Self-sensing and Self-healing ‘Smart’ Cement-based Materials – A Review of the State of the Art

The paper reviews recent research on self-sensing and self-healing cement-based materials as part of Smart Civil Engineering Infrastructures. Incorporated in Structural Health Monitoring systems, these materials are likely to play an important role in making future infrastructure robust, resilient and sustainable. Smart or intelligent cement-based materials have attracted extensive attention in the last decade or so with strong implications for improving structural durability and service life. Additions of carbon fibres, carbon nano-tubes and various nano-powders giving cement-based matrix electrical properties used for self-sensing have been known for over a decade and a half. In addition, the strong capacity of Strain-Hardening Cement-based Composites (SHCC) for autogenous healing is aided by tight crack-width control, and the application of different mineral and bio-additive based materials to accelerate autonomic self-healing of cracks have been noted with great interest. Monitoring of the durability of concrete structures is often neglected in favour of the structural safety against catastrophic failure. The present review summarizes the latest literature with a focus on identifying and documenting key innovations and field applications, and the performance based design approach to tailoring material solutions for long service life, sustainability and resiliency. Smart infrastructures including Smart Buildings and Smart Cities are being constructed at an increasing pace around the world. One of the major driving force is the explosion of low-cost Internet-enabled sensors as part of the new wave of ‘Internet of Things (IoT)’. At a fraction of the cost that is being invested into the latest IoT products for incrementally more comfortable living space, a much more resilient and sustainable infrastructure can be ensured by investing in commercialization of self-sensing and self-healing materials. For this to happen the research community need to identify the gaps between the ‘Industry Pull’ and ‘Technology Push’ first instead of inventing solutions waiting for a problem

Efficient ECG Compression and QRS Detection for E-Health Applications

Current medical screening and diagnostic procedures have shifted toward recording longer electrocardiogram (ECG) signals, which have traditionally been processed on personal computers (PCs) with high-speed multi-core processors and efficient memory processing. Battery-driven devices are now more commonly used for the same purpose and thus exploring highly efficient, low-power alternatives for local ECG signal collection and processing is essential for efficient and convenient clinical use. Several ECG compression methods have been reported in the current literature with limited discussion on the performance of the compressed and the reconstructed ECG signals in terms of the QRS complex detection accuracy. This paper proposes and evaluates different compression methods based not only on the compression ratio (CR) and percentage root-mean-square difference (PRD), but also based on the accuracy of QRS detection. In this paper, we have developed a lossy method (Methods III) and compared them to the most current lossless and lossy ECG compression methods (Method I and Method II, respectively). The proposed lossy compression method (Method III) achieves CR of 4.5×, PRD of 0.53, as well as an overall sensitivity of 99.78% and positive predictivity of 99.92% are achieved (when coupled with an existing QRS detection algorithm) on the MIT-BIH Arrhythmia database and an overall sensitivity of 99.90% and positive predictivity of 99.84% on the QT database.This work was made possible by NPRP grant #7-684-1-127 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Computational Imaging Systems for High-speed, Adaptive Sensing Applications

Driven by the advances in signal processing and ubiquitous availability of high-speed low-cost computing resources over the past decade, computational imaging has seen the growing interest. Improvements on spatial, temporal, and spectral resolutions have been made with novel designs of imaging systems and optimization methods. However, there are two limitations in computational imaging. 1), Computational imaging requires full knowledge and representation of the imaging system called the forward model to reconstruct the object of interest. This limits the applications in the systems with a parameterized unknown forward model such as range imaging systems. 2), the regularization in the optimization process incorporates strong assumptions which may not accurately reflect the a priori distribution of the object. To overcome these limitations, we propose 1) novel optimization frameworks for applying computational imaging on active and passive range imaging systems and achieve 5-10 folds improvement on temporal resolution in various range imaging systems; 2) a data-driven method for estimating the distribution of high dimensional objects and a framework of adaptive sensing for maximum information gain. The adaptive strategy with our proposed method outperforms Gaussian process-based method consistently. The work would potentially benefit high-speed 3D imaging applications such as autonomous driving and adaptive sensing applications such as low-dose adaptive computed tomography(CT)

An overview on structural health monitoring: From the current state-of-the-art to new bio-inspired sensing paradigms

In the last decades, the field of structural health monitoring (SHM) has grown exponentially. Yet, several technical constraints persist, which are preventing full realization of its potential. To upgrade current state-of-the-art technologies, researchers have started to look at nature’s creations giving rise to a new field called ‘biomimetics’, which operates across the border between living and non-living systems. The highly optimised and time-tested performance of biological assemblies keeps on inspiring the development of bio-inspired artificial counterparts that can potentially outperform conventional systems. After a critical appraisal on the current status of SHM, this paper presents a review of selected works related to neural, cochlea and immune-inspired algorithms implemented in the field of SHM, including a brief survey of the advancements of bio-inspired sensor technology for the purpose of SHM. In parallel to this engineering progress, a more in-depth understanding of the most suitable biological patterns to be transferred into multimodal SHM systems is fundamental to foster new scientific breakthroughs. Hence, grounded in the dissection of three selected human biological systems, a framework for new bio-inspired sensing paradigms aimed at guiding the identification of tailored attributes to transplant from nature to SHM is outlined.info:eu-repo/semantics/acceptedVersio

A bearing fault detection method with low-dimensional compressed measurements of vibration signal

The traditional bearing fault detection method is achieved often by sampling the bearing vibration data under the Shannon sampling theorem. Then the information of the bearing state can be extracted from the vibration data, which is used in fault detection. A long-term and continuous monitoring needs to sample and store large amounts of raw vibration signals, which will burden the data storage and transmission greatly. For this problem, a new bearing fault detection method based on compressed sensing is presented, which just needs to sample and store a small amount of compressed observation data and uses these data directly to achieve the fault detection. Firstly, an over-complete dictionary is trained, on which the vibration signals corresponded to normal state can be decomposed sparsely. Then, the bearing fault detection can be achieved based on the difference of the sparse representation errors between the compressed signals in normal state and fault state on this dictionary. The fault detection results of the proposed method with different parameters are analyzed. The effectiveness of the method is validated by the experimental tests