Real-Time Vibration-Based Bearing Fault Diagnosis Under Time-Varying Speed Conditions

Detection of rolling-element bearing faults is crucial for implementing proactive maintenance strategies and for minimizing the economic and operational consequences of unexpected failures. However, many existing techniques are developed and tested under strictly controlled conditions, limiting their adaptability to the diverse and dynamic settings encountered in practical applications. This paper presents an efficient real-time convolutional neural network (CNN) for diagnosing multiple bearing faults under various noise levels and time-varying rotational speeds. Additionally, we propose a novel Fisher-based spectral separability analysis (SSA) method to elucidate the effectiveness of the designed CNN model. We conducted experiments on both healthy bearings and bearings afflicted with inner race, outer race, and roller ball faults. The experimental results show the superiority of our model over the current state-of-the-art approach in three folds: it achieves substantial accuracy gains of up to 15.8%, it is robust to noise with high performance across various signal-to-noise ratios, and it runs in real-time with processing durations five times less than acquisition. Additionally, by using the proposed SSA technique, we offer insights into the model's performance and underscore its effectiveness in tackling real-world challenges

A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras

Social distancing is crucial to restrain the spread of diseases such as COVID-19, but complete adherence to safety guidelines is not guaranteed. Monitoring social distancing through mass surveillance is paramount to develop appropriate mitigation plans and exit strategies. Nevertheless, it is a labor-intensive task that is prone to human error and tainted with plausible breaches of privacy. This paper presents a privacy-preserving adaptive social distance estimation and crowd monitoring solution for camera surveillance systems. We develop a novel person localization strategy through pose estimation, build a privacy-preserving adaptive smoothing and tracking model to mitigate occlusions and noisy/missing measurements, compute inter-personal distances in the real-world coordinates, detect social distance infractions, and identify overcrowded regions in a scene. Performance evaluation is carried out by testing the system's ability in person detection, localization, density estimation, anomaly recognition, and high-risk areas identification. We compare the proposed system to the latest techniques and examine the performance gain delivered by the localization and smoothing/tracking algorithms. Experimental results indicate a considerable improvement, across different metrics, when utilizing the developed system. In addition, they show its potential and functionality for applications other than social distancing.Peer reviewe

DroneRF dataset : A dataset of drones for RF-based detection, classification and identification

Modern technology has pushed us into the information age, making it easier to generate and record vast quantities of new data. Datasets can help in analyzing the situation to give a better understanding, and more importantly, decision making. Consequently, datasets, and uses to which they can be put, have become increasingly valuable commodities. This article describes the DroneRF dataset: a radio frequency (RF) based dataset of drones functioning in different modes, including off, on and connected, hovering, flying, and video recording. The dataset contains recordings of RF activities, composed of 227 recorded segments collected from 3 different drones, as well as recordings of background RF activities with no drones. The data has been collected by RF receivers that intercepts the drone's communications with the flight control module. The receivers are connected to two laptops, via PCIe cables, that runs a program responsible for fetching, processing and storing the sensed RF data in a database. An example of how this dataset can be interpreted and used can be found in the related research article “RF-based drone detection and identification using deep learning approaches: an initiative towards a large open source drone database” (Al-Sa'd et al., 2019).publishedVersionPeer reviewe

Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies.

