A neural fuzzy framework for system mapping applications

A novel neural fuzzy (NF) mapping framework is developed in this paper to convert linear systems and a class of nonlinear systems from the crisp-domain to a NF representation. The resulting neural fuzzy system (NFS) is guaranteed to be functionally identical to the original system. Therefore, the proposed mapping technique provides a well-defined prototype for one type of NFS design. The resulting fuzzy reasoning representation facilitates the investigation in linguistic terms into the system operations, whereas the system performance can be further improved by properly incorporating expertise knowledge or by online/offline training via this NF structure. The developed technique is to extend our previously-developed techniques to NF modeling/mapping applications and its effectiveness is demonstrated by simulations using a flexible-link robot.     

Optimization-based mapping framework for parallel applications

The mapping of tasks of a parallel program onto nodes of a parallel computing system has a remarkable impact on application performance. In this paper we propose an optimization framework to solve the mapping problem, which takes into account the communication matrix of the application and a cost matrix that depends on the topology of the parallel system. This cost matrix is usually a distance matrix (the classic approach), but we propose a novel definition of the cost criterion, applicable to torus networks, that tries to distribute traffic evenly over the different axes; we call this the Traffic Distribution criterion. As the mapping problem can be seen as a particular instance of the Quadratic Assignment Problem (QAP), we can apply any QAP solver to this problem. In particular, we use a greedy randomized algorithm. Using simulation, we test the performance levels of the optimization-based mappings, and compare them with those of trivial mappings (consecutive, random), in two different environments: single application (one application uses all system resources all the time) and space sharing (several applications run simultaneously, on different system partitions), using systems with 2D and 3D topologies and real application traffic. Experimental results show that some applications do not benefit from optimization-based mappings: those in which there is a match between virtual and physical topologies, and those that carry out massive all-to-all communications. In other cases, optimization-based mappings with the TD criterion provide excellent performance levels.     

A task-level adaptive MapReduce framework for real-time streaming data in healthcare applications

Healthcare scientific applications, such as body area network, require of deploying hundreds of interconnected sensors to monitor the health status of a host. One of the biggest challenges is the streaming data collected by all those sensors, which needs to be processed in real time. Follow-up data analysis would normally involve moving the collected big data to a cloud data center for status reporting and record tracking purpose. Therefore, an efficient cloud platform with very elastic scaling capacity is needed to support such kind of real time streaming data applications. The current cloud platform either lacks of such a module to process streaming data, or scales in regard to coarse-grained compute nodes.In this paper, we propose a task-level adaptive MapReduce framework. This framework extends the generic MapReduce architecture by designing each Map and Reduce task as a consistent running loop daemon. The beauty of this new framework is the scaling capability being designed at the Map and Task level, rather than being scaled from the compute-node level. This strategy is capable of not only scaling up and down in real time, but also leading to effective use of compute resources in cloud data center. As a first step towards implementing this framework in real cloud, we developed a simulator that captures workload strength, and provisions the amount of Map and Reduce tasks just in need and in real time.To further enhance the framework, we applied two streaming data workload prediction methods, smoothing and Kalman filter, to estimate the unknown workload characteristics. We see 63.1% performance improvement by using the Kalman filter method to predict the workload. We also use real streaming data workload trace to test the framework. Experimental results show that this framework schedules the Map and Reduce tasks very efficiently, as the streaming data changes its arrival rate.     

A framework for inverse tone mapping

In recent years many tone mapping operators (TMOs) have been presented in order to display high dynamic range images (HDRI) on typical display devices. TMOs compress the luminance range while trying to maintain contrast. The inverse of tone mapping, inverse tone mapping, expands a low dynamic range image (LDRI) into an HDRI. HDRIs contain a broader range of physical values that can be perceived by the human visual system. We propose a new framework that approximates a solution to this problem. Our framework uses importance sampling of light sources to find the areas considered to be of high luminance and subsequently applies density estimation to generate an expand map in order to extend the range in the high luminance areas using an inverse tone mapping operator. The majority of today’s media is stored in the low dynamic range. Inverse tone mapping operators (iTMOs) could thus potentially revive all of this content for use in high dynamic range display and image based lighting (IBL). Moreover, we show another application that benefits quick capture of HDRIs for use in IBL.     

