Tracking coherence-related contention delays in real-time multicore systems

The prevailing use of multicores in Embedded Critical Systems (ECS) is multi-application workloads in which independent applications run in different cores with data sharing restricted to the communication between applications and the real-time operating system. However, thread-level parallelism is increasingly used, e.g., OpenMP, in ECS to improve individual applications' performance. At the hardware level, we are witnessing increased research efforts to master and improve multicore cache coherence that plays a key role enabling efficient data sharing among threads. Despite these efforts, the limited information provided by performance monitoring counters on cache coherence limits the understanding of coherence's impact on tasks execution time and hence, poses severe constraints to estimate tight worst-case execution time bounds. In this line, this work contributes with an analysis of the impact that cache coherence can have on application timing behavior, and a new set of low-overhead performance monitoring counters that can be used to track the coherence-related contention that different threads can cause on each other when sharing data. Our results show that the proposed performance monitoring counters effectively capture all coherence-related contention that tasks can suffer and hence are key for parallel software timing validation and verification in ECS. Furthermore, they help application optimization by providing key information about data sharing among the application threads.The research leading to these results has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 772773). This work has also been partially supported by Grant PID2019-107255GB-C21 funded by MCIN/AEI/ 10.13039/501100011033.Peer ReviewedPostprint (author's final draft

Assessing the Effectiveness of Assistance in Capacity Development

{Excerpt} Feedback is a circular causal process whereby some portion of a system’s output is returned to the input to control the dynamic behavior of the system. In organizations, feedback is the process of sharing observations, concerns, and suggestions to improve performance. In work that seeks to address the increasingly complex challenges of development, often with limited resources, feedback is essential to maximize development impact. Knowledge Solutions: Monthly Progress Notes asserts that the essential first steps of feedback are the processes of monitoring and evaluation. They identify challenges, recognize common constraints, and note that the submission of monthly progress notes on activities and accomplishments is too infrequently provided in the scope of projects and programs. There are opportunities too for more systematic capture and storage of feedback from executing agencies on the effectiveness of assistance in capacity development, prior to knowledge sharing and learning. Capacity development is the process whereby people, organizations, and society as awhole unleash, strengthen, create, adapt, and maintain capacity over time. In 2005, the Paris Declaration on Aid Effectiveness called for capacity development to be an explicit objective of the national development and poverty reduction strategies of partner countries. Bilateral and multilateral agencies, among others, have responded by elevating capacity development in their operations, and given attention to factors that drive success and factors that deter from it

Virtual enterprise collaborative processes monitoring through a project business approach

In order to design a system for managing performance in collaborative project-based enterprises, it is necessary to undertake real-time monitoring of its business processes and activities. This paper presents a systematic approach to project business process monitoring (BPM) and identifies the key aspects of virtual enterprise (VE) process evaluation. A framework for VE BPM is presented with special emphasis in linking interest groups to the development of their targets, information and knowledge sharing. The proposed model defines the exclusive performance metrics, which are needed during BPM. This interdisciplinary study examines BPM through peer-to-peer information exchange in the VE domain, which is currently a research gap. The fundamental metrics to define business process performance monitoring are elaborated in the research reported in this paper. The identified performance metrics can be used to measure the overall performances for both a project business and VE. A reference architecture is also highlighted with a case example with the objective to measure the performance in a project business VE. An overall definition of collaborative BPM and performance management systems is also presented accordingly. Future research directions are identified regarding the nature of collaboration in a project business VE and the characteristics of performance indicators to support it.fi=vertaisarvioitu|en=peerReviewed

