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تعداد مقالات یافته شده: 76
ردیف عنوان نوع
1 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
2 Deep learning based computer vision approaches for smart agricultural applications
رویکردهای بینایی کامپیوتری مبتنی بر یادگیری عمیق برای کاربردهای کشاورزی هوشمند-2022
The agriculture industry is undergoing a rapid digital transformation and is growing powerful by the pillars of cutting-edge approaches like artificial intelligence and allied technologies. At the core of artificial intelligence, deep learning-based computer vision enables various agriculture activities to be performed automatically with utmost precision enabling smart agriculture into reality. Computer vision techniques, in conjunction with high-quality image acquisition using remote cameras, enable non-contact and efficient technology-driven solutions in agriculture. This review contributes to providing state-of-the-art computer vision technologies based on deep learning that can assist farmers in operations starting from land preparation to harvesting. Recent works in the area of computer vision were analyzed in this paper and categorized into (a) seed quality analysis, (b) soil analysis, (c) irrigation water management, (d) plant health analysis, (e) weed management (f) livestock management and (g) yield estimation. The paper also discusses recent trends in computer vision such as generative adversarial networks (GAN), vision transformers (ViT) and other popular deep learning architectures. Additionally, this study pinpoints the challenges in implementing the solutions in the farmer’s field in real-time. The overall finding indicates that convolutional neural networks are the corner stone of modern computer vision approaches and their various architectures provide high-quality solutions across various agriculture activities in terms of precision and accuracy. However, the success of the computer vision approach lies in building the model on a quality dataset and providing real-time solutions.
keywords: Agriculture automation | Computer vision | Deep learning | Machine learning | Smart agriculture | Vision transformers
مقاله انگلیسی
3 Attention-based model and deep reinforcement learning for distribution of event processing tasks
مدل مبتنی بر توجه و یادگیری تقویتی عمیق برای توزیع وظایف پردازش رویداد-2022
Event processing is the cornerstone of the dynamic and responsive Internet of Things (IoT). Recent approaches in this area are based on representational state transfer (REST) principles, which allow event processing tasks to be placed at any device that follows the same principles. However, the tasks should be properly distributed among edge devices to ensure fair resources utilization and guarantee seamless execution. This article investigates the use of deep learning to fairly distribute the tasks. An attention-based neural network model is proposed to generate efficient load balancing solutions under different scenarios. The proposed model is based on the Transformer and Pointer Network architectures, and is trained by an advantage actorcritic reinforcement learning algorithm. The model is designed to scale to the number of event processing tasks and the number of edge devices, with no need for hyperparameters re-tuning or even retraining. Extensive experimental results show that the proposed model outperforms conventional heuristics in many key performance indicators. The generic design and the obtained results show that the proposed model can potentially be applied to several other load balancing problem variations, which makes the proposal an attractive option to be used in real-world scenarios due to its scalability and efficiency.
keywords: Web of Things (WoT) | Representational state transfer (REST) | application programming interface (APIs) | Edge computing | Load balancing | Resource placement | Deep reinforcement leaning | Transformer model | Pointer networks | Actor critic
مقاله انگلیسی
4 A Cryo-CMOS Oscillator With an Automatic Common-Mode Resonance Calibration for Quantum Computing Applications
یک نوسان ساز Cryo-CMOS با کالیبراسیون رزونانس حالت مشترک خودکار برای برنامه های محاسباتی کوانتومی-2022
This article presents a 4-to-5 GHz LC oscillator operating at 4.2 K for quantum computing applications. The phase noise (PN) specification of the oscillator is derived based on the control fidelity for a single-qubit operation. To reveal the substantial gap between the theoretical predictions and measurement results at cryogenic temperatures, a new PN expression for an oscillator is derived by considering the shot-noise effect. To reach the optimum performance of an LC oscillator, a common-mode (CM) resonance technique is implemented. Additionally, this work presents a digital calibration loop to adjust the CM frequency automatically at 4.2 K, reducing the oscillator’s PN and thus improving the control fidelity. The calibration technique reduces the flicker corner of the oscillator over a wide temperature range (10 × and 8 × reduction at 300 K and 4.2 K, respectively). At 4.2 K, our 0.15-mm 2 oscillator consumes a 5-mW power and achieves a PN of − 153.8 dBc/Hz at a 10 MHz offset, corresponding to a 200-dB FOM. The calibration circuits consume only a 0.4-mW power and 0.01-mm 2 area.
Index Terms—Quantum computing | qubit | cryogenic | oscillator | PLL | common-mode resonance calibration | phase noise | frequency noise | flicker noise | shot noise.
مقاله انگلیسی
5 The role of peripheral ocular length and peripheral corneal radius of curvature in determining refractive error
نقش طول چشم محیطی و شعاع انحنای قرنیه محیطی در تعیین خطای شکست-2021
Purpose: The purpose of this study was to extend the knowledge of peripheral biometric component and its relationship to refractive status in healthy individuals by determining the correlation between peripheral ocular length to peripheral corneal radius ratio and the refractive error.
