MOUSSE: scaling MOdelling and verification to complex heterogeneoUS embedded systems evolution

Universities and other educational institutions may find it difficult to afford the cost of obtaining cutting-edge teaching resources. This study introduces the adoption of a novel open prototyping framework in the context of mechatronics education, employing low-cost commercial off-the-shelf (COTS) components and tools for the motion control module. The goal of this study is to propose a novel structure for the motion control module in the engineering mechatronics curriculum. The objective is to foster a new teaching method. From a methodology perspective, students are involved in a series of well-organised theoretical lectures as well as practical, very engaging group projects in the lab. To help students understand, draw connections, and broaden their knowledge, the methods of surface learning and deep learning are frequently mixed thoroughly. The structure of the course as well as the key topics are discussed. The proposed open framework, which consists of an elevator model, is presented in details. Students’ early evaluation indicates that the course organisation and subjects are successful and beneficial.

Chapter 3 - Scaling of Food-Web Properties with Diversity and Complexity Across Ecosystems

Virtual prototyping of pressure driven microfluidic systems with SystemC-AMS extensions

Reconciling scene realism with interactivity has emerged as one of the most important areas in making virtual reality feasible for large-scale CAD data sets consisting of several millions of primitives. Level of detail (LoD) and multi-resolution modeling techniques in virtual reality can be used to speed up the process of virtual design and virtual prototyping. In this paper, we present an automatic LoD generation and rendering algorithm, which is suitable for CAD models and propose a new multi-resolution representation scheme called MRM (multi-resolution model), which can support efficient extraction of fixed resolution and variable resolution for multiple objects in the same scene. MRM scheme supports unified selective simplifications and selective refinements over the mesh. Furthermore, LoD and multi-resolution models may be used to support real-time geometric transmission in collaborative virtual design and prototyping.

Networks-on-Chip (NoCs) are fast becoming the defacto interconnection fabric for System-on-Chip (SoC) architectures. Early stage performance modeling is a critical step in NoC design for design space exploration and achieving efficient topologies within Time-To-Market (TTM) demands. In this paper, we propose an automatic traffic generation framework for early and comprehensive analysis of NoCs. Using behavioral input and five independent control parameters, it generates a wide range of spatio-temporal traffic stimuli, including bursty traffic while honoring all design constraints. Generated profiles include use-case scenarios representative of actual SoC traffic and plethora of scenarios for out-of-bound performance analysis. Evaluation on production level SoCs shows that the profiles generated are within 5% margin of actual traffic, enables quick iterations from early stages and shrinks TTM from months to few weeks. Final evaluation using RTL testbenches meets performance in all cases.

For patients suffering from central nervous system tumors, prognosis estimation, treatment decisions, and postoperative assessments are made from the analysis of a set of magnetic resonance (MR) scans. Currently, the lack of open tools for standardized and automatic tumor segmentation and generation of clinical reports, incorporating relevant tumor characteristics, leads to potential risks from inherent decisions’ subjectivity. To tackle this problem, the proposed Raidionics open-source software has been developed, offering both a user-friendly graphical user interface and stable processing backend. The software includes preoperative segmentation models for each of the most common tumor types (i.e., glioblastomas, lower grade gliomas, meningiomas, and metastases), together with one early postoperative glioblastoma segmentation model. Preoperative segmentation performances were quite homogeneous across the four different brain tumor types, with an average Dice around 85% and patient-wise recall and precision around 95%. Postoperatively, performances were lower with an average Dice of 41%. Overall, the generation of a standardized clinical report, including the tumor segmentation and features computation, requires about ten minutes on a regular laptop. The proposed Raidionics software is the first open solution enabling an easy use of state-of-the-art segmentation models for all major tumor types, including preoperative and postsurgical standardized reports.

