and extracted movement features were compared to the ground truth Optotrak motion tracking. Results: Over 96% (529/552) of DLC measures were within +∕−0.5 Hz of the Optotrak measures. At tapping frequencies >4 Hz, there was progressive decline in accuracy, attributed to motion blur associated with the laptop camera’s low FPS. Computer vision methods hold potential for moving us towards intelligent telemedicine by providing human movement analysis during consultations. However, further developments are required to accurately measure the fastest movements.
keywords: پزشکی از راه دور | ضربه زدن با انگشت | موتور کنترل | کامپیوتری | Telemedicine | DeepLabCut | Finger tapping | Motor control | Computer vision
مقاله انگلیسی
2 The physical and mechanical properties for flexible biomass particles using computer vision
خواص فیزیکی و مکانیکی ذرات زیست توده انعطاف پذیر با استفاده از بینایی کامپیوتری-2022
The combustion and fluidization behavior of biomass depend on the physical properties (size, morphology, and density) and mechanical performances (elastic modulus, Poisson’s ratio, tensile strength and failure strain), but their quantitative models have rarely been focused in previous researchers. Hence, a static image measurement for particle physical properties is studied. Combining the uniaxial tension and digital image correlation tech- nology, the dynamic image measurement method for the mechanical properties is proposed. The results indicate that the average roundness, rectangularity, and sphericity of present biomass particles are 0.2, 0.4, and 0.16, respectively. The equivalent diameter and density obey the skewed normal distribution. The tensile strength and failure stress are sensitive to stretching rate, fiber size and orientation. The distribution intervals of elastic modulus and Poisson’s ratio are 30–600 MPa and 0.25–0.307, respectively. The stress–strain curves obtained from imaging experiments agree well with the result of finite element method. This study provides the operating parameters for the numerical simulation of particles in the fluidized bed and combustor. Furthermore, the computer vision measurement method can be extended to the investigations of fossil fuels.
keywords: ذرات زیست توده | مشخصات فیزیکی | اجرای مکانیکی | تست کشش | آزمایش تصویربرداری | بینایی کامپیوتر | Biomass particle | Physical properties | Mechanical performances | Tensile testing | Imaging experiment | Computer vision
مقاله انگلیسی
3 A robust structural vibration recognition system based on computer vision
یک سیستم قوی تشخیص ارتعاش ساختاری بر اساس بینایی کامپیوتری-2022
Vibration-based structural health monitoring (SHM) systems are useful tools for assessing structural safety performance quantitatively. When employing traditional contact sensors, achieving high-resolution spatial measurements for large-scale structures is challenging, and fixed contact sensors may also lose dependability when the lifetime of the host structure is surpassed. Researchers have paid close attention to computer vision because it is noncontact, saves time and effort, is inexpensive, and has high efficiency in giving visual perception. In advanced noncontact measurements, digital cameras can capture the vibration information of structures remotely and swiftly. Thus, this work studies a system for recognizing structural vibration. The system ensures acquiring high-quality structural vibration signals by the following: 1) Establishing a novel image preprocessing, which includes visual partitioning measurement and image enhancement techniques; 2) initial recognition of structural vibration using phase-based optical flow estimation (POFE), which introduces 2-D Gabor wavelets to extract the independent phase information of the image to track the natural texture targets on the surface of the structure; 3) extracting the practical vibration information of the structure using mode decomposition to remove the complex environment of the camera vibration and other noises; 4) employing phase-based motion magnification (PMM) techniques to magnify small vibration signals, and then recognizing the complete information on the vibration time range of the structure. The research results of the laboratory experiments and field testing conducted under three different cases reveal that the system can recognize structural vibration in complicated environments.
keywords: Computer vision | Phase | Motion estimation | Motion magnification | Mode decomposition | Structural vibration
مقاله انگلیسی
4 Eigen-Spectrum Estimation and Source Detection in a Massive Sensor Array Based on Quantum Assisted Hamiltonian Simulation Framework
تخمین طیف ویژه و تشخیص منبع در یک آرایه حسگر عظیم بر اساس چارچوب شبیه‌سازی همیلتونی به کمک کوانتومی-2022
In this work, we propose quantum assisted eigenvalue estimation and target detection algorithms for a large sensor array via Hamiltonian simulation. Quantum algorithms provide complexity advantage of a certain class of problems on a quantum computer with fewer physical resources as compared to their classical counterparts. The proposed algorithms make use of the quantum phase estimation (QPE) as its core computing component. We have introduced an analytical quantum framework to map from classical to quantum in the context of target detection. Target detection involves an appropriate choice of threshold based on the probability of detection or false alarm. We exploited the massive sensor array structure and invoked the random matrix theory to propose an optimal threshold. It also takes into account the quantum measurement noise in the framework. Numerical simulations are performed to ascertain the efficacy of the proposed framework. The results suggest near term applications of the quantum algorithm for large-scale linear systems. Index Terms: Quantum signal processing | quantum eigenvalue estimation | quantum phase estimation | Hamiltonian simulation | array signal processing.