A timeband framework for modelling real-time systems

Complex real-time systems must integrate physical processes with digital control, human operation and organisational structures. New scientific foundations are required for specifying, designing and implementing these systems. One key challenge is to cope with the wide range of time scales and dynamics inherent in such systems. To exploit the unique properties of time, with the aim of producing more dependable computer-based systems, it is desirable to explicitly identify distinct time bands in which the system is situated. Such a framework enables the temporal properties and associated dynamic behaviour of existing systems to be described and the requirements for new or modified systems to be specified. A system model based on a finite set of distinct time bands is motivated and developed in this paper.     

A framework for simulating real-time multi-agent systems

In this paper, we describe an implementation of use in demonstrating the effectiveness of architectures for real-time multi-agent systems. The implementation provides a simulation of a simplified RoboCup Search and Rescue environment, with unexpected events, and includes a simulator for both a real-time operating system and a CPU. We present experimental evidence to demonstrate the benefit of the implementation in the context of a particular hybrid architecture for multi-agent systems that allows certain agents to remain fully autonomous, while others are fully controlled by a coordinating agent. In addition, we discuss the value of the implementation for testing any models for the construction of real-time multi-agent systems and include a comparison to related work.

A framework for real-time discrete event control

The TTM/RTTL (timed transition model with real-time temporal logic) framework is presented for modeling, specifying, and analyzing real-time discrete-event systems. TTMs are used to represent the process of the plant and its controller. RTTL is the assertion language for specifying plant behavior and verifying that a controller satisfies the specification. The framework adapts features from the program verification literature which are useful for posing problems of interest to the control engineer, such as modular synthesis and design. Examples are used to illustrate the ideas presented. The authors' published analytical results are summarized and referenced     

Thermal-aware task allocation and scheduling for periodic real-time applications in mesh-based heterogeneous NoCs

Real-Time Systems - With continuous technology scaling, the power density and hence the temperature of Network-on-Chip (NoC) may increase rapidly. This in-turn degrades the performance of the chip...     

A formal framework for real-time information flow analysis

We view Multi-Level Secure (MLS) real-time systems as systems in which MLS real-time tasks are scheduled and execute, according to a scheduling algorithm employed by the system. From this perspective, we develop a general trace-based framework that can carry out a covert-timing channel analysis of a real-time system. In addition, we propose a set of covert-timing channel free policies: If a system satisfies one of our proposed security policies, we demonstrated that the system can achieve a certain level of real-time information flow security. Finally, we compare the relative strength of the proposed covert-timing channel free security policies and analyze whether each security policy can be regarded as a property (a set of execution sequences).     

A hardware-software real-time operating system framework for SoCs

The /spl delta/ framework for RTOS-SoC codesign helps designers simultaneously build a SoC or platform-ASIC architecture and a customized hardware-software RTOS. Examples generated by this prototype tool include RTOS designs that speed up applications by 27% or more, using a small amount of hardware area.     

A framework for real-time semantic social media analysis

This paper presents a framework for collecting and analysing large volume social media content. The real-time analytics framework comprises semantic annotation, Linked Open Data, semantic search, and dynamic result aggregation components. In addition, exploratory search and sense-making are supported through information visualisation interfaces, such as co-occurrence matrices, term clouds, treemaps, and choropleths. There is also an interactive semantic search interface (Prospector), where users can save, refine, and analyse the results of semantic search queries over time. Practical use of the framework is exemplified through three case studies: a general scenario analysing tweets from UK politicians and the public’s response to them in the run up to the 2015 UK general election, an investigation of attitudes towards climate change expressed by these politicians and the public, via their engagement with environmental topics, and an analysis of public tweets leading up to the UK’s referendum on leaving the EU (Brexit) in 2016. The paper also presents a brief evaluation and discussion of some of the key text analysis components, which are specifically adapted to the domain and task, and demonstrate scalability and efficiency of our toolkit in the case studies.     

A generic framework for modeling heterogeneous real-time systems

Complex real-time systems usually consist of heterogeneous components. These components interact with different semantics. Modeling these systems normally need integrating several domain-specific tools such as UML, Simulink. But interchanging data between these tools is very difficult. UML is a standard modeling language for object-oriented software development, used more and more in real-time domain. It provides several extensibility mechanisms to allow modeling special domains. This paper presents a generic framework, which is based on UML notations and metamodels, for heterogeneous modeling real-time systems. So engineers from different domains can work together on a unified platform.     

