Recent Submissions

  • Recent Advancement of Interdigital Sensor for Nitrate Monitoring in Water

    Alahi, Md Eshrat E.; Hui, Yun; Tina, Fahmida Wazed; Akhter, Fowzia; Nag, Anindya; Wu, Tianzhun; Mukhopadhyay, S. C. (Springer Nature, 2021-02-16) [Book Chapter]
    Water contamination is a significant problem in all over the world, and it is crucial to monitor the contaminating nutrients regularly for keeping the groundwater or drinking water safe. The nitrate ion has a remarkable impact on human health and the environment, and excessive use of this ion might damage the ecological system and the natural environments. Nitrate ions can be detected through various laboratory-based methods or in-situ sensor-based methods to develop a monitoring system. But for the last few years, the Interdigital sensor is used to detect the nitrate ions due to their reasonable fabrication costs and secure sensing mechanism. Some such sensors have high sensitivity with a reasonable limit of detection (LOD). Others might have a reasonable sensitivity with the reduced cost, where the proposed detection method uses a sensor-based portable sensing system. This chapter discusses the different working principles and fabrication methods of the Interdigital sensor for nitrate ions detection in water.
  • Fabrication of Interdigitated Sensors: Issues and Resolution

    Nag, Anindya; Mukhopadhyay, S. C.; Gooneratne, C. P. (Springer Nature, 2021-02-16) [Book Chapter]
    The design and implementation of interdigital sensors in the sensing world have been pivotal in the last few decades. Due to the advantages imparted by these designs, researchers all over the world have practiced developing prototypes based on them. The variation in the physical and material specificities of the interdigital sensors varies with their individual applications. In order to develop a range of sensing prototypes for biomedical, industrial and environmental applications, the optimization of the fabrication of these interdigitated electrodes has been done over the years. This chapter highlights some of the research works done on their design, development and implementation, based on a range of fabrication processes. Some of the issues faced by each of these fabrication techniques, as well as the formed prototypes, have also been discussed in the chapter. Finally, some of the possible remedies to address the existing issues have been elucidated, along with a brief market survey that has been mentioned at the end of the chapter.
  • Theory and practice of orbital angular momentum and beyond

    Trichili, Abderrahmen; Cox, Mitchell A.; Perez-Garcia, Benjamin; Ooi, Boon S.; Alouini, Mohamed-Slim (Accepted by Wiley, 2021-02) [Book Chapter]
    Nearly three decades since its discovery, orbital angular momentum (OAM) has proven to be highly versatile for a wide range of applications. It is an indispensable tool in quantum optics, has made a significant impact in optical tweezing, enabled higher contrast and more detailed imaging, and offers a convenient way to harness the space degree of freedom in telecommunications. In this paper, we present a review of a wide range of applications of OAM as well as describing the creation and detection of OAM modes, with a focus on the use of OAM in communications. In addition, we detail various similar higher-order optical modes, such as vector vortex modes, and provide an introduction to the use of OAM in quantum optics, pitched for readers new to the field.
  • RISCuer: a reliable multi-UAV search and rescue testbed

    Abdelkader, Mohamed; Fiaz, Usman A.; Toumi, Noureddine; Mabrok, Mohamed A.; Shamma, Jeff S. (Elsevier BV, 2021-01-29) [Book Chapter]
    We present the Robotics Intelligent Systems & Control (RISC) Lab multiagent testbed for reliable search and rescue and aerial transport in outdoor environments. The system consists of a team of three multirotor unmanned aerial vehicles (UAVs), which are capable of autonomously searching, picking up, and transporting randomly distributed objects in an outdoor field. The method involves vision-based object detection and localization, passive aerial grasping with our novel design, GPS-based UAV navigation, and safe release of the objects at the drop zone. Our cooperative strategy ensures safe spatial separation between UAVs at all times and we prevent any conflicts at the drop zone using communication-enabled consensus. All computation is performed onboard each UAV. We describe the complete software and hardware architecture for the system and demonstrate its reliable performance using comprehensive outdoor experiments and by comparing our results with some recent, similar works.
  • Co-creation of Pediatric Physical Therapy Environments: Humanistic Co-design Process