Purpose: XNAT is an informatics software platform to support imaging research, particularly in the context of large, multicentre studies of the type that are essential to validate quantitative imaging biomarkers. XNAT provides import, archiving, processing and secure distribution facilities for image and related study data. Until recently, however, modern data visualisation and annotation tools were lacking on the XNAT platform. We describe the background to, and implementation of, an integration of the Open Health Imaging Foundation (OHIF) Viewer into the XNAT environment. We explain the challenges overcome and discuss future prospects for quantitative imaging studies. Materials and methods: The OHIF Viewer adopts an approach based on the DICOM web protocol. To allow operation in an XNAT environment, a data-routing methodology was developed to overcome the mismatch between the DICOM and XNAT information models and a custom viewer panel created to allow navigation within the viewer between different XNAT projects, subjects and imaging sessions. Modifications to the development environment were made to allow developers to test new code more easily against a live XNAT instance. Major new developments focused on the creation and storage of regions-of-interest (ROIs) and included: ROI creation and editing tools for both contour- and mask-based regions; a "smart CT" paintbrush tool; the integration of NVIDIA's Artificial Intelligence Assisted Annotation (AIAA); the ability to view surface meshes, fractional segmentation maps and image overlays; and a rapid image reader tool aimed at radiologists. We have incorporated the OHIF microscopy extension and, in parallel, introduced support for microscopy session types within XNAT for the first time. Results: Integration of the OHIF Viewer within XNAT has been highly successful and numerous additional and enhanced tools have been created in a programme started in 2017 that is still ongoing. The software has been downloaded more than 3700 times during the course of the development work reported here, demonstrating the impact of the work. Conclusions: The OHIF open-source, zero-footprint web viewer has been incorporated into the XNAT platform and is now used at many institutions worldwide. Further innovations are envisaged in the near future

Time-Frequency Analysis : Application to Electroencephalogram Signal Processing

Time-frequency (TF) signal analysis and processing techniques provide adequate tools to investigate non-stationary signals such as electroencephalogram (EEG). Nonetheless, the body of TF signal analysis and EEG processing holds literature gaps that mandate remedies to ensure successful adoption. Besides, the gravitas of epileptic seizures in newborns invites further efforts to understand its EEG manifestation, spatial characteristics, and non-stationary behavior. In this thesis, we hypothesize that multi-channel non-stationary signal applications must utilize the piece-wise spline Wigner-Ville distribution (PW-WVD) to design or select best performing TF signal processing techniques and incorporate inter-sensor awareness to comprehend spatiotemporal systems. Specifically, the thesis delivers the following contributions: (1) introducing the PW-WVD as an optimal TF distribution (TFD) and proposing new TFD selection strategies; (2) designing a process and deriving novel accuracy and resolution measures to evaluate the TFD performance; (3) offering new feature domains to extract meaningful information from multi-channel EEG recordings; (4) developing a multi-sensor newborn EEG model that takes into account its temporal, spectral, and spatial characteristics; and (5) proposing an adaptive multi-user multiple-modality signal compression paradigm based on deep learning techniques. In this thesis, we divide the stated hypothesis into two parts; the TF prospect and the EEG aspect. First, we present the proposed optimal PW-WVD along with the new TFD performance evaluation process and measures. We confirm the PW-WVD optimality for arbitrary multi-component non-stationary signals with non-linear frequency and amplitude laws. Besides, we demonstrate the proposed process ability to quantify the TFD accuracy and resolution separately, identify the performance relative gain/loss among different TFDs, and its adequacy for signals with arbitrary parameters. Afterward, we introduce and validate the developed multi-sensor newborn EEG model. We report various comparisons that verify the model's ability to mimic real normal and seizure EEG patterns. Finally, we describe the deep learning-based adaptive multiple-modality signal compression scheme and illustrate its advantages in a multi-user mobile-health setup. The thesis findings shed the light on a fundamental flaw in the design process of computationally expensive TFDs; they do not maximize both the TFD accuracy and resolution. Moreover, the EEG electrical manifestation is confined to some set of electrodes; hence, channel selection is paramount and inter-sensor understanding can improve the efficacy of EEG applications. The evidence from this dissertation appears to support the candidate's hypothesis and assert the contributions' role and significance to the body of knowledge. In addition, it reveals new research questions in need of exploration and invites further investigation

A real-time multi-sensor 3D surface shape measurement system using fringe analysis

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Comparing business intelligence systems in the information environment Analytical study on Microsoft Power BI and Oracle Analytics