مقاله انگلیسی
5 Spatiotemporal flow features in gravity currents using computer vision methods
ویژگی های جریان مکانی-زمانی در جریان های گرانشی با استفاده از روش های بینایی کامپیوتری-2022
Relationships between the features visually identified at the front of the flow’s current and parameters regarding its velocity and turbulence were observed in early experimental works on the characterization of gravity currents. Researches have associated front features, like lobes and clefts, with the flow’s turbulence, and have used these associations ever since. In more recent works using numerical simulations, these connections were still being validated for various flow parameters at higher front velocities. The majority of works regarding measurements at the front of a gravity current rely on the front’s images for making its analysis and establish relationships. Besides that, there is an interdisciplinary field related to computer science called computer vision, devoted to study how digital images can be analyzed and how these results can be automated. This paper describes the use of computer vision algorithms, particularly corner detection and optical flow, to automatically track features at the front of gravity currents, either from physical or numerical experiments. To determine the proposed approach’s accuracy, we establish a ground-truth method and apply it to numerical simulation results data sets. The technique used to trace the front features along the flow showed promising results, especially with higher Reynolds numbers flows.
keywords: جریان های گرانشی | ساختارهای لوب و شکاف | روش های کامپیوتری | ویژگی ردیابی | Gravitycurrents | Lobesandcleftsstructures | Computervisionmethods | Featurepointtracking
مقاله انگلیسی
6 A computer vision-based method for bridge model updating using displacement influence lines
یک روش مبتنی بر بینایی کامپیوتری برای به‌روزرسانی مدل پل با استفاده از خطوط موثر جابجایی-2022
This paper presents a new computer vision-based method that simultaneously provides the moving vehicle’s tire loads, the location of the loads on a bridge, and the bridge’s response displacements, based on which the bridge’s influence lines can be constructed. The method employs computer vision techniques to measure the displacement influence lines of the bridge at different target positions, which is then later used to perform model updating of the finite element models of the monitored structural system.
The method is enabled by a novel computer vision-based vehicle weigh-in-motion method which the coauthors recently introduced. A correlation discriminating filter tracker is used to estimate the displacements at target points and the location of single or multiple moving loads, while a low-cost, non-contact weigh-in-motion technique evaluates the magnitude of the moving vehicle loads.
The method described in this paper is tested and validated using a laboratory bridge model. The system was loaded with a vehicle with pressurized tires and equipped with a monitoring system consisting of laser displacement sensors, accelerometers, and cameras. Both artificial and natural targets were considered in the experimental tests to track the displacements with the cameras and yielded robust results consistent with the laser displacement measurements.
The extracted normalized displacement influence lines were then successfully used to perform model updating of the structure. The laser displacement sensors were used to validate the accuracy of the proposed computer vision-based approach in deriving the displacement measurements, while the accelerometers were used to derive the system’s modal properties employed to validate the updated finite element model. As a result, the updated finite element model correctly predicted the bridge’s displacements measured during the tests. Furthermore, the modal parameters estimated by the updated finite element model agreed well with those extracted from the experimental modal analysis carried out on the bridge model. The method described in this paper offers a low-cost non-contact monitoring tool that can be efficiently used without disrupting traffic for bridges in model updating analysis or long-term structural health monitoring.
keywords: Computer vision | Displacement influence line | Vehicle weigh-in-motion | Structural identification | Finite element method model | Model updating | Modal analysis | Bridge systems
مقاله انگلیسی
7 A novel method of fish tail fin removal for mass estimation using computer vision
یک روش جدید حذف باله دم ماهی برای تخمین جرم با استفاده از بینایی کامپیوتر-2022
Fish mass estimation is extremely important for farmers to get fish biomass information, which could be useful to optimize daily feeding and control stocking densities and ultimately determine optimal harvest time. However, fish tail fin mass does not contribute much to total body mass. Additionally, the tail fin of free-swimming fish is deformed or bent for most of the time, resulting in feature measurement errors and further affecting mass prediction accuracy by computer vision. To solve this problem, a novel non-supervised method for fish tail fin removal was proposed to further develop mass prediction models based on ventral geometrical features without tail fin. Firstly, fish tail fin was fully automatically removed using the Cartesian coordinate system and image processing. Secondly, the different features were respectively extracted from fish image with and without tail fin. Finally, the correlational relationship between fish mass and features was estimated by the Partial Least Square (PLS). In this paper, tail fins were completely automatically removed and mass estimation model based on area and area square has been the best tested on the test dataset with a high coefficient of determination (R2) of 0.991, the root mean square error (RMSE) of 7.10 g, the mean absolute error (MAE) of 5.36 g and the maximum relative error (MaxRE) of 8.46%. These findings indicated that mass prediction model without fish tail fin can more accurately estimate fish mass than the model with tail fin, which might be extended to estimate biomass of free- swimming fish underwater in aquaculture.