    Alomrani, Hadeel; Aljabr, Rana; Almansoury, Rneem; Alsinan, Abduallah (Springer Nature, 2021-01-26) [Book Chapter]
    In this paper, we describe the co-design process for creating interactive pediatric physical therapy (PT) spaces by applying gamification techniques, with a focus on helping children with gross motor delay which impact their mobility, independence, quality of movement, and balance coordination. The co-design of activities for age-appropriate gross motor skills was conducted through iterative cycles of co-creation with physical therapists and occupational therapists. Consequently, we propose exergaming interactive user interfaces, designed in a way to support improving a child’s gross motor performance. The design involves personalized illustrative graphic characters drawn on the room’s surfaces. The efficacy of such immersive space will be examined in experimental user acceptance studies and the design implications will be discussed.
  • Backflash Light as a Security Vulnerability in Quantum Key Distribution Systems

    Vybornyi, Ivan; Trichili, Abderrahmen; Alouini, Mohamed-Slim (Springer Nature, 2021-01-25) [Book Chapter]
    Based on the fundamental rules of quantum mechanics, two communicating parties can generate and share a secret random key that can be used to encrypt and decrypt messages sent over an insecure channel. This process is known as quantum key distribution (QKD). Contrary to classical encryption schemes, the security of a QKD system does not depend on the computational complexity of specific mathematical problems. However, QKD systems can be subject to different kinds of attacks, exploiting engineering, and technical imperfections of the components forming the systems. Here, we review the security vulnerabilities of QKD. We mainly focus on a particular effect known as backflash light, which can be a source of eavesdropping attacks. We equally highlight the method for quantifying backflash emission and the different ways to mitigate this effect.
  • Energy Geoscience and Engineering

    Santamarina, Carlos; Rached, Rached (Springer Nature, 2021-01-15) [Book Chapter]
    Quality of life is strongly correlated with power consumption. The geo-disciplines have a crucial role to play in the energy challenge by contributing solutions to all kind of energy resources from resource recovery to energy and waste storage. Energy geoengineering requires a broad understanding of physical processes (sediments, fractured rocks and complex multiphase fluids), coupled phenomena, constitutive models for extreme conditions, and wide-ranging spatial and time scales. Numerical methods are critical for the analysis, design, and optimal operation of energy geosystems under both short and long-term conditions. Furthermore, they allow “numerical experiments” at temporal and spatial scales that are unattainable in the laboratory. Yet, computer power can provide a false sense of reality and unjustified confidence; simulations face uncertainties related to the validation of complex multi-physics codes, limited data, excessive numbers of degrees of freedom, ill-conditioning, and uncertain model parameters. Dimensional analyses help identify the governing processes and allow for simpler and more reliable simulations. Educational programs must evolve to address the knowledge needs in energy geoscience and engineering.
  • Linear latent variable regression (LVR)-based process monitoring

    Harrou, Fouzi; Sun, Ying; Hering, Amanda S.; Madakyaru, Muddu; Dairi, Abdelkader (Elsevier BV, 2021) [Book Chapter]
    Fast-paced developments in data acquisition, instrumentation technology and the era of the Internet-of-Things have resulted in large amounts of data produced by modern industrial processes. The ability to extract useful information from these multivariate datasets has vital benefits that could be utilized in process monitoring. In the absence of a physics-based process model, data-driven approaches such as latent variable modeling have proved to be practical for process monitoring over the past four decades. The aim of this chapter is to review and show the challenges in multivariate process monitoring based on linear models. Specifically, after presenting the limitations of the full-rank regression model, we provide a brief overview of linear latent variable models such as principal component analysis, principal component regression, and partial least squares regression. To deal with dynamic systems, we present dynamic extensions of these methods that capture both static and dynamic features in multivariate processes. We then provide a brief overview of univariate monitoring schemes, such as exponentially-weighted moving average and cumulative sum and generalized likelihood ratio monitoring schemes and their multivariate counterparts. To apply such tools to multivariate data, we employ appropriate multivariate dimension-reduction techniques according to the features of a process, and we use monitoring schemes to monitor more informative variables in a lower dimension. Next, we aim to identify which process variables contribute to abnormal change; conventional contribution plots and radial visualization tool are briefed. Lastly, the effectiveness of the presented inferential modeling techniques is assessed using simulated data. We also present a study on monitoring influent measurements at a water resource recovery facility. Finally, we discuss limitations of the presented monitoring approaches and give some possible directions to rectify these limitations.
  • Modeling air pollution by atmospheric desert

