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This paper introduces REHAB-C, a goal-based context-aware recommendation system that supports users in transforming their energy habits. A combination of smart sensors and actuators collects information about user actions and their context and evaluates the actions to be proposed. Based on user decisions, REHAB-C either triggers an energy saving action, postpones or cancels it and records user preferences. The recommendation system continuously evaluates the user context and action against a rule base along with user goals and prioritizes the recommended actions, also considering other user preferences. The prototype implementation of REHAB-C is evaluated on two real-world scenarios, in an office setup. The user actions and presence are recorded for four weeks, as well as the user behavior towards energy saving, and his preferences on the recommended actions. The analysis of recorded results shows that goal-based context-aware recommendations can help users to improve their energy efficiency and in the long term can automatically adapt to user habits and reduce their energy footprint. 2020 Elsevier B.V. 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

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

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

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

The increased consumption of energy worldwide has boosted the interest of people for energy-efficient solutions at every level of daily life, from goods production and transportation to the use of household and office appliances. This gave rise to monitoring applications that monitor the daily user interaction with the electrical and electronic appliances, detect unnecessary or extensive usage and recommend corrective actions. In this direction, this work presents the anatomy of the Consumer Engagement Towards Energy Saving Behavior by means of Exploiting Micro Moments and Mobile Recommendation Systems (EM)3 recommendation engine, which supports household and office users with real-time personalized recommendations for avoiding unnecessary energy consumption and reducing the overall household (or office) energy footprint. The recommendation engine is based on a set of sensors that monitor energy usage, room occupancy, and environmental conditions inside and outside the living space, and a set of actuators that allow the remote control of devices, (e.g. on and off actions, set to eco or standby mode, etc.). The innovating feature of this recommendation engine is that it puts the human in the loop of energy efficiency by recommending actions at the right moment, in real-time, with user approval and rejection options. In addition, it provides savings related facts in order to increase the persuasiveness of the recommendations. Initial results show that users respond positively to personalized recommendations and are further persuaded when specific types of facts are chosen. 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

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

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

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

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

Cyber-Physical Systems (CPSs) comprise multiple cyberparts, physical processes, and human participants (end-users) that affect them, and vice versa. During the design of such systems, it is critical for the designer to take into account the end-user-perceived quality of provided services, as well as their cost, and integrate them into the CPSs; striking a satisfactory balance between quality and affordability is critical to system acceptance. In this work, we propose a model-based approach, using the Systems Modeling Language (SysML), to explore system design, encapsulating Quality of Service (QoS) and cost aspects, as system requirements, into a core model. Via this approach, the designer can define the system structure, configure it, measure and evaluate the quality, while analyzing cost, and find the best solution(s) for a correct design. As a use case, this approach is applied to a healthcare CPS, namely the Remote Elderly Monitoring System (REMS). In that context, managing REMS QoS and cost requirements, can contribute to an effective system design and implementation, enhancing the end-user satisfaction. 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

The Internet of Things (IoT) has started redesigning the paradigm of the connected health sector by leveraging the availability of low power, low-cost sensors and efficient communication protocols. Consequently, IoT based connected health platforms are expected to further enhance the patient connectivity and everyday convenience. Nevertheless, issues related to power consumption and user security limit the performance of such systems. The conventional approaches that incorporate biometric measures into the IoT design rise high concerns regarding the cost and the complexity of the implementation. This paper proposes an identification approach integrated within a patient�s heart monitoring system based on the theory of compressive sensing (CS). CS is an emerging theory that promotes both power optimization and security by transmitting random measurements with fewer samples rather than transmitting the whole raw signal. The proposed system uses the electrocardiogram (ECG) as a biometric measure to identify the patient. The advantage of such system is that it does not require any additional complexity to acquire and process the data. The obtained results showed a successful identification rate up to 98.88% by compressing the transmitted signal to only half the original one. - Springer Nature Switzerland AG 2019. Scopus

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

Selecting the best classifier plays a significant role in the current electronic nose systems that can be deployed for gas applications. For this purpose, this paper presents an empirical study on the performance of three different classifiers, namely, binary decision tree (BDT), K-nearest neighbours (KNN) and extended nearest neighbours (ENN) on gas identification. It has been observed that with BDT and ENN a maximum classification accuracy of up to 96.4 % and 96.7 % can be obtained, respectively, whereas in the case of KNN up to 97.0 % accuracy can be achieved. In addition to the individual classifiers, a committee machine (CM) based on the three classifiers has been designed, with and without feedback mechanism to determine the improvement gained by combining these classifiers. The performance attained by the CM with feedback is 97.44 % and it is slightly better than the one without feedback, that is 97.2 %. - Springer International Publishing AG 2018. Acknowledgment. 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 statements made herein are solely the responsibility of the authors.

