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The emerging field of flexible bio-mechatronic technologies, such as wearable sensors, actuators and robots, are playing more and more crucial roles in the design and development of next generation healthcare and rehabilitation systems that safely and smartly interact with human patients. To create a complex and intelligent healthcare and rehabilitation system, it is necessary to design, develop and implement advanced wearable sensors, Internet of Things (IoT) and intelligent algorithms. The papers of this special issue discuss various research and development achievements in the field of IoT systems for intelligent healthcare and rehabilitation, including developments, methodologies, and applications to biological sensors, IoT infrastructures, soft robotics, biomechanical modeling, novel algorithms, big data analysis, as well as devices and tools for health monitoring, etc. This special issue provides readers with a comprehensive collection of the state-of-the-art advances in the field of intelligent healthcare and rehabilitation.

Edge computing is attracting an increasing attention presently even though most of the building energy efficiency solutions are still using cloud computing for gathering, pre-processing and analyzing energy data. However, edge computing still requires more power in order to be used alone to meet the high computation demand of artificial intelligence based energy saving solutions. Meanwhile, a hybrid edge-cloud architecture can be the best current approach to implement energy efficiency systems. It provides end-users and utility companies with a flexible control of their energy usage footprints, minimizes the cost of cloud hosting, and improves privacy-preservation. Accordingly, in this paper, we present a novel energy efficiency system based on a hybrid edge-cloud computing architecture. To analyze energy and occupancy data collected through different smart meters and occupancy sensors, we use a micro-moment approach to cluster energy observations into different categories representing both normal and abnormal energy usage. Following, a deep micro-moments (deepM2) scheme is deployed to automate the Anomaly Detection task, where a new approach called deepM2-AD is developed. Moving forward, deepM2-AD is implemented on three different architectures, defined as edge-only, cloud-only and hybrid edge-cloud to evaluate their performance and identify their merits and demerits. Overall, the hybrid edge-cloud architecture has presented the best compromise in terms of improving the processing speed, curtailing the cost of cloud hosting, and reducing the communication latency. Therefore, it has a great potential for supporting real-time energy consumption anomaly detection applications that help in minimizing wasted energy. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. Acknowledgments. This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and precise recommender systems. Such datasets often include sensitive information, yet most recommender systems are focusing on the models' accuracy and ignore issues related to security and the users' privacy. Despite the efforts to overcome these problems using different risk reduction techniques, none of them has been completely successful in ensuring cryptographic security and protection of the users' private information. To bridge this gap, the blockchain technology is presented as a promising strategy to promote security and privacy preservation in recommender systems, not only because of its security and privacy salient features, but also due to its resilience, adaptability, fault tolerance and trust characteristics. This paper presents a holistic review of blockchain-based recommender systems covering challenges, open issues and solutions. Accordingly, a well-designed taxonomy is introduced to describe the security and privacy challenges, overview existing frameworks and discuss their applications and benefits when using blockchain before indicating opportunities for future research., 25 pages, 6 figures, 3 tables

Despite many advancements in extracorporeal membrane oxygenation (ECMO), the procedure is still correlated with a high risk of patient complications. Simulation-based training provides the opportunity for ECMO staff to practice on real-life scenarios without exposing ECMO patients to medical errors while practicing. At Hamad Medical Corporation (HMC) in Qatar, there is a critical need of expert ECMO staff. Thus, a modular ECMO simulator is being developed to enhance the training process in a cost-effective manner. This ECMO simulator gives the instructor the ability to control the simulation modules and run common simulation scenarios through a tablet application. The core modules of the simulation system are placed in the patient unit. The unit is designed modularly such that more modules can be added throughout the simulation sessions to increase the realism of the simulation sessions. The new approach is to enclose the patient unit in a trolley, which is custom-designed and made to include all the components in a modular fashion. Each module is enclosed in a separate box and then mounted to the main blood simulation loop box using screws, quick connect/disconnect liquid fittings, and electrical plugs. This method allows fast upgrade and maintenance for each module separately as well as upgrading modules easily without modifying the trolley’s design. The prototype patient unit has been developed for portability, maintenance, and extensibility. After implementation and testing, the prototype has proven to successfully simulate the main visual and audio cues of the real emergency scenarios, while keeping costs to a minimum Qatar University Scopus

Internet of Medical Things (IoMT) systems are envisioned to provide high-quality healthcare services to patients in the comfort of their home, utilizing cutting-edge Internet of Things (IoT) technologies and medical sensors. Patient comfort and willingness to participate in such efforts is a prominent factor for their adoption. As IoT technology has provided solutions for all technical issues, patient concerns are those that seem to restrict their wider adoption. To enhance patient awareness of the system properties and enhance their willingness to adopt IoMT solutions, this paper presents a novel methodology to integrate patient concerns in the design requirements of such systems. It comprises a number of straightforward steps that an IoMT designer can follow, starting from identifying patient concerns, incorporating them in system design requirements as criticalities, proceeding to system implementation and testing, and finally, verifying that it fulfills the concerns of the patients. To showcase the effectiveness of the proposed methodology, the paper applies it in the design and implementation of a fall detection system for elderly patients remotely monitored in their homes. The Author(s) 2021. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors wish to acknowledge Qatar National Research Fund project EMBIoT (Proj. No. NPRP 9-114-2-055) project, under the auspices of which the work presented in this paper has been carried out. Scopus

Extracorporeal membrane oxygenation (ECMO) is a modified form of CPB that supports intensive care patients' vital functions during recovery from cardiac or pulmonary trauma. ECMO, although lifesaving, is vulnerable to a plethora of mechanical complications which can cause mortality. This is why developing advanced training systems is of crucial importance. In this paper, as part of an ECMO simulator for training management, a novel thermochromic heater-cooler system is presented. The need of such contribution arises from the lack of high-realism blood simulation methodologies Hence, developed upon thermochromic ink, cost-effective blood simulation is achieved by temperature adjustment, simulating oxygenation and hypoxemia. The system has been developed as a prototype with successful and reversible transitions between dark and bright red blood color. After addressing the limitations, the heater-cooler will be integrated with the ECMO simulator, allowing unpreceded cost-efficient simulation possibilities. Qatar University Scopus

Internet of Energy (IoE) is revolutionizing the building energy industry by introducing numerous innovations that help in data collection, interpretation, and behavioural improvement. Consequently, collecting and analyzing big data is fiercely impacting every field and research area in the context of energy utilization, and therefore, the concept of micro-moments is a timed encapsulation of the user's interaction with the building's appliances. In this article, a high-performance yet cost-efficient edge-based IoE platform for Energy Efficiency in buildings, called Edge-IoE3 is presented, which allows sensing, processing and analyzing energy data in residential buildings. The proposed platform, in the form of a smart plug, consists of two units: a data collection unit and a data processing unit. The data collection unit includes multiple sensors that measure energy consumption along with temperature, humidity, luminosity, and room occupancy. Innovative micro-moment analysis based on ensemble bagging trees is performed to extract pertinent energy consumption micro-moments, which aid in improving consumption behavior with recommender systems. Obtained results have shown a classification accuracy up to 99% of the sensed data and a computation time of 0.1 sec. A number of case studies is presented as guidance on the applications of the proposed solution, and as a building block for improving energy efficiency in buildings. 2001-2012 IEEE. This work was supported by the National Priorities Research Program (NPRP) through the Qatar National Research Fund (a member of Qatar Foundation) under Grant 10-0130-170288. Scopus