Explicit schemes for time propagating many-body wavefunctions

Accurate theoretical data on many time-dependent processes in atomic and molecular physics and in chemistry require the direct numerical solution of the time-dependent Schr\"odinger equation, thereby motivating the development of very efficient time propagators. These usually involve the solution of very large systems of first order differential equations that are characterized by a high degree of stiffness. We analyze and compare the performance of the explicit one-step algorithms of Fatunla and Arnoldi. Both algorithms have exactly the same stability function, therefore sharing the same stability properties that turn out to be optimum. Their respective accuracy however differs significantly and depends on the physical situation involved. In order to test this accuracy, we use a predictor-corrector scheme in which the predictor is either Fatunla's or Arnoldi's algorithm and the corrector, a fully implicit four-stage Radau IIA method of order 7. We consider two physical processes. The first one is the ionization of an atomic system by a short and intense electromagnetic pulse; the atomic systems include a one-dimensional Gaussian model potential as well as atomic hydrogen and helium, both in full dimensionality. The second process is the decoherence of two-electron quantum states when a time independent perturbation is applied to a planar two-electron quantum dot where both electrons are confined in an anharmonic potential. Even though the Hamiltonian of this system is time independent the corresponding differential equation shows a striking stiffness. For the one-dimensional Gaussian potential we discuss in detail the possibility of monitoring the time step for both explicit algorithms. In the other physical situations that are much more demanding in term of computations, we show that the accuracy of both algorithms depends strongly on the degree of stiffness of the problem.Comment: 24 pages, 14 Figure

Design and Test of an Autonomy Monitoring Service to Detect Divergent Behaviors on Unmanned Aerial Systems

Operation of Unmanned Aerial Vehicles (UAV) support many critical missions in the United State Air Force (USAF). Monitoring abnormal behavior is one of many responsibilities of the operator during a mission. Some behaviors are hard to be detect by an operator, especially when flying one or more autonomous vehicles; as such, detections require a high level of attention and focus to flight parameters. In this research, a monitoring system and its algorithm are designed and tested for a target fixed-wing UAV. The Autonomy Monitoring Service (AMS) compares the real vehicle or simulated Vehicle with a similar simulated vehicle using Software in the Loop (SITL).It is hypothesized that the resulting design has the potential to reduce monotonous monitoring, reduce risk of losing vehicles, and increase mission effectiveness. Performance of the prototyped AMS model was examined by several measures, including divergence detection rate, synchronization time, and Upper Control Limit (UCL) of aircraft location variability in different scenarios. Results showed 100 rate of divergence detection out of all divergent events occurred. The weighted mean of AMS synchronization time was 4.02 seconds, and the weighted mean for aircraft location variability was 44.8 meters. The overarching AMS functionality was achieved. AMS supports the concept that humans and machines should be designed to complement each other by sharing responsibilities and behaviors effectively, making final system safer and more reliable

Potential Benefits and Costs of Concurrent Task Engagement to Maintain Vigilance: A Driving Simulator Investigation

Permissions were not obtained for sharing the full text of this article.Objective: The objective of this study was to investigate the nature of concurrent task interference during a vigilance task and to determine whether a concurrent task improves performance with decreased vigilance. Background: Research has repeatedly shown that engaging in a cell phone conversation while driving increases the risk of getting into crashes. At the same time, it has also been found that task monotony could lead to an increase in crash risk. There is evidence that suggests that engaging in a concurrent task reduces the effects of monotony, leading to an improvement in vigilance task performance. Method: A monotonous drive in a driving simulator was used to investigate the effects of a concurrent verbal task. Three task conditions were used: no verbal task, continuous verbal task, and late verbal task. Results: When engaged in a secondary verbal task, drivers showed improved lane-keeping performance and steering control when vigilance was lowest. Conclusion: A strategically placed concurrent task can improve performance when vigilance is at its lowest. Application: There is potential for the design of a countermeasure system that can be strategically activated by an automated system monitoring driver performance