This chapter presents a generalized and versatile connected health solution for patient monitoring. It consists of a mobile system that can be used at home, an ambulance and a hospital. The system uses the Shimmer sensor device to collect three axes (x, y and z) accelerometer data as well as electrocardiogram signals. The accelerometer data is used to implement a fall detection system using the k-Nearest Neighbors classifier. The classification algorithm is implemented on various platforms including a PC and the Zynq system on chip platform where both programmable logic and processing system of the Zynq are explored. In addition, the electrocardiogram signals are used to extract vital information, the signals are also encrypted using the Advanced Encryption Standard and sent wirelessly using Wi-Fi for further processing. Implementation results have shown that the best overall accuracy reaches 90% for the fall detection while meeting real-time performances when implemented on the Zynq and while using only 48% of Look-up Tables and 22% of Flip-Flops available on chip. 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). Scopus

Lately, Internet-based solutions, brought by the Internet of Things (IoT) and cloud computation and storage technologies, have been driving revolutionary approaches in innumerable domains, including the sensitive domain of healthcare. Indicatively, real-time diagnosis of medical issues, telemedicine, remote monitoring of patients, as well as computer-assisted smart transportation in case of emergencies, are anticipated as Systems-of-Systems (SoS) that can execute several applications of different criticality, thus necessitating mission-critical and non-critical peripheral components. Therefore, managing the criticality of a specific component, application or service in such an environment, is of fundamental importance. In this respect, this paper discusses an approach to identify and model criticalities of healthcare IoT systems, as a first step to effectively manage them in system implementation and deployment. To do so, it explains the mixed-criticality characteristics of such systems, describing two principal use cases, that stem from the combination of novel technologies with classic health care practices, namely (a) a remote elderly monitoring platform, as well as (b) a smart ambulance system. The main cricalities anticipated in such systems are described, as well as open areas for future research are also identified. 2017 IEEE. The authors wish to acknowledge the 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

The last decade has witnessed tremendous efforts to shape the internet of thing (IoT) platforms to be well suited for healthcare applications. These applications involve the deployment of remote monitoring platforms to collect different information about several vital signs such as electroencephalogram (EEG). However, the deployment of these platforms faces several limitations in terms of high power consumption and system complexity. High energy consumption associated with the continuous wireless data transmission can be optimized by exploring efficient compression techniques such as compressive sensing (CS). CS is an emerging theory that enables a compressed acquisition using well chosen sensing matrices to take random projections of the data in sub-Nyquist sampling rates. In addition, system complexity can be reduced by using hardware friendly structured sensing matrices. This paper quantifies the performance of CS-based scheme for vital sign acquisition for a connected health application over an IoT platform taking multi-channel EEG signals as a case study. In addition, the paper exploits the joint sparsity of multi-channel EEG signals as well as a designed sparsifying basis to increase the sparsity of the EEG signal to improve the reconstruction quality, hence, increase the efficiency of the system. 2017 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

This paper presents a reconfigurable connected health platform for fall and cardiac disease detection to be used in smart home environments using Shimmer sensing device and ZYNQ System on Chip (SoC) platform. The system can also be deployed in ambulances to equip them with health monitoring technologies. The system is designed to be used by elderly, diabetics, muscular and neurological patients who are likely to fall, in addition to heart patients who are probable to get heart attacks causing falls. This project aims to provide users and their families with a sense of mental and physical security in their houses. It will also help them pass through the abstraction of money, since they will not need costly home care nursing services, and they will enjoy relief without having an observer, hence providing a significant socioeconomic impact. The system has three main features: (1) Sensing data gathered from the accelerometer and Electrocardiogram (ECG) electrodes embedded in the Shimmer sensing device; (2) Real time monitoring and alerting system; and (3) Medical logging consists of time, position, strength, and location of the fall. The real-time classification of the fall detection has been achieved with an accuracy of 90% using K-Nearest Neighbors (KNN) algorithm. Moreover, the KNN hardware implementation requires 48% of Look-Up Tables (LUTs) and 22% of Flip-Flops (FFs) available on the Zedboard while consuming 582 Mw. Springer International Publishing AG 2018. Acknowledgment. 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 statements made herein are solely the responsibility of the authors. The work is also supported by Qatar University student grant QUST-CENG-FALL-15/16-1. Scopus

Electronic nose or machine olfaction are systems used for detection and identification of odorous com- pounds and gas mixtures. An electronic nose system is mainly made of two parts, the sensing part which takes the form of a single or a set of sensors and the processing part which takes the form of some pat- tern recognition algorithms. As an alternative solution to pure software or hardware implementation of the processing part of a gas identification system, this paper proposes a hardware/software co-design ap- proach using the Zynq platform for the implementation of an electronic nose system based on principal component analysis as a dimensionality reduction technique and decision tree as a classification algo- rithm using two different sensors array, a 4 ×4 in-house fabricated sensor and a commercial one based on 7 Figaro sensors, for comparison purpose. The system was successfully trained and simulated in MAT- LAB environment prior to the implementation on the Zynq platform. Various scenarios were explored and discussed including the investigation of different combination of principal components as well as the uti- lization of drift compensation technique to improve the identification accuracy. High level synthesis was carried out on the proposed designs using different optimization directives including loop unrolling, ar- ray partitioning and pipelining. Hardware implementation results on the Zynq system on chip show that real-time performances can be achieved for proposed electronic nose systems using hardware/software co-design approach with a single ARM processor running at 667 MHz and the programmable logic run- ning at 142 MHz. In addition, using the designed IP cores and for the best scenarios, a gas can be iden- tified in 3.46 μs using the 4 ×4 sensor and 0.55 μs using the Figaro sensors. Furthermore, it has been noticed that the choice of the sensor array has an important impact on performances in terms of ac- curacy and processing time. Finally, it has been demonstrated that the programmable logic of the Zynq platform consumes much less power than the processing system. 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 statements made herein are solely the responsibility of the authors. Scopus