    Lelieveld, Jos; Abdelkader, Mohamed; Astitha, Marina; Karydis, Vlassis A.; Klingmüller, Klaus (Elsevier BV, 2021) [Book Chapter]
    High concentrations of aeolian dust affect the air quality and climate in large regions across Northern Africa, the Middle East, and parts of Asia. To assess the environmental impacts, numerical models have been developed that include mineral dust emissions, atmospheric transport and chemistry, and deposition processes. Since the dust can disperse across continents and oceans, there is a need to model a large geographical area. Here we present a state-of-the-art global atmospheric chemistry–climate model, with detailed representations of these processes. One unique model feature is the chemical interaction of dust with air pollution (chemical aging), which alters the microphysics of particles relevant for their atmospheric lifetime, e.g., the hygroscopic growth behavior, optical properties, and aerosol–cloud interactions, thus influencing the hydrologic cycle and climate. Based on recent developments and published results, we present a comparison of model calculations with satellite and ground-based remote sensing data as well as surface observations of dust concentrations and deposition. The model results are used to evaluate the consequences of aeolian dust for climate and public health.
  • Introduction

    Harrou, Fouzi; Sun, Ying; Hering, Amanda S.; Madakyaru, Muddu; Dairi, Abdelkader (Elsevier BV, 2021) [Book Chapter]
    With today's competitive automation environment, demands for efficiency, safety, and high productivity are continuously increasing. Thus, process monitoring is vital for maintaining the desired process performance and specifications. Process monitoring aims to detect potential anomalies that can occur in a monitored process and identify their potential sources. This chapter provides an overview of process monitoring methods. To begin, we present the motivation for using process monitoring, followed by an introduction and a reminder of some of the key definitions, fundamental concepts, and terminology that are used throughout this chapter. We also briefly explain the distinction between different types of faults, such as drift, abrupt, and intermittent faults. In the following section, we discuss the different monitoring methods including model-, knowledge-, and data-based techniques. Finally, we describe the most commonly used metrics for the evaluation of the performance of the different fault detection approaches.
  • Unsupervised recurrent deep learning scheme for process monitoring

    Harrou, Fouzi; Sun, Ying; Hering, Amanda S.; Madakyaru, Muddu; Dairi, Abdelkader (Elsevier BV, 2021) [Book Chapter]
    Precisely detecting anomalies in process monitoring is beneficial to enhance the operation of the monitored process by avoiding catastrophic failures and reducing maintenance costs. Unsupervised deep learning techniques are increasingly popular because of their capacity to uncover relevant information from large and complex datasets without using labeled data. In this chapter, we review and evaluate the detection performance of recurrent neural networks (RNNs)-based approaches based on a multivariate time series. RNNs are a powerful tool to appropriately model temporal dependencies in multivariate time series data. We first offer a brief overview of RNNs, from the simplest RNNs with no memory states, to sophisticated architectures with several gates and memory components. Particularly, we focus on those that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a gated recurrent unit (GRU). We then present hybrid deep learning models that integrate the desirable features of RNNs and LSTM, which are capable of approximating complex distributions of deep belief networks and restricted Boltzmann machines. We then apply these models with numerous clustering algorithms for uncovering anomalies. We finally demonstrate these methods on real measurements of effluents from a coastal municipal wastewater treatment plant in Saudi Arabia.
  • Nonlinear latent variable regression methods

