Globally, the aging and the lifestyle lead to rabidly increment of the number of type two diabetes (T2D) patients. Critically, T2D considers as one of the most challenging healthcare issue. Importantly, physical activity (PA) plays a vital role of improving glycemic control T2D. However, daily monitoring of T2D using wearable devices/ sensors have a crucial role to monitor glucose levels in the blood. Nowadays, daily physical activity (PA) and exercises have been used to manage T2D. The main contribution of the proposed study is to review the literature about managing and monitoring T2D with daily PA through wearable devices and sensors. Finally, challenges and future trends are also highlighted.

/pdf/a-review-of-wearable-sensors-based-monitoring-with-daily-47xtk2zjdc.pdf

A review of wearable sensors based monitoring with daily physical activity to manage type 2 diabetes

Nowadays, induction motor (IM) is extensively used in industry, including mechanical and electrical applications However, three main types of IM faults have been discussed in the literature, bearing, stator, and rotor Importantly, stator and rotor faults represent approximately 50% Traditional condition monitoring (CM) and fault diagnosis (FD) methods require a high processing cost and much experience knowledge To tackle this challenge, artificial intelligent (AI) based CM and FD techniques are extensively developed However, there have been many review research papers for intelligent CM and FD machine learning methods of rolling elements bearings of IM in the literature Whereas there is a lack in the literature, and there are not many review papers for both stator and rotor intelligent CM and FD Thus, the proposed study's main contribution is in reviewing the CM and FD of IM, especially for the stator and the rotor, based on AI methods The paper also provides discussions on the main challenges and possible future works

/pdf/a-review-of-intelligent-methods-for-condition-monitoring-and-20ln8cm2j3.pdf

A review of intelligent methods for condition monitoring and fault diagnosis of stator and rotor faults of induction machines

Frei-Chen bases based lossy digital image compression technique

Condition monitoring (CM) and fault diagnosis and detection (FDD) of rotating machines (RM) have an important role in today's industrial world. Importantly, protecting valuable machines from a sudden breakdown is vital to improve system availability and to reduce maintenance costs. Undoubtedly, wind turbines consider being a key element of sustainable and renewable energy. The Gearboxes are considered to be one of the vital and main parts of electromechanical equipment. Thus, many kinds of research and engineers aim to develop fault diagnostic techniques by considering some critical points such as diagnostic performance, accuracy, continuity, etc. Image processing is a two-dimensional space analysis, and it is simple to obtain data using this type of analysis. In addition, image analysis techniques are non-contact, non-intrusive, and cheap. The proposed study's main contribution is to review image processing-based methods for CM and FDD of two types of RMs; wind turbines and gearboxes. Challenges and future trends are highlighted.

A Review of Condition Monitoring and Fault Diagnosis and Detection of Rotating Machinery Based on Image Aspects

Managing, diagnosis, prognosis, continuous monitoring, early detection, and preventing chronic diseases for patients and elderly people have been gained a crucial role nowadays. However, elderly people with chronic health conditions such as diabetes, cardiovascular disease, and mental diseases, need special health care. With the help of the internet of things (IoT) technologies, remote health monitoring (RHM) helps patients, caregivers, and countries for improving healthcare services, such as medical files services, mobile healthcare (mhealth), telemedicine services, and sensing technology. Moreover, RHM aims to reduce hospitalized demands and costs. The main contribution of the proposed study is to review RHM studies based on IoT technologies. Moreover, the challenges and possible future trends of RMH are highlighted.

/pdf/a-review-of-remote-health-monitoring-based-on-internet-of-4q9s25xgwx.pdf

A review of remote health monitoring based on internet of things

Lossy Digital Image Compression Technique Using Run-Length Encoding and Frei-Chen Basis، للحصول على النص الكامل يرجى زيارة مكتبة الحسين بن طلال في جامعة اليرموك او زيارة موقعها الالكتروني

Al_Khassaweneh

Papers

Lossy Digital Image Compression Technique Using Run-Length Encoding and Frei-Chen Basis