The increasing need for validating heterogeneous Systems on Chip (SoC) before their costly fabrication gives rise to the development of numerous virtual prototyping tools, allowing not only the modeling of such systems, but also the simulation of the environment in which they will be embedded, including the crucial software. SoC designers indeed require flexible simulation frameworks that follow a component-based approach and that cover a wide field of physical/engineering disciplines… and no questions asked about the simulation intrinsics. Furthermore, for SMEs, laboratories, and enthusiastic makers with limited funding, the cost of these frameworks makes them unaffordable, especially when the target system hardware is supposed to be Open Source too. In this paper, we detail the principles of an Open Source SystemC Multi-Disciplinary Virtual Prototyping (SystemC MDVP) framework, which directly tackles with all these issues. We introduce the key concepts and methodology (relationships between Models of Computation and overall synchronization techniques) and insist on the importance of a “correct by construction” global model elaboration that allows to generate the hierarchy of solvers that make up the final simulator. We then show that a new Model of Computation, Electrical Network, has been added to SystemC MDVP to handle components with non-linear beahviors. The paper concludes with a quick presentation of the echOpen project which aims at designing a low-cost portable echography device modeled and simulated with the nresented nrincinles.

As today's computer architectures are becoming more and more heterogeneous, a plethora of options including CPUs, GPUs, DSPs, reconfigurable logic (FPGAs), and other application-specific processors come into consideration for close-to-sensor processing. Especially, in the domain of image processing on mobile devices, among numerous design challenges, a very stringent energy budget is of utmost importance, making embedded GPUs and FPGAs ideal targets for implementation. Recently, the HIPAcc framework was proposed as a means for automatic code generation of image processing algorithms for embedded GPUs, based on a Domain-Specific Language (DSL). Despite of huge advancements in High-Level Synthesis (HLS) for FPGAs, designers are still required to have detailed knowledge about coding techniques and the targeted architecture to achieve efficient solutions. As a remedy, in this work, we propose code generation techniques for C-based HLS from a common high-level DSL description targeting FPGAs. Our approach includes FPGA-specific memory architectures for handling point and local operators, numerous high-level transformations, and automatic test bench generation. We evaluate our approach by comparing the resulting hardware accelerators to existing frameworks in terms of performance and resource requirements. Moreover, we assess the achieved energy efficiency in contrast to software implementations, generated by HIPAcc from the same code base, executed on GPUs.

Component-Based Software Development (CBSD) is an approach that has many benefits, such as improving application developer productivity, reducing costs and complexity by reusing of existing codes. Programming within this approach is like assembly (i.e. composing software out of prefabricated components) rather than development, which reduces skill requirements, and allows expertise focuses on domain problems. The foundation of any CBSD methodology is its underlying component model, which defines what components are, how they can be constructed, and specifies the standards and conventions that are needed to enable composition of independently developed component. The current component models do not support composition in both design and deployment phase. They also focus on the specification and packaging of components but provide almost no support for the easy composition of components. Component in these models uses either direct or indirect message passing as connection schemes, which leads to tightly coupling (i.e. components mix computation with control). It is conclude that this research has proposed an effective way for component composition which provides loosely coupling between composed components. For system maintenance and evolution, this decoupling should make it simpler to manage changes in the components, and also changes in the connector separately. This research has resulted in the proposed of mediator connector which is similar to a communication hub. It initiates method calls and manages the returns, and also provides total loosely coupling between components and also itself. Mediator connector is a framework and can be reused without any modification. The components composition using mediator connector belongs to the deployment phase. Our approach is based on interactions between components as a subset of behavior in a system. In order to minimize coupling between components and mediator connector, we have designed and developed an XML-based language, called Component Interaction Markup Language (CIML), where components as well as their interactions are described in a CIML document which are used by mediator connector. For evaluation of mediator connector in order to measure the loosely coupling it provides, four case studies have been tested. To measure coupling we applied Coupling Between Object (CBO) software metric. The result shows that mediator connector provides totally loosely coupling between software components composing in the deployment phase.