    Harrou, Fouzi; Sun, Ying; Hering, Amanda S.; Madakyaru, Muddu; Dairi, Abdelkader (Elsevier BV, 2021) [Book Chapter]
    Detecting anomalies is crucially important for improving the operation, reliability, and profitability of complex industrial processes. Traditional linear data-driven methods, such as the principal component analysis (PCA) and partial least squares (PLS) method, are extensively exploited for detecting anomalies in multivariate correlated processes. Since most of the data observed in practical applications are innately nonlinear, the development of models able to learn such nonlinearity are indispensable. In this chapter, in order to handle nonlinearity, we use nonlinear latent variable regression (LVR) modeling methods, which are powerful tools for processing nonlinearities. First, we use nonlinear functions using polynomials an adaptive network-based fuzzy-inference system as an inner model of the LVR model (i.e., nonlinear relation between latent variables and output). We then offer a brief overview of nonlinear LVR-based monitoring approaches and how they can be used for anomaly detection. We also present an alternative for dealing with nonlinearities in-process data by using kernel PCA, which captures the nonlinear features in high-dimensional feature spaces via nonlinear kernel functions. Lastly, the methods presented are applied to simulated synthetic data, plug flow reactor data, and real data from a wastewater treatment plant located in Saudi Arabia.
  • Multiscale latent variable regression-based process monitoring methods

    Harrou, Fouzi; Sun, Ying; Hering, Amanda S.; Madakyaru, Muddu; Dairi, Abdelkader (Elsevier BV, 2021) [Book Chapter]
    Data acquired from industrial processes, usually via sensors, are generally noisy, correlated in time and nonstationary; this makes the implementation of the monitoring process difficult, as most techniques are designed for Gaussian and uncorrelated observations. As conventional monitoring methods, their efficiency may be significantly affected by typical uncertainties in industrial processes. Assumptions of Gaussianity, dependence in time, and stationarity are typically not verified in industrial processes. These properties make wavelet-based fault detection approaches especially appropriate. Wavelet methods are also helpful when the characteristics of the fault are unknown. This chapter discusses wavelet-based monitoring approaches that are flexible and designed with fewer structural assumptions. In this chapter, we present a brief overview of wavelets and their desirable characteristics, as well as the discrete wavelet transform. We then assess the effect of violating these assumptions (in addition to the effect of noise levels), based on the performances of the univariate monitoring methods, provide an overview of the univariate wavelet-based technique. And then discuss and illustrate the wavelet-based multivariate extension of LVR methods. At the end of the chapter, the methods are demonstrated on distillation column data.
  • Fault isolation

    Harrou, Fouzi; Sun, Ying; Hering, Amanda S.; Madakyaru, Muddu; Dairi, Abdelkader (Elsevier BV, 2021) [Book Chapter]
    When multivariate observations are monitored, detection of a fault is merely the first step of the process. Knowledge of the existence of the fault is not particularly informative in high-dimensional settings. Thus, the field of fault isolation has developed to identify those variables that have been affected by the fault. Variables affected by a fault are termed shifted variables, regardless of the nature of the fault. In this chapter, we will first present some pitfalls to be avoided when performing fault isolation and also illustrate the importance of isolating important variables associated with the fault, which can also improve the speed of fault detection. Traditional approaches to removing variables and recalculating monitoring statistics, such as and Q, to isolate the variables will be presented. Next, more modern approaches using variable selection techniques, which typically involve penalized regression, will be described. Both prior approaches work in unsupervised settings where the types of faults that will be observed cannot be anticipated in advance. In settings where a process is very well-studied, a catalogue of data associated with multiple types of faults may exist, so supervised classification methods may be used. We close with some metrics that may be used to assess the performance of fault isolation methods along with two detailed case studies for illustration.
  • Unsupervised deep learning-based process monitoring methods

    Harrou, Fouzi; Sun, Ying; Hering, Amanda S.; Madakyaru, Muddu; Dairi, Abdelkader (Elsevier BV, 2021) [Book Chapter]
    In this chapter, first we provide an overview of some of the shallow-machine learning approaches used in anomaly detection and outlier detection in data mining, namely data clustering techniques. Then, we give a brief description of two frequently used unsupervised machine learning algorithms for one-class classification or detection, namely one-class SVM and support vector data description (SVDD). Particular attention is paid to deep learning models. We present the commonly used deep learning models based on autoencoders (Variational Autoencoder, Denoising Autoencoder, and Contrastive Autoencoder), probabilistic models (Boltzmann Machine and Restricted Boltzmann Machine) and deep neural models (Deep Belief Network and Deep Boltzmann Machine), and we show their capacity and limitations. Finally, we merge the desirable properties of shallow learning approaches, such as one-class support vector machine and k-nearest neighbors and unsupervised deep-learning approaches to develop more sophisticated and efficient monitoring techniques.
  • Case studies

