Time Stamped Fault Tolerance in Real Time Systems

We present a methodology for building timed models of real-time systems by adding time constraints to their application software. The applied constraints take into account execution times of atomic statements, the behavior of the system's external environment, and scheduling policies. The timed models of the application obtained in this manner can be analyzed by using time analysis techniques to check relevant real-time properties. We show an instance of the methodology developed in the TAXYS project for the modeling and analysis of real-time systems programmed in the Esterel language. This language has been extended to describe, by using pragmas, time constraints characterizing the execution platform and the external environment. An analyzable timed model of the real-time system is produced by composing instrumented C-code generated by the compiler. The latter has been re-engineered in order to take into account the pragmas. Finally, we report on applications of TAXYS to several nontrivial examples.

Artificial neural network (ANN) is a simplified model of the biological nervous system consisting of nerve cells or neurons. The application of ANN to food process engineering is relatively novel. ANN had been employed in diverse applications like food safety and quality analyses, food image analysis, and modeling of various thermal and non-thermal food-processing operations. ANN has the ability to map nonlinear relationships without any prior knowledge and predicts responses even with incomplete information. Every neural network possesses data in the form of connection weights interconnecting lines between the input to hidden layer neurons and weights of hidden to output layer neurons, which has a significant role in predicting the output data. The applications of ANN in different unit operations in food processing were described that includes theoretical developments using intelligent characteristics for adaptability, automatic learning, classification, and prediction. The parallel architecture of ANN resulted in a fast response and low computational time making it suitable for application in real-time systems of different food process operations. The predicted responses obtained by the ANN model exhibited high accuracy due to lower relative deviation and root mean squared error and higher correlation coefficient. This paper presented the various applications of ANN for modeling nonlinear food engineering problems. The application of ANN in the modeling of the processes such as extraction, extrusion, drying, filtration, canning, fermentation, baking, dairy processing, and quality evaluation was reviewed. HIGHLIGHTS 1. This paper discusses application of ANN in different emerging trends in food process. 2. Application of ANN to develop non-linear multivariate modeling is illustrated. 3. ANNs have been shown to be useful tool for prediction of outcomes with high accuracy. 4. ANN resulted in fast response making it suitable for application in real time systems.

The increase of the world population, especially in the global competition, together with the increasing use of fossil fuel resources to meet energy needs, leads to more greenhouse gases (more than one CO2, methane etc.) emissions and the global climate crisis. In this process, changes in meteorological events such as temperature, precipitation, and wind, attract attention moreover but when considered as a whole, we know that these negative changes in the ecosystem negatively affect many living groups. Sea Surface Temperature (SST) as measured meteorologically is the most important environmental parameter where these changes are monitored and observed. It draws attention to the fact that changes in SST are not limited to living organisms as habitats, but also catalyze many chain reactions, especially socio-economic impacts. Therefore, much of the work is devoted to forecasting studies to adapt to changing habitats and take the necessary precautions against potential risks. Feed-forward artificial neural networks have been commonly used to address these emerging needs. Artificial neural networks, which are a simple imitation of the human neurological system, have been used as an artificial intelligence method in forecasting problems due to their superior performance and not having the limitations of classical time series. In this study, the forecasting of the time series of monthly mean SST temperature obtained from Rize station between the years 2010 and 2020 is performed by using feed-forward artificial neural networks, and the forecasting performance of the corresponding time series is compared with many forecasting methods with different characteristics. The comparison of the methods used the mean square error and mean absolute percentage error criteria, which are commonly used in the forecasting literature. The analysis results showed that the analysis results obtained with the feed-forward artificial neural networks have the best prediction performance. As a result, it can be stated that the sea surface temperature can be forecasted with a very high accuracy using the feed-forward artificial neural networks.

A large body of human image processing techniques use skin detection as a first primitive for subsequent feature extraction. Well established methods of colour modelling, such as histograms and Gaussian mixture models have enabled the construction of suitably accurate skin detectors. However such techniques are not ideal for use in adaptive real time environments. We describe methods of skin detection using a Self-Organising Map or SOM, and show performance comparable (94% accuracy on facial images) to conventional techniques. We also introduce the AXEON Learning Processor as the basis for a hardware skin detector, and outline the potential benefits of using this system in a demanding environment, such as filtering Internet traffic, to which conventional techniques are not best suited.

