977 resultados para data processing in real-time
Resumo:
The automatic interpolation of environmental monitoring network data such as air quality or radiation levels in real-time setting poses a number of practical and theoretical questions. Among the problems found are (i) dealing and communicating uncertainty of predictions, (ii) automatic (hyper)parameter estimation, (iii) monitoring network heterogeneity, (iv) dealing with outlying extremes, and (v) quality control. In this paper we discuss these issues, in light of the spatial interpolation comparison exercise held in 2004.
Resumo:
Wireless sensor networks have been identified as one of the key technologies for the 21st century. In order to overcome their limitations such as fault tolerance and conservation of energy, we propose a middleware solution, In-Motes. In-Motes stands as a fault tolerant platform for deploying and monitoring applications in real time offers a number of possibilities for the end user giving him in parallel the freedom to experiment with various parameters, in an effort the deployed applications to run in an energy efficient manner inside the network. The proposed scheme is evaluated through the In-Motes EYE application, aiming to test its merits under real time conditions. In-Motes EYE application which is an agent based real time In-Motes application developed for sensing acceleration variations in an environment. The application was tested in a prototype area, road alike, for a period of four months.
Resumo:
Purpose: To examine the use of real-time, generic edge detection, image processing techniques to enhance the television viewing of the visually impaired. Design: Prospective, clinical experimental study. Method: One hundred and two sequential visually impaired (average age 73.8 ± 14.8 years; 59% female) in a single center optimized a dynamic television image with respect to edge detection filter (Prewitt, Sobel, or the two combined), color (red, green, blue, or white), and intensity (one to 15 times) of the overlaid edges. They then rated the original television footage compared with a black-and-white image displaying the edges detected and the original television image with the detected edges overlaid in the chosen color and at the intensity selected. Footage of news, an advertisement, and the end of program credits were subjectively assessed in a random order. Results: A Prewitt filter was preferred (44%) compared with the Sobel filter (27%) or a combination of the two (28%). Green and white were equally popular for displaying the detected edges (32%), with blue (22%) and red (14%) less so. The average preferred edge intensity was 3.5 ± 1.7 times. The image-enhanced television was significantly preferred to the original (P < .001), which in turn was preferred to viewing the detected edges alone (P < .001) for each of the footage clips. Preference was not dependent on the condition causing visual impairment. Seventy percent were definitely willing to buy a set-top box that could achieve these effects for a reasonable price. Conclusions: Simple generic edge detection image enhancement options can be performed on television in real-time and significantly enhance the viewing of the visually impaired. © 2007 Elsevier Inc. All rights reserved.
Resumo:
An array of FBG curvature sensors are wavelength-interrogated and the recovered data combined with a three-dimensional algorithm to reconstruct in real time the enveloped object with a 1% to 9% volumetric error. © 2012 OSA.
Resumo:
This work looks into video quality assessment applied to the field of telecare and proposes an alternative metric to the more traditionally used PSNR based on the requirements of such an application. We show that the Pause Intensity metric introduced in [1] is also relevant and applicable to heterogeneous networks with a wireless last hop connected to a wired TCP backbone. We demonstrate through our emulation testbed that the impairments experienced in such a network architecture are dominated by continuity based impairments rather than artifacts, such as motion drift or blockiness. We also look into the implication of using Pause Intensity as a metric in terms of the overall video latency, which is potentially problematic should the video be sent and acted upon in real-time. We conclude that Pause Intensity may be used alongside the video characteristics which have been suggested as a measure of the overall video quality. © 2012 IEEE.
Resumo:
In recent years, depth cameras have been widely utilized in camera tracking for augmented and mixed reality. Many of the studies focus on the methods that generate the reference model simultaneously with the tracking and allow operation in unprepared environments. However, methods that rely on predefined CAD models have their advantages. In such methods, the measurement errors are not accumulated to the model, they are tolerant to inaccurate initialization, and the tracking is always performed directly in reference model's coordinate system. In this paper, we present a method for tracking a depth camera with existing CAD models and the Iterative Closest Point (ICP) algorithm. In our approach, we render the CAD model using the latest pose estimate and construct a point cloud from the corresponding depth map. We construct another point cloud from currently captured depth frame, and find the incremental change in the camera pose by aligning the point clouds. We utilize a GPGPU-based implementation of the ICP which efficiently uses all the depth data in the process. The method runs in real-time, it is robust for outliers, and it does not require any preprocessing of the CAD models. We evaluated the approach using the Kinect depth sensor, and compared the results to a 2D edge-based method, to a depth-based SLAM method, and to the ground truth. The results show that the approach is more stable compared to the edge-based method and it suffers less from drift compared to the depth-based SLAM.
Resumo:
[EN]This paper describes a real-time approach for face detection and selection of frontal views, for further processing. Typically, face detection papers provide results for a set of single images but the problem of face detection in video streams rarely is tackled. Instead of performing an exhaustive search for every video stream frame a set of opportunistic ideas applied in a cascade fashion and based on temporal and spatial coherence provide promising results in real-time.
