ISEP - CISTER - Research Centre in Real-Time Computing Systems
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CISTER (Research Centre in Real-Time and Embedded Computing Systems) is a top-ranked Research Unit based at the School of Engineering (ISEP) of the Polytechnic Institute of Porto (IPP), Portugal.
The IPP-HURRAY research group, created in mid 1997, is the core and genesis of the CISTER Research Unit.
HURRAY stands for HUgging Real-time and Reliable Architectures for computing sYstems. Therefore, the research unit focuses its activity in the analysis, design and implementation of real-time and embedded computing systems.
CISTER was, in the 2004 evaluation process, the only research unit in Portugal, in the areas of electrical engineering and computer science and engineering, to be awarded the level of Excellent. This excellent rating was confirmed in the last evaluation process (2007) CISTER, in which only one other research unit in these areas received this rating.
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- A 12*(1+|R|/(4m))-speed algorithm for scheduling constrained-deadline sporadic real-time tasks on a multiprocessor comprising m processors where a task may request one of |R| sequentially-reusable shared resourcesPublication . Andersson, Björn; Easwaran, ArvindWe present a 12*(1+|R|/(4m))-speed algorithm for scheduling constrained-deadline sporadic real-time tasks on a multiprocessor comprising m processors where a task may request one of |R| sequentially-reusable shared resources.
- 3D convolutional neural networks based automatic modulation classification in the presence of channel noisePublication . Khan, Rahim; Yang, Qiang; Ullah, Inam; Rehman, Ateeq Ur; Tufail, Ahsan Bin; NOOR, ALAM; Rehman, Abdul; Cengiz, KorhanAutomatic modulation classification is a task that is essentially required in many intelligent communication systems such as fibre-optic, next-generation 5G or 6G systems, cognitive radio as well as multimedia internet-ofthings networks etc. Deep learning (DL) is a representation learning method that takes raw data and finds representations for different tasks such as classification and detection. DL techniques like Convolutional Neural Networks (CNNs) have a strong potential to process and analyse large chunks of data. In this work, we considered the problem of multiclass (eight classes) classification of modulated signals, which are, Binary Phase Shift Keying, Quadrature Phase Shift Keying, 16 and 64 Quadrature Amplitude Modulation corrupted by Additive White Gaussian Noise, Rician and Rayleigh fading channels using 3D-CNN architectures in both frequency and spatial domains while deploying three approaches for data augmentation, which are, random zoomed in/out, random shift and random weak Gaussian blurring augmentation techniques with a cross-validation (CV) based hyperparameter selection statistical approach. Simulation results testify the performance of 10-fold CV without augmentation in the spatial domain to be the best while the worst performing method happens to be 10-fold CV without augmentation in the frequency domain and we found learning in the spatial domain to be better than learning in the frequency domain.
- 5G network as key-enabler for vehicular platooningPublication . Duarte, Paulo; Soyturk, Mujdat; Robles, Ramiro; Araújo, Marco; Yaman, Berkay; Goes, Adriano; Mendes, Bruno; Javanmardi, Gowhar; Gutiérrez Gaitán, MiguelThe future of goods transportation will rely on increased efficiency, lower risks, and diminished delays through the use of vehicle platoons that benefit from vehicular connectivity using V2X (Vehicle to Everything) applications. This article describes a system that offers the aforementioned vehicular connectivity to platoons, based on AI-enhanced 5G for resource allocation in wireless platoon intra-communications under three scenarios (latency emergency braking, platoon wireless resource management in tunnels, V2X communications interference in a traffic congestion). Demos are described for each of the scenarios, targeting different layers, starting by the PHY (physical) layer where propagation models are implemented, then a simulation-based MAC (medium access control) layer that allows the allocation of resources to the connected User Equipments (UE) and finally a management and orchestration layer capable of monitoring and managing the radio network, offering features such as network slicing management using O-RAN (Open Radio Access Network) standards.