keywords: برداشتن باله دم | اتوماسیون | ماهی | تخمین انبوه | بینایی کامپیوتر | Tail fin removal | Automation | Fish | Mass estimation | Computer vision
مقاله انگلیسی
8 Prediction of total volatile basic nitrogen (TVB-N) and 2-thiobarbituric acid (TBA) of smoked chicken thighs using computer vision during storage at 4 °C
پیش‌بینی کل نیتروژن بازی فرار (TVB-N) و اسید ۲-تیوباربیتوریک (TBA) ران مرغ دودی با استفاده از بینایی رایانه در طول نگهداری در دمای ۴ درجه سانتی‌گراد-2022
As the traditional indicators of freshness measurement of meat products, TVB-N and TBA have the disadvantage of time-consuming, labor-intensive and destructive to the sample. The objective of this study was to investigate the possibility of computer vision techniques to visualize the variation of TVB-N and TBA during the storage of smoked chicken thighs. In this study, freshness indicators (TVB-N and TBA) and images of smoked chicken thighs were obtained simultaneously every 3 days during storage at 4 ◦C. Then, the RGB color space was converted to HSI and L*a*b* color spaces by color conversion algorithm, and the color parameters (RGB, HSI and L*a*b*) were correlated with TVB-N and TBA, respectively, for establishing multiple regression models. Finally, visu- alization maps of the spoilage were established by applying the multiple regression model to each pixel in the image. The results showed that the multiple linear regression models of TBA and TVB-N based on the color parameters L*, a*, I, S and R were well correlated (R 2 = 0.993 for TBA and R 2 = 0.970 for TVB-N). Distribution maps of TBA and TVB-N changed color gradually from blue to red during storage, respectively. In conclusion, this study demonstrated that distribution maps can be employed as a rapid, objective, and non-destructive method to predict the TBA and TVB-N values of smoked chicken thighs during storage.
keywords: ران مرغ دودی | بینایی کامپیوتر | خنکی | TVB-N | TBA | Smoked chicken thigh | Computer vision | Freshness
مقاله انگلیسی
9 Assessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data
ارزیابی شرایط زهکشی سطحی در مقیاس خیابان و محله: یک روش دید کامپیوتری و جهت جریان اعمال شده به داده های لیدار-2022
Surface drainage at the neighborhood and street scales plays an important role in conveying stormwater and mitigating urban flooding. Surface drainage at the local scale is often ignored due to the lack of up-to-date fine- scale topographical information. This paper addresses this issue by providing a novel method for evaluating surface drainage at the neighborhood and street scales based on mobile lidar (light detection and ranging) measurements. The developed method derives topographical properties and runoff accumulation by applying a semantic segmentation (SS) model (a computer vision technique) and a flow direction model (a hydrology technique) to lidar data. Fifty lidar images representing 50 street blocks were used to train, validate, and test the SS model. Based on the test dataset, the SS model has 80.3% IoU and 88.5% accuracy. The results suggest that the proposed method can effectively evaluate surface drainage conditions at both the neighborhood and street scales and identify problematic low points that could be susceptible to water ponding. Municipalities and property owners can use this information to take targeted corrective maintenance actions.
keywords: تقسیم بندی معنایی | جهت جریان | لیدار موبایل | زهکشی سطحی | زیرساخت های زهکشی | Semantic segmentation | Flow direction | Mobile lidar | Surface drainage | Drainage infrastructure
مقاله انگلیسی
10 Generation of Accessible Sets in the Dynamical Modeling of Quantum Network Systems
تولید مجموعه‌های قابل دسترس در مدل‌سازی دینامیکی سیستم‌های شبکه کوانتومی-2022
In this article, we consider the dynamical modeling of a class of quantum network systems consisting of qubits, where information extraction is allowed by performing measurement on several selected qubits of the system. For a variety of applications, a state space model is a useful approach to modeling the system dynamics. To construct a state space model for a quantum network system, the major task is to find an accessible set containing all of the operators coupled to the measurement operators. This article focuses on the generation of a proper accessible set for a given system and measurement scheme. We provide analytic results on simplifying the process of generating accessible sets for systems with a time-independent Hamiltonian. Since the order of elements in the accessible set determines the form of state space matrices, guidance is provided to effectively arrange the ordering of elements in the state vector. Defining a system state according to the accessible set, one can develop a state space model with a special pattern inherited from the system structure. As a demonstration, we specifically consider a typical 1-D-chain system with several common measurements and employ the proposed method to determine its accessible set.
Index Terms: Accessible set | dynamical modeling | quantum network system | quantum system.
مقاله انگلیسی
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