Automatic code generation from Matlab/Simulink for critical applications

Križan, J.; Ertl, L.; Bradac, M.G.; Jasansky, M.; Andreev, Andrey

doi:10.1109/ccece.2014.6901058

Cited by 29 publications

(11 citation statements)

References 2 publications

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“…These models have time-continuous behaviors (described using differential equations) since they are expected to capture and continuously interact with the physical world [26,22]. Code generation models, on the other hand, capture software parts only, and have discrete time behavior (described using some form of discrete logic or discrete state machines) [46,25]. This is because the generated code will run on platforms that support discrete computations, and further, the code will receive input data as discrete sequences of events.…”

Section: Introductionmentioning

confidence: 99%

Automated test suite generation for time-continuous simulink models

Matinnejad

Nejati

Briand

et al. 2016

Proceedings of the 38th International Conference on Software Engineering

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All engineering disciplines are founded and rely on models, although they may differ on purposes and usages of modeling. Interdisciplinary domains such as Cyber Physical Systems (CPSs) seek approaches that incorporate different modeling needs and usages. Specifically, the Simulink modeling platform greatly appeals to CPS engineers due to its seamless support for simulation and code generation. In this paper, we propose a test generation approach that is applicable to Simulink models built for both purposes of simulation and code generation. We define test inputs and outputs as signals that capture evolution of values over time. Our test generation approach is implemented as a meta-heuristic search algorithm and is guided to produce test outputs with diverse shapes according to our proposed notion of diversity. Our evaluation, performed on industrial and public domain models, demonstrates that: (1) In contrast to the existing tools for testing Simulink models that are only applicable to a subset of code generation models, our approach is applicable to both code generation and simulation Simulink models. (2) Our new notion of diversity for output signals outperforms random baseline testing and an existing notion of signal diversity in revealing faults in Simulink models. (3) The fault revealing ability of our test generation approach outperforms that of the Simulink Design Verifier, the only testing toolbox for Simulink.

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Section: Introductionmentioning

confidence: 99%

Automated test suite generation for time-continuous simulink models

Matinnejad

Nejati

Briand

et al. 2016

Proceedings of the 38th International Conference on Software Engineering

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“…The work presented in Reference [27] discusses Simulink usage for automatic generation of C code in critical applications, according to DO-178C and DO-331 standards. They also discussed the possibility of automatic code generation of a whole application or of separate parts, or tasks.…”

Section: Related Workmentioning

confidence: 99%

Analysis and Identification of Possible Automation Approaches for Embedded Systems Design Flows

2020

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Sophisticated and high performance embedded systems are present in an increasing number of application domains. In this context, formal-based design methods have been studied to make the development process robust and scalable. Models of computation (MoC) allows the modeling of an application at a high abstraction level by using a formal base. This enables analysis before the application moves to the implementation phase. Different tools and frameworks supporting MoCs have been developed. Some of them can simulate the models and also verify their functionality and feasibility before the next design steps. In view of this, we present a novel method for analysis and identification of possible automation approaches applicable to embedded systems design flow supported by formal models of computation. A comprehensive case study shows the potential and applicability of our method.

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“…To benchmark the real-time performance of existing EMG classification systems of the two types outlined in Section 2, optimised C++ derived via MATLAB's Embedded Coder [12] from the models used to compare the performance in [11] is considered. This is deployed on a 1.4 GHz ARM Cortex-A53 processor on a Raspberry Pi 3 B+ model.…”

Section: Embedded Emg Analysismentioning

confidence: 99%

EMG Wrist-hand Motion Recognition System for Real-time Embedded Platform

Raurale

McAllister

Rincón

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Electromyography (EMG) signal analysis is a popular method for controlling prosthetic and gesture control equipment. For portable systems, such as prosthetic limbs, real-time lowpower operation on embedded processors is critical, but to date there has been no record of how existing EMG analysis approaches support such deployments. This paper presents a novel approach to time-domain classification of multichannel EMG signals harnessed from randomly-placed sensors according to the wrist-hand movements which caused their occurrence. It shows how, by employing a very small set of time-domain features, Kernel Fisher discriminant feature projection and Radial Bias Function neural network classifiers, nine wrist-hand movements can be detected with accuracy exceeding 99% -surpassing the state-of-the-art on record. It also shows how, when deployed on ARM Cortex-A53, the processing time is not only sufficient to enable real-time processing but is also a factor 50 shorter than the leading time-frequency techniques on record.

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Automatic code generation from Matlab/Simulink for critical applications

Cited by 29 publications

References 2 publications

Automated test suite generation for time-continuous simulink models

Automated test suite generation for time-continuous simulink models

Analysis and Identification of Possible Automation Approaches for Embedded Systems Design Flows

EMG Wrist-hand Motion Recognition System for Real-time Embedded Platform

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