- Abstract Timers and their Implementation onto the ARM Cor tex-M family of MCUsPublication . Lindgren, Per; Fresk, Emil; Lindner, Marcus; Lindner, Andreas; Pereira, David; Pinho, Luís MiguelReal-Time For the Masses (RTFM) is a set of languages andto ols b eing develop ed to facilitate emb edded software development and provide highly efficient implementations gearedto static verification. The RTFM-kernel is an architecturedesigned to provide highly efficient and predicable Stack Resource Policy based scheduling, targeting bare metal (singlecore) platforms.We contribute b eyond prior work by intro ducing a platform independent timer abstraction that relies on existingRTFM-kernel primitives. We develop two alternative implementations for the ARM Cortex-M family of MCUs: ageneric implementation, using the ARM defined SysTick-/DWT hardware; and a target sp ecific implementation, using the match compare/free running timers. While sacrificing generality, the latter is more flexible and may reduceoverall overhead. Invariants for correctness are presented,and metho ds to static and run-time verification are discussed. Overhead is b ound and characterized. In b oth casesthe critical section from release time to dispatch is less than2us on a 100MHz MCU. Queue and timer mechanisms aredirectly implemented in the RTFM-core language and canb e included in system-wide scheduling analysis.
- Active Flow Control for Aerospace Operations by means of a Dense Wireless Sensor and Actuator NetworkPublication . Robles, Ramiro; Loureiro, João; Tovar, Eduardo; Viana, Júlio; Cintra, João; Rocha, AndréThis paper presents the design of an active flow control (AFC) system for commercial aircraft based on a dense wired/wireless sensor and actuator network. The goal is to track gradients of pressure across the surface of the fuselage of commercial aircraft. This collected information will be used to activate a set of actuators that will attempt to reduce the skin drag effect produced by the separation between laminar and turbulent flows. This will be translated into increased lift-off forces, higher speeds, longer ranges and reduced fuel consumption. The paper describes the architecture of the system in the context of the European research project DEWI (dependable embedded wireless infrastructure) using the concept of the DEWI Bubble. A simulator architecture is also proposed to model each process of the AFC system and the DEWI Bubble. To the best of our knowledge this is the first approach towards the use of wireless sensor technologies in the field of active flow control.
- Active Flow Control using Dense Wireless Sensor and Actuator NetworksPublication . Robles, Ramiro; Viana, Júlio; Loureiro, João; Cintra, João; Rocha, André; Tovar, EduardoThis paper describes the design of an active flow control (AFC) system for aeronautics applications based on dense wireless sensor and actuator networks (WSANs). The objective of this AFC system is to track gradients of pressure (or wall shear stress) across the surface of the fuselage of commercial aircraft. This collected information is used to activate a set of actuators that will attempt to reduce the skin drag effect produced by the separation between laminar and turbulent flows. This is expected to be translated into increased lift-off forces, higher vehicle speeds, longer ranges and reduced fuel consumption. The paper describes the architecture of the system in the context of the European research project DEWI (dependable embedded wireless infrastructure) using the concept of the DEWI Bubble and its three-tier architecture especially designed to ensure dependability and interoperability in industrial WSANs. A system-level simulator is also proposed to model each process of the AFC system and the aeronautics DEWI Bubble infrastructure, highlighting the interactions between the network simulation and the results of the computational fluid dynamics (CFD) simulation. The key element in the proposed solution is a polygonal patch of wired sensors and actuators. This patch is provided with a wireless link to a central coordinator or access point conveniently located in the aircraft to maximize coverage to a network of distributed patches. A trade-off between scalability, size of the patches, fluid speed/viscosity, sampling sensor and actuator rates in space and time, and the capacity/delay characteristic of the wireless inter-patch and the wireline intra-patch communication technologies is also here discussed. The hybrid wireless/wired sensor and actuator network achieves great flexibility, scalability, manageability, troubleshooting, and modularity as compared to a solution exclusively based on wireline or wireless components. The final details of the prototype and results in a wind tunnel test-bed are here described, demonstrating the validity of the concept and the use of wireless technologies for aeronautical applications (flexible architecture and innovative services). Future issues regarding security, safety and trustiness of the AFC system are also briefly introduced in the context of the spin-off European project SCOTT (secure connected trusted things).
