The virtual landing pad: facilitating rotary-wing landing operations in degraded visual environments

The safety of rotary-wing operations is significantly affected by the local weather conditions, especially during key phases of flight including hover and landing. Despite the operational flexibility of rotary-wing craft, such craft accounts for a significantly greater proportion of accidents than their fixed-wing counterparts. A key period of risk when operating rotary-wing aircraft is during operations that occur in degraded visual environments, for example as a result of thick fog. During such conditions, pilots’ workload significantly increases and their situation awareness can be greatly impeded. The current study examines the extent to which providing information to pilots via the use of a head-up display (HUD) influenced perceived workload and situation awareness, when operating in both clear and degraded visual environments. Results suggest that whilst the HUD did not benefit pilots during clear conditions, workload was reduced when operating in degraded visual conditions. Overall results demonstrate that access to the HUD reduces the difficulties associated with flying in degraded visual environments.     

RTTES: Real-time search in dynamic environments

In this paper we propose a real-time search algorithm called Real-Time Target Evaluation Search (RTTES) for the problem of searching a route in grid worlds from a starting point to a static or dynamic target point in real-time. The algorithm makes use of a new effective heuristic method which utilizes environmental information to successfully find solution paths to the target in dynamic and partially observable environments. The method requires analysis of nearby obstacles to determine closed directions and estimate the goal relevance of open directions in order to identify the most beneficial move. We compared RTTES with other competing real-time search algorithms and observed a significant improvement on solution quality.     

异构Hadoop环境下的实时作业调度算法

为提升Hadoop集群在异构环境下处理硬实时作业的性能,提出一种基于历史进度自动调整作业优先级的调度算法（HAPS）。该算法实时监控作业进度信息,对作业进度率进行指数平滑预测,计算作业剩余执行时间,动态估算作业空闲时间。并据此实时更新作业队列中作业的优先级顺序,优先调度空闲时间小的作业。实验结果表明,HAPS有效地提高了异构环境下硬实时作业的执行成功率。     

Real-time hierarchical stereo Visual SLAM in large-scale environments

In this paper we present a new real-time hierarchical (topological/metric) Visual SLAM system focusing on the localization of a vehicle in large-scale outdoor urban environments. It is exclusively based on the visual information provided by a cheap wide-angle stereo camera. Our approach divides the whole map into local sub-maps identified by the so-called fingerprints (vehicle poses). At the sub-map level (low level SLAM), 3D sequential mapping of natural landmarks and the robot location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. A higher topological level (high level SLAM) based on fingerprints has been added to reduce the global accumulated drift, keeping real-time constraints. Using this hierarchical strategy, we keep the local consistency of the metric sub-maps, by mean of the EKF, and global consistency by using the topological map and the MultiLevel Relaxation (MLR) algorithm. Some experimental results for different large-scale outdoor environments are presented, showing an almost constant processing time.     

Construction of the initial deorbit region for landing at the given place

The algorithm of the two-point boundary problem is developed for choice the nominal deorbit point, from which a descent vehicle is enabled to land at a given place without a cross-range maneuver. The method is suggested and the example is given of constructing the initial deorbit region for landing at the Vostochny cosmodrome.     

Analyzing place metaphors in 3D educational collaborative virtual environments

In the recent years, the usage of three dimensional (3D) collaborative virtual environments (CVEs) for educational purposes has increased. The metaphors behind the design of virtual places are quite diverse, from replication of real universities to art museums and scientific labs. This paper reports the results of a case study where the students of our university, as a part of their course assignment, analyzed place metaphors used in a range of 3D educational CVEs vs. the corresponding educational goals. The students suggested a design for a virtual campus representing the Norwegian University of Science and Technology (NTNU). The results of this study provide some suggestions concerning the characterization of different design features in educational CVEs and the suitability of such features for different educational goals. Also, a preliminary set of design guidelines for an ideal virtual campus representing a real university is presented. Finally, some challenges associated with using 3D CVEs in various educational situations are discussed.     

