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353 results on '"Din, Sadia"'

2. Bayesian Multidimensional Scaling for Location Awareness in Hybrid-Internet of Underwater Things

Author

Khalil, Ruhul Amin, Saeed, Nasir, Babar, Mohammad Inayatullah, Jan, Tariqullah, and Din, Sadia

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Networking and Internet Architecture, Electrical Engineering and Systems Science - Systems and Control
Abstract

Localization of sensor nodes in the Internet of Underwater Things (IoUT) is of considerable significance due to its various applications, such as navigation, data tagging, and detection of underwater objects. Therefore, in this paper, we propose a hybrid Bayesian multidimensional scaling (BMDS) based localization technique that can work on a fully hybrid IoUT network where the nodes can communicate using either optical, magnetic induction, and acoustic technologies. These technologies are already used for communication in the underwater environment; however, lacking localization solutions. Optical and magnetic induction communication achieves higher data rates for short communication. On the contrary, acoustic waves provide a low data rate for long-range underwater communication. The proposed method collectively uses optical, magnetic induction, and acoustic communication-based ranging to estimate the underwater sensor nodes' final locations. Moreover, we also analyze the proposed scheme by deriving the hybrid Cramer Rao lower bound (HCRLB). Simulation results provide a complete comparative analysis of the proposed method with the literature., Comment: Accepted for Pulication in IEEE/CAA Journal of Automatica Sinica

Published
2021

36. Design and development of patient health tracking, monitoring and big data storage using Internet of Things and real time cloud computing.

Author

Shafi, Imran, Din, Sadia, Farooq, Siddique, Díez, Isabel de la Torre, Breñosa, Jose, Espinosa, Julio César Martínez, and Ashraf, Imran

Subjects
*CLOUD storage, *PULSE oximeters, *REAL-time computing, *DATA warehousing, *PATIENT-professional relations, *MEDICAL personnel as patients, *MEDICAL personnel
Abstract

With the outbreak of the COVID-19 pandemic, social isolation and quarantine have become commonplace across the world. IoT health monitoring solutions eliminate the need for regular doctor visits and interactions among patients and medical personnel. Many patients in wards or intensive care units require continuous monitoring of their health. Continuous patient monitoring is a hectic practice in hospitals with limited staff; in a pandemic situation like COVID-19, it becomes much more difficult practice when hospitals are working at full capacity and there is still a risk of medical workers being infected. In this study, we propose an Internet of Things (IoT)-based patient health monitoring system that collects real-time data on important health indicators such as pulse rate, blood oxygen saturation, and body temperature but can be expanded to include more parameters. Our system is comprised of a hardware component that collects and transmits data from sensors to a cloud-based storage system, where it can be accessed and analyzed by healthcare specialists. The ESP-32 microcontroller interfaces with the multiple sensors and wirelessly transmits the collected data to the cloud storage system. A pulse oximeter is utilized in our system to measure blood oxygen saturation and body temperature, as well as a heart rate monitor to measure pulse rate. A web-based interface is also implemented, allowing healthcare practitioners to access and visualize the collected data in real-time, making remote patient monitoring easier. Overall, our IoT-based patient health monitoring system represents a significant advancement in remote patient monitoring, allowing healthcare practitioners to access real-time data on important health metrics and detect potential health issues before they escalate. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Loss-of-function mutations in ADCY3 cause monogenic severe obesity

Author

Saeed, Sadia, Bonnefond, Amélie, Tamanini, Filippo, Mirza, Muhammad Usman, Manzoor, Jaida, Janjua, Qasim M., Din, Sadia M., Gaitan, Julien, Milochau, Alexandra, Durand, Emmanuelle, Vaillant, Emmanuel, Haseeb, Attiya, De Graeve, Franck, Rabearivelo, Iandry, Sand, Olivier, Queniat, Gurvan, Boutry, Raphaël, Schott, Dina A., Ayesha, Hina, Ali, Muhammad, Khan, Waqas I., Butt, Taeed A., Rinne, Tuula, Stumpel, Connie, Abderrahmani, Amar, Lang, Jochen, Arslan, Muhammad, and Froguel, Philippe

Published
2018
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49. Data transmission enhancement using optimal coding technique over in vivo channel for interbody communication

Author

Mezher, Mohanad Ahmed, Din, Sadia, Ilyas, Muhammad, Bayat, Oguz, Abbasi, Qammer Hussain, and Ashraf, Imran

Abstract

Wireless in vivo actuators and sensors are examples of sophisticated technologies. Another breakthrough is the use of in vivo wireless medical devices, which provide scalable and cost-effective solutions for wearable device integration. In vivo wireless body area networks devices reduce surgery invasiveness and provide continuous health monitoring. Also, patient data may be collected over a long period of time. Given the large fading in in vivo channels due to the signal path going through flesh, bones, skins, and blood, channel coding is considered a solution for increasing the efficiency and overcoming inter-symbol interference in wireless communications. Simulations are performed by using 50 MHz bandwidth at Ultra-Wideband frequencies (3.10–10.60 GHz). Optimal channel coding (Turbo codes, Convolutional codes, with the help of polar codes) improves data transmission performance over the in vivo channel in this research. Moreover, the results reveal that turbo codes outperform polar and convolutional codes in terms of bit error rate. Other approaches perform similarly when the information block length is increased. The simulation in this work indicates that the in vivo channel shows less performance than the Rayleigh channel due to the dense structure of the human body (flesh, skins, blood, bones, muscles, and fat).

Published
2022

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