- Activity Monitoring of Islamic Prayer (Salat) Postures using Deep LearningPublication . Koubaa, Anis; Ammar, Adel; Benjdira, Bilel; Al Hadid, Abdullatif; Kawaf, Belal; Al Yahri, Saleh Ali; Babiker, Abdelrahman; Assaf, Koutaiba; Ba Ras, MohannadIn the Muslim community, the prayer (i.e. Salat) is the second pillar of Islam, and it is the most essential and fundamental worshiping activity that believers have to perform five times a day. From a gestures' perspective, there are predefined human postures that must be performed in a precise manner. However, for several people, these postures are not correctly performed, due to being new to Salat or even having learned prayers in an incorrect manner. Furthermore, the time spent in each posture has to be balanced. To address these issues, we propose to develop an artificial intelligence assistive framework that guides worshippers to evaluate the correctness of the postures of their prayers. This paper represents the first step to achieve this objective and addresses the problem of the recognition of the basic gestures of Islamic prayer using Convolutional Neural Networks (CNN). The contribution of this paper lies in building a dataset for the basic Salat positions, and train a YOLOv3 neural network for the recognition of the gestures. Experimental results demonstrate that the mean average precision attains 85% for a training dataset of 764 images of the different postures. To the best of our knowledge, this is the first work that addresses human activity recognition of Salat using deep learning.
- AdaptC: programming adaptation policies for WSN applicationsPublication . Gaur, Shashank; Almeida, Luis; Tovar, EduardoEvolution in both hardware and software technologies has enabled Wireless Sensor Networks(WSNs) to target a multiplicity of domains. Programming for such advanced WSNs remains a challenging process for users, especially as the WSN may need to make changes as per outcomes from different scenarios during execution. Usually, various adaptation policies are written while programming such applications to enable changes. However it is difficult for the programmer to anticipate changes for new scenarios. It also becomes difficult to reuse these adaptation policies. In this paper, we propose AdaptC, an abstraction for such adaptation policies that facilitates re-usability and expansion across various WSNs. We also present concepts for the design and implementation of AdaptC. We evaluate the abstraction for multiple use cases and compare it against existing work.
- Adaptive Fuzzy Model-free Control For 3d Trajectory Tracking Of QuadrotorPublication . Chekakta, Zakaria; Zerikat, Mokhtar; Bouzid, Yasser; Koubaa, AnisThis paper presents a novel adaptive control strategy with rejection ability for unmanned aerial vehicles (UAVs), namely fuzzy model-free control (FMFC). It is based on the model-free control (MFC) concept, where the control parameters are tuned online using fuzzy logic. The controller assumes an ultra-local model that can compensate unknown/unmodelled dynamics, uncertainties and external disturbances, ensuring a good robustness level. Moreover, the fuzzy logic system is used to tune online the proportional-derivative terms due to its heuristic aspect. These compensation and adaptation mechanisms allow ensuring good compromise robustness-performance even in the presence of disturbances. Several experiments, using RotorS Gazebo micro aerial vehicle (MAV) simulator, are provided to demonstrate the effectiveness of the proposed controller compared with other techniques. The fuzzy model-free controller shows superior performance without the time-consuming and tedious tuning task.
- Adaptive offloading for infotainment systemsPublication . Ferreira, Luís Lino; Pinho, Luís Miguel; Albano, Michele; Teixeira, CésarInfotainment applications in vehicles are currently supported both by the in-vehicle platform, as well as by user’s smart devices, such as smartphones and tablets. More and more the user expects that there is a continuous service of applications inside or outside of the vehicle, provided in any of these devices (a simple but common example is hands-free mobile phone calls provided by the vehicle platform). With the increasing complexity of ‘apps’, it is necessary to support increasing levels of Quality of Service (QoS), with varying resource requirements. Users may want to start listening to music in the smartphone, or video in the tablet, being this application transparently ‘moved’ into the vehicle when it is started. This paper presents an adaptable offloading mechanism, following a service-oriented architecture pattern, which takes into account the QoS requirements of the applications being executed when making decisions.