Introduction: Almost two million people worldwide die annually due to hepatic-related diseases. Half of these diseases are attributed to cirrhosis and the other half are related to hepatitis and hepatocellular carcinoma (HCC). The liver is also a metastasis hub from adjacent organs. This research aims to create an accurate high-quality delineation of the human liver and prepare them to be 3D printed for medical analysis to help aid medical practitioners in pre-procedural planning. Materials and Methods: Convolutional neural networks (ConvNets) are used to perform the liver tissues delineation. A famous ConvNet, named U-net, is used as the basis benchmark architecture that is also known for its great outcomes in the medical segmentation field. Contrast-enhanced computerized tomography (CT) scans are used from the famous Medical Segmentation Decathlon Challenge (Task 8: Hepatic Vessel), abbreviated as MSDC-T8. It contains 443 CT scans, which is considered the largest dataset that contains both the tumors and vessels ground-truth segmentation. Some researchers also generated the liver masks for this dataset, making it a complete dataset that contains all the relevant tissues’ ground-truth masks. Results: Currently, the liver delineation has been successfully done with very high DICE = 98.12% (higher than the state-of-the-art results DICE = 97.61%), where a comparison between two famous schedulers namely, ReduceLRonPlateau and OneCycleLR has been conducted. Moreover, the 3D liver volume creation has also been completed and built via the marching cube algorithm. Conclusions/Future Directions: The developed ConvNet can segment livers with high confidence. The tumor(s) and vessels tissues segmentation are also under investigation now. Moreover, newly devised self-organized neural networks (Self-ONN) look promising and will be investigated soon. Lastly, a GUI will be built so that the medical practitioner can just insert the CT volume and get the 3D liver volume with all the segmented tissues.

Fall detection is a serious healthcare issue that needs to be solved. Falling without quick medical intervention would lower the chances of survival for the elderly, especially if living alone. Hence, the need is there for developing fall detection algorithms with high accuracy. This paper presents a novel IoT-based system for fall detection that includes a sensing device transmitting data to a mobile application through a cloud-connected gateway device. Then, the focus is shifted to the algorithmic aspect where multiple features are extracted from 3-axis accelerometer data taken from existing datasets. The results emphasize on the significance of Continuous Wavelet Transform (CWT) as an influential feature for determining falls. CWT, Signal Energy (SE), Signal Magnitude Area (SMA), and Signal Vector Magnitude (SVM) features have shown promising classification results using K-Nearest Neighbors (KNN) and E-Nearest Neighbors (ENN). For all performance metrics (accuracy, recall, precision, specificity, and F1 Score), the achieved results are higher than 95% for a dataset of small size, while more than 98.47% score is achieved in the aforementioned criteria over the UniMiB-SHAR dataset by the same algorithms, where the classification time for a single test record is extremely efficient and is real-time, 30 Pages, 9 figures, 9 tables. This is a the Accepted Manuscript version of the article published in Biomedical Signal Processing and Control (URL: https://doi.org/10.1016/j.bspc.2021.102532)

Human population are striving against energy-related issues that not only affects society and the development of the world, but also causes global warming. A variety of broad approaches have been developed by both industry and the research community. However, there is an ever increasing need for comprehensive, end-to-end solutions aimed at transforming human behavior rather than device metrics and benchmarks. In this paper, a micro-moment-based smart plug system is proposed as part of a larger multi-appliance energy efficiency program. The smart plug, which includes two sub-units: the power consumption unit and environmental monitoring unit collect energy consumption of appliances along with contextual information, such as temperature, humidity, luminosity and room occupancy respectively. The plug also allows home automation capability. With the accompanying mobile application, end-users can visualize energy consumption data along with ambient environmental information. Current implementation results show that the proposed system delivers cost-effective deployment while maintaining adequate computation and wireless performance., Comment: This paper has been accepted in SCA2020: The Fifth international conference on Smart City Applications

Energy efficiency is a crucial factor in the well-being of our planet. In parallel, Machine Learning (ML) plays an instrumental role in automating our lives and creating convenient workflows for enhancing behavior. So, analyzing energy behavior can help understand weak points and lay the path towards better interventions. Moving towards higher performance, cloud platforms can assist researchers in conducting classification trials that need high computational power. Under the larger umbrella of the Consumer Engagement Towards Energy Saving Behavior by means of Exploiting Micro Moments and Mobile Recommendation Systems (EM)3 framework, we aim to influence consumers behavioral change via improving their power consumption consciousness. In this paper, common cloud artificial intelligence platforms are benchmarked and compared for micro-moment classification. The Amazon Web Services, Google Cloud Platform, Google Colab, and Microsoft Azure Machine Learning are employed on simulated and real energy consumption datasets. The KNN, DNN, and SVM classifiers have been employed. Superb performance has been observed in the selected cloud platforms, showing relatively close performance. Yet, the nature of some algorithms limits the training performance., This paper has been accepted in IEEE RTDPCC 2020: International Symposium on Real-time Data Processing for Cloud Computing

The recent advances in artificial intelligence namely in machine learning and deep learning, have boosted the performance of intelligent systems in several ways. This gave rise to human expectations, but also created the need for a deeper understanding of how intelligent systems think and decide. The concept of explainability appeared, in the extent of explaining the internal system mechanics in human terms. Recommendation systems are intelligent systems that support human decision making, and as such, they have to be explainable in order to increase user trust and improve the acceptance of recommendations. In this work, we focus on a context-aware recommendation system for energy efficiency and develop a mechanism for explainable and persuasive recommendations, which are personalized to user preferences and habits. The persuasive facts either emphasize on the economical saving prospects (Econ) or on a positive ecological impact (Eco) and explanations provide the reason for recommending an energy saving action. Based on a study conducted using a Telegram bot, different scenarios have been validated with actual data and human feedback. Current results show a total increase of 19\% on the recommendation acceptance ratio when both economical and ecological persuasive facts are employed. This revolutionary approach on recommendation systems, demonstrates how intelligent recommendations can effectively encourage energy saving behavior., 19 pages, 8 figures, 1 table

This paper proposes a new appliance identification scheme by introducing a novel approach for extracting highly discriminative characteristic sets that can considerably distinguish between various appliance footprints. In this context, a precise and powerful characteristic projection technique depending on fuzzy-neighbors-preserving analysis based QR-decomposition (FNPA-QR) is applied on the extracted energy consumption time-domain features. The FNPA-QR aims to diminish the distance among the between class features and increase the gap among features of dissimilar categories. Following, a novel bagging decision tree (BDT) classifier is also designed to further improve the classification accuracy. The proposed technique is then validated on three appliance energy consumption datasets, which are collected at both low and high frequency. The practical results obtained point out the outstanding classification rate of the time-domain based FNPA-QR and BDT. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Acknowledgements. This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

In recent years, the automatic identification of electrical devices through their power consumption signals finds a variety of applications in smart home monitoring and non-intrusive load monitoring (NILM). This work proposes a novel appliance identification scheme and introduces a new feature extraction method that represents power signals in a 2D space, similar to images and then extracts their properties. In this context, the local binary pattern (LBP) and other variants are investigated on their ability to extract histograms of 2D binary patterns of power signals. Specifically, by moving to a 2D representation space, each power sample is surrounded by eight neighbors at least. This can help extracting pertinent characteristics and providing more possibilities to encode power signals robustly. Moreover, the proposed identification technique has the main advantage of accurately recognizing the electrical devices independently of their states and on/off events, unlike existing models. Three public databases including real household power consumption measurements at the appliance-level are employed to assess the performance of the proposed system while considering various machine learning classifiers. The promising performance obtained in terms of accuracy and F-score proves the successful application of the 2D LBP in recognizing electrical devices and creates new possibilities for energy efficiency based on NILM models. Springer Nature Switzerland AG 2021. This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

Energy conservation is a critical task for domestic households and office buildings, mainly because of the shortage of energy resources and the uprising contemporary environmental issues. The development of an IoT ecosystem that monitors energy consumption habits and timely recommends actions to promote energy efficiency can be beneficial for the mainstream. In this work, we present the EM3 project, which combines data collection, information abstraction, timed recommendations for saving actions and automations that promote energy saving in a household or office setup. The article focuses on the data and information processing aspects of the EM3 solution, which efficiently handles thousands of sensor events on a daily basis and provides useful analytics and recommendations to the end user to support habit change. 2020 IEEE. This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

With the development of the Internet of Things (IoT), various large-scale real-time data processing applications for handling real-time sensor data are becoming one of the important applications in cloud computing. The academia, the industry, and even the government institutions have already begun to pay close attention to how to efficiently process large amounts of sensor data in real-time using cloud computing technology. Although cloud computing technology has attracted much attention with high-performance, there are strong needs for improving data processing efficiency of large-scale real-time data for IoT-based applications. In addition to this, currently the IoT paradigm is facing increasing difficulty to handle the data generated from IoT applications. As a result of this, it is challenging to ensure low latency and network bandwidth consumption, optimal utilization of computational recourses, scalability, security, and energy efficiency of IoT devices while moving all data to the cloud. Therefore, this centralized computing model is starting to shift to a decentralized model termed as edge computing, that allows data to be handled from the cloud to local devices such as smartphones, smart gateways or routers, local PCs or sensor nodes on a smaller scale in real-time.