Real Time Vehicle Identification: A Synchronous-Transmission Based Approach

Identification of the vehicles passing over the roads is a very important component of traffic monitoring/surveillance. There have been many attempts to design and develop efficient strategies to carry out the job. However, from the point of view of practical usefulness and real-time operation, most of them do not score well. In the current work, we perceive the problem as efficient real-time communication and data-sharing between the units in charge of recording the identities of the vehicles, i.e., Vehicle Recorders (VR), and the Vehicles (VE). We propose a strategy to address the issue with the help of Synchronous-Transmission (ST), which is a newer paradigm of communication compared to the traditional paradigm based on Asynchronous-Transmission (AT). First, we theoretically show that the presence of the physical layer phenomena called Capture-Effect in ST brings a significant benefit. Next, we also implement the strategy in a well-known IoT-Operating System Contiki, and compare its performance with the existing best-known strategy

Dynamic Speed and Separation Monitoring with On-Robot Ranging Sensor Arrays for Human and Industrial Robot Collaboration

This research presents a flexible and dynamic implementation of Speed and Separation Monitoring (SSM) safety measure that optimizes the productivity of a task while ensuring human safety during Human-Robot Collaboration (HRC). Unlike the standard static/fixed demarcated 2D safety zones based on 2D scanning LiDARs, this research presents a dynamic sensor setup that changes the safety zones based on the robot pose and motion. The focus of this research is the implementation of a dynamic SSM safety configuration using Time-of-Flight (ToF) laser-ranging sensor arrays placed around the centers of the links of a robot arm. It investigates the viability of on-robot exteroceptive sensors for implementing SSM as a safety measure. Here the implementation of varying dynamic SSM safety configurations based on approaches of measuring human-robot separation distance and relative speeds using the sensor modalities of ToF sensor arrays, a motion-capture system, and a 2D LiDAR is shown. This study presents a comparative analysis of the dynamic SSM safety configurations in terms of safety, performance, and productivity. A system of systems (cyber-physical system) architecture for conducting and analyzing the HRC experiments was proposed and implemented. The robots, objects, and human operators sharing the workspace are represented virtually as part of the system by using a digital-twin setup. This system was capable of controlling the robot motion, monitoring human physiological response, and tracking the progress of the collaborative task. This research conducted experiments with human subjects performing a task while sharing the robot workspace under the proposed dynamic SSM safety configurations. The experiment results showed a preference for the use of ToF sensors and motion capture rather than the 2D LiDAR currently used in the industry. The human subjects felt safe and comfortable using the proposed dynamic SSM safety configuration with ToF sensor arrays. The results for a standard pick and place task showed up to a 40% increase in productivity in comparison to a 2D LiDAR

A development of IoT based basal body temperature (BBT) device with ovulation and pregnancy prediction system using fuzzy logic method

Fertility Awareness Method (FAM) is a natural family planning method that is based on body signs, commonly basal body temperature (BBT) changes during each menstrual cycle in response to hormonal changes in a woman's body. There are several products on the BBT devices that can help in charting, monitoring, and tracking the fertility automatically. However, most of them are less used for a consultation purpose because of time-consuming to meet the physician. Besides, the products are lack of clinical studies being reported on the algorithm used to derive the information needed for fertility monitoring. Therefore, this research has developed a prototype named TempIoT1.0 which is a BBT device that's equipped with a smart fertility prediction using fuzzy logic intelligence computational method that can predict ovulation and pregnancy. This prototyped has been integrated with an Internet-of-Things (IoT) for automatic BBT charting and monitoring and accessible data sharing for consultation through an Android application. The smart fertility prediction system has been verified on 60 datasets of the BBT cycles that give an accuracy of 78.3% and 95% for ovulation and pregnancy prediction, respectively. Through performance evaluation of TempIoT1.0 with Omron and iBasal on a healthy subject, comparable results in terms of BBT data pattern with a correlation of 0.984 and 0.972, respectively were observed. TempIoT1.0 is comparably able to predict the occurrence of the ovulation with 67% similarity in the prediction of the ovulation phase and 100% similarity in the prediction of pregnancy. In conclusion, TempIoT1.0 could enhance women’s understandings of their own unique menstrual cycle in a deeper level towards a better healthcare and to the best of found knowledge, this will be among the leading IoT device for the automatic BBT charting and monitoring with a smart fertility prediction system