This paper presents a new evolutionary fuzzy system modeling strategy alternative to fuzzy rule bases, and does not entail if...then rule base structure. The new approach, which is based on improved fuzzy functions with genetic algorithms, is proposed to reduce complexity of earlier fuzzy system models and improve modeling accuracy. Structure identification of the new approach is based on a supervised improved fuzzy clustering (IFC) method with a dual optimization algorithm, which yields improved membership values. The merit of the proposed FSM is that uncertain information on natural grouping of data samples, i.e., membership values, is utilized as additional predictors while structuring fuzzy functions. Presented model is applied to desulphurization process of a steel company in Canada. It is shown that proposed approach is superior in comparison to earlier fuzzy, neuro-fuzzy, and non-fuzzy system models in terms of robustness and error reduction.

This article introduces the rationale for developing a special issue on methodological and statistical advancements in clinical assessment, which will appear approximately 25 years after a similar 1995 issue that produced many seminal and highly cited articles. We commissioned articles from a range of expert scholars in important areas of psychometrics (reliability, validity, scale construction, item response theory, and factor analysis) and assessment methodology in specific contexts (multicultural, clinical prediction, response bias, experience-sampling, and neuroscience) in addition to a lead article that considers clinical assessment research in the broader open science framework. We briefly summarize these contributions and their impact. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Several methods have been proposed to automatically generate fuzzy rule-based systems (FRBSs) from data. At the beginning, the unique objective of these methods was to maximize the accuracy with the result of often neglecting the most distinctive feature of the FRBSs, namely their interpretability. Thus, in the last years, the automatic generation of FRBSs from data has been handled as a multiobjective optimization problem, with accuracy and interpretability as objectives. Multi-objective evolutionary algorithms (MOEAs) have been so often used in this context that the FRBSs generated by exploiting MOEAs have been denoted as multi-objective evolutionary fuzzy systems. In this paper, we introduce a taxonomy of the different approaches which have been proposed in this framework. For each node of the taxonomy, we describe the relevant works pointing out the most interesting features. Finally, we highlight current trends and future directions.

Network Exposure Function (NEF) enables third-party application providers utilize the network capabilities and services that the 5G System offers. The 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> Generation Partnership Project (3GPP) provides open standardized Application programming Interfaces (API) through NEF; however, a big gap appears in the market between mobile operators and service providers since no commercial solutions are available. In this context, the NEF’s standardized APIs offered by 3GPP are examined. As a result of this analysis an open-source simulator “NEFSim” is presented. NEFSim realizes the aspects of the NEF and enables application developers to experiment with the northbound APIs under a simulated, configurable environment.

LM 26 is a category of cast aluminum alloy which is having exceptional characteristics that leads to use of this alloy as primary choice for making automobile components. Metal matrix composite (MMC) materials are having improved characteristics and believed as an alternate for plentiful engineering industries. Due to reinforcement added, the materials became harder and result in inferior machining performance by traditional approaches. Wire electrical discharge machining (WEDM) is one among the advanced and also a conversant approach engaged for making intricate forms. In this exploration, an investigation on machinability of WEDM of stir-casted hybrid MMC (LM26+ Graphite+Fly ash) and evolution of hybrid adaptive neuro fuzzy inference system (ANFIS) model for predicting the desired variables. The ascendance of variables named as pulse on (Ton), pulse off (Toff), dielectric fluid flushing pressure, wire feed, and servo voltage in contrast to preferred output measures like material removal rate, surface finish, dimensional deviation, and form/orientation tolerance errors were investigated. A hybrid approach combining grey and ANFIS model has been developed to foretell the desired measures, and comparison has been done on the experimental and predicted results. The performance of the model is investigated and proved that the model can predict the desired measures.

We introduce Backstroke, a new open source framework for the automatic generation of reverse code for functions written in C++. Backstroke enables reverse computation for optimistic parallel discrete event simulations. It is built using the ROSE open-source compiler infrastructure, and handles complex C++ features including pointers and pointer types, arrays, function and method calls, class types, inheritance, polymorphism, virtual functions, abstract classes, templated classes and containers. Backstroke also introduces new program inversion techniques based on advanced compiler analysis tools built into ROSE. We explore and illustrate some of the complex language and semantic issues that arise in generating correct reverse code for C++ functions.