Matlab2Trace: A Matlab to Trace translator to visualise and analyse concurrent system activities and execution traces

Matlab provides an environment to analyse and visualise data and develop algorithms. However, there is limited support for visualising and analysing system activities executing concurrently, for instance, on a multiprocessor platform. Trace (https://esi.nl/research/output/tools/trace) is software that specialises in visualising and analysing concurrent system activities and execution traces. We present a Matlab to Trace translator that directly generates a trace-input file from the Matlab environment. Concurrent system activities and execution traces of the algorithms developed inside the Matlab environment can be visualised and analysed in Trace using the generated trace-input file. The translator takes as input the logical or absolute starting and ending time of the algorithmic execution, and the number (and labels) of processing cores. TRACE visualizes concurrent activities in a Gantt-chart-like view which provides colouring, grouping and filtering options. TRACE also provides several analysis methods, which sets it apart from the many other Gantt-chart visualization tools: i) Critical-path analysis can be used to detect tasks and resources that are bottlenecks for performance; ii) Distance analysis can be used to compare execution traces with respect to structure, e.g. to check a model trace against an implementation trace; iii) MTL checking provides a means to formally specify and verify properties of execution traces using Metric Temporal Logic. It is useful to express and check, for instance, performance properties such as the “processing latency is at most 50 ms”; iv) The streaming performance DSL is a domain-specific language that captures often-used performance properties for stream-processing systems (e.g., image or video processing), and which eases the use of the MTL checker; and v) The resource usage feature can quickly give insight in the details of the resource usage. The Matlab2Trace can be downloaded from https://github.com/TUE-EE-ES/Matlab2Trace

DASA:an open-source design, analysis and simulation framework for automotive image-based control systems

Image-Based Control (IBC) systems are a class of data-intensive feedback control systems whose feedback is provided by image-based sensing using a camera. IBC has become popular with the advent of efficient image processing systems and low-cost CMOS cameras with high resolution. The combination of the camera and image processing (sensing) gives necessary information on parameters such as relative position, geometry, relative distance, depth perception and tracking of the object-of-interest. This enables the effective use of low-cost camera sensors to enable new functionality or replace expensive sensors in cost-sensitive industries like automotive.The state-of-the-art design, analysis, and simulation of IBC assumes that the sensing algorithm is executing correctly with an assumed or estimated worst-case delay. The sensing algorithm is simulated and validated using static pre-captured image streams and is normally decoupled from the control algorithm. However, in reality, the camera is fixed to the vehicle body and any steering change would affect the region captured by the image. This dynamism cannot be captured in a static image stream and a dynamic image stream that considers the change in vehicle dynamics due to IBC actuation is needed.We present an open-source design, analysis, and simulation framework for automotive IBC systems that can consider the change in vehicle dynamics in real-time and produces real-time dynamic image stream as per the control algorithm. Our framework models the 3D environment in 3ds Max, simulates the vehicle dynamics, camera position, environment and traffic in V-REP and computes the control output in Matlab. Our framework runs Matlab as a server and V-REP as a client in synchronous mode. We show the effectiveness of our framework using a vision-based lateral control system.<br/

Model-driven quality and resource management for CPSs

A Cyber-Physical System (CPS) integrates cyber systems, human users, networks and physical systems. Thus, a CPS needs visual context and awareness to make autonomous and correct decisions. Advanced image and video processing is computationally intensive and challenging. Moreover, a CPS comprises increasingly complex and distributed configurations, which is reflectedin the growing number of sensors, actuators and other smart devices. This leadsto an exponential number of dynamic system configurations. To make mattersworse, a CPS needs to simultaneously satisfy many rigorous constraints, e.g.,hard deadlines, safety, quality, and performance. Hence, the system designeris confronted with an immense number of potential configurations of which anumber meet the constraints and only a fraction are optimal regarding certainqualities. This makes finding the optimal configurations hard, especially duringrun-time. A domain-specific language (DSL) for quality and resource managment (QRM) is presented to specify these configurations conveniently and reasonabout them in an automated manner

Simultaneous Budget and Buffer Size Computation for Throughput-Constrained Task Graphs

Modern embedded multimedia systems process multiple concurrent streams of data processing jobs. Streams often have throughput requirements. These jobs are implemented on a multiprocessor system as a task graph. Tasks communicate data over buffers, where tasks wait on sufficient space in output buffers before producing their data. For cost reasons, jobs share resources. Because jobs can share resources with other jobs that include tasks with data-dependent execution rates, we assume run-time scheduling on shared resources. Budget schedulers are applied, because they guarantee a minimum budget in a maximum replenishment interval. Both the buffer sizes as well as the budgets influence the temporal behaviour of a job. Interestingly, a trade-off exists: a larger buffer size can allow for a smaller budget while still meeting the throughput requirement. This work is the first to address the simultaneous computation of budget and buffer sizes. We solve this non-linear problem by formulating it as a second-order cone program. We present tight approximations to obtain a non-integral second-order cone program that has polynomial complexity. Our experiments confirm the non-linear trade-off between budget and buffer sizes

Interface Modeling for Quality and Resource Management

We develop an interface-modeling framework for quality and resource management that captures configurable working points of hardware and software components in terms of functionality, resource usage and provision, and quality indicators such as performance and energy consumption. We base these aspects on partially-ordered sets to capture quality levels, budget sizes, and functional compatibility. This makes the framework widely applicable and domain independent (although we aim for embedded and cyber-physical systems). The framework paves the way for dynamic (re-)configuration and multi-objective optimization of component-based systems for quality- and resource-management purposes