Real-time scheduling of divisible loads in cluster computing environments

Cluster computing has become an important paradigm for solving large-scale problems. To enhance the quality of service (QoS) and provide performance guarantees in a cluster computing environment, various real-time scheduling algorithms and workload models have been investigated. Computational loads that can be arbitrarily divided into independent tasks represent many real-world applications. However, the problem of providing performance guarantees to divisible load applications has only recently been studied systematically. In this work, three important and necessary design decisions, (1) workload partitioning, (2) node assignment, and (3) task execution order, are identified for real-time divisible load scheduling. A scheduling framework that can configure different policies for each of the three design decisions is proposed and used to generate various algorithms. This paper systematically studies these algorithms and identifies scenarios where the choices of design parameters have significant effects.     

大规模虚拟环境中实时阴影生成技术

为了解决大规模场景中阴影生成的真实性和实时性问题,通过平均分割和对数分割相结合的方法,利用平行于视觉投影面的分割面将视截体划分成不同的深度部分;然后对每个分割部分生成对应的阴影图;最后利用多个阴影图代替单个阴影图进行渲染。这种方法既减少了运行时的缓冲空间,又提高了阴影质量,尤其适合于动态大规模环境中实时阴影的生成。用VS2005和OpenSceneGraph实现了算法的绘制,达到满意的视觉效果和生成速度。     

Real-time fault detection in manufacturing environments using face recognition techniques

New image processing techniques as well digital image capture equipment provide an opportunity for fast detection and diagnosis of quality problems in manufacturing environments compared with traditional dimensional measurement techniques. This paper proposes a new use of image processing to detect in real-time quality faults using images traditionally obtained to guide manufacturing processes. The proposed method utilizes face recognition tools to eliminate the need of specific feature detection on determining out-of-specification parts. The focus of the proposed methodology is on computational efficiency to ensure that the algorithm runs in real time in high volume manufacturing environments. The algorithm is trained with previously classified images. New images are then classified into two groups, healthy and unhealthy. This paper proposes a method that combines Discrete Cosine Transform with Fisher’s Linear Discriminant Analysis to detect faults, such as cracks, directly from aluminum stamped parts.     

Real-time 6-DOF multi-session visual SLAM over large-scale environments

This paper describes a system for performing real-time multi-session visual mapping in large-scale environments. Multi-session mapping considers the problem of combining the results of multiple simultaneous localisation and mapping (SLAM) missions performed repeatedly over time in the same environment. The goal is to robustly combine multiple maps in a common metrical coordinate system, with consistent estimates of uncertainty. Our work employs incremental smoothing and mapping (iSAM) as the underlying SLAM state estimator and uses an improved appearance-based method for detecting loop closures within single mapping sessions and across multiple sessions. To stitch together pose graph maps from multiple visual mapping sessions, we employ spatial separator variables, called anchor nodes, to link together multiple relative pose graphs.The system architecture consists of a separate front-end for computing visual odometry and windowed bundle adjustment on individual sessions, in conjunction with a back-end for performing the place recognition and multi-session mapping. We provide experimental results for real-time multi-session visual mapping on wheeled and handheld datasets in the MIT Stata Center. These results demonstrate key capabilities that will serve as a foundation for future work in large-scale persistent visual mapping.     

Real-time exploration and photorealistic reconstruction of large natural environments

This paper presents a hybrid (geometry- and image-based) framework suitable for providing photorealistic walkthroughs of large, complex outdoor scenes, based only on a small set of real images from the scene. To this end, a novel data representation of a 3D scene is proposed, which is called morphable 3D panoramas. Motion is assumed to be taking place along a predefined path of the 3D environment and the input to the system is a sparse set of stereoscopic views at certain positions (key positions) along that path (one view per position). An approximate local 3D model is constructed from each view, capable of capturing the photometric and geometric properties of the scene only locally. Then, during the rendering process, a continuous morphing (both photometric as well as geometric) takes place between successive local 3D models, using what we call a ‘morphable 3D model’. For the estimation of the photometric morphing, a robust algorithm capable of extracting a dense field of 2D correspondences between wide-baseline images is used, whereas, for the geometric morphing, a novel method of computing 3D correspondences between local models is proposed. In this way, a physically valid morphing is always produced, which is thus kept transparent from the user. Moreover, a highly optimized rendering path is used during morphing. Thanks to the use of appropriate pixel and vertex shaders, this rendering path can be run fully in 3D graphics hardware and thus allows for high frame rates. Our system can be extended to handle multiple stereoscopic views (and therefore multiple local models) per key position of the path (related by a camera rotation). In this case, one local 3D panorama (per key position) is constructed, comprising all local 3D models therein, and so a ‘morphable 3D panorama’ is now used during the rendering process. For handling the geometric consistency of each 3D panorama, a technique which is based on solving a partial differential equation is adopted. The effectiveness of our framework is demonstrated by using it for the 3D visual reconstruction of the Samaria Gorge in Crete.     

