A novel PRFB decomposition for non-stationary time-series and image analysis

This work presents a novel perfect reconstruction filterbank decomposition (PRFBD) method for nonlinear and non-stationary time-series and image data representation and analysis. The Fourier decomposition method (FDM), an adaptive approach based on Fourier representation (FR), is shown to be a special case of the proposed PRFBD. In addition, adaptive Fourier–Gauss decomposition (FGD) based on FR and Gaussian filters, and adaptive Fourier–Butterworth decomposition (FBD) based on Butterworth filters are developed as the other special cases of the proposed PRFBD method. The proposed theory of PRFBD can decompose any signal (time-series, image, or other data) into a set of desired number of Fourier intrinsic band functions (FIBFs) that follow the amplitude-modulation and frequency-modulation (AM-FM) representations. A generic filterbank representation, where perfect reconstruction can be ensured for any given set of lowpass or highpass filters, is also presented. We performed an extensive analysis on both simulated and real-life data (COVID-19 pandemic, Earthquake and Gravitational waves) to demonstrate the efficacy of the proposed method. The resolution results in the time-frequency representation demonstrate that the proposed method is more promising than the state-of-the-art approaches. © 2023 Elsevier B.V.

FastSS: Fast and smooth segmentation of JPEG compressed printed text documents using DC and AC signal analysis.

With the surge of COVID-19 pandemic, the world is moving towards digitization and automation more than it was presumed. The Internet is becoming one of the popular mediums for communication, and multimedia (image, audio, and video) combined with data compression techniques play a pivotal role in handling a huge volume of data that is being generated on a daily basis. Developing novel algorithms for automatic analysis of compressed data without decompression is the need of the present hour. JPEG is a popular compression algorithm supported in the digital electronics world that achieves compression by dividing the whole image into non-overlapping blocks of 8 × 8 pixels, and subsequently transforming each block using Discrete Cosine Transform (DCT). This research paper proposes to carry out Fast and Smooth Segmentation (FastSS) directly in JPEG compressed printed text document images at text-line and word-level using DC and AC signals. From each 8 × 8 block, DC and AC signals are analyzed for accomplishing Fast and Smooth segmentation, and subsequently, two Faster segmentation (MFastSS) algorithms are also devised using low resolution-images generated by mapping the DC signal (DC Reduced Image) and encoded DCT (ECM Image) coefficients separately. Proposed models are tested on various JPEG compressed printed text document images created with varied space and fonts. The experimental results have demonstrated that the direct analysis of compressed streams is computationally efficient, and has achieved speed gain more than 90% when compared to uncompressed domains.

A novel PRFB decomposition for non-stationary time-series and image analysis

This work presents a novel perfect reconstruction filterbank decomposition (PRFBD) method for nonlinear and non-stationary time-series and image data representation and analysis. The Fourier decomposition method (FDM), an adaptive approach based on Fourier representation (FR), is shown to be a special case of the proposed PRFBD. In addition, adaptive Fourier–Gauss decomposition (FGD) based on FR and Gaussian filters, and adaptive Fourier–Butterworth decomposition (FBD) based on Butterworth filters are developed as the other special cases of the proposed PRFBD method. The proposed theory of PRFBD can decompose any signal (time-series, image, or other data) into a set of desired number of Fourier intrinsic band functions (FIBFs) that follow the amplitude-modulation and frequency-modulation (AM-FM) representations. A generic filterbank representation, where perfect reconstruction can be ensured for any given set of lowpass or highpass filters, is also presented. We performed an extensive analysis on both simulated and real-life data (COVID-19 pandemic, Earthquake and Gravitational waves) to demonstrate the efficacy of the proposed method. The resolution results in the time-frequency representation demonstrate that the proposed method is more promising than the state-of-the-art approaches.

A dual watermarking scheme for identity protection.

A novel dual watermarking scheme with potential applications in identity protection, media integrity maintenance and copyright protection in both electronic and printed media is presented. The proposed watermarking scheme uses the owner's signature and fingerprint as watermarks through which the ownership and validity of the media can be proven and kept intact. To begin with, the proposed watermarking scheme is implemented on continuous-tone/greyscale images, and later extended to images achieved via multitoning, an advanced version of halftoning-based printing. The proposed watermark embedding is robust and imperceptible. Experimental simulations and evaluations of the proposed method show excellent results from both objective and subjective view-points.