Resumo:
The Cherenkov Telescope Array (CTA) will be the next-generation ground-based observatory to study the universe in the very-high-energy domain. The observatory will rely on a Science Alert Generation (SAG) system to analyze the real-time data from the telescopes and generate science alerts. The SAG system will play a crucial role in the search and follow-up of transients from external alerts, enabling multi-wavelength and multi-messenger collaborations. It will maximize the potential for the detection of the rarest phenomena, such as gamma-ray bursts (GRBs), which are the science case for this study. This study presents an anomaly detection method based on deep learning for detecting gamma-ray burst events in real-time. The performance of the proposed method is evaluated and compared against the Li&Ma standard technique in two use cases of serendipitous discoveries and follow-up observations, using short exposure times. The method shows promising results in detecting GRBs and is flexible enough to allow real-time search for transient events on multiple time scales. The method does not assume background nor source models and doe not require a minimum number of photon counts to perform analysis, making it well-suited for real-time analysis. Future improvements involve further tests, relaxing some of the assumptions made in this study as well as post-trials correction of the detection significance. Moreover, the ability to detect other transient classes in different scenarios must be investigated for completeness. The system can be integrated within the SAG system of CTA and deployed on the onsite computing clusters. This would provide valuable insights into the method's performance in a real-world setting and be another valuable tool for discovering new transient events in real-time. Overall, this study makes a significant contribution to the field of astrophysics by demonstrating the effectiveness of deep learning-based anomaly detection techniques for real-time source detection in gamma-ray astronomy.
Resumo:
This paper presents a means of structuring specifications in real-time Object-Z: an integration of Object-Z with the timed refinement calculus. Incremental modification of classes using inheritance and composition of classes to form multi-component systems are examined. Two approaches to the latter are considered: using Object-Z's notion of object instantiation and introducing a parallel composition operator similar to those found in process algebras. The parallel composition operator approach is both more concise and allows more general modelling of concurrency. Its incorporation into the existing semantics of real-time Object-Z is presented.
Resumo:
This work proposes a real-time algorithm to generate a trajectory for a 2 link planar robotic manipulator. The objective is to minimize the space/time ripple and the energy requirements or the time duration in the robot trajectories. The proposed method uses an off line genetic algorithm to calculate every possible trajectory between all cells of the workspace grid. The resultant trajectories are saved in several trees. Then any trajectory requested is constructed in real-time, from these trees. The article presents the results for several experiments.
Resumo:
Report for the scientific sojourn at the German Aerospace Center (DLR) , Germany, during June and July 2006. The main objective of the two months stay has been to apply the techniques of LEO (Low Earth Orbiters) satellites GPS navigation which DLR currently uses in real time navigation. These techniques comprise the use of a dynamical model which takes into account the precise earth gravity field and models to account for the effects which perturb the LEO’s motion (such as drag forces due to earth’s atmosphere, solar pressure, due to the solar radiation impacting on the spacecraft, luni-solar gravity, due to the perturbation of the gravity field for the sun and moon attraction, and tidal forces, due to the ocean and solid tides). A high parameterized software was produced in the first part of work, which has been used to asses which accuracy could be reached exploring different models and complexities. The objective was to study the accuracy vs complexity, taking into account that LEOs at different heights have different behaviors. In this frame, several LEOs have been selected in a wide range of altitudes, and several approaches with different complexity have been chosen. Complexity is a very important issue, because processors onboard spacecrafts have very limited computing and memory resources, so it is mandatory to keep the algorithms simple enough to let the satellite process it by itself.
Resumo:
In type I diabetes mellitus, islet transplantation provides a moment-to-moment fine regulation of insulin. Success rates vary widely, however, necessitating suitable methods to monitor islet delivery, engraftment and survival. Here magnetic resonance-trackable magnetocapsules have been used simultaneously to immunoprotect pancreatic beta-cells and to monitor, non-invasively in real-time, hepatic delivery and engraftment by magnetic resonance imaging (MRI). Magnetocapsules were detected as single capsules with an altered magnetic resonance appearance on capsule rupture. Magnetocapsules were functional in vivo because mouse beta-cells restored normal glycemia in streptozotocin-induced diabetic mice and human islets induced sustained C-peptide levels in swine. In this large-animal model, magnetocapsules could be precisely targeted for infusion by using magnetic resonance fluoroscopy, whereas MRI facilitated monitoring of liver engraftment over time. These findings are directly applicable to ongoing improvements in islet cell transplantation for human diabetes, particularly because our magnetocapsules comprise clinically applicable materials.
Resumo:
Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior
Resumo:
PURPOSE: To implement real-time myocardial strain-encoding (SENC) imaging in combination with tracking the tissue displacement in the through-plane direction. MATERIALS AND METHODS: SENC imaging was combined with the slice-following technique by implementing three-dimensional (3D) selective excitation. Certain adjustments were implemented to reduce scan time to one heartbeat. A total of 10 volunteers and five pigs were scanned on a 3T MRI scanner. Spatial modulation of magnetization (SPAMM)-tagged images were acquired on planes orthogonal to the SENC planes for comparison. Myocardial infarction (MI) was induced in two pigs and the resulting SENC images were compared to standard delayed-enhancement (DE) images. RESULTS: The strain values computed from SENC imaging with slice-following showed significant difference from those acquired without slice-following, especially during systole (P < 0.01). The strain curves computed from the SENC images with and without slice-following were similar to those computed from the orthogonal SPAMM images, with and without, respectively, tracking the tag line displacement in the strain direction. The resulting SENC images showed good agreement with the DE images in identifying MI in infarcted pigs. CONCLUSION: Correction of through-plane motion in real-time cardiac functional imaging is feasible using slice-following. The strain measurements are more accurate than conventional SENC measurements in humans and animals, as validated with conventional MRI tagging.
Resumo:
The optimization of most pesticide and fertilizer applications is based on overall grove conditions. In this work we measurements. Recently, Wei [9, 10] used a terrestrial propose a measurement system based on a ground laser scanner to LIDAR to measure tree height, width and volume developing estimate the volume of the trees and then extrapolate their foliage a set of experiments to evaluate the repeatability and surface in real-time. Tests with pear trees demonstrated that the accuracy of the measurements, obtaining a coefficient of relation between the volume and the foliage can be interpreted as variation of 5.4% and a relative error of 4.4% in the linear with a coefficient of correlation (R) of 0.81 and the foliar estimation of the volume but without real-time capabilities. surface can be estimated with an average error less than 5 %.