Background. Simulation-based learning (SBL) employs the synergy between technology and people to immerse learners in highly-realistic situations in order to achieve quality clinical education. Due to the ever-increasing popularity of extracorporeal membrane oxygenation (ECMO) SBL, there is a pressing need for a proper technological infrastructure that enables high-fidelity simulation to better train ECMO specialists to deal with related emergencies. In this article, we tackle the control aspect of the infrastructure by presenting and evaluating an innovative cloud-based instructor, simulator controller, and simulation operations specialist application that enables real-time remote control of full-scale immersive ECMO simulation experiences for ECMO specialists as well as creating custom simulation scenarios for standardized training of individual healthcare professionals or clinical teams. Aim. This article evaluates the intuitiveness, responsiveness, and convenience of the ECMO instructor application as a viable ECMO simulator control interface. Method. A questionnaire-based usability study was conducted following institutional ethical approval. Nineteen ECMO practitioners were given a live demonstration of the instructor application in the context of an ECMO simulator demonstration during which they also had the opportunity to interact with it. Participants then filled in a questionnaire to evaluate the ECMO instructor application as per intuitiveness, responsiveness, and convenience. Results. The collected feedback data confirmed that the presented application has an intuitive, responsive, and convenient ECMO simulator control interface. Conclusion. The present study provided evidence signifying that the ECMO instructor application is a viable ECMO simulator control interface. Next steps will comprise a pilot study evaluating the educational efficacy of the instructor application in the clinical context with further technical enhancements as per participants? This article was also made possible by an UREP grant #19-062-2-026 from the Qatar National Research Fund (a member of Qatar foundation). This article also was supported by Qatar University Internal Grant No. QUST-CENG-SPR-15/16-8. The statements made herein are solely the responsibility of the authors. Scopus

This paper presents the design of an interactive graphical user interface (GUI) to monitor and quantify a developed electronic nose (EN) platform for gas identification. To this end, an EN system has been implemented using a multi-sensing embedded platform comprised of a data acquisition unit, an RFID module and a signal processing unit. The gas data are collected using two different types of gas sensors, namely, seven commercial Figaro sensors and in-house fabricated 4?4 tin-oxide gas array sensor. The collected gas data are processed for identification by means of dimensionality reduction algorithms and classification techniques where the software implementation and the quantification of these algorithms have been carried out. Subsequently, the GUI was designed to enable several operations. The GUI allows the user to visualize the sensors responses for any selected gas at any point of the acquisition process as well as visualizing the data distribution. Beside, it provides an easy approach to evaluate the EN system performance in terms of data identification and execution time by computing the classification accuracy using a 10-fold cross validation technique. Furthermore, the GUI, which is freely distributed, grants the users the privilege to upload other types of data to enable different pattern recognition applications. ? 2017 Elsevier B.V. This paper was made possible by National Priorities Research Program (NPRP) grant No. 5-080-2-028 from the Qatar National Research Fund (a member of Qatar Foundation). The statement made herein are solely the responsibility of the authors. Scopus

This paper presents the development of a wireless low power reconfigurable self-calibrated multi-sensing platform for gas sensing applications. The proposed electronic nose (EN) system monitors gas temperatures, concentrations, and mixtures wirelessly using the radio-frequency identification (RFID) technology. The EN takes the form of a set of gas and temperature sensors and multiple pattern recognition algorithms implemented on the Zynq system on chip (SoC) platform. The gas and temperature sensors are integrated on a semi-passive RFID tag to reduce the consumed power. Various gas sensors are tested, including an in-house fabricated 4× 4 SnO 2 based sensor and seven commercial Figaro sensors. The data is transmitted to the Zynq based processing unit using a RFID reader, where it is processed using multiple pattern recognition algorithms for dimensionality reduction and classification. Multiple algorithms are explored for optimum performance, including principal component analysis (PCA) and linear discriminant analysis (LDA) for dimensionality reduction while decision tree (DT) and k-nearest neighbors (KNN) are assessed for classification purpose. Different gases are targeted at diverse concentration, including carbon monoxide (CO), ethanol (C 2 H 6 O), carbon dioxide (CO 2 ), propane (C 3 H 8 ), ammonia (NH 3 ), and hydrogen (H 2 ). An accuracy of 100% is achieved in many cases with an overall accuracy above 90% in most scenarios. Finally, the hardware/software heterogeneous solution to implementation PCA, LDA, DT, and KNN on the Zynq SoC shows promising results in terms of resources usage, power consumption, and processing time. Manuscript received December 25, 2017; revised March 17, 2018; accepted March 18, 2018. Date of publication April 3, 2018; date of current version May 9, 2018. This paper was supported by the National Priorities Research Program under Grant 5-080-2-028 from the Qatar National Research Fund (a member of Qatar Foundation). The associate editor coordinating the review of this paper and approving it for publication was Prof. Mehmet Yuce. (Corresponding author: Ali Farhat.) A. Ait Si Ali is with the Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, U.K. (e-mail: amine.ali@northumbria.ac.uk). Scopus

Designing power aware wearable devices is the main key in building compact size autonomous smart devices to successfully connect health Internet of things solutions. With their ability to perform tasks ranging from simple self-monitoring to complex interactive tasks, these devices hold great promises in providing a large scale cost effective solution to the challenges facing nowadays healthcare systems. Despite the advances in sensing and hardware design, there still remain several technical challenges facing the research community to build devices that meet the computational requirements with a self-powered capability. Overcoming these challenges require major improvements in all the building blocks of wearable devices including sensors, power management, signal processing, computing architectures, and communication. This paper surveys some of the past milestones related to these subsystems and discusses promising research directions addressing their limitations.

When designing a large scale IoT ecosystem, it is important to provide economical solutions at all levels, from sensors and actuators to the software used for analytics and orchestration. It is of equal importance to provide non-intrusive solutions that do not violate users' privacy, but above all, it is important to guarantee the accuracy and integrity of the provided solution. In this work, we present a research prototype solution that has been developed as part of an ongoing project called (EM)3. The project involves IoT sensors and actuators, realtime data analytics modules and cutting edge recommendation algorithms in an ecosystem that improves energy efficiency in office buildings. The main concept of the (EM)3 is to recommend energy saving actions at the right moment to the right user. At the core of the (EM)3 vision is to detect when is the right moment for an energy saving action and sensors play a vital role in this. This article focuses on the model that predicts room occupancy using only data from a motion sensor. The predictions of the model, are used to trigger automations and notifications that turn-off office devices (e.g. air conditioning, lights, monitors, etc.) as soon as the office becomes empty, or a few minutes before this happens, in order to further promote efficient energy consumption habits. The evaluation of the model, using data from a camera sensor for validation, demonstrates a very low error rate and a very short delay on the detection of when the room is actually empty. 2020 IEEE. ACKNOWLEDGMENT This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