A Deep Learning-based 3D CNN for Automated COVID-19 Lung Lesions Segmentation from 3D Chest CT Scans

This paper presents an automated COVID-19 lung lesions segmentation method based on a deep three-dimensional convolutional neural network model which automatically detects and extracts multifocal, bilateral and peripheral lung lesions from chest 3D-CT scans. The proposed CNN model is based on a modified 11-layer U-net architecture and employs a loss function that combines Dice coefficient and Cross-Entropy. It has been tested and evaluated on Covid-19-20-v2 training dataset containing a total of 199 3D-CT scans of different subjects with COVID-19 lesions representing different sizes, shapes and locations in CT images. The obtained results have proven to be satisfactory and objective, as well as similar and close to ground truth data provided by medical experts. On these challenging CT data, the proposed CNN obtained average scores of 0.7639, 0.8129 and 0.9986 corresponding to Dice Similarity Coefficient, Sensitivity and Specificity metrics respectively. © 2022 IEEE.

The acceptability of testing contacts of confirmed COVID-19 cases using serial, self-administered lateral flow devices as an alternative to self-isolation.

Introduction. Evidence suggests that although people modify their behaviours, full adherence to self-isolation guidance in England may be suboptimal, which may have a detrimental impact on COVID-19 transmission rates.Hypothesis. Testing asymptomatic contacts of confirmed COVID-19 cases for the presence of SARS-CoV-2 could reduce onward transmission by improving case ascertainment and lessen the impact of self-isolation on un-infected individuals.Aim. This study investigated the feasibility and acceptability of implementing a 'test to enable approach' as part of England's tracing strategy.Methodology. Contacts of confirmed COVID-19 cases were offered serial testing as an alternative to self-isolation using daily self-performed lateral flow device (LFD) tests for the first 7 days post-exposure. Asymptomatic participants with a negative LFD result were given 24 h of freedom from self-isolation between each test. A self-collected confirmatory PCR test was performed on testing positive or at the end of the LFD testing period.Results. Of 1760 contacts, 882 consented to daily testing, of whom 812 individuals were within 48 h of exposure and were sent LFD testing packs. Of those who declined to participate, 39.1% stated they had already accessed PCR testing. Of the 812 who were sent LFD packs, 570 (70.2%) reported one or more LFD results; 102 (17.9%) tested positive. Concordance between reported LFD result and a supplied LFD image was 97.1%. In total, 82.8% of PCR-positive samples and 99.6% of PCR-negative samples were correctly detected by LFD. The proportion of secondary cases from contacts of those who participated in the study and tested positive (6.3%; 95% CI: 3.4-11.1%) was comparable to a comparator group who self-isolated (7.6%; 95% CI: 7.3-7.8%).Conclusion. This study shows a high acceptability, compliance and positivity rates when using self-administered LFDs among contacts of confirmed COVID-19 cases. Offering routine testing as a structured part of the contact tracing process is likely to be an effective method of case ascertainment.

A feasibility study on adoption of autonomous vans for relief distribution in rural India

In the wake of the COVID-19 global health emergency, governments in different countries of the world have restricted the movement of people and goods intra-country or even intrastate to reduce the spread of infections. Developing countries like India face an acute shortage of distribution centers for disaster management and recovery. Residents in semi-urban and rural areas in India continue to face tremendous scarcity of daily basic supplies and food grains. The safety concern stemming from the delivery options for essential goods from one point to another has been under serious concern considering the widespread of the coronavirus in India. The purpose of this study is to understand the shortcomings, needs and requirements of existing food and logistics supply in disaster scenarios by NGOs and local government and then perform a gap analysis to propose Connected Refrigerated Driverless Vans (CRDVs). The adoption of driverless car technology (DCT) is still a long-term effort, and this research aims to delve into the ways in which the proposed CRDV framework can be adopted in relief and supply distribution in rural India. © 2022 IEEE.

Impact of environmental scanning on organizational resilience and competitive advantage: a study of Egyptian SMEs

Purpose>This paper aims to examine the impact of environmental scanning (ES) on competitive advantage (CA) through the mediation of organizational resilience dimensions within manufacturing small and medium-sized enterprises (SMEs) in Egypt.Design/methodology/approach>This study adopts a cross-sectional design to collect data. This study used a self-administered questionnaire to collect data from a sample of 249 Egyptian SMEs. This study employed the Smart partial least square structural equation modeling technique to test the hypotheses.Findings>ES positively affects CA both directly and indirectly through the mediation of organizational resilience dimensions, namely, robustness and agility. However, ES does not affect integrity;therefore, integrity does not mediate the ES–CA relationship. These results indicate that organizational resilience partially mediates the relationship between ES and CA.Research limitations/implications>The sample size was small, covering only Egyptian manufacturing SMEs. The results may be different in the service sector and other countries. The study was cross-sectional which could not trace the long-term effects of ES and organizational resilience on CA. Therefore, a longitudinal study should be conducted, based on resource availability.Practical implications>Managers in Egyptian SMEs should scan their environments to build organizational resilience and, in turn, enhance their CA.Originality/value>To the best of the authors’ knowledge, this research is among the first endeavors to investigate the role of ES in building CA through organizational resilience in the context of Egyptian SMEs.

