A Systematic Review of Convolutional Neural Network-Based Structural Condition Assessment Techniques

With recent advances in non-contact sensing technology such as cameras, unmanned aerial and ground vehicles, the structural health monitoring (SHM) community has witnessed a prominent growth in deep learning-based condition assessment techniques of structural systems. These deep learning methods rely primarily on convolutional neural networks (CNNs). The CNN networks are trained using a large number of datasets for various types of damage and anomaly detection and post-disaster reconnaissance. The trained networks are then utilized to analyze newer data to detect the type and severity of the damage, enhancing the capabilities of non-contact sensors in developing autonomous SHM systems. In recent years, a broad range of CNN architectures has been developed by researchers to accommodate the extent of lighting and weather conditions, the quality of images, the amount of background and foreground noise, and multiclass damage in the structures. This paper presents a detailed literature review of existing CNN-based techniques in the context of infrastructure monitoring and maintenance. The review is categorized into multiple classes depending on the specific application and development of CNNs applied to data obtained from a wide range of structures. The challenges and limitations of the existing literature are discussed in detail at the end, followed by a brief conclusion on potential future research directions of CNN in structural condition assessment

Bridge damage identification using deep learning-based Convolutional Neural Networks (CNNs)

In this paper, a novel method is proposed based on a windowed-one-dimensional convolutional neural network for multiclass damage detection using acceleration responses. The data is pre-processed and augmented by extracting samples of windows of the original acceleration time series. 1D CNN is developed to classify the signals in multiple classes. The damage is detected if the predicted classification is one of the indicated damage levels. The damage is quantified using the predicted class probabilities. Various signals from the accelerometers are provided as input to the 1D CNN model, and the resulting class probabilities are used to identify the location of the damage. The proposed method is validated using Z24 bridge benchmark data for multiclass classification for two damage scenarios. The results show that the proposed 1D CNN method performs with superior accuracy for severe damage cases and works well with different types of damage types

Compressive Sensing with Low-Power Transfer and Accurate Reconstruction of EEG Signals

Tele-monitoring of EEG in WBAN is essential as EEG is the most powerful physiological parameters to diagnose any neurological disorder. Generally, EEG signal needs to record for longer periods which results in a large volume of data leading to huge storage and communication bandwidth requirements in WBAN. Moreover, WBAN sensor nodes are battery operated which consumes lots of energy. The aim of this research is, therefore, low power transmission of EEG signal over WBAN and its accurate reconstruction at the receiver to enable continuous online-monitoring of EEG and real time feedback to the patients from the medical experts. To reduce data rate and consequently reduce power consumption, compressive sensing (CS) may be employed prior to transmission. Nonetheless, for EEG signals, the accuracy of reconstruction of the signal with CS depends on a suitable dictionary in which the signal is sparse. As the EEG signal is not sparse in either time or frequency domain, identifying an appropriate dictionary is paramount. There are a plethora of choices for the dictionary to be used. Wavelet bases are of interest due to the availability of associated systems and methods. However, the attributes of wavelet bases that can lead to good quality of reconstruction are not well understood. For the first time in this study, it is demonstrated that in selecting wavelet dictionaries, the incoherence with the sensing matrix and the number of vanishing moments of the dictionary should be considered at the same time. In this research, a framework is proposed for the selection of an appropriate wavelet dictionary for EEG signal which is used in tandem with sparse binary matrix (SBM) as the sensing matrix and ST-SBL method as the reconstruction algorithm. Beylkin (highly incoherent with SBM and relatively high number of vanishing moments) is identified as the best dictionary to be used amongst the dictionaries are evaluated in this thesis. The power requirements for the proposed framework are also quantified using a power model. The outcomes will assist to realize the computational complexity and online implementation requirements of CS for transmitting EEG in WBAN. The proposed approach facilitates the energy savings budget well into the microwatts range, ensuring a significant savings of battery life and overall system’s power. The study is intended to create a strong base for the use of EEG in the high-accuracy and low-power based biomedical applications in WBAN

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

A Deep Learning Approach for Vital Signs Compression and Energy Efficient Delivery in mhealth Systems