Identifying domestic appliances in the smart grid leads to a better power usage management and further helps in detecting appliance-level abnormalities. An efficient identification can be achieved only if a robust feature extraction scheme is developed with a high ability to discriminate between different appliances on the smart grid. Accordingly, we propose in this paper a novel method to extract electrical power signatures after transforming the power signal to 2D space, which has more encoding possibilities. Following, an improved local binary patterns (LBP) is proposed that relies on improving the discriminative ability of conventional LBP using a post-processing stage. A binarized eigenvalue map (BEVM) is extracted from the 2D power matrix and then used to post-process the generated LBP representation. Next, two histograms are constructed, namely up and down histograms, and are then concatenated to form the global histogram. A comprehensive performance evaluation is performed on two different datasets, namely the GREEND and WITHED, in which power data were collected at 1 Hz and 44000 Hz sampling rates, respectively. The obtained results revealed the superiority of the proposed LBP-BEVM based system in terms of the identification performance versus other 2D descriptors and existing identification frameworks., Comment: 8 pages, 10 figures and 5 tables

This paper conveys the importance of using suitable data visualizations for electrical energy consumption and the effect it carries on reducing said consumption. Data visualization tools construct an important pillar in energy micro-moments, i.e., the concept of providing the right information at the right time in the right way for a specific power consumer. Such behavioral change can be triggered with the help of good recommendations and suitable visualizations to convey the right message. A questionnaire is built as a mobile application to evaluate different groups of conventional and novel visualizations. Conventional charts are restricted to bar, line and stacked area charts, while novel visualizations contain heatmap, spiral and appliance-level stacked bar charts. Significant findings gathered from participants' responses indicate that they are slightly inclined towards conventional charts. However, their understanding of the novel charts is better by 8% when the analysis questions are investigated. Finally, a question is answered on whether a group of visualizations should be discarded completely, or some modifications can be applied., Comment: This work has been accepted in GPECOM2020 and it is to be published in its proceedings

With the proliferation of wearable wireless sensors and adequate communication protocols, the internet of things (IoT) started to shape the healthcare sector into a new frame of connection between patients with health providers. Although the huge opportunities granted by this paradigm, it raises some concerns as well. Two main issues that could be highlighted are power consumption and medical record security. This paper aims to address both issues in an IoT-based remote health monitoring systems by exploring compressive sensing (CS). The presented solution exploits shift registers with sparse sensing matrices in order to construct an encryption key to implement a lightweight efficient encryption scheme. The obtained results show that CS can reduce power consumption by a factor of 35% by only transmitting 70% of the data while preserving the ECG information. In addition, the paper demonstrates that transmission is kept secure even if the illegitimate attacker can access 95% of the information needed to recover the data. 2019 Elsevier Ltd This paper was made possible by National Priorities Research Program (NPRP) grant No. 9-114-2-055 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Hamza Djelouat received the B.Eng. degree in telecommunication from the Institute of Electrical and Electronic Engineering, Boumerdes, Algeria, in 2013, and the M.Sc. degree in electronic systems majoring telecommunication from the Ecole Militaire Polytechnique, Algiers, Algeria, in 2015. He is currently a Research Assistant with Qatar University. His research interests are mainly in digital signal processing and the applications of compressive sensing in wireless networks and connected health Abbes Amira (S99M01SM07) received the Ph.D. degree in computer engineering from Queens University Belfast, Belfast, U.K., in 2001.,In 2001, he undertook many academic and consultancy positions in the U.K., Asia, and the Middle East. He is currently the Associate Dean for research and graduate studies and a Full Professor in computer engineering with Qatar University, Doha, Qatar. He has taken visiting professor positions with the University of Tun Hussein Onn, Parit Raja, Malaysia, and the University of Nancy Henri Poincare, Nancy, France. He has been successful in securing substantial funding from government agencies and industry. He has supervised over 20 Ph.D. students and has authored or co-authored around 300 publications in top journals and conferences in the area of embedded computing, and image and signal processing.,Prof. Amira was a recipient of many international awards, including the 2008 VARIAN Prize of the Swiss Society of Radiobiology and Medical Physics. He has been invited to give keynote talks, short courses, and tutorials at many universities and international conferences. He is also a member of the IEEE Technical Committee for Biomedical Circuits and Systems. He is a Fellow of the IET and the Higher Education Academy and a Senior Member of the ACM. Faycal Bensaali (S03M06SM15) received the Dipl.-Ing. (M.Eng.) degree in electronics from the University of Constantine, Constantine, Algeria, and the Ph.D. degree in electronic and computer engineering from Queens University Belfast, Belfast, U.K.,He is currently an Associate Professor in electrical engineering with Qatar University, Doha, Qatar. He took other academic positions with Queens University Belfast and the University of Hertfordshire, Hertfordshire, U.K. He has authored/co-authored over 100 scientific papers in international journals and conference proceedings. His current research interests include embedded systems and high performance computing, custom computing using field programmable gate arrays, image and vision systems, and connected health.,Dr. Bensaali is a regular Reviewer for a number of international journals and conferences, such as the IEEE Transactions on Circuits and Systems for Video Technology, IEEE Sensors Journal, Journal of Real-Time Image Processing (Springer), EURASIP Journal on Image and Video Processing, IEEE International Symposium on Circuits and Systems, IEEE Workshop on Embedded Computer Vision, IEEE GCC conference, ACS/IEEE International Conference on Computer Systems and Applications, and IEEE International Conference on Electronics Circuits and Systems. He also serves as the Guest Editor of the IEEE Internet of Things Journal and the General Chair, the Workshop Chair, and a TPC member of a number of international conferences and workshops. He is an Associate of the Higher Education Academy. Issam Boukhennoufa received the B.Eng. degree in control from the Institute of Electrical and Electronic Engineering, Boumerdes, Algeria, in 2013, and the M.Sc. degree in electronic systems majoring sensors and associated systems from Montpellier university, Montpellier, France, in 2015. He is currently a Research Assistant with Qatar University. His research interests are mainly in digital signal processing and the applications of compressive sensing in wireless networks and connected health Scopus

Extracorporeal Membrane Oxygenation (ECMO) is a lifesaving procedure developed for the care of patients with short-term respiratory and/or cardiac issues (MacLaren et al., 2011). ECMO patients must be monitored twenty-four hours a day by an ECMO-trained multidisciplinary team. The trained healthcare professional needs to watch over fifty variables and rapidly intervene to assess and resolve any given emergency. Hence hands on training is very important for ECMO professionals to develop rapid and correct actions upon different scenarios. Simulation based training (SBT) offers ECMO practitioners an opportunity to develop the skills needed for the initiation of the ECMO procedure and the care of ECMO patients without exposing patients to undue risks (Al Disi et al., 2018). For ECMO to work, cannulation is required to reroute the blood flow to the machine rather than the lung and/or heart. Cannulation is the insertion of a cannula through the blood vessels. In collaboration with Hamad Medical Corporation (HMC), the main healthcare provider in Qatar, the aim of this project is to develop an effective, economical realistic, user-friendly, low-cost, and a multi-functional high-fidelity cannulation simulation mannequin. Keywords: Extracorporeal Membrane Oxygenation, ECMO, Medical training, Cannulation, Healthcare