Methods: This prospective study was conducted on thirty-three healthy adult participants. Refractive error was assessed objectively and subjectively and recorded as the mean spherical equivalent. Central and peripheral ocular lengths at 30◦ were assessed using partial coherence interferometry under dilation with 1% tropicamide. Central and peripheral corneal radius of curvature was assessed using Scheimpflug topography. Peripheral ocular lengths at 30◦ were paired with peripheral corneal curvatures at the incident points of the IOLMaster beam (3.8 mm away from corneal apex) superiorly, inferiorly, temporally and nasally to calculate the peripheral ocular length-peripheral corneal radius ratio. Descriptive statistics were used to describe the distribution and spread of the data. Pearson’s correlation analysis was used to present the association between biometric and refractive variables.
Results: Refractive error was negatively correlated with the axial length-central corneal radius ratio (r = −0.91; p < 0.001) and with 30◦ peripheral ocular length-peripheral corneal radius ratio in all four meridians (r ≤ −0.76; p < 0.001). The strength of the correlation was considerably lower when only axial length or peripheral ocular lengths were used.
Conclusion: Using the ratios of peripheral ocular length-peripheral corneal radius to predict refractive error is more effective than using peripheral corneal radius or peripheral ocular length alone.
KEYWORDS: Refractive error | Myopia | Peripheral ocular length | Peripheral corneal radius of curvature | Axial length
مقاله انگلیسی
6 Computer-vision classification of corn seed varieties using deep convolutional neural network
طبقه بندی بینایی ماشین انواع بذر ذرت با استفاده از شبکه عصبی پیچیده عمیق-2021
Automated classification of seed varieties is of paramount importance for seed producers to maintain the purity of a variety and crop yield. Traditional approaches based on computer vision and simple feature extraction could not guarantee high accuracy classification. This paper presents a new approach using a deep convolutional neural network (CNN) as a generic feature extractor. The extracted features were classified with artificial neural network (ANN), cubic support vector machine (SVM), quadratic SVM, weighted k-nearest-neighbor (kNN), boosted tree, bagged tree, and linear discriminant analysis (LDA). Models trained with CNN-extracted features demonstrated better classification accuracy of corn seed varieties than models based on only simple features. The CNN-ANN classifier showed the best performance, classifying 2250 test instances in 26.8 s with classification accuracy 98.1%, precision 98.2%, recall 98.1%, and F1-score 98.1%. This study demonstrates that the CNN-ANN classifier is an efficient tool for the intelligent classification of different corn seed varieties.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Machine vision | Deep learning | Feature extraction | Non-handcrafted features | Texture descriptors
مقاله انگلیسی
7 Compensating over- and underexposure in optical target pose determination
Compensating over- and underexposure in optical target pose determination-2021
Optical coded targets allow to determine the relative pose of a camera, on a metric scale, from one image only. Furthermore, they are easily and efficiently detected, opening to a wide range of applications in robotics and computer vision. In this work we describe the effect of pixel saturation and non-ideal lens Point Spread Function, causing the apparent position of the corners and the edges of the target to change as a function of the camera exposure time. This effect, which we call exposure bias, is frequent in over- or underexposed images and introduces a systematic error in the estimated camera pose. We propose an algorithm that is able to estimate and correct for the exposure bias exploiting specific geometric features of a common target design based on concentric circles. Through rigorous laboratory experiments carried out in a highly controlled environment, we demonstrate that the proposed algorithm is seven times more precise and three times more accurate in the target distance estimation than the algorithms available in the literature.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Optical target | Target orientation | Image processing algorithm | Geometry | Ellipse fitting | Computer vision | Overexposure | Exposure compensation | Resection
مقاله انگلیسی
8 Ocular Biometric Characteristics Measured by Swept-Source Optical Coherence Tomography in Individuals Undergoing Cataract Surgery
مشخصات بیومتریک چشم اندازه گیری شده توسط توموگرافی انسجام نوری منبع جارو در افراد تحت عمل جراحی آب مروارید-2021
PURPOSE: To study the distribution of ocular biometric parameters utilizing a swept-source optical coherence tomography (SS-OCT) biometer in adult candidates for cataract surgery.
Design: A retrospective cross-sectional study
METHODS: SETTING: A single-center analysis of consecutive eyes measured with the IOLMaster 700 SS-OCT biometer at a large tertiary medical center between February 2018 and June 2020.
RESULTS: 3836 eyes of 3836 patients were included in the study. The mean age was 72.3±12.8 years and 53% were females. The mean biometric values were: total corneal power (44.17±1.70D), total corneal astigmatism (TCA) (1.11±0.87D), mean posterior keratometry (- 5.87±0.26D), posterior corneal astigmatism (-0.26±0.15D), axial length (AL) (23.95±1.66mm), anterior chamber depth (ACD) (3.18±0.42mm), lens thickness (LT) (4.49±0.47mm); white-towhite distance (WTW) (11.92±0.44mm), central corneal thickness (CCT) (0.54 ± 0.04mm), angle alpha (0.49±0.17mm), and angle kappa (0.34±0.17mm). There were sex-related differences in all biometric parameters with the exception of LT (P=.440), angle kappa (P=.216), and corneal astigmatism (P=.103). Biometric parameters demonstrated correlations between AL, WTW distance, ACD, and LT (P<.001). Age correlated with all parameters (P<.001), with the exception of CCT and posterior keratometry. Angle alpha and angle kappa magnitudes also correlated (P<.001). The prevalence of patients with TCA ≥0.75D, 1.0D and 1.5D were 59.1%, 43.4% and22.6%,respectively.
CONCLUSIONS: Age significantly correlated with most of the biometric parameters and significant differences between sexes were noted. Furthermore, the high prevalence of TCA and relatively large angle alpha and angle kappa magnitudes were noted among subjects. These data can be relevant in planning local and national health economics.
مقاله انگلیسی
9 Uncalibrated stereo vision with deep learning for 6-DOF pose estimation for a robot arm system
دید استریو کالیبره نشده با یادگیری عمیق برای برآورد 6-DOF برای سیستم بازوی ربات-2021
This paper proposes a novel method for six degrees of freedom pose estimation of objects for the application of robot arm pick and place. It is based on the use of a stereo vision system, which does not require calibration. Using both cameras, four corner points of the object are detected. A deep-neural- network (DNN) is trained for the prediction of the 6 DOF pose of the object from the four detected corner points’ coordinates in each image of both cameras. The stereo vision used is a low-end vision system placed in a custom-made setup. Before the training phase of the DNN, the robot is set to auto collect data in a predefined workspace. This workspace is defined dependently on the spatial feasibility of the robot arm and the shared field of view of the stereo vision system. The collected data represent images of a 2D marker attached to the robot arm gripper. The 2D marker is used for data collection to ease the detection of the four corner points. The proposed method succeeds in estimating the six degrees of freedom pose of the object, without the need for the determination of neither the intrinsic nor the extrinsic parameters of the stereo vision system. The optimum design of the proposed DNN is obtained after comparing different activation functions and optimizers associated with the DNN. The proposed uncalibrated DNN-based method performance is compared to that of the traditional calibration-based method. In the calibration-based method, the rotational matrix relating the robot coordinates to the stereo vision coordinates is computed using two approaches. The first approach uses Singular Value Decomposition (SVD) while the second approach uses a novel proposed modification of particle swarm optimization (PSO) called Hyper particle Scouts optimization (HPSO). HPSO outperforms other metaheuristic optimization algorithms such as PSO and genetic algorithm (GA).Exhaustive tests are performed, and the proposed DNN-based method is shown to outperform all tested alternatives.© 2021 Elsevier B.V. All rights reserved.
Keywords: Deep learning | Pose estimation | Robot vision | Stereo vision | Optimization techniques | Levenberg–Marquardt algorithm
مقاله انگلیسی
10 Adaptive finite element eye model for the compensation of biometric influences on acoustic tonometry
مدل چشم اجزای محدود تطبیقی برای جبران تأثیرات بیومتریک بر تونومتری آکوستیک-2021
Background and objective: Glaucoma is currently a major cause for irreversible blindness worldwide. A risk factor and the only therapeutic control parameter is the intraocular pressure (IOP). The IOP is determined with tonometers, whose measurements are inevitably influenced by the geometry of the eye. Even though the corneal mechanics have been investigated to improve accuracy of Goldmann and air pulse tonometry, influences of geometric properties of the eye on an acoustic self-tonometer approach are still unresolved.
Methods: In order to understand and compensate for measurement deviations resulting from the geometric uniqueness of eyes, a finite element eye model is designed that considers all relevant eye components and is adjustable to all physiological shapes of the human eye.
Results: The general IOP-dependent behavior of the eye model is validated by laboratory measurements on porcine eyes. The difference between simulation and measurement is below 8 μm for IOP levels from 5 to 40 mmHg. The adaptive eye model is then used to quantify systematic uncertainty contributions of a variation of eye length and central corneal thickness based on input statistics of a clinical trial series. The adaptive eye model provides the required relation between biometric eye parameters and the corneal deflection amplitude, which here is the measured quantity to trace back to the IOP. Implementing the relations provided by the eye model in a Gaussian uncertainty propagation calculation now allows the quantification of the uncertainty contributions of the biometric parameters on the overall measurement uncertainty of the acoustic self-tonometer. As a result, a systematic uncertainty contribution resulting from deviations in eye length dominate stochastic deviations of the sensor equipment by a factor of 3.5.
Conclusion: As perspective, the proposed adaptive eye model provides the basis to compensate for systematic deviations of (but not only) the acoustic self-tonometer.
Keywords: Corneal vibration | Transient simulation | FEM | Eye model | Intraocular pressure | Glaucoma
مقاله انگلیسی
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