© 2013 IEEE. Due to the increasing number of chronic disease patients, continuous health monitoring has become the top priority for health-care providers and has posed a major stimulus for the development of scalable and energy efficient mobile health systems. Collected data in such systems are highly critical and can be affected by wireless network conditions, which in return, motivates the need for a preprocessing stage that optimizes data delivery in an adaptive manner with respect to network dynamics. We present in this paper adaptive single and multiple modality data compression schemes based on deep learning approach, which consider acquired data characteristics and network dynamics for providing energy efficient data delivery. Results indicate that: 1) the proposed adaptive single modality compression scheme outperforms conventional compression methods by 13.24% and 43.75% reductions in distortion and processing time, respectively; 2) the proposed adaptive multiple modality compression further decreases the distortion by 3.71% and 72.37% when compared with the proposed single modality scheme and conventional methods through leveraging inter-modality correlations; and 3) adaptive multiple modality compression demonstrates its efficiency in terms of energy consumption, computational complexity, and responding to different network states. Hence, our approach is suitable for mobile health applications (mHealth), where the smart preprocessing of vital signs can enhance energy consumption, reduce storage, and cut down transmission delays to the mHealth cloud.This work was supported by NPRP through the Qatar National Research Fund (a member of the Qatar Foundation) under Grant 7-684-1-127

Data Compression in Multi-Hop Large-Scale Wireless Sensor Networks

Data collection from a multi-hop large-scale outdoor WSN deployment for environmental monitoring is full of challenges due to the severe resource constraints on small battery-operated motes (e.g., bandwidth, memory, power, and computing capacity) and the highly dynamic wireless link conditions in an outdoor communication environment. We present a compressed sensing approach which can recover the sensing data at the sink with good accuracy when very few packets are collected, thus leading to a significant reduction of the network traffic and an extension of the WSN lifetime. Interplaying with the dynamic WSN routing topology, the proposed approach is efficient and simple to implement on the resource-constrained motes without motes storing of a part of random measurement matrix, as opposed to other existing compressed sensing based schemes. We provide a systematic method via machine learning to find a suitable representation basis, for the given WSN deployment and data field, which is both sparse and incoherent with the measurement matrix in the compressed sensing. We validate our approach and evaluate its performance using our real-world multi-hop WSN testbed deployment in situ in collecting the humidity and soil moisture data. The results show that our approach significantly outperforms three other compressed sensing based algorithms regarding the data recovery accuracy for the entire WSN observation field under drastically reduced communication costs. For some WSN scenarios, compressed sensing may not be applicable. Therefore we also design a generalized predictive coding framework for unified lossless and lossy data compression. In addition, we devise a novel algorithm for lossless compression to significantly improve data compression performance for variouSs data collections and applications in WSNs. Rigorous simulations show our proposed framework and compression algorithm outperform several recent popular compression algorithms for wireless sensor networks such as LEC, S-LZW and LTC using various real-world sensor data sets, demonstrating the merit of the proposed framework for unified temporal lossless and lossy data compression in WSNs

Graph Signal Processing: Overview, Challenges and Applications

Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing. We then summarize recent developments in developing basic GSP tools, including methods for sampling, filtering or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning. We finish by providing a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE

Intelligent Feature Extraction, Data Fusion and Detection of Concrete Bridge Cracks: Current Development and Challenges

As a common appearance defect of concrete bridges, cracks are important indices for bridge structure health assessment. Although there has been much research on crack identification, research on the evolution mechanism of bridge cracks is still far from practical applications. In this paper, the state-of-the-art research on intelligent theories and methodologies for intelligent feature extraction, data fusion and crack detection based on data-driven approaches is comprehensively reviewed. The research is discussed from three aspects: the feature extraction level of the multimodal parameters of bridge cracks, the description level and the diagnosis level of the bridge crack damage states. We focus on previous research concerning the quantitative characterization problems of multimodal parameters of bridge cracks and their implementation in crack identification, while highlighting some of their major drawbacks. In addition, the current challenges and potential future research directions are discussed.Comment: Published at Intelligence & Robotics; Its copyright belongs to author