How the governance of and through digital contact tracing technologies shapes geographies of power

In this article, we use the COVID-19 pandemic to study governance through digital technologies. We investigate ???digital contact tracing??? (DCT) apps developed in Austria and Norway and find their emergence, contestation and stabilisation as moments in which norms and values are puzzled through, and distributions of power change. We show that debates on DCT apps involved disputes on ???digital citizenship???, that is, on the scope and nature of data that authorities are allowed to collect from citizens. Remarkably, these disputes were settled through the enrolment of a framework developed jointly by Apple and Google. Software became akin to a constitution that enshrined understandings of good citizenship into technological design, while also being a means through which geographies of power materialised. This article contributes to literature on technological governance by showing how the rising salience of technologies in governance transform political geographies and, as a consequence, democratic lives.

Linking environmental scanning and organizational learning with organizational resilience of Egyptian SMEs: the moderating role of environmental uncertainty

Purpose: This study aims to examine the impact of environmental scanning on organizational resilience through organizational learning based on organizational information processing theory (OIPT) in Egyptian small and medium-sized enterprises (SMEs) during the COVID-19 pandemic. Furthermore, this study aims to examine the moderating role of environmental uncertainty in this relationship. Design/methodology/approach: The data for the mediation analysis was obtained using a cross-sectional design. Using a self-administered questionnaire, the authors collected data from a sample of 249 Egyptian SMEs. The authors tested the hypotheses using the smart partial least square structural equation modeling approach. Findings: Organizational learning affects organizational resilience. Environmental scanning does not have a direct effect on organizational resilience. However, organizational learning fully mediates the relationship between environmental scanning and organizational resilience. Furthermore, environmental uncertainty does not moderate the indirect relationship between environmental scanning and resilience. Research limitations/implications: The sample included only Egyptian manufacturing SMEs. The results in the service sector and in other countries may differ. This study was cross-sectional, which was limited in its ability to trace the long-term effects of environmental scanning and organizational learning on organizational resilience. Practical implications: Egyptian SMEs’ managers should experience organizational learning as a pathway for environmental scanning to build organizational resilience. Originality/value: To the best of the authors’ knowledge, this study is the first to investigate the role of environmental scanning in building organizational resilience through organizational learning and the moderating role of environmental uncertainty in this relationship. © 2022, Emerald Publishing Limited.

Optimized implementation of an improved KNN classification algorithm using intel FPGA platform: Covid-19 case study

The improved k-nearest neighbor (KNN) algorithm based on class contribution and feature weighting (DCT-KNN) is a highly accurate approach. However, it requires complex computational steps which consumes much time for the classification process. A field programmable gate array (FPGA) can be used to solve this drawback. However, using traditional hardware description language (HDL) to implement FPGA-based accelerators requires a high design time. Fortunately, the open computing language (OpenCL) high level parallel programming tool allows rapid and effective design on FPGA-based hardware accelerators. In this study, OpenCL has been used to examine speeding up the DCT-KNN algorithm on the FPGA parallel computing platform through applying numerous parallelization and optimization techniques. The optimized approach of the improved KNN could be used in various engineering problems that require a high speed of classification process. Classification of the COVID-19 disease is the case study used to examine this work. The experimental results show that implementing the DCT-KNN algorithm on the FPGA platform (Intel De5a-net Arria-10 device was used) gives an extremely high performance when compared to the traditional single-core-CPU based implementation. The execution time for our optimized design on the FPGA accelerator is 44 times faster than the conventional design implemented on the regular CPU-based computational platform.

Authentication of NIfTI Neuroimages Using Lifting Wavelet Transform, Arnold Cat Map, Z-Transform, and Hessenberg Decomposition

The technological progress in digital medical imaging has enabled the diagnosis of various ailments, and thus upgraded the global healthcare system. In the era of coronavirus 2019 (COVID-19), telemedicine plays the crucial role of supporting remote medical consultation in rural locations. During the remote consultation, numerous medical images are sent to each radiologist via the Internet. There has been a surge in the number of attacks on digital medical images worldwide, which severely threatens authenticity and ownership. To mitigate the threat, this paper proposes a robust and secure watermarking approach for NIfTI images. Our approach painstakingly incorporates a watermark into the chosen NIfTI image slice, aiming to accurately fit the watermark, while preserving the medical information contained in the slice. Specifically, the original image was converted through the lifting wavelet transform (LWT), realizing excellent modification during insertion. Next, Z transform was applied over the low-low (LL) band, and the Hessenberg decomposition (HD) was performed on the transformed band, which contains the maximum energy of the image. Afterwards, Arnold Cat map was employed to scramble the watermark, before inserting it into the slice. Simulation results show that our approach strikes a perfect balance between security, imperceptibility, and robustness against various attacks, as suggested by metrics like peak signal-to-noise ratio (PSNR), normalized correlation (NC), structural similarity index measure (SSIM), and universal image quality index Q.

