Application Performance of Physical System Simulations

Various parallel computer benchmarking projects have been around since early 1990s but the adopted so far approaches for performance analysis require a significant revision in view of the recent developments of both the application domain and the computer technologies. This paper presents a novel performance evaluation methodology based on assessing the processing rate of two orthogonal use cases – dense and sparse physical systems – as well as the energy efficiency for both. Evaluation results with two popular codes — HPL and HPCG — validate our approach and demonstrate its use for analysis and interpretation in order to identify and confirm current technological challenges as well as to track and roadmap the future application performance of physical system simulations

Semantic-Functional Communications in Cyber-Physical Systems

This paper explores the use of semantic knowledge inherent in the cyber-physical system (CPS) under study in order to minimize the use of explicit communication, which refers to the use of physical radio resources to transmit potentially informative data. It is assumed that the acquired data have a function in the system, usually related to its state estimation, which may trigger control actions. We propose that a semantic-functional approach can leverage the semantic-enabled implicit communication while guaranteeing that the system maintains functionality under the required performance. We illustrate the potential of this proposal through simulations of a swarm of drones jointly performing remote sensing in a given area. Our numerical results demonstrate that the proposed method offers the best design option regarding the ability to accomplish a previously established task -- remote sensing in the addressed case -- while minimising the use of radio resources by controlling the trade-offs that jointly determine the CPS performance and its effectiveness in the use of resources. In this sense, we establish a fundamental relationship between energy, communication, and functionality considering a given end application

PhyNetLab: An IoT-Based Warehouse Testbed

Future warehouses will be made of modular embedded entities with communication ability and energy aware operation attached to the traditional materials handling and warehousing objects. This advancement is mainly to fulfill the flexibility and scalability needs of the emerging warehouses. However, it leads to a new layer of complexity during development and evaluation of such systems due to the multidisciplinarity in logistics, embedded systems, and wireless communications. Although each discipline provides theoretical approaches and simulations for these tasks, many issues are often discovered in a real deployment of the full system. In this paper we introduce PhyNetLab as a real scale warehouse testbed made of cyber physical objects (PhyNodes) developed for this type of application. The presented platform provides a possibility to check the industrial requirement of an IoT-based warehouse in addition to the typical wireless sensor networks tests. We describe the hardware and software components of the nodes in addition to the overall structure of the testbed. Finally, we will demonstrate the advantages of the testbed by evaluating the performance of the ETSI compliant radio channel access procedure for an IoT warehouse

Design and simulation of automotive radar for autonomous vehicles

Modern automobile technology is pushing towards maximizing road safety, connected vehicles, autonomous vehicles, etc. Automotive RADAR is core sensor technology used for ADAS (Advanced Driver Assistance Technology), ACC (Adaptive Cruise Control), AEB (Automatic Emergency Braking System), traffic assistance, parking aid, and obstacle/pedestrian detection. Despite being inexpensive, RADAR technology provides robust results in harsh conditions such as harsh weather, extreme temperature, darkness, etc. However, the performance of these systems depends on the position of the RADAR and its characteristics like frequency, beamwidth, and bandwidths. Moreover, the characterization of varied materials like layers of paint, polish, primer, or layer of rainwater needs to be analyzed. This performance can be predicted through real-time simulation using advanced FEM software like Altair FEKO&WinProp. These simulations can provide valuable insight into the performance of the system, allowing engineers to optimize the system for specific use cases. For example, simulation can be used to determine the optimal parameters of the RADAR system for a given application. This information can then be used to design and build a physical model or prototype that is optimized for the desired performance. These simulations play a prominent role in determining appropriate data collection and sensor fusion, which reduces the cost and time required for the development of a physical model or prototype. The continued growth and demand for advanced safety features in vehicles further highlight the importance of RADAR technology in modern automobile technology. By accurately characterizing the environment and simulating the system's behavior in real time, engineers can optimize RADAR systems for specific use cases, contributing to safer and more efficient driving experience

A new analysis strategy for detection of faint gamma-ray sources with Imaging Atmospheric Cherenkov Telescopes

A new background rejection strategy for gamma-ray astrophysics with stereoscopic Imaging Atmospheric Cherenkov Telescopes (IACT), based on Monte Carlo (MC) simulations and real background data from the H.E.S.S. [High Energy Stereoscopic System, see [1].] experiment, is described. The analysis is based on a multivariate combination of both previously-known and newly-derived discriminant variables using the physical shower properties, as well as its multiple images, for a total of eight variables. Two of these new variables are defined thanks to a new energy evaluation procedure, which is also presented here. The method allows an enhanced sensitivity with the current generation of ground-based Cherenkov telescopes to be achieved, and at the same time its main features of rapidity and flexibility allow an easy generalization to any type of IACT. The robustness against Night Sky Background (NSB) variations of this approach is tested with MC simulated events. The overall consistency of the analysis chain has been checked by comparison of the real gamma-ray signal obtained from H.E.S.S. observations with MC simulations and through reconstruction of known source spectra. Finally, the performance has been evaluated by application to faint H.E.S.S. sources. The gain in sensitivity as compared to the best standard Hillas analysis ranges approximately from 1.2 to 1.8 depending on the source characteristics, which corresponds to an economy in observation time of a factor 1.4 to 3.2.Comment: 26 pages, 13 figure

Cloud Workload Prediction by Means of Simulations

Clouds hide the complexity of maintaining a physical infrastructure with a disadvantage: they also hide their internal workings. Should users need to know about these details e.g., to increase the reliability or performance of their applications, they would need to detect slight behavioural changes in the underlying system. Existing solutions for such purposes offer limited capabilities. This paper proposes a technique for predicting background workload by means of simulations that are providing knowledge of the underlying clouds to support activities like cloud orchestration or workflow enactment. We propose these predictions to select more suitable execution environments for scientific workflows. We validate the proposed prediction approach with a biochemical application