Browsing by Author "Bensaali, F."
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Item Open Access Heterogeneous System-on-Chip based Lattice-Boltzmann Visual Simulation System(IEEE, 2019-11) Amira, Abbes; Zhai, X.; Chen, M.; Esfahani, S.S.; Bensaali, F.; Abinahed, J.; Dakua, S.; Richardson, R.A.; Coveney, P.V.Cerebral aneurysm is a cerebrovascular disorder caused by a weakness in the wall of an artery or vein, that causes a localised dilation or ballooning of the blood vessel. It is life-threatening, hence an early and accurate diagnosis would be a great aid to medical professionals in making the correct choice of treatment. HemeLB is a massively parallel lattice-Boltzmann simulation software which is designed to provide the radiologist with estimates of flow rates, pressures and shear stresses throughout the relevant vascular structures, intended to eventually permit greater precision in the choice of therapeutic intervention. However, in order to allow surgeries and doctors to view and visualise the results in real-time at medical environments, a cost-efficient, practical platform is needed. In this paper, we have developed and evaluated a version of HemeLB on various heterogeneous system-on-chip platforms, allowing users to run HemeLB on a low cost embedded platform and to visualise the simulation results in real-time. A comprehensive evaluation of implementation on the Zynq SoC and Jetson TX1 embedded graphic processing unit platforms are reported. The achieved results show that the proposed Jetson TX1 implementation outperforms the Zynq implementation by a factor of 19 in terms of site updates per second.Item Open Access Moving object tracking in clinical scenarios: application to cardiac surgery and cerebral aneurysm clipping(Springer, 2019-07-15) Amira, Abbes; Dakua, S.P.; Abinahed, J.; Zakaria, A.; Balakrishnan, S.; Younes, G.; Navkar, N.; Al-Ansari, A.; Zhai, X.; Bensaali, F.Surgical procedures such as laparoscopic and robotic surgeries are popular since they are invasive in nature and use miniaturized surgical instruments for small incisions. Tracking of the instruments (graspers, needle drivers) and field of view from the stereoscopic camera during surgery could further help the surgeons to remain focussed and reduce the probability of committing any mistakes. Tracking is usually preferred in computerized video surveillance, traffic monitoring, military surveillance system, and vehicle navigation. Despite the numerous efforts over the last few years, object tracking still remains an open research problem, mainly due to motion blur, image noise, lack of image texture, and occlusion. Most of the existing object tracking methods are time-consuming and less accurate when the input video contains high volume of information and more number of instruments.Item Open Access Real-time ECG Monitoring using Compressive sensing on a Heterogeneous Multicore Edge-Device(Elesivier, 2019-06-27) Amira, Abbes; Djelouat, H.; Bensaali, F.; Kotronis, C.; Politi, E.; Nikolaidou, M.; Dimitrakopoulos, G.In a typical ambulatory health monitoring systems, wearable medical sensors are deployed on the human body to continuously collect and transmit physiological signals to a nearby gateway that forward the measured data to the cloud-based healthcare platform. However, this model often fails to respect the strict requirements of healthcare systems. Wearable medical sensors are very limited in terms of battery lifetime, in addition, the system reliance on a cloud makes it vulnerable to connectivity and latency issues. Compressive sensing (CS) theory has been widely deployed in electrocardiogramme ECG monitoring application to optimize the wearable sensors power consumption. The proposed solution in this paper aims to tackle these limitations by empowering a gatewaycentric connected health solution, where the most power consuming tasks are performed locally on a multicore processor. This paper explores the efficiency of real-time CS-based recovery of ECG signals on an IoT-gateway embedded with ARM’s big.littleTM multicore for different signal dimension and allocated computational resources. Experimental results show that the gateway is able to reconstruct ECG signals in real-time. Moreover, it demonstrates that using a high number of cores speeds up the execution time and it further optimizes energy consumption. The paper identifies the best configurations of resource allocation that provides the optimal performance. The paper concludes that multicore processors have the computational capacity and energy efficiency to promote gateway-centric solution rather than cloud-centric platforms.Item Open Access Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree(Elsevier, 2020-04-20) Himeur, Y.; Alsalemi, A.; Bensaali, F.; Amira, AbbesProviding the user with appliance-level consumption data is the core of each energy efficiency system. To that end, non-intrusive load monitoring is employed for extracting appliance specific consumption data at a low cost without the need of installing separate submeters for each electrical device. In this context, we propose in this paper a novel non-intrusive appliance recognition system based on (i) detecting events in the aggregated power signal using a novel and powerful scheme, (ii) applying multiscale wavelet packet tree to collect comprehensive energy consumption features, and (iii) adopting an ensemble bagging tree classifier along with comparing its performance with various machine learning schemes. Moreover, to validate the proposed model, an empirical investigation is conducted on two real and public energy consumption datasets, namely, the GREEND and REDD, in which consumption readings are collected at low-frequencies. In addition, a comprehensive review of recent non-intrusive load monitoring approaches has been conducted and presented, in which their characteristics, performances and limitations are described. The proposed non-intrusive load monitoring system shows a high appliance recognition performance in terms of the accuracy, F1 score and low time complexity when it has been applied to different households from the GREEND and REDD repositories, in which every house includes various domestic appliances. Obtained results have described, e.g., that average accuracies of 97.01% and 96.36% have been reached on the GREEND and REDD datasets, respectively, which outperformed almost existing solutions considered in this framework.