Resilient Design for Process and Runtime Variations

The main objective of this thesis is to tackle the impact of parameter variations in order to improve the chip performance and extend its lifetime

Towards fog-driven IoT eHealth:Promises and challenges of IoT in medicine and healthcare

Internet of Things (IoT) offers a seamless platform to connect people and objects to one another for enriching and making our lives easier. This vision carries us from compute-based centralized schemes to a more distributed environment offering a vast amount of applications such as smart wearables, smart home, smart mobility, and smart cities. In this paper we discuss applicability of IoT in healthcare and medicine by presenting a holistic architecture of IoT eHealth ecosystem. Healthcare is becoming increasingly difficult to manage due to insufficient and less effective healthcare services to meet the increasing demands of rising aging population with chronic diseases. We propose that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other. This patient-centric IoT eHealth ecosystem needs a multi-layer architecture: (1) device, (2) fog computing and (3) cloud to empower handling of complex data in terms of its variety, speed, and latency. This fog-driven IoT architecture is followed by various case examples of services and applications that are implemented on those layers. Those examples range from mobile health, assisted living, e-medicine, implants, early warning systems, to population monitoring in smart cities. We then finally address the challenges of IoT eHealth such as data management, scalability, regulations, interoperability, device–network–human interfaces, security, and privacy

Relationship between Distribution of Coronary Artery Lesions and Renal Artery Stenosis in Patients Undergoing Simultaneous Coronary and Renal Angiography

Aims We evaluated the relationship between distribution of lesions in coronary tree and atherosclerotic renal artery stenosis (RAS). Methods and Results Data collected prospectively on 500 consecutive patients who underwent simultaneous renal angiography following coronary angiography. Overall prevalence of RAS was 26.2% (131 patients). Significant (≥ 50% luminal diameter stenosis) RAS was present in 70 patients (14%). In 346 individuals of the study population, significant CAD was present (69.2%). Significant RAS was more common (18.4%) in this group. Older age, higher intra-arterial systolic blood pressure (SBP) and pulse pressure (PP) at the time of catheterization, and 3-vessel coronary artery disease (CAD) were associated with significant RAS in univariate analysis. Relationship between involved locations of coronary arteries [Left anterior descending (LAD), left circumflex (LCX), Right Coronary Artery (RCA), and their ostioproximal portions] and RAS were significant except for left main (LM) disease. In multivariate model, age more than 62 years, SBP greater than 150 mmHg, PP in excess of 60 mmHg and RCA involvement were independent predictors of significant RAS. Conclusion Simultaneous renal angiography following coronary angiography might be justified in patients with significant RCA disease who are older with increased levels of intra-arterial SBP and PP

Immediate Results of Percutaneous Trans-Luminal Mitral Commissurotomy in Pregnant Women with Severe Mitral Stenosis

Background Valvular heart diseases and mainly rheumatic heart diseases complicate about 1% of pregnancies. During pregnancy physiological hemodynamic changes of the circulation are the main cause of mitral stenosis (MS) decompensation. Prior to introduction of percutaneous mitral balloon commissuroplasty (PTMC), surgical comissurotomy was the preferred method of treatment in patients with refractory symptoms. PTMC is an established non-surgical treatment of rheumatic mitral stenosis. The study aimed to assess the safety and efficacy of PTMC in pregnant women with severs mitral stenosis. Material and Method Thirty three consecutive patients undergoing PTMC during pregnancy enrolled in this prospective study. Mitral valve area (MVA), transmitral valve gradient (MVG), and severity of mitral regurgitation (MR) were assessed before and 24 hour after the procedure by transthoracic and transesophageal echocardiography. Mitral valve morphology was evaluated before the procedure using Wilkin's criteria. Patient followed for one month and neonates monitored for weight and height and adverse effect of radiation. Result Mitral valve area increased from 0.83 ± 0.13 cm 2 to 1.38 ± 0.29 cm 2 ( P = 0.007). Mean gradient of mitral valve decreased from 15.5 ± 7.4 mmHg to 2.3 ± 2.3 mmHg ( P = <0.001). Pulmonary artery pressure decreased from 65.24 ± 17.9 to 50.45 ± 15.33 ( P = 0.012). No maternal death, abortion, intrauterine growth restriction was observed and only one stillbirth occurred. Conclusion PTMC in pregnant women has favorable outcome and no harmful effect on children noted

Does fasting plasma glucose values 5.1-5.6 mmol/l in the first trimester of gestation a matter?