Real-time path planning in dynamic virtual environments using multiagent navigation graphs

We present a novel approach for efficient path planning and navigation of multiple virtual agents in complex dynamic scenes. We introduce a new data structure, Multi-agent Navigation Graph (MaNG), which is constructed using first- and second-order Voronoi diagrams. The MaNG is used to perform route planning and proximity computations for each agent in real time. Moreover, we use the path information and proximity relationships for local dynamics computation of each agent by extending a social force model [Helbing05]. We compute the MaNG using graphics hardware and present culling techniques to accelerate the computation. We also address undersampling issues and present techniques to improve the accuracy of our algorithm. Our algorithm is used for real-time multi-agent planning in pursuit-evasion, terrain exploration and crowd simulation scenarios consisting of hundreds of moving agents, each with a distinct goal.     

Resource failures risk assessment modelling in distributed environments

Service providers offer access to resources and services in distributed environments such as Grids and Clouds through formal Service level Agreements (SLA), and need well-balanced infrastructures so that they can maximise the Quality of Service (QoS) they offer and minimise the number of SLA violations. We propose a mathematical model to predict the risk of failure of resources in such environments using a discrete-time analytical model driven by reliability functions fitted to observed data. The model relies on the resource historical data so as to predict the risk of failure for a given time interval. The model is evaluated by comparing the predicted risk of failure with the observed risk of failure, and is shown to accurately predict the resources risk of failure, allowing a service provider to selectively choose which SLA request to accept.     

Real-time stress assessment using thermal imaging

Thermal imaging is one of the most promising methods of probing the psychological status of human beings because of its non-invasiveness. This study develops a new method of assessing the stress status in real time. The differential energy between philtrum (located in the maxillary area) and forehead (DEFP) algorithm is developed to amplify and extract stress-induced thermal imprints. The algorithm is then validated against the clinical standard in a controlled lab experiment in which neurophysiologic responses are invoked from subjects via Trier Social Stress Test. The correlation between the extracted thermal imprints and established stress markers (heart beat rate and cortisol level) is significant. Experimental result demonstrates that DEFP has the capacity to classify stress and baseline status. An accuracy rate of over 90 % suggests the feasibility of the real-time assessment of stress, disregarding personal factors.     

Real-time deep satellite image quality assessment

A method for deep satellite image quality assessment based on no-reference satellite images is proposed. We design suitable deep convolutional neural networks, which are named satellite image quality assessment of deep convolutional neural networks (SIQA-DCNN) and SIQA-DCNN++. These sophisticated methods can remove various distorted satellite images in real-time remote sensing. The novelty of this method lies in the objective assessment and restoration of the deep model which understands various distorted satellite images in high- and low-resolution problems. The activation function has a lower computational time and ensures the deactivation of noise by making the mean activators close to zero. Our methods are also effective for transfer learning, which can be used to adequately investigate satellite image classification in deep satellite image quality assessment. Using Spearman’s rank order correlation coefficient (SROCC) and linear correlation coefficient (LCC) evaluations, we demonstrated that our methods show better performance than other algorithms, with more than 0.90 of SROCC and LCC values compared to the full-reference and no-reference satellite image in MODIS/Terra and USGS datasets. Regarding computational complexity, we obtained better performance in operational function times. As compared to other methods, SIQA-DCNN and SIQA-DCNN++ also reduced computational time by more than 40 and 56%, respectively, when applied to the USGS dataset, and by more than 46 and 60% respectively, when applied to the MODIS/Terra dataset.     

Real-time map building and navigation for autonomous robots inunknown environments

An algorithmic solution method is presented for the problem of autonomous robot motion in completely unknown environments. Our approach is based on the alternate execution of two fundamental processes: map building and navigation. In the former, range measures are collected through the robot exteroceptive sensors and processed in order to build a local representation of the surrounding area. This representation is then integrated in the global map so far reconstructed by filtering out insufficient or conflicting information. In the navigation phase, an A*-based planner generates a local path from the current robot position to the goal. Such a path is safe inside the explored area and provides a direction for further exploration. The robot follows the path up to the boundary of the explored area, terminating its motion if unexpected obstacles are encountered. The most peculiar aspects of our method are the use of fuzzy logic for the efficient building and modification of the environment map, and the iterative application of A*, a complete planning algorithm which takes full advantage of local information. Experimental results for a NOMAD 200 mobile robot show the real-time performance of the proposed method, both in static and moderately dynamic environments.     

Real-time scheduling algorithm for safety-critical systems on faulty multicore environments

An algorithm (called FTM) for scheduling of real-time sporadic tasks on a multicore platform is proposed. Each task has a deadline by which it must complete its non-erroneous execution. The FTM algorithm executes backups in order to recover from errors caused by non-permanent and permanent hardware faults. The worst-case schedulability analysis of FTM algorithm is presented considering an application-level error model, which is independent of the stochastic behavior of the underlying hardware-level fault model. Then, the stochastic behavior of hardware-level fault model is plugged in to the analysis to derive the probability of meeting all the deadlines. Such probabilistic guarantee is the level of assurance (i.e., reliability) regarding the correct functional and timing behaviors of the system. One of the salient features of FTM algorithm is that it executes some backups in active redundancy to exploit the parallel multicore architecture while other backups passively to avoid unnecessary execution of too many active backups. This paper also proposes a scheme to determine for each task the number of backups that should run in active redundancy in order to increase the probability of meeting all the deadlines. The effectiveness of the proposed approach is demonstrated using an example application.     

Real-time control architecture using Xenomai for intelligent service robots in USN environments

This paper describes the implementation of a dual-kernel software architecture, based on standard Linux and real-time embedded Linux, for real-time control of service robots in ubiquitous sensor network environments. Mobile robots are used in active service for the assisted living of elderly people, monitoring their mental and physiological data with wireless sensor nodes. The data collected from sensor nodes are routed back to a sink node through multi-hop communication. The moving sink node installed on the main controller of the robot collects data and transmits it to the main controller. To be able to handle emergency situations, the robot needs to satisfy real-time requirements when processing the data collected, and invoking tasks to execute. This paper realizes a multi-hop sensor network and proposes real-time software architecture based on Xenomai. The real-time tasks were implemented, with priority, to rapidly respond to urgent sensor data. In order to validate the deterministic response of the proposed system, the performance measurements for the delay in handling the sensed data transmission and the trajectory control with a feedback loop were evaluated on the non real-time standard Linux.     

Risk assessment in work environments: modeling and simulation

The paper proposes a characterization of risks and a service‐oriented prototype to face risky situations in work environments, such as in industrial plants or building construction areas. A risk is the overture of emergencies that produce human and/or material damages. Therefore, it is particularly critical to identify and manage risks to avoid their evolution into emergencies. In this paper, we outline the technological features of a risk environment and propose a risk model and a service‐based simulation prototype aimed to improve safety in work environments. We discuss engineering issues concerning risk modeling and management. Furthermore, we propose a risk management system solution and its related implemented prototype composed of services able to detect and also to prevent the occurrence of risk conditions. Copyright © 2012 John Wiley & Sons, Ltd.     

Evaluating peer learning and assessment in online collaborative learning environments

Transformation of learning and teaching in higher education now offers greater educational equality through enhanced access and collaboration within the framework of lifelong learning in the digital age. This study aims to evaluate online peer learning and assessment in the collaborative learning process in higher education practices. The study also investigates the impact of online peer learning on the development of skills within collaborative learning through the use of volunteered responses from learners concerning their experiences with and perceptions of online learning. Therefore, a quantitative approach is applied through the administration of a survey with 32 items that is distributed to 715 participants. According to the objective of the study, a set of inferential statistical analyses are performed. The theoretical framework of this study is the CHAT (cultural historical activity theory) which reconstructs the knowledge of learners through the application of the Adobe Connect program to demonstrate how learners can be collaborative and social with their peers in an online context. The results revealed that the collaborative online peer learning process in higher education encourages critical reflection and self-assessment. The study contributes to the understanding of the value of learner satisfaction in online collaborative learning environments through the experiences of learners.