Internet of Things (IoT) technology has attracted much attention in recent years for its potential to alleviate the strain on healthcare systems caused by an aging population and a rise in chronic illness. Standardization is a key issue limiting progress in this area, and thus this paper proposes a standard model for application in future IoT healthcare systems. This survey paper then presents the state-of-the-art research relating to each area of the model, evaluating their strengths, weaknesses, and overall suitability for a wearable IoT healthcare system. Challenges that healthcare IoT faces including security, privacy, wearability, and low-power operation are presented, and recommendations are made for future research directions.

Internet of Things for Smart Healthcare: Technologies, Challenges, and Opportunities

Emerging artificial intelligence methods in structural engineering

Facebook recently deployed Facebook Messages, its first ever user-facing application built on the Apache Hadoop platform. Apache HBase is a database-like layer built on Hadoop designed to support billions of messages per day. This paper describes the reasons why Facebook chose Hadoop and HBase over other systems such as Apache Cassandra and Voldemort and discusses the applicationBs requirements for consistency, availability, partition tolerance, data model and scalability. I explore the enhancements made to Hadoop to make it a more effective realtime system, the tradeoffs we made while configuring the system, and how this solution has significant advantages over the sharded MySQL database scheme used in other applications at Facebook and many other web-scalecompanies. I discuss the motivations behind my design choices, the challenges that we face in day-to-day operations, and future capabilities and improvements still under development.I offer these observations on the deployment as a model for other companies who are contemplating a Hadoop-based solution over traditional sharded RDBMS deployments.

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Apache Hadoop Goes Realtime at Facebook

The modern manufacturing industry is investing in new technologies such as the Internet of Things (IoT), big data analytics, cloud computing and cybersecurity to cope with system complexity, increa...

The Internet of Things for Smart Manufacturing: A Review

Internet of Things: A survey of enabling technologies in healthcare and its applications

Internet of Things (IoT) has recently received a great attention due to its potential and capacity to be integrated into any complex system. As a result of rapid development of sensing technologies such as radio-frequency identification, sensors and the convergence of information technologies such as wireless communication and Internet, IoT is emerging as an important technology for monitoring systems. This paper reviews and introduces a framework for structural health monitoring (SHM) using IoT technologies on intelligent and reliable monitoring. Specifically, technologies involved in IoT and SHM system implementation as well as data routing strategy in IoT environment are presented. As the amount of data generated by sensing devices are voluminous and faster than ever, big data solutions are introduced to deal with the complex and large amount of data collected from sensors installed on structures.

Structural Health Monitoring Framework Based on Internet of Things: A Survey

Electromagnetic Acoustic Transducer (EMAT) has become one of the fastest-growing solutions for pipeline weld inspection over the past decade due to its non-contact advantage. One primary problem of EMAT is that it has relatively lower energy transition efficiency compared to widely used piezoelectric transducers, coupled with the effect of lift-off and the non-uniformity issue of welding material, the Signal-to-Noise Ratio (SNR) can be significantly restricted. This brings great difficulty in interpreting the EMAT signal measured from pipeline girth welds. To overcome this challenge, this paper presents a deep learning-based ultrasonic pattern recognition method to identify the pipeline girth weld cracking automatically. The proposed method utilizes a deep Convolution Neural Network (CNN) integrated with a pre-trained Support Vector Machine (SVM) classifier to extract the high-level features from the time-frequency representation of A-scan signals measured by bulk-wave EMAT and classify these signals into defective or non-defective groups. To validate the proposed method, a set of experiments is carried out to classify A-scan signals measured from the girth welds of an ex-service type 813-X70 gas pipeline. A comparative investigation is also undertaken to demonstrate the superiority of the proposed method against the conventional ultrasonic pattern recognition methods for evaluation.

A Deep Learning-Based Ultrasonic Pattern Recognition Method for Inspecting Girth Weld Cracking of Gas Pipeline

The invention discloses the measurement method of the equivalent electrical parameter and the resonant frequency of a piezoelectric resonator. The method is characterized by measuring the resonant frequency and anti-resonant frequency through measuring the phase-frequency curve of the piezoelectric resonator; and then, measuring the slopes of the phase-frequency curve at the resonant frequency andthe anti-resonant frequency, and calculating a resonant angular frequency and an anti-resonant angular frequency, and using the nonlinear equation set to solve the equivalent electrical parameter ofthe piezoelectric resonator.

Measurement method of equivalent electrical parameter and resonant frequency of piezoelectric resonator

In a multi-temporal (MT) interferometric synthetic aperture radar (InSAR) algorithm, if the Itoh condition is not met, the closure phase of zero or near zero is no longer true. To resolve it, we apply a phase gradient network (PGNet) to assess a cell's phase gradient not subjected to the Itoh condition. The PGNet has been trained over rugged terrain in Mao County (32.06°N, 103.65°E), Sichuan Province, China. Then, the network was applied to predict phase gradients of three differential interferograms, i.e., 18 July-30 July, 30 July-11 August, and 11 August-18 July of 2017, Jiuzhaigou, Sichuan. A Ms (surface-wave magnitude) 7.0 earthquake (33.20° N, 103.82° E) occurred on 8 August 2017 in Jiuzhaigou. The PGNet has 1,175,000 cells with 0 for the horizontal closure phase gradient and 1,149,000 for the vertical closure phase gradient. (n = 1,413,120) For the Itoh condition-based method, the cell numbers are 984,800 horizontally and 1,094,000 vertically, less than those after the PGNet. Thus, the PGNet is more effective in minimizing the closure phase than the Itoh condition, improving an MT-InSAR algorithm's performance.

Application of the PGNet to Minimize Closure Phases in a Multi-Temporal InSAR Algorithm

In this paper, an innovative Compressed Sensing (CS) framework has been proposed with the purpose of providing a sub-Nyquist sampling mechanism to Electromagnetic Acoustic Transducer (EMAT) testing. Particularly, a novel sparse representation method based on dictionary learning was introduced into the CS framework aimed at a more efficient signal sparse representation and more accurate signal reconstruction. The proposed CS framework was examined through a process of thickness measurement of an 813-X70 pipeline. Experimental results indicated that the thickness feature of the specimen could be precisely extracted from the recovered data with 20% of compression ratio compared with conventional sampling method. It demonstrates that the CS theorem can significantly reduce the required sampling rate of EMAT testing system without loss of signal resolution, thus improves the overall testing efficiency.

Yan Yan

Papers

Structural Health Monitoring Framework Based on Internet of Things: A Survey

A Deep Learning-Based Ultrasonic Pattern Recognition Method for Inspecting Girth Weld Cracking of Gas Pipeline

Measurement method of equivalent electrical parameter and resonant frequency of piezoelectric resonator

Application of the PGNet to Minimize Closure Phases in a Multi-Temporal InSAR Algorithm

Application of Compressed Sensing on Eletromagetic Transducer Testing