ObjectivesThe aim of the study was to investigate the effect of treatment on pregnancy outcomes among women who had fasting plasma glucose (FPG) 5.1-5.6 mmol/l in the first trimester of pregnancy.MethodsWe performed a secondary-analysis of a randomized community non-inferiority trial of gestational diabetes mellitus (GDM) screening. All pregnant women with FPG values range 5.1-5.6 mmol/l in the first trimester of gestation were included in the present study (n=3297) and classified to either the (i) intervention group who received treatment for GDM along with usual prenatal care (n=1,198), (ii) control group who received usual-prenatal-care (n=2,099). Macrosomia/large for gestational age (LGA) and primary cesarean-section (C-S) were considered as primary-outcomes. A modified-Poisson-regression for binary outcome data with a log link function and robust error variance was used to RR (95%CI) for the associations between GDM status and incidence of pregnancy outcomes.ResultsThe mean maternal age and BMI of pregnant women in both study groups were similar. There were no statistically significant differences in the adjusted risks of adverse pregnancy outcomes, including macrosomia, primary C-S, preterm birth, hyperbilirubinemia, preeclampsia, NICU-admission, birth trauma, and LBW both groups.ConclusionsIt is found that treating women with first-trimester FPG values of 5.1-5.6 mmol/l could not improve adverse pregnancy outcomes including macrosomia, Primary C-S, Preterm birth, hypoglycemia, hypocalcemia, preeclampsia, NICU admission, Birth trauma and LBW. Therefore, extrapolating the FPG cut-off point of the second trimester to the first –which has been proposed by the IADPSG, might therefore not be appropriate.Clinical Trial Registrationhttps://www.irct.ir/trial/518, identifier IRCT138707081281N1

Harnessing the Power of Smart and Connected Health to Tackle COVID-19:IoT, AI, Robotics, and Blockchain for a Better World

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), Artificial Intelligence (AI) &#x2014; including Machine Learning (ML) and Big Data analytics &#x2014; as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This paper provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas where IoT can contribute are discussed, namely, i) tracking and tracing, ii) Remote Patient Monitoring (RPM) by Wearable IoT (WIoT), iii) Personal Digital Twins (PDT), and iv) real-life use case: ICT/IoT solution in Korea. Second, the role and novel applications of AI are explained, namely: i) diagnosis and prognosis, ii) risk prediction, iii) vaccine and drug development, iv) research dataset, v) early warnings and alerts, vi) social control and fake news detection, and vii) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including i) crowd surveillance, ii) public announcements, iii) screening and diagnosis, and iv) essential supply delivery. Finally, we discuss how Distributed Ledger Technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19

Federated clustered multi-domain learning for health monitoring

Abstract Wearable Internet of Things (WIoT) and Artificial Intelligence (AI) are rapidly emerging technologies for healthcare. These technologies enable seamless data collection and precise analysis toward fast, resource-abundant, and personalized patient care. However, conventional machine learning workflow requires data to be transferred to the remote cloud server, which leads to significant privacy concerns. To tackle this problem, researchers have proposed federated learning, where end-point users collaboratively learn a shared model without sharing local data. However, data heterogeneity, i.e., variations in data distributions within a client (intra-client) or across clients (inter-client), degrades the performance of federated learning. Existing state-of-the-art methods mainly consider inter-client data heterogeneity, whereas intra-client variations have not received much attention. To address intra-client variations in federated learning, we propose a federated clustered multi-domain learning algorithm based on ClusterGAN, multi-domain learning, and graph neural networks. We applied the proposed algorithm to a case study on stress-level prediction, and our proposed algorithm outperforms two state-of-the-art methods by 4.4% in accuracy and 0.06 in the F1 score. In addition, we demonstrate the effectiveness of the proposed algorithm by investigating variants of its different modules