    Harrou, Fouzi; Sun, Ying; Hering, Amanda S.; Madakyaru, Muddu; Dairi, Abdelkader (Elsevier BV, 2021) [Book Chapter]
    Addressing anomaly detection and attribution is essential to promptly detect abnormalities, and it aids the decision-making of operators, allowing them to better optimize performance, take corrective actions, and maintain downstream processes. Recently, deep learning models have developed rapidly, especially in terms of their learning capabilities. In this chapter, we propose a novel hybrid deep-learning-based anomaly detection method. In particular, we focus on the benefits of deep learning models due to their greedy learning features and the sensitivity of clustering approaches to reveal anomalies in the monitoring process. In this chapter, we discuss and present applications of some deep-learning-based monitoring methods. We apply the developed approaches to monitor many processes, such as detection of obstacles in driving environments for autonomous robots and vehicles, monitoring of wastewater treatment plants, and detection of ozone pollution.
  • Conclusion and further research directions

    Harrou, Fouzi; Sun, Ying; Hering, Amanda S.; Madakyaru, Muddu; Dairi, Abdelkader (Elsevier BV, 2021) [Book Chapter]
    Developing efficient anomaly detection and isolation schemes that offer early detection of potential anomalies in the monitored process and identify and isolate the source of the detected anomalies is indispensable to monitor process operations in an efficient manner. This will further enhance availability, operation reliability, and profitability of monitored processes and reduce manpower costs. This book is mainly devoted to data-driven fault detection and isolation methods based on multivariate statistical monitoring techniques and deep learning methods. In this chapter, conclusions and further research directions are drawn.
  • Nanocomposite sensors for smart textile composites

    Nauman, Saad; Lubineau, Gilles (Elsevier BV, 2021) [Book Chapter]
    Textile composites are an emerging class of materials pulled by the emerging demand in wearable electronics. These composites use some aspect of textile technologies in their fabrication, ranging from fiber placement techniques such as weaving and knitting to various coating practices such as printing or dying. Contrary to most of structural composites that use fibers in various forms and types as reinforcement, smart textile composites mainly take advantage of exceptional properties of nanofillers, both carbonaceous and noncarbonaceous. These fillers can be used to create reinforcement network with exceptional properties even at very low filler concentrations. These fillers have not only been used to improve mechanical properties but also been incorporated for their exceptional electrical and thermal conductivities. These latter attributes have been successfully exploited for the development of smart composites capable of sensing changes in their environment.
  • Downlink resource allocations of satellite–airborne–terrestrial networks integration

    Alsharoa, Ahmad; Zedini, Emna; Alouini, Mohamed-Slim (Elsevier BV, 2021) [Book Chapter]
    This chapter studies the potential improvement in the Internet broadband of the data rate available to ground users by integrating terrestrial, airborne, and satellite stations. The goal is to establish dynamic downlink wireless services in remote or infrastructure-less areas. This integration uses satellite and high-altitude platforms (HAPs) the exosphere and stratosphere, respectively, for better altitude reuse. Hence, it offers a significant increase in scarce spectrum aggregate efficiency. However, managing resource allocation with deployment in this integrated system still faces difficulties. This chapter tackles resource management challenges by formulating and solving optimization problem to find the best HAPs’ location, access and backhaul associations, and transmit power allocation. Finally, we show how our results illustrate the advantages of the proposed scheme followed by some potential future works.
  • Simulation of Nitride Semiconductor MOVPE

    Ohkawa, Kazuhiro (Wiley, 2020-12-31) [Book Chapter]
    This article seeks to help readers understand the MOVPE growth of nitride semiconductors as a part of science. MOVPE is the abbreviation for metalorganic vapor-phase epitaxy. Therefore, the precursors used are metalorganic gases and ammonia. The precursors decompose or react with others in the gas phase. The obtained reactive molecules form semiconductor layers on substrates. Those growth reaction pathways and the polymer formation will be discussed numerically for GaN, InN, InGaN, AlN, and AlGaN in this article. For those materials, the numerical analyses of growth rate and alloy composition exhibited the qualitative and quantitative agreements with experiments. The reader can see the growth mechanism, and experts will understand the current MOVPE conditions of nitride semiconductors.

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