Background: Cannulation, in extracorporeal membrane oxygenation (ECMO), is the act of inserting a cannula through the body1. For femoral veins, femoral arteries, and the jugular vein, the cannula stops at the inferior vena cava (IVC) beside the hepatic vein and at the beginning of the distal aorta, and the superior vena cava at the right atrium, respectively. Cannulation is considered a critical operation and requires intensive training. Simulation-based training (SBT) is the gold standard, allowing for training in risk-free, versatile, and realistic environments2. A research collaboration was established between Hamad Medical Corporation and Qatar University College of Engineering to support the development of the ECMO training programme. Initially an ECMO machine simulator was developed with thermochromic ink to simulate blood and modules that simulate common emergencies practitioners may face during ECMO runs3. This cannulation simulator is now being designed to close the gap in the market in relation to cost and fidelity4,5. Methods: The cannulation simulator is composed of several modules. Firstly, a 3D-printed femoral pad mold was constructed to facilitate the production of cannulation pads (Figure 1(a), (c)). Secondly, cannulation pads were designed so they are anatomically correct and ultrasound compatible. For the arteries, the superficial artery was added at the access point to simulate possible incorrect routes for the cannula. Furthermore, the orientation of the veins and arteries were set to further resemble the human anatomy, where the arteries are situated above the veins (Figure 1(a), (b)). In addition to the implementation of a closed loop linking the jugular to the femoral, cannulation access points with a pump connected to a tank between them to regulate the flow. The blood flow in the arteries was enhanced with a pump to simulate a pulsatile flow while the flow in the veins is laminar as seen in the single loop implementation (Figure 1(h)). The connection of the pump to the embedded system is shown in Figure 1(g). The junctional point in the IVC was designed in the venous loop to allow for two cannulas to pass and an alternative path simulating the renal vein was added. A force sensing resistor (FSR) was connected to detect and measure incorrect entry of the guide-wire as this, in real-time scenarios, could cause internal bleeding to the patient (Figure 1(g)). Lastly, the Y-connector showing the renal vein entry is shown in Figure 1(d) and (e). Results: Tests were done on the system namely on the FSR to recalibrate it in the presence of liquid. Tests on the pulsatile flow were conducted to optimize for realism in terms of pressure. Since both jugular and femoral cannulation access points are included, the simulator can be used for training for all ECMO modes including veno-arterial and veno-venous. After testing, the main limitations of the current prototype include the flexibility of the tubes, limits on FSR measurements, and the rigidity of the available 3D printing material. Conclusion: After implementing the stated features, the anticipated outcome is a realistic and cost-efficient ECMO cannulation simulator.

Cerebral aneurysm is a cerebrovascular disorder caused by a weakness in the wall of an artery or vein, which causes a localized 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 visualize the results in real-time at medical environments, a cost-efficient, practical platform is needed. In this article, 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 visualize 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. 2007-2012 IEEE. Manuscript received February 19, 2019; revised September 20, 2019; accepted November 4, 2019. Date of publication December 2, 2019; date of current version June 3, 2020. This paper was supported by the National Priorities Research Program (NPRP) under Grant 5-792-2-328 from the Qatar National Research Fund (a member of Qatar Foundation). X. Zhai is partially supported by the UK Engineering and Physical Sciences Research Council under Grants EP/R02572X/1 and EP/P017487/1. (Corresponding author: Xiaojun Zhai.) X. Zhai is with the School of Computer Science and Electronic Engineering, University of Essex, CO4 3SQ Colchester, U.K. (e-mail: xzhai@essex.ac.uk). Scopus

Extracorporeal Membrane Oxygenation (ECMO) is a highly complex/critical lifesaving procedure known to support patients with cardiac and respiratory issues. Patients on ECMO are monitored 24/7 by a team of highly trained ECMO team comprising nurses, physicians, respiratory therapists, and perfusionists promptly intervening to any potential emergency situation. Simulation-Based Training (SBT) allows clinicians to experience and practice realistic hands-on procedures and scenarios without any risk. In ECMO, cannulation is a critical procedure performed to externally reroute the blood flow so it can be re-oxygenated by the ECMO machine before being recirculated through the patient's body. In a close collaboration with Hamad Medical Corporation (HMC), this project aims to develop a cost effective, realistic, and user-friendly ECMO simulator focusing on the venous and arterial cannulation procedure, The main features of this simulator include cannulation emergencies caused by low pressure flow, excessive force, recirculation, or mispositioned wire/cannula. Therefore, the ECMO cannulation simulator will not only greatly contribute to the initial and ongoing local training of HMC ECMO clinicians but also contribute to improving patient care by lowering the risks associated with the cannulation process. - 2019 IEEE. This publication was made possible by Qatar University internal grant and the UREP award UREP22-003-2-001 from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

Due to global energy demands and overwhelming environmental dilemmas, exorbitant domestic energy usage is a colossal barrier towards energy efficiency. Tremendous research efforts have been poured into a plethora of methods for behavioral change. However, there is an expanse of opportunity to capitalize on evidence-based, technology-enabled energy efficiency solutions. Therefore, we present a novel application that effectively visualizes domestic energy data, presents meaningful and simplified benchmarks pertaining the efficiency of the end-user's usage, and contextually presents micro-moment based recommendations for better energy efficiency. The solution is part of a larger Consumer Engagement Towards Energy Saving Behavior by means of Exploiting Micro Moments and Mobile Recommendation Systems (EM)3 platform. Simulated and real energy data tests signifies the premise of the application and pinpoints areas of future work including more comprehensive data visualization, a richer recommendation system, and a full-fledged micro-moment behavior observer. Springer Nature Switzerland AG 2020. This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

Monitoring elderlies living alone has been a rising issue that caregivers are interested in solving since many elderlies are at risk of experiencing a fall. In the absence of urgent help, serious consequences may occur. This paper presents a complete communication system to monitor elderlies by checking their Electrocardiogram (ECG) and accelerometer data through a cloud-based server anytime on a mobile application ensuring that they are unharmed. This has been implemented by having a Multi-core Processing Unit (MPU), acting as a gateway, at the elderly's side monitoring signals coming from a wearable sensing device. It will classify ECG and accelerometer data using Machine Learning algorithms, stream the data upon request, alert caregivers through a mobile application and store the data on the database for further analysis in case of a fall. Fall detection had an accuracy of 95% using Extended Nearest Neighbor (E-NN) learning algorithm. 2019 IEEE. ACKNOWLEDGMENTS This paper was made possible by the National Priorities Research Program (NPRP) grant No. 9-114-2-055 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

Domestic user behavior shapes overall power consumption, necessitating the development of systems that analyze and help foster energy efficient behavior. The most important step in the process is the collection and management of comprehensive data on end-user power consumption behavior. This paper presents an appliance-based energy data collection system for domestic households. It revolves around the concept of micro-moments, which are short-timed and energy-based events that form the overall energy behavior of the end-user. The system comprises sensing modules for recording energy consumption, occupancy, temperature, humidity, and luminosity storing recordings on a database server. Sensing parameters were tested in terms of connection stability and measurement accuracy. High stability and accuracy benchmarks have been reached with future work focused on deploying the system for multi-users in addition to enhancing overall data security and integrity. 2019 IEEE. This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

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 gateway-centric 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.LITTLE 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. 2019 This paper was made possible by National Priorities Research Program (NPRP) grant no. 9-114-2-055 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Hamza Djelouat received a BEng degree in telecommunication from the Institute of Electrical and Electronic Engineering, Boumerdes, Algeria, in 2013 and an M.Sc. degree in electronic systems majoring telecommunication from The Ecole Militaire Polytechnique (EMP), Algiers, Algeria, in 2015. He is currently a research assistant at Qatar University. His research interests are mainly in digital signal processing and the applications of compressive sensing in wireless networks and connected health. Mohammed Al Disi received his B.S. in electrical engineering from Qatar University. Mohammed is currently a research assistant at Qatar University working on heterogeneous multicore platforms for connected health applications. His research interests include biomedical signal processing and connected health implementations. Al Disi was a recipient of best poster presentation and oral presentation awards in Extracorporeal Life Support Organization South West Asia Conference (ESLO-SWAC) 2017 for the works titled Using Thermochromic Ink for Medical Simulation and Design and implementation of a modular ECMO simulator. Issam Boukhenoufa received a BEng degree in Control from the Institute of Electrical and Electronic Engineering, Boumerdes, Algeria, in 2013 and an MSc degree in Electronic systems majoring Sensors and Associated Systems from Montpelier University, France in 2015. He is currently a research assistant at Qatar University. Abbes Amira received the Ph.D. degree in the area of computer engineering from Queen's University, Belfast, U.K., in 2001. He took many academic and consultancy positions, including his recent positions as Associate Dean for Research and Innovation at De Montfort University, Leicester, U.K. He published more than 300 papers in top journals and conferences and secured significant funding from government and industry. His research interests include reconfigurable computing, signal and image processing, IoT, connected health and security. He is a Fellow IET, Fellow of HEA, Senior Member of IEEE and ACM. Faycal Bensaali (S'03-M'06-SM'15) obtained a Dipl-Ing (M.Eng.) in electronics from University of Constantine and a Ph.D. in Electronic and Computer Engineering from Queen's University, Belfast. He is currently an Associate Professor in Electrical Engineering at Qatar University. Dr. Bensaali took other academic positions at the University of Hertfordshire-UK and Queen's University Belfast-UK. His research interests are mainly in embedded systems and high performance computing, custom computing using FPGAs, image and vision systems and connected health. Dr. Bensaali serves as Guest Editor of IEEE IoT journal and General Chair, Workshop Chair and TPC Member of a number of international conferences and workshops. He has authored/co-authored over 100 scientific papers in international journals and conference proceedings. He is an HEA Associate and IEEE senior member. Christos Kotronis received his B.Sc. in Informatics & Telematics from Harokopio University in 2014 and M.Sc. in Web Engineering from the Department of Informatics and Telematics of Harokopio in 2016. He is currently a Ph.D. candidate at the same department, under the supervision of Professor Mara Nikolaidou. He gained a three-year 2017-2019 Engineering and Technology Sciences scholarship from the General Secretariat for Research and Technology (GSRT) and the Hellenic Foundation for Research and Innovation (HFRI) for conducting his doctoral research. This research, titled "Systems-of-Systems engineering: An integrated model-driven methodology for complex systems", proposes model-driven approaches, utilizing SysML as a modeling language to automatically generate performance and cost models. Case studies on transportation, e-health, and sensor networks, are explored, taking into account the dynamic behavior of such Systems-of-Systems (SoS) under real-time conditions. His current position is teaching assistant at the Dept. of Informatics & Telematics of Harokopio University of Athens 2016. Elena Politis holds a B.Sc. in Physics from the National and Kapodistrian University of Athens and a M.Sc in Informatics and Telematic Services with honors from Harokopio University. She is currently a Ph.D. candidate at the department of Informatics and Telematics at Harokopio University. Her dissertation is dedicated to the Internet of Underwater Things. She speaks fluent Greek and English and has a very good of the German language. Her research interests revolve around the Internet of Things, related applications and services and optimization algorithms for autonomous underwater vehicles. Mara Nikolaidou received the B.Sc. and Ph.D. degrees from the Department of Informatics, University of Athens, Athens, Greece. She is a Professor with the Department of Informatics and Telematics, Harokopio University of Athens, Kallithea, Greece, where she also currently serves as the Rector. Over the last years, she actively participated in numerous projects on system engineering, service-oriented architectures, digital libraries, and E-government. She is currently participating in research project focusing on systems-of-systems (SoS) engineering, Internet of Things, and smart cities. She has published over 100 papers in international journals and conferences. Her current research interests include SoS engineering, system modeling and simulation, service-oriented architectures, and BPM. George Dimitrakopoulos has received his bachelor degree in Electrical and Computer Engineering in 2002 and his Ph.D. in 2007, from the National Technical University of Athens, and the University of Piraeus, respectively. He is currently an assistant professor at the department of Informatics and Telematics of Harokopio University of Athens (HUA) (2010), as well as a coordinator of funded projects at Infineon Technologies A.G. (2018) and a senior consultant and project manager at Intrasoft International S.A. (2015). He is the author of about 120 publications in international journals and conferences, whereas he has been involved in more than 20 internationally funded RD projects. His research interests include the design and development of safety and emergency management algorithms for smart devices, as well as the design and development of strategies for the optimization of wireless networks based on cognitive networking principles. Scopus

The use of Internet of Things (IoT) with medical devices within a connected health environment promotes the quick flow of information and enables easy access to it [...]

Internet-of-Things (IoT) is an appealing service to revolutionise Smart City (SC) initiatives across the globe. IoT interconnects a plethora of digital devices known as Sensor Nodes (SNs) to the Internet. Due to their high performance and exceptional Quality-of-Service (QoS) Multiprocessor System-on-Chip (MPSoC) computing architectures are gaining increasing popularity for the computationally extensive workloads in both IoT and consumer electronics. In this survey, we have explored balance between the IoT paradigm and its applications in SC while introducing Wireless Sensor Network (WSN), including the structure of the SN. We considered MPSoCs systems in relation to characteristics such as architecture and the communication technology involved. This provides an insight into the benefits of coupling MPSoCs with IoT. This paper, also investigates prevalent software level energy optimisation techniques and extensively reviews workload mapping and scheduling approaches since 2001 until today for energy savings using (1) Dynamic Voltage and Frequency Scaling (DVFS) and/or Dynamic Power Management (DPM) (2) Inter-processor communication reduction (3) Coarse-grained software pipelining integrated with DVFS. This paper constructively summarises the findings of these approaches and algorithms identifying insightful directions to future research avenues.

Increasing the number of sensors in a gas identification system generally improves its performance as this will add extra features for analysis. However, this affects the computational complexity, especially if the identification algorithm is to be implemented on a hardware platform. Therefore, feature reduction is required to extract the most important information from the sensors for processing. In this paper, linear discriminant analysis (LDA) and principal component analysis (PCA)-based feature reduction algorithms have been analyzed using the data obtained from two different types of gas sensors, i.e., seven commercial Figaro sensors and in-house fabricated $4 \times 4$ tin-oxide gas array sensor. A decision tree-based classifier is used to examine the performance of both the PCA and LDA approaches. The software implementation is carried out in MATLAB and the hardware implementation is performed using the Zynq system-on-chip (SoC) platform. It has been found that with the $4 \times 4$ array sensor, two discriminant functions (DF) of LDA provide 3.3% better classification than five PCA components, while for the seven Figaro sensors, two principal components and one DF show the same performances. The hardware implementation results on the programmable logic of the Zynq SoC shows that LDA outperforms PCA by using 50% less resources as well as by being 11% faster with a maximum running frequency of 122 MHz. 2001-2012 IEEE. Scopus

This Paper presents an Internet of Things (IoT) based framework to monitor ECG for biometric recognition and acceleration for fall detection. To this end, an-IoT based Remote Elderly Monitoring System (REMS) platform is described. REMS consists of a Shimmer3TM device transmitting physiological signal wirelessly to a nearby gateway which routes the data to a remote IoT-platform, able to accommodate dynamically changing configurations. The Shimmer firmware has been modified to send data based on the compressive sensing theory in order to ameliorate energy consumption in addition of real data, and the analysis and processing are done locally on a heterogeneous multicore edge device in order to solve latency issues related to cloud reliance. Subsequently the framework has been designed to handle the different parameter settings and multiple scenarios in a user-friendly way. Furthermore, it allows the user to monitor physiological data and acquire some feedback related to their analysis. Depending on a scenario (energy save, secure communication) the system can be configured manually or automatically to monitor ECG or acceleration data and displays them, it can also identify the subject based on ECG recognition and detect fall if it occurs. - 2019 IEEE. This paper was made possible by National Priorities Research Program (NPRP) grant No. 9-114-2-055 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

A weakness in the wall of a cerebral artery causing a dilation or ballooning of the blood vessel is known as a cerebral aneurysm. Optimal treatment requires fast and accurate diagnosis of the aneurysm. HemeLB is a fluid dynamics solver for complex geometries developed to provide neurosurgeons with information related to the flow of blood in and around aneurysms. On a cost efficient platform, HemeLB could be employed in hospitals to provide surgeons with the simulation results in real-time. In this work, we developed an improved version of HemeLB for GPU implementation and result visualization. A visualization platform for smooth interaction with end users is also presented. Finally, a comprehensive evaluation of this implementation is reported. The results demonstrate that the proposed implementation achieves a maximum performance of 15,168,964 site updates per second, and is capable of speeding up HemeLB for deployment in hospitals and clinical investigations. - 2019 IEEE. This paper was made possible by National Priorities Research Program (NPRP) grant No. 5-792-2-328 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

Medical simulators are advancing in technology and complexity to enhance realism and hence implementing a real-time communication system for such simulators is a challenge that engineers face. This paper follows up on the Firebase communication system solution that was employed for the modular extracorporeal membrane oxygenation (ECMO) simulator and addresses its challenges and limitations by presenting an alternative CouchDB based communication system. Testing results shows that CouchDB overcomes Firebase limitations in terms of latency, compatibility, and ongoing maintenance costs. The new approach features automatic application program interfaces (APIs), extremely low latency, and long term reliability. This paper was supported by Qatar university Internal Grant No. QUCG-CENG-2018-1. The findings achieved herein are solely the responsibility of the authors. Scopus

Since electricity consumption of households in developing countries is dramatically increasing every year, it is now more prudent than ever to utilize technology-based solutions that assist energy end-users to improve energy efficiency without affecting quality of life. User behavior is the most important factor that influences household energy consumption and recommender systems can be the technology enabler for shaping the users' behavior towards energy efficiency. The current literature mostly focuses on energy usage monitoring and home automation and fails to engage and motivate users, who are not as committed and self-motivated. In this work, we present a context-aware recommender system that analyses user activities and understands their habits. Based on the output of this analysis, the system synchronizes with the user activities and presents personalized energy efficiency recommendations at the right moment and place. The recommendation algorithm considers user preferences, energy goals, and availability in order to maximize the acceptance of a recommended action and increase the efficiency of the recommender system. The results from the evaluation on a publicly available dataset comprising energy consumption data from multiple devices shows that micro-moments repeatedly occur within user's timeline (covering more than 35% of user future activities) and can be learned from user logs. Copyright 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

Cerebral aneurysm is a life-threatening disease that may enlarge and bleed into surrounding area. Therefore, diagnosis of aneurysm is highly important as early as possible to help doctors to decide the right treatment. The HemeLB is a lattice-Boltzmann simulation framework that allows surgeons to view the simulation results of cerebral blood vessels. The HemeLB simulation framework was designed for high performance computer (HPC), which is not user friendly for the users who do not have computer science background. In this paper, we present a solution for designing and implementing HemeLB on a cost efficient embedded platform, in order to allow the HemeLB simulation framework to be potentially implemented in the local environment of hospital. The proposed work has been developed using a Zynq system-on-chip (SoC), which is a heterogeneous multi-processor system-on-chip (MPSoC) platform widely used in high performance embedded applications. Moreover, a comprehensive evaluation for the implementation is also reported in this paper. The results demonstrate that the proposed implementation is able to support the HemeLB framework on a low-cost MPSoC platform and achieving a maximum performance of 215,751 sites updates per second with only 2 cores. 2019 IEEE. This paper was made possible by National Priorities Research Program (NPRP) grant No. 5-792-2-328 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. We would also like to thank Dr Robin Richardson Scopus

With the ever-growing rise of energy consumption and its devastating financial and environmental repercussions, it is of utmost significance to moderate energy usage with proper energy efficiency tools. This is particularly applicable to domestic energy end-users, where an accurate profile is a prerequisite for motivating energy saving behavior. This article presents an innovative method for accurately understanding domestic energy usage patterns through a classification system. It capitalizes on the emerging concept of micro-moments, short energy-related events, and builds a comprehensive profile of end-user's energy activities with unprecedented accuracy. Micro-moments are classified based on a set of criteria per the given appliance. Five classifiers with different parameter settings were trained and tested on 10-fold cross-validated simulated data, with ensemble bagged trees topping with an accuracy of 88.0%. We also observed that linear classifiers lack in accuracy due to their inability to capture the dataset's specific structure and patterns. Fused with the other components of our framework, the proposed classification system is a novel contribution to domestic energy profiling in an effort to step energy efficiency up to the next level. - 2019 Elsevier Ltd This paper was made possible by National Priorities Research Program (NPRP) Grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

The sensitive domain of healthcare intensifies the shortcomings associated with internet of things (IoT) based remote health monitoring systems in terms of their high-energy consumption and big data issues such as latency and privacy, caused by, the continuous stream of raw data. Hence, in the development of their remote elderly monitoring system (REMS), the authors focus on using embedded multicore architectures as powerful IoT edge devices and energy efficient signal acquisition and processing techniques to elevate such limitations. This study addresses the design of sparsifying matrices for electroencephalogram (EEG) signals in the context of compressed sensing. These signals are known to be non-sparse in both time and standard transform domains. The designed matrices are adapted to the data and are based on the autoregressive modeling of the signal and the singular value decomposition (SVD) of the impulse response matrix of the linear predictive coding (LPC) filter. To facilitate the hardware implementation and to prolong the life of the wearable node, the measurement matrix is chosen to be binary. The proposed algorithm has been applied to the EEGLab dataset 'eeglab data set' with an average normalized mean square error of 0.068. This paper was made possible by National Priorities Research Program (NPRP) Grant No. 9-114-2-055 from the Qatar National Research Fund (a member of Qatar Foundation). Scopus

Introduction: Patients under the error-prone and complication-burdened extracorporeal membrane oxygenation (ECMO) are looked after by a highly trained, multidisciplinary team. Simulation-based training (SBT) affords ECMO centers the opportunity to equip practitioners with the technical dexterity required to manage emergencies. The aim of this article is to review ECMO SBT activities and technology followed by a novel solution to current challenges. ECMO simulation: The commonly-used simulation approach is easy-to-build as it requires a functioning ECMO machine and an altered circuit. Complications are simulated through manual circuit manipulations. However, scenario diversity is limited and often lacks physiological and/or mechanical authenticity. It is also expensive to continuously operate due to the consumption of highly specialized equipment. Technological aid: Commercial extensions can be added to enable remote control and to automate circuit manipulation, but do not improve on the realism or cost-effectiveness. A modular ECMO simulator: To address those drawbacks, we are developing a standalone modular ECMO simulator that employs affordable technology for high-fidelity simulation.

Introduction: Extracorporeal membrane oxygenation (ECMO) training programs employ real ECMO components, causing them to be extremely expensive while offering little realism in terms of blood oxygenation and pressure. To overcome those limitations, we are developing a standalone modular ECMO simulator that reproduces ECMO’s visual, audio and haptic cues using affordable mechanisms. We present a central component of this simulator, capable of visually reproducing blood oxygenation color change using thermochromism. Methods: Our simulated ECMO circuit consists of two physically distant modules, responsible for adding and withdrawing heat from a thermochromic fluid. This manipulation of heat creates a temperature difference between the fluid in the drainage line and the fluid in the return line of the circuit and, hence, a color difference. Results: Thermochromic ink mixed with concentrated dyes was used to create a recipe for a realistic and affordable blood-colored fluid. The implemented “ECMO circuit” reproduced blood’s oxygenation and deoxygenation color difference or lack thereof. The heat control circuit costs 300 USD to build and the thermochromic fluid costs 40 USD/L. During a ten-hour in situ demonstration, nineteen ECMO specialists rated the fidelity of the oxygenated and deoxygenated “blood” and the color contrast between them as highly realistic. Conclusions: Using low-cost yet high-fidelity simulation mechanisms, we implemented the central subsystem of our modular ECMO simulator, which creates the look and feel of an ECMO circuit without using an actual one.

Typical real-time remote health monitoring architectures consist of wearable medical devices continuously transmitting physiological signals to a nearby gateway which routes the data to an remote internet of things (IoT)-platform. Unfortunately, this model falls-short under the strict requirements of healthcare systems. Wearable medical devices have short battery lifespans, the system reliance on a cloud makes it vulnerable to connectivity and latency issues, and there are privacy concerns related to streaming sensitive medical data to remote servers. The compressive sensing (CS) scheme has been explored in the context of bio-signals to reduce the energy consumption of wearable sensors. However, CS does not address the other limitations caused by the model's reliance on cloud-computing but exacerbates the associated computing latency by requiring a computationally complex reconstruction process. In our remote elderly monitoring system, we attempt to address this weakness by developing a gateway-centric connected health system, where most signal processing and analysis occurs locally on heterogeneous multicore edge-devices. This paper explores the efficacy of real-time reconstruction of ECG signals, compressed under the CS scheme, on an IoT-gateway powered by ARM's big. LITTLE multicore solution at different signal dimension and allocated computational resources. Experimental results show the gateway's capability to reconstruct ECG signals in real-time, even when considering dimensionally large windows and minimum computational resources. Moreover, they demonstrate that utilizing more cores for the reconstruction process has a higher impact on execution time and is more energy efficient than increasing the cores' frequency. The optimal resource allocation for the majority of cases is a single big (A15) core at minimum frequency as it provides extreme fast reconstruction while consuming less or slightly more energy than its LITTLE (A7) counterpart. Heterogeneous multicore devices have the computational capacity and energy efficiency to elevate some of the limitations of a cloud-based remote health monitoring and can help create a more sustainable IoT-based connected health. Scopus

Introduction/Aim: The patient’s condition and high-risk nature of extracorporeal membrane oxygenation (ECMO) therapy force clinical services to ensure clinicians are properly trained and always ready to deal effectively with critical situations. Simulation-based education (SBE), from the simplest approaches to the most immersive modalities, helps promote optimum individual and team performance. The risks of SBE are negative learning, inauthenticity in learning and over-reliance on the participants’ suspension of disbelief. This is especially relevant to ECMO SBE as circuit/patient interactions are difficult to fully simulate without confusing circuit alterations. Methods: Our efforts concentrate on making ECMO simulation easier and more realistic in order to reduce the current gap there is between SBE and real ECMO patient care. Issues to be overcome include controlling the circuit pressures, system failures, patient issues, blood colour and cost factors. Key to our developments are the hospital-university collaboration and research funding. Results: A prototype ECMO simulator has been developed that allows for realistic ECMO SBE. The system emulates the ECMO machine interface with remotely controllable pressure parameters, haemorrhaging, line chattering, air bubble noise and simulated blood colour change. Conclusion: The prototype simulator allows the simulation of common ECMO emergencies through innovative solutions that enhance the fidelity of ECMO SBE and reduce the requirement for suspension of disbelief from participants. Future developments will encompass the patient cannulation aspect.

Systems based on wireless gas sensor networks offer a powerful tool to observe and analyze data in complex environments over long monitoring periods. Since the reliability of sensors is very important in those systems, gas classification is a critical process within the gas safety precautions. A gas classification system has to react fast in order to take essential actions in the case of fault detection. This paper proposes a low latency real-time gas classification service system, which uses a multi-layer perceptron (MLP) artificial neural network to detect and classify the gas sensor data. An accurate MLP is developed to work with the data set obtained from an array of tin oxide (SnO2) gas sensor, based on convex micro hotplates. The overall system acquires the gas sensor data through radio-frequency identification (RFID), and processes the sensor data with the proposed MLP classifier implemented on a system on chip (SoC) platform from Xilinx. Hardware implementation of the classifier is optimized to achieve very low latency for real-time application. The proposed architecture has been implemented on a ZYNQ SoC using fixed-point format and the achieved results have shown that an accuracy of 97.4% has been obtained. 2013 IEEE. Scopus

Internet of things (IoT) is shifting the healthcare delivery paradigm from in-person encounters between patients and providers to an «anytime, anywhere» model delivery. Connected health has become more profound than ever due to the availability of wireless wearable sensors, reliable communication protocols and storage infrastructures. Wearable sensors would offer various insights regarding the patient's health (electrocardiogram (ECG), electroencephalography (EEG), blood pressure, etc.) and their daily activities (hours slept, step counts, stress maps,) which can be used to provide a thorough diagnosis and alert healthcare providers to medical emergencies. Remote elderly monitoring system (REMS) is the most popular sector of connected health, due to the spread of chronic diseases amongst the older generation. Current REMS use low power sensors to continuously collect patient's records and feed them to a local computing unit in order to perform real-time processing and analysis. Afterward, the local processing unit, which acts as a gateway, feeds the data and the analysis report to a cloud server for further analysis. Finally, healthcare providers can then access the data, visualize it and provide the proper medical assistant if necessary. Nevertheless, the state-of-the-art IoT-based REMS still face some limitations in terms of high energy consumption due to raw data streaming. The high energy consumption decreases the sensor's lifespan immensely, hence, a severe degradation in the overall performance of the REMS platform. Therefore, sophisticated signal acquisition and analysis methods, such as compressed sensing (CS), should be incorporated. CS is an emerging sampling/compression theory, which guarantees that an N-length sparse signals can be recovered from M-length measurement vector (M<

Internet-based solutions, enhanced by Internet of Things (IoT) and Cloud computing, are constantly driving revolutionary approaches in multiple domains, including healthcare. Indicatively, telemedicine, real-time diagnosis and remote monitoring of patients, are expected to transform the healthcare domain. These systems may offer several services of different criticality, necessitating safety-/mission-critical core components and non-critical peripheral components; in other words, they are complex, mixed-criticality System-of-Systems (SoS). To understand and design such systems, engineers must be facilitated with the appropriate modeling tools. In this work, we explore the application of model-based design, using the Systems Modeling Language (SysML), of IoT e-Health systems, emphasizing criticality requirements. We focus on the Remote Elderly Monitoring System (REMS) use case, combining IoT technologies with classic healthcare practices, to demonstrate the potential of the proposed approach. Requirements comprise a basic concept of a systematic model-driven methodology that enables the successful management of the criticalities in system design, implementation and deployment. In the REMS use case, identified criticalities are modeled as SysML requirements, while SysML constraints and parametric diagrams are employed to describe and verify quantitative criticality requirements. ? 2018 IEEE. Scopus

The ever-increasing demand for biometric solutions for the Internet-of-Things (IoT)-based connected health applications is mainly driven by the need to tackle fraud issues, along with the imperative to improve patient privacy, safety, and personalized medical assistance. However, the advantages offered by the IoT platforms come with the burden of big data and its associated challenges in terms of computing complexity, bandwidth availability, and power consumption. This paper proposes a solution to tackle both privacy issues and big data transmission by incorporating the theory of compressive sensing and a simple, yet, efficient identification mechanism using the electrocardiogram (ECG) signal as a biometric trait. Moreover, the paper presents the hardware implementation of the proposed solution on a system-on-chip (SoC) platform with an optimized architecture to further reduce the hardware resource usage. First, we investigate the feasibility of compressing the ECG data while maintaining a high identification quality. The obtained results show a 98.88% identification rate using only a compression ratio of 30%. Furthermore, the proposed system has been implemented on a Zynq SoC using heterogeneous software/hardware solution, which is able to accelerate the software implementation by a factor of 7.73 with a power consumption of 2.318 W. ManuscriptreceivedJuly21,2018;revisedAugust19,2018;acceptedSeptember13,2018.DateofpublicationSeptember19,2018;dateofcurrentversionNovember13,2018.ThisworkwassupportedbytheQatarNationalResearchFund(amemberofQatarFoundation),throughtheNationalPrioritiesResearchProgram,underGrant9-114-2-055.TheassociateeditorcoordinatingthereviewofthispaperandapprovingitforpublicationwasDr.FerranReverter.(Correspondingauthor:XiaojunZhai.)H.Djelouat,M.A.Disi,A.Amira,andF.BensaaliarewiththeCollegeofEngineering,QatarUniversity,Doha,Qatar. Scopus