Towards A Semiformal Development Methodology for Embedded Systems

In recent days, the amount of functions has increased significantly in embedded products so that systems development methodologies play an important role to ensure the product’s quality, cost, and time. Furthermore, this complexity coupled with constantly evolving specifications, has led to propose a semiformal development methodology to support the building of embedded real-time systems. A platform-based design approach has been used to balance costs and time-to-market in relation to performance and functionality constraints. We performed three expressive case studies and we concluded that the proposed methodology significantly reduces design time and improves software modularity and reliability

Deep Learning for predicting disease progression of clinical endpoints in ALS

Tese de Mestrado, Engenharia Informática, 2022, Universidade de Lisboa, Faculdade de CiênciasAmyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease vastly known for its rapid progression, usually leading to death within a few years, by respiratory failure. Since there is no cure currently known, the main objective is treatment in order to improve symptoms and prolong survival. A treatment that is known to be effective, is Non-invasive Ventilation (NIV), being capable of extending life expectancy and improving quality of life. Therefore it is imperative to administrate it preemptively. However Amyotrophic Lateral Sclerosis (ALS) affects most muscles of the body, so there will exist many clinical conditions that require some kind of treatment. In this work, we propose two approaches that use deep learning to predict Amyotrophic Lateral Sclerosis (ALS) disease progression, to know when a patient should be treated or not for a given clinical endpoint. In the first approach, we use the various snapshots of the patients as input without taking into consideration the temporal dependence between the available features, so instead we use each assessment of the patient as a single instance to feed the models. In this approach, we propose the use of a MLP and a CNN, to predict the outcome for the time windows available (90, 180 and 365 days), using the instances mentioned before and perform class resampling on the training sets, using SMOTE on the minority class and Random Undersampling on the majority class. In order to have more data to train the models and have a balanced set, which helps achieving a better performing model on the test set. In the second approach, we take the snapshots of the patients and group them by the patient reference, and proceed to only used those that have length of 3 and 4. On the sequences of length 3, we performed padding in order to have the same length as the sequences of length 4, by simply taking the first instance of this sequence and use it as the first and second assessment on the sequences. We proceed to use the sequences to feed them into a LSTM model, and train for the several datasets and retrieve the scores obtained. On this approach we do not perform any type of class resampling, as the first approach does. The results obtained show some promising insights, on the first approach in which we use the instances of the patients, the preprocessing performed was one of the main factors to the great results obtained. On the second approach, which the temporal sequences of length 3 and 4 are used, the results obtained are not as promissing as the first approach, however there are still improvements that could be done

A Formal Method for Modeling, Verification and Synthesis of Embedded Reactive Systems

Embedded reactive systems are now invisible and everywhere, and are adopted, for instance, to monitor and control critical tasks in cars, airplanes, traffic, and industrial plants. However, the increasing amount of new functionalities being moved to software leads to difficulties in verifying the design correctness. In this context, we propose a novel design method called BARE Model, which is a formal abstraction to design, verify and synthesize software in embedded reactive applications. The method consists in designing the application using an extension of the well-known finite state machine, called X-machine. We thus propose to translate this model to a tabular data structure, which is a kind of state transition table augmented with memory input, memory output, and condition (or guard). This tabular structure may be automatically translated to the input of the NuSMV model checker in order to verify the system’s properties. We also propose a runtime environment to execute the system (expressed as a tabular data structure) in a specific platform. In this way, we can convert the high-level specification into executable code that runs on a target platform. To show the practical usability of our proposed method, we experimented it with the Envirotrack case study. The experiment shows that the proposed method is able to not only model the system, but also to verify safety and liveness properties, and synthesize executable code of real-world applications

Surface Structure Determination of Black Phosphorus Using Photoelectron Diffraction

Atomic structure of single-crystalline black phosphorus was studied by high resolution synchrotron-based photoelectron diffraction (XPD). The results show that the topmost phosphorene layer in the black phosphorus is slightly displaced compared to the bulk structure and presents a small contraction in the direction perpendicular to the surface. Furthermore, the XPD results show the presence of a small buckling among the surface atoms, in agreement with previously reported scanning tunneling microscopy results. The contraction of the surface layer added to the presence of the buckling indicates an uniformity in the size of the sp3 bonds between P atoms at the surface

A Platform-Based Software Design Methodology for Embedded Control Systems: An Agile Toolkit

A discrete control system, with stringent hardware constraints, is effectively an embedded real-time system and hence requires a rigorous methodology to develop the software involved. The development methodology proposed in this paper adapts agile principles and patterns to support the building of embedded control systems, focusing on the issues relating to a system's constraints and safety. Strong unit testing, to ensure correctness, including the satisfaction of timing constraints, is the foundation of the proposed methodology. A platform-based design approach is used to balance costs and time-to-market in relation to performance and functionality constraints. It is concluded that the proposed methodology significantly reduces design time and costs, as well as leading to better software modularity and reliability