Fast object detection in digital grayscale images The problem of specific object detection in digital grayscale images is considered under the following conditions: relatively small image fragments can be analysed (a priori information about the size of objects is available); images contain a varying undefined background (clutter) of larger objects; processing time should be minimised and must be independent from the image contents; proposed methods should provide for efficient implementation in application-specific electronic circuits. The last two conditions reflect the aim to propose approaches suitable for application in real time systems where known sophisticated methods would be inapplicable. The research is motivated by potential applications in the food industry (detection of contaminants in products from their X-ray images), medicine (detection of anomalies in fragments of computer tomography images etc.). Possible objects to be detected may include compact small objects, curved lines in different directions, and small regions of pixels with brightness different from the background. The paper describes proposed image processing approaches to detection of such objects and the results obtained from processing of sample food images.

This paper presents a novel application of a hybrid learning approach to the optimisation of membership and nonmembership functions of a newly developed interval type-2 intuitionistic fuzzy logic system (IT2 IFLS) of a Takagi–Sugeno–Kang (TSK) fuzzy inference system with neural network learning capability. The hybrid algorithms consisting of decoupled extended Kalman filter (DEKF) and gradient descent (GD) are used to tune the parameters of the IT2 IFLS for the first time. The DEKF is used to tune the consequent parameters in the forward pass while the GD method is used to tune the antecedents parts during the backward pass of the hybrid learning. The hybrid algorithm is described and evaluated, prediction and identification results together with the runtime are compared with similar existing studies in the literature. Performance comparison is made among the proposed hybrid learning model of IT2 IFLS, a TSK-type-1 intuitionistic fuzzy logic system (IFLS-TSK), and a TSK-type interval type-2 fuzzy logic system (IT2 FLS-TSK) on two instances of the datasets under investigation. The empirical comparison is made on the designed systems using three artificially generated datasets and three real world datasets. Analysis of results reveal that IT2 IFLS outperforms its type-1 variants, IT2 FLS and most of the existing models in the literature. Moreover, the minimal run time of the proposed hybrid learning model for IT2 IFLS also puts this model forward as a good candidate for application in real time systems.

A real-time operating system (RTOS) provides a platform for the design and implementation of a wide range of applications in real-time systems, embedded systems, and mission-critical systems. This paper presents a formal design model for a general RTOS known as RTOS+ that enables a specific target RTOS to be rigorously and efficiently derived in real-world applications. The methodology of a denotational mathematics, Real-Time Process Algebra (RTPA), is described for formally modeling and refining architectures, static behaviors, and dynamic behaviors of RTOS+. The conceptual model of the RTOS+ system is introduced as the initial requirements for the system. The architectural model of RTOS+ is created using RTPA architectural modeling methodologies and refined by a set of Unified Data Models (UDMs). The static behaviors of RTOS+ are specified and refined by a set of Unified Process Models (UPMs). The dynamic behaviors of the RTOS+ system are specified and refined by the real-time process scheduler and system dispatcher. This work is presented in two papers; the conceptual and architectural models of RTOS+ is described in this paper, while the static and dynamic behavioral models of RTOS+ will be elaborated in a forthcoming paper.

research-article Free Access Share on Embedded systems in real time applications, design & architecture Authors: A. L. Suseela View Profile , V. Lalith Kumar View Profile Authors Info & Claims UbiquityVolume 2005Issue AugustAugust 2005 pp 2https://doi.org/10.1145/1088431.1086450Online:01 August 2005Publication History 0citation170,093DownloadsMetricsTotal Citations0Total Downloads170,093Last 12 Months1,290Last 6 weeks56 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF

We propose a method for high-order image subsampling using feedforward artificial neural networks (FANNs). In our method, the high-order subsampling process is decomposed into a sequence of first-order subsampling stages. The first stage employs a tridiagonally symmetrical FANN, which is obtained by applying the design algorithm introduced by Dumitras and Kossentini (see IEEE Trans. Signal Processing, vol.48, p.1446-55, 2000). The second stage employs a small fully connected FANN. The algorithm used to train both FANNs employs information about local edges (extracted using pattern matching) to perform effective subsampling of both high detail and smooth image areas. We show that our multistage first-order subsampling method achieves excellent speed-performance tradeoffs, and it consistently outperforms traditional lowpass filtering and subsampling methods both subjectively and objectively.

Since the grid system is implemented on a network framework with heterogeneous remote resources, it is a hazardous environment. The possibility of failure occurrence on the grid resources are not negligible and should be considered. On the other hand, the reliability is a major criterion for the safety-critical and real time applications. This paper focuses on dependability aspects of grid scheduling strategies such as reliability and availability by using various forms of redundancy (e.g. hardware, software, and data). A hybrid method including fault masking and fault tolerance is used in this paper to improve the reliability of grid scheduling system. The proposed scheduling model integrates an active redundancy and backup majority voting together. After the detection of an error, multiple check-pointing as a recovery technique is invoked by the proposed scheduler. This method can be used at each level of scheduling based on a hierarchical and dynamic architecture. This scheduling model does not have a significant time overhead on other performance criteria such as waiting time, completion time and throughput. The proposed scheduling model also tries to reduce the resource cost in economic grids. Simulation results of proposed model in the presence of faults and resource failure show the improvement of reliability when compared with non-fault tolerance scheduler and other classic methods.

Fault tolerance (FT) is a topic of major concern in achieving dependable systems, for both real time as well as non real time systems. The paper provides a model of achieving fault tolerance, based on the ISO/ITU Reference Model for Open Distributed Processing (RM-ODP). This reference model provides a system software engineering methodology for fault tolerance, an object based model of fault tolerance, system requirements for achieving fault tolerance in an open manner, modeling constructs and rules to enable a proper system specification of fault tolerance, and business rules in terms of policies to achieve a well formed system specification. All these aspects are discussed at some depth, but the author primarily focuses on how certain behavior can be specified and achieved in an object based system, the constructs of the Unified Modeling Language (UML) and the Object Constraint Language (OCL).

Task automata: Schedulability, decidability and undecidability

<p>This paper presents an inverse dynamic model estimation based on an artificial neural network of a complete new parallel robot manipulator prototype 6- PGK with six degrees of freedom, built at Petru Maior University of Tirgu-Mures. The model estimation of the parallel robot manipulator is performed with a feedforward artificial neural network. In the control engineering domain there are control structures that need the direct or inverse model of the process for ensuring the process control at the imposed performances. Usually, the determination of the direct/inverse mathematical model is a difficult or impossible task to be achieved. In these cases different non-parametric or parametric, off-line or on-line identification methods are used. A solution that may support the on-line parametric methods is represented by the feedforward artificial neural networks. By implementing feedforward artificial neural networks as a nonlinear autoregressive model with exogenous inputs, the authors investigate the possibility of choosing the optimum parameters that characterize the neural network so that it approximates as better as possible the model of the 6-PGK prototype robot. Finally an innovative algorithm is developed for obtaining the optimal configuration parameters set of the feedforward artificial neural network. The proposed algorithm helps in setting the optimal parameters of the neural network that offer high opportunities to provide satisfactory identification of the robot model. Experimental results obtained by a structure derived from the proposed solution demonstrate a good approximation related to the studied system, which is characterized by nonlinearities and high complexity.</p>

A feedforward artificial neural network has been realized and trained to individualize subharmonics frequencies in an electric network. The adopted sampling window is fixed in 20 ms correspondent to a period of the fundamental frequency of 50 Hz, nevertheless the net is able to identify the subharmonics frequencies even if their period is not completed. The particular binary successive approximation structure realized for the ANN forecasts the scalability of the same net so allowing to recognize the subharmonics with a prefixed resolution. The net has been widely tested on synthesized signals.

Improved likelihood ratio test based voice activity detector applied to speech recognition