The study revolves around business intelligence systems and their importance, and due to the rapid changes in business environments in the data they store and produce, and the intensification of competition, the study aims to analyze and compare the two business intelligence systems that are considered. The most popular and most widely used in this era (Oracle Analytics, Microsoft Power BI), which collect, monitor, and analyze enterprise data for the enterprise. The study also identified the objectives that lie in defining business intelligence systems, their importance, and functions, and defining a number of criteria for evaluating business intelligence systems, and then comparing and analyzing these two systems and determining the availability of criteria presented in business intelligence systems in with them. The study was approved to achieve these goals on the comparative approach, and the descriptive analytical approach for its relevance to achieving the results of the study represented in achieving the system of 97% in the first criterion compared to Oracle analyzes only, it was 85%. And the second criterion of data management, Power B achieved 89%, while Oracle Analytics 82%. The third criterion was about cloud computing. Power B achieved 92%, while Oracle Analytics achieved 86%. As for the last criterion, which was related to support and customer service, Power B achieved 84%, while the other system achieved 57%. The study showed the development of Microsoft in all four criteria by a small percentage, except for the last criterion. The discrepancy was rather large, and the total percentage in all criteria for Microsoft Power B was 89%, while its counterpart was 75

Design and implementation of a multi-sensor newborn EEG seizure and background model with inter-channel field characterization

This paper presents a novel multi-sensor non-stationary EEG model; it is obtained by combining state of the art mono-sensor newborn EEG simulators, a multilayer newborn head model comprised of four homogeneous concentric spheres, a multi-sensor propagation scheme based on array processing and optical dispersion to calculate inter-channel attenuation and delay, and lastly, a multi-variable optimization paradigm using particle swarm optimization and Monte-Carlo simulations to validate the model for optimal conditions. Multi-sensor EEG of 7 newborns, comprised of seizure and background epochs, are analyzed using time-space, time-frequency, power maps and multi-sensor causality techniques. The outcomes of these methods are validated by medical insights and serve as a backbone for any assumptions and as performance benchmarks for the model to be evaluated against. The results obtained with the developed model show 85.7% averaged time-frequency correlation (which is the selected measure for similarity with real EEG)with 5.9% standard deviation, and the averaged error obtained is 34.6% with 8% standard deviation. The resulting performances indicate that the proposed model provides a suitable matching fit with real EEG in terms of their probability density function, inter-sensor attenuation and translation, and multi-sensor causality. They also demonstrate the model flexibility to generate new unseen samples by utilizing user-defined parameters, making it suitable for other relevant applications. - 2019This research was funded by Qatar Foundation grants NPRP 6-885-2-364 and NPRP 6-680-2-282 . In addition, this work includes the outcome of the first author's Master thesis written under Prof Boashash supervision [86] . The real newborn EEG data and other related materials used in this paper were provided by Prof Paul Colditz, UQCCR, as part of the grant NPRP 6-885-2-364. The authors wish to thank Dr Samir Ouelha for his technical comments and review of this paper. In addition, the authors thank Dr Abdelaziz Gdoura, Paris, France for his general feedback. Appendix AScopu

Design of an Optimal Piece-Wise Spline Wigner-Ville Distribution for TFD Performance Evaluation and Comparison

This paper proposes a new performance evaluation process for time-frequency distributions (TFD) by designing a reference optimal TFD and novel accuracy and resolution measures. The motivation comes from the need for a TFD performance evaluation method that is objective, capable of quantifying the TFD accuracy and resolution, can determine the performance difference among different TFDs, and suitable for signals with an arbitrary number of components, instantaneous frequency and amplitude. We formulate the proposed optimal TFD, namely the piece-wise spline Wigner-Ville distribution (PW-WVD), by decomposing a standard non-stationary signal model using piece-wise linear frequency modulated (LFM) basis and by exploiting the Wigner-Ville distribution optimality for LFM signals. We compare the designed PW-WVD to conventional optimal TFDs and show that the former is more suitable to serve as a reference for TFD performance evaluation. Using the PW-WVD we derive TFD accuracy and resolution measures, compare them to conventional approaches, and analyze their sensitivity to form a TFD selection criteria. We evaluate the accuracy and resolution of twelve different TFDs and develop precise TFD selection strategies with or without prior information on the signal parameters. Results indicate that the compact kernel distribution is the best performing TFD given no prior information on the signal parameters and different TFDs must be selected upon the availability of prior information.publishedVersionPeer reviewe

Multisensor Time-Frequency Signal Processing Toolbox

LogicielMTFSP software packag