A map point on the role of the telemedicine and e-Health in the digital contact tracing during the COVID-19 pandemic

In public health, independently by the technology use, contact tracing (CT) is the process of identifying people who may have met an infected person and subsequent collection of further information on these contacts. The differences between the potential methods of carrying out CT in 2003, during the SARS epidemic, and the current one SARS-CoV-2 are considerable. During the previous pandemic, current mobile technologies were not available (in particular the smartphone as we know it today). The role of the mobile technology—and therefore of the mobile Health (mHealth)—was and is basic during this pandemic for the digital contact tracing (DCT). The review, starting from the introduction of contact tracing performed manually, faced the potentialities and the technologies used for DCT based on dedicated APPs, interrogating on the state of development and on the aspects affecting the effectiveness of the DCT. From this review, various phases of the dissemination of medical knowledge around these Apps emerged. In a first phase, the novelty was high as well as the consequent difficulty on the part of epidemiology to set a concrete approach on them. Subsequently, scientific knowledge has spread, publications have increased and even the great IT giants have moved in the development of solutions. It was highlighted that hundreds of Apps have been/ proposed and/or are under development in the World according to different approaches in terms of the (I) technologies, (II) protocols (Bluetooth and Global Positioning System), (III) centralized governmental choice. The review in a first part extracted some important experiences in this Area captured during the first period;In a second part extracted some important outcomes from research of the next phases. The review ends pointing out the reasons for success/failure of the DCT and the lessons for the future for the epidemiologist. © Journal of Public Health and Emergency. All rights reserved.

Detection of Iris Presentation Attacks Using Hybridization of Discrete Cosine Transform and Haar Transform With Machine Learning Classifiers and Ensembles

Iris biometric identification allows for contactless authentication, which helps to avoid the transmission of diseases like COVID-19. Biometric systems become unstable and hazardous due to spoofing attacks involving contact lenses, replayed video, cadaver iris, synthetic Iris, and printed iris. This work demonstrates the iris presentation attacks detection (Iris- PAD) approach that uses fragmental coefficients of transform iris images as features obtained using Discrete Cosine Transform (DCT), Haar Transform, and hybrid Transform. In experimental validations of the proposed method, three main types of feature creation are investigated. The extracted features are utilized for training seven different machine learning classifiers alias Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), and decision tree(J48) with ensembles of SVM CRF CNB, SVM CRF CRT, and RF CSVM CMLP (multi-layer perceptron) for proposed iris liveness detection. The proposed iris liveness detection variants are evaluated using various statistical measures: accuracy, Attack Presentation Classification Error Rate (APCER), Normal Presentation Classification Error Rate (NPCER), Average Classification Error Rate (ACER). Six standard datasets are used in the investigations. Total nine iris spoofing attacks are getting identified in the proposed method. Among all investigated variations of proposed iris-PAD methods, the 4 x 4 of fragmental coefficients of a Hybrid transformed iris image with RF algorithm have shown superior iris liveness detection with 99.95% accuracy. The proposed hybridization of transform for features extraction has demonstrated the ability to identify all nine types of iris spoofing attacks and proved it robust. The proposed method offers exceptional performances against the Synthetic iris spoofing images by using a random forest classifier. Machine learning has massive potential in a similar domain and could be explored further based on the research requirements.

The use of careFiji app for contact tracing during the COVID-19 pandemic: Digital gap and challenges faced in Fiji.

Globally, digital contact tracing initiatives has been used as a tool to combat the COVID-19 pandemic. The Fijian Government and Ministry of Health are promoting the use of the "careFiji" app to help in contact tracing. This paper will discuss the rollout of the careFiji app which helps in combating COVID-19 in Fiji, and the challenges caused by the digital gap that has surfaced during the pandemic.

Modeling and prediction of COVID-19 pandemic using Gaussian mixture model.

COVID-19 is caused by a novel coronavirus and has played havoc on many countries across the globe. A majority of the world population is now living in a restricted environment for more than a month with minimal economic activities, to prevent exposure to this highly infectious disease. Medical professionals are going through a stressful period while trying to save the larger population. In this paper, we develop two different models to capture the trend of a number of cases and also predict the cases in the days to come, so that appropriate preparations can be made to fight this disease. The first one is a mathematical model accounting for various parameