Code Generation from Hybrid Systems Models for Distributed Embedded Systems

Code generation from hybrid system models is a promising approach to producing reliable embedded systems. This approach presents new challenges as the precise semantics of the model are hard to capture in the code. A framework for generating code was introduced for single threaded/processor environments. Here, we extend it by considering code generation for distributed environments. We also define criteria for faithful implementation of the model. To this end, we define faulty and missed transitions. For preventing faulty transitions, we build on the idea of instrumentation we have developed for sound simulation of hybrid systems. Finally, we present sufficient conditions to avoid missed transitions and provide examples

A Comparison of Compositional Schedulability Analysis Techniques for Hierarchical Real-Time Systems

Schedulability analysis of hierarchical real-time embedded systems involves defining interfaces that represent the underlying system faithfully and then compositionally analyzing those interfaces. Whereas commonly used abstractions, such as periodic and sporadic tasks and their interfaces, are simple and well studied, results for more complex and expressive abstractions and interfaces based on task graphs and automata are limited. One contributory factor may be the hardness of compositional schedulability analysis with task graphs and automata. Recently, conditional task models, such as the recurring branching task model, have been introduced with the goal of reaching a middle ground in the tradeoff between expressivity and ease of analysis. Consequently, techniques for compositional analysis with conditional models have also been proposed, and each offer different advantages. In this work, we revisit those techniques, compare their advantages using an automotive case study, and identify limitations that would need to be addressed before adopting these techniques for use with real-world problems

An Analysis Framework for Network-Code Programs

Distributed real-time systems require a predictable and verifiable mechanism to control the communication medium. Current real-time communication protocols are typically independent of the application and have intrinsic limitations that impede customizing or optimizing them for the application. Therefore, either the developer must adapt her application and work around these subtleties or she must limit the capabilities of the application being developed. Network Code, in contrast, is a more expressive and flexible model that specifies real-time communication schedules as programs. By providing a programmable media access layer on the basis of TDMA, Network Code permits creating application-specific protocols that suit the particular needs of the application. However, this gain in flexibility also incurrs additional costs such as increased communication and run-time overhead. Therefore, engineering an application with network code necessitates that these costs are analyzed, quantified, and weighted against the benefits. In this work, we propose a framework to analyze network code programs for commonly used metrics such as overhead, schedulability, and average waiting time. We introduce Timed Tree Communication Schedules, based on timed automata to model such programs and define metrics in the context of deterministic and probabilistic communication schedules. To demonstrate the utility of our framework, we study an inverted pendulum system and show that we can decrease the cumulative numeric error in the model’s implementation through analyzing and improving the schedule based on the presented metrics

Resource Scopes: Toward Language Support for Compositional Determinism

Complex real-time embedded systems should be compositional and deterministic in the resource, time, and value domains. Determinism eases the engineering of correct systems and compositionality simplifies the assembly of complex systems out of smaller modules. This paper describes the PEACOD framework that is developed to support deterministic behavior for resource consumption, value passing, and timing. The paper introduces the notions of determinism in the context of the resource, value, and temporal domains, and present the resource-scope language construct that can be used to program such deterministic behaviors. Furthermore, the paper also provides semantics for the resource scope construct and uses these semantics to show that the program behavior is preserved under composition. The paper briefly describes the current implementation of PEACOD

Sound Code Generation From Hybrid System Models: Some Theoretical Results

Code generation from hybrid system models, a promising approach for producing reliable embedded systems, has been our research focus for some time now. In this report, we summarize the progress made thus far and provide directions for research towards realization of this goal

