Configurable Component Middleware for Distributed Real-Time Systems with Aperiodic and Periodic Tasks

Many distributed real-time applications must handle mixed periodic and aperiodic tasks with diverse requirements. However, existing middleware lacks flexible configuration mechanisms needed to manage end-to-end timing easily for a wide range of different applications with both periodic and aperiodic tasks. The primary contribution of this work is the design, implementation and performance evaluation of the first configurable component middleware services for admission control and load balancing of aperiodic and periodic tasks in distributed real-time systems. Empirical results demonstrate the need for and effectiveness of our configurable component middleware approach in supporting different applications with periodic and aperiodic tasks

Reconfigurable Real-Time Middleware for Distributed Cyber-Physical Systems with Aperiodic Events

Different distributed cyber-physical systems must handle aperiodic and periodic events with diverse requirements. While existing real-time middleware such as Real-Time CORBA has shown promise as a platform for distributed systems with time constraints, it lacks flexible configuration mechanisms needed to manage end-to-end timing easily for a wide range of different cyber-physical systems with both aperiodic and periodic events. The primary contribution of this work is the design, implementation and performance evaluation of the first configurable component middleware services for admission control and load balancing of aperiodic and periodic event handling in distributed cyber-physical systems. Empirical results demonstrate the need for, and the effectiveness of, our configurable component middleware approach in supporting different applications with aperiodic and periodic events, and providing a flexible software platform for distributed cyber-physical systems with end-to-end timing constraints

Real-Time Performance and Middleware on Multicore Linux Platforms

An increasing number of distributed real-time applications are running on multicore platforms. However, existing real-time middleware (e.g., Real-Time CORBA) lacks support for scheduling soft real-time tasks on multicore platforms while guaranteeing their time constraints will be satisfied. This paper makes three contributions to the state of the art in real-time system software for multicore platforms. First, it offers what is to our knowledge the first experimental analysis of real-time performance for vanilla Linux primitives on multicore platforms. Second, it presents MC-ORB, the first real-time object request broker (ORB), designed to exploit the features of multicore platforms, with admission control and task allocation services that can provide schedulability guarantees for soft real-time tasks on multicore platforms. Third, it gives a performance evaluation of MC-ORB on a Linux multicore testbed, the results of which demonstrate the efficiency and effectiveness of MC-ORB

Customizing Component Middleware for Distributed Real-Time Systems with Aperiodic and Periodic Tasks

Many distributed real-time applications must handle mixed aperiodic and periodic tasks with diverse requirements. However, existing middleware lacks flexible configuration mechanisms needed to manage end-to-end timing easily for a wide range of different applications with both aperiodic and periodic tasks. The primary contribution of this work is the design, implementation and performance evaluation of the first configurable component middleware services for admission control and load balancing of aperiodic and periodic tasks in distributed real-time systems. Empirical results demonstrate the need for, and the effectiveness of, our configurable component middleware approach in supporting different applications with aperiodic and periodic tasks

Low Light Image Enhancement and Saliency Object Detection

University of Technology Sydney. Faculty of Engineering and Information Technology.Low light images represent a series of image types with great potential. Their research focuses on images and videos of the environment at dusk and near darkness. It can be widely used in night safety monitoring, license plate recognition, night scene shot, special target recognition at dusk, and other emergency events that occur under light scenes. After the environment is enhanced and combined with other tasks in computer vision and pattern recognition, it can bring many results, such as saliency detection and object detection under low illumination, and abnormal detection in crowded places under low-light environment. For the enhancement of low light and low light scenes, using traditional methods often results in over-exposure and halo conditions. Therefore, using deep learning network technology can fix and improve these specific shortcomings. For low light image enhancement, a series of qualitative and quantitative experimental comparisons conducted on a benchmark dataset demonstrate the superiority of our approach, which overcomes the drawbacks of white and colour distortion. At present, most of the research works on visual saliency have concentrated on the field of visible light, and there are few studies on night scenes. Due to insufficient lighting conditions in night scenes, and relatively lower contrasts and signal-to-noise ratios, the effectiveness of available visual features is greatly reduced. Moreover, without sufficient depth information, many features and clues are lost in the original images. Therefore, the detection of salient targets in night scenes is also difficult and it is a focus of current research in the field of computer vision. The performance leads to vague effects when the existing methods are directly con-ducted, so we adopt a new “enhance firstly, detection secondly” mechanism that firstly enhances the low-light images in order to improve the contrast and visibility, and then combines it with relevant saliency detection methods with depth information. Furthermore, we concern about the feature aggregation schemes for deep RGB-D saliency object detection and propose novel feature aggregation methods. Meanwhile, for the monocular vision, of which the depth information is hard to acquire, a novel RGB-D image saliency detection method is proposed to leverage depth cues for enhancing the saliency detection performance but without actually using depth data. The model not only outperforms the state-of-the-art RGB saliency models, but also achieves comparable or even better results compared with the state-of-the-art RGB-D saliency models