A real-time framework for eye detection and tracking

Epidemiological studies indicate that automobile drivers from varying demographics are confronted by difficult driving contexts such as negotiating intersections, yielding, merging and overtaking. We aim to detect and track the face and eyes of the driver during several driving scenarios, allowing for further understanding of a driver’s visual search pattern behavior. Traditionally, detection and tracking of objects in visual media has been performed using specific techniques. These techniques vary in terms of their robustness and computational cost. This research proposes a real-time framework that is built upon a foundation synonymous to boosting, which we extend from learners to trackers and demonstrate that the idea of an integrated framework employing multiple trackers is advantageous in forming a globally strong tracking methodology. In order to model the effectiveness of trackers, a confidence parameter is introduced to help minimize the errors produced by incorrect matches and allow more effective trackers with a higher confidence value to correct the perceived position of the target.     

A hierarchical consistency framework for real-time supervisory control

The control framework of hierarchical consistency of timed discrete-event systems (TDES’s) is investigated in a standard two-level hierarchy. Real-time concepts and the associated theoretical results supporting consistent TDES hierarchies are developed. Where the given low-level system model of the hierarchy possesses time fidelity, a consistency version that assures time fidelity of the high-level system model is also developed. Importantly, this version furnishes a sound real-time high-level specification design foundation for hierarchical control. An example illustrates the new time-fidelity control foundation. Given that in general, a given two-level TDES hierarchy is not hierarchically consistent between the levels, the structural existence and synthesis of the sufficiency structure for hierarchical consistency is investigated. Both the timed versions of hierarchical consistency - without and with output-time fidelity guarantee - are successively treated. The abstraction or output-system refinement procedures for the version without output-time fidelity guarantee are first developed for a class of TDES hierarchies under mild output-system design restrictions. The abstraction methods for the version with output-time fidelity are then developed for a subclass ‘linearly’ structured under further output-system design restrictions. A detailed example explains and illustrates the use of an overarching method developed.     

A component-driven distributed framework for real-time video dehazing

Traditional dehazing techniques, as a well studied topic in image processing, are now widely used to eliminate the haze effects from individual images. However, the state-of-the-art dehazing algorithms may not provide sufficient support to video analytics, as a crucial pre-processing step for video-based decision making systems (e.g., robot navigation), due to poor coherence and low processing efficiency of the present algorithms. This paper presents a new framework, particularly designed for video dehazing, to output coherent results in real time, with two novel techniques. Firstly, we decompose the dehazing algorithms into three generic components, namely transmission map estimator, atmospheric light estimator and haze-free image generator. They can be simultaneously processed by multiple threads in the distributed system, such that the processing efficiency is optimized by automatic CPU resource allocation based on the workloads. Secondly, a cross-frame normalization scheme is proposed to enhance the coherence among consecutive frames, by sharing the parameters of atmospheric light from consecutive frames in the distributed computation platform. The combination of the above three components enables our framework to generate highly consistent and accurate dehazing results in real-time, by using only 5 PCs connected by Ethernet.

     

A new algorithm for scheduling periodic,real-time tasks

Algorithmica - We consider the problem of preemptively scheduling a set of periodic, real-time tasks on a multiprocessor computer system. We give a new scheduling algorithm, the so-called...     

A new algorithm for scheduling periodic, real-time tasks

We consider the problem of preemptively scheduling a set of periodic, real-time tasks on a multiprocessor computer system. We give a new scheduling algorithm, the so-called Slack-Time Algorithm, and show that it is more effective than the known Deadline Algorithm. We also give an (exponential-time) algorithm to decide if a task system is schedulable by the Slack-Time or the Deadline Algorithm. The same algorithm can also be used to decide if a task system is schedulable by any given fixed-priority scheduling algorithm. This resolves an open question posed by Leung and Whitehead. Finally, it is shown that the problem of deciding if a task system is schedulable by the Slack-Time, the Deadline, or any given fixed-priority scheduling algorithm is co-NP-hard for each fixedm.     

A parallel neural processor for real-time applications

Training and testing artificial neural networks can be challenging and time-consuming. Experiments with two real-time applications were performed to compare three approaches for implementing a multilayer perceptron neural network. In both applications, the special-purpose processor performed best     

A security framework for reflective Java applications

The advent of component‐based reflective applications raises the issue of protecting baselevel components from the actions performed by metalevel components. However, by their very nature, reflective applications are far more difficult to secure than non‐reflective applications, which certainly explains why the problem has received very little attention so far. In this paper we present a security framework for enforcing access control between metalevel components and the baselevel components they reflect on. Rather than designing a new security architecture from scratch, we extend the standard security architecture of Java to provide security for a fully‐functional proxy‐based MOP for Java. We implement a number of well‐known meta‐level behaviors and study their security requirements, the results of which support our design choices. Copyright © 2003 John Wiley & Sons, Ltd.