Quantifying Eavesdropping Vulnerability in Sensor Networks

With respect to security, sensor networks have a number of considerations that separate them from traditional distributed systems. First, sensor devices are typically vulnerable to physical compromise. Second, they have significant power and processing constraints. Third, the most critical security issue is protecting the (statistically derived) aggregate output of the system, even if individual nodes may be compromised. We suggest that these considerations merit a rethinking of traditional security techniques: rather than depending on the resilience of cryptographic techniques, in this paper we develop new techniques to tolerate compromised nodes and to even mislead an adversary. We present our initial work on probabilistically quantifying the security of sensor network protocols, with respect to sensor data distributions and network topologies. Beginning with a taxonomy of attacks based on an adversary’s goals, we focus on how to evaluate the vulnerability of sensor network protocols to eavesdropping. Different topologies and aggregation functions provide different probabilistic guarantees about system security, and make different trade-offs in power and accuracy

Compositional Feasibility Analysis for Conditional Real-Time Task Models

Conditional real-time task models, which are generalizations of periodic, sporadic, and multi-frame tasks, represent real world applications more accurately. These models can be classified based on a tradeoff in two dimensions – expressivity and hardness of schedulability analysis. In this work, we introduce a class of conditional task models and derive efficient schedulability analysis techniques for them. These models are more expressive than existing models for which efficient analysis techniques are known. In this work, we also lay the groundwork for schedulability analysis of hierarchical scheduling frameworks with conditional task models. We propose techniques that abstract timing requirements of conditional task models, and support compositional analysis using these abstractions

Unit & Dynamic Typing in Hybrid Systems Modeling with CHARON

In scientific applications, dimensional analysis forms a basis for catching errors as it introduces a type-discipline into the equations and formulae. Dimensions in physical quantities are measured via their standard units. However, many programming and modeling tools provide limited support for incorporating these units into the variables. Thus, it is quite difficult for a programmer to ensure dimensional consistency in the code. Different existing standards for units further complicates this problem and an incautious use could cause inconsistencies, often with catastrophic results. In this paper, we propose an extension of the basic type system in CHARON, a language for modeling of hybrid systems, to include Unit and Dynamic data types. Through a combination of indirect user-guided annotations and typeinference, we address the problem of ensuring both dimensional consistency, and consistency with respect to different unitsystems. Further, we also introduce dynamic data typing, that allows programmers to specify entities that bind at runtime. Such abstractions are particularly useful to program controllers for dynamic environments. We illustrate these benefits with an example on mobile robots

Optical Coherence Tomography and its application in prognosis of disease through ayurveda.

A review of OCT as diagnostic tool and its application in ayurved. Optical Coherence Tomography (OCT) is one of the elite diagnostic measures in modern science which not only helps in diagnosing diseases but also in establishing relation between modern science and ancient science. OCT is usually used in diagnosing and managing diseases like Diabetic Macular Edema (DME), Myopia, Diabetic Retinopathy (DR), Central Serous Retinopathy (CSR), Glaucoma, etc. Depending on the disease condition; result of OCT can be co-related with modern aspect as well as ancient aspect. The primary objective of this literary review is concerned with gunas of vataj, pittaj, kaphaj dosha with clinical findings as seen in OCT. Ayurvedic classics state that guna of vata, guna of pitta, guna of pitta, guna of kapha are evident in shrotas and shrotojanya vyadhi. In the present era changes in lifestyle, food habits, uninhibited use of steroids had led to disorders of retina which is visible as changes in normative findings of OCT. Therefore, a proper understanding of doshas and its guna will help in decoding the findings of OCT with ayurvedic perspective

Evaluation of Local Feature Detectors for the Comparison of Thermal and Visual Low Altitude Aerial Images

Local features are key regions of an image suitable for applications such as image matching, and fusion. Detection of targets under varying atmospheric conditions, via aerial images is a typical defence application where multi spectral correlation is essential. Focuses on local features for the comparison of thermal and visual aerial images in this study. The state of the art differential and intensity comparison based features are evaluated over the dataset. An improved affine invariant feature is proposed with a new saliency measure. The performances of the existing and the proposed features are measured with a ground truth transformation estimated for each of the image pairs. Among the state of the art local features, Speeded Up Robust Feature exhibited the highest average repeatability of 57 per cent. The proposed detector produces features with average repeatability of 64 per cent. Future works include design of techniques for retrieval of corresponding regions