Extracorporeal membrane oxygenation (ECMO) is a high-complexity life-saving procedure where quick and coordinated interventions are required to avoid mortality. ECMO perfusionists and nurses must be well trained to respond to time-critical emergencies, using simulation-based education, which currently rely on rudimental ECMO simulation processes. Implementing a simulation system for such complex medical procedure necessitates a robust, well-coordinated underlying architecture. In this paper, the use of Internet of Things (IoT) service Firebase is presented in the case study of an ECMO simulator. Features of the proposed solution are discussed and justified in the given use case including latency, hardware compatibility, and data management. For the used case of ECMO simulation system, Firebase proved to be a proper communication solution that satisfied the urgent nature of medical training. 2017 IEEE. This publication was made possible by an internal Qatar University grant and UREP grant #19-062-2-026 from the Qatar National Research Fund (a member of Qatar foundation). The statements made herein are solely the responsibility of the authors. Scopus

Meeting application requirements under a tight power budget is of a primary importance to enable connected health internet of things applications. This paper considers using sparse representation and well-defined inequality indexes drawn from the theory of inequality to distinguish ventricular ectopic beats (VEBs) from non-VEBs. Our approach involves designing a separate dictionary for each arrhythmia class using a set of labeled training QRS complexes. Sparse representation, based on the designed dictionaries of each new test QRS complex is then calculated. Following this, its class is predicted using the winner-Takes-All principle by selecting the class with the highest inequality index. The experiments showed promising results ranging between 80% and 100% for the detection of VEBs considering the patient-specific approach, 80% using cross validation and 70% on unseen data using independent sets for training and testing, respectively. An efficient hardware implementation of the alternating direction method of multipliers algorithm is also presented. The results show that the proposed hardware implementation can classify a QRS complex in 69.3ms that use only 0.934 W energy. 2013 IEEE. This work was supported by the National Priorities Research Program through the Qatar National Research Fund (a member of Qatar Foundation) under Grant 9-114-2-055. Scopus

Connected health is a technology that associates medical devices, security devices and communication technologies. It enables patients to be monitored and treated remotely from their home. Patients data and medical records within a connected health system should be securely stored and transmitted for further analysis and diagnosis. This paper presents a set of security solutions that can be deployed in a connected health environment, which includes the advanced encryption standard (AES) algorithm and electrocardiogram (ECG) identification system. Efficient System-on-Chip (SoC) implementations for the proposed algorithms have been carried out on the Xilinx ZC702 prototyping board. The Achieved hardware implementation results have shown that the proposed AES and ECG identification based system met the real-time requirements and outperformed existing field programmable gate array (FPGA)-based systems in different key performance metrics such as processing time, hardware resources and power consumption. The proposed systems can process an ECG sample in 10.71ms and uses only 30% of the available hardware resources with a power consumption of 107mW. An ECG based solution for connected health systems.ECG based solution on a reconfigurable hardware.Implementation of ECG algorithms on Zynq SoC.

Recently, Automatic Number Plate Recognition (ANPR) systems have become widely used in safety, security, and commercial aspects. The whole ANPR system is based on three main stages: Number Plate Localization (NPL), Character Segmentation (CS), and Optical Character Recognition (OCR). In recent years, to provide better recognition rate, High Definition (HD) cameras have started to be used. However, most known techniques for standard definition are not suitable for real-time HD image processing due to the computationally intensive cost of localizing the number plate. In this paper, algorithms to implement the three main stages of a high definition ANPR system for Qatari number plates are presented. The algorithms have been tested using MATLAB and two databases as a proof of concept. Implementation results have shown that the system is able to process one HD image in 61 ms, with an accuracy of 98.0% in NPL, 99.75% per character in CS, and 99.5% in OCR. 2016 IEEE. Scopus

A gas discrimination system is mainly made of two parts, the sensing part and the processing part. As an alternative solution to pure software or hardware implementation of the processing part of a gas identification system, this paper proposes a gas discrimination system and its implementation on the Zynq system on chip platform using hardware/software co-design approach. In addition, the recommended system uses principal component analysis for dimensionality reduction, binary decision tree for classification and a 4�4 in-house gas sensor array for sensing. Moreover, k-nearest neighbors classifier is also used and compared with decision tree. MATLAB is used for simulation and validation before the final implementation on the Zynq. Algorithms are implemented using high level synthesis and different optimization directives are applied. Hardware implementation results on the Zynq show that real-time performances can be achieved for proposed e-nose system using hardware/software co-design approach with a single ARM processor running at 667 MHz and the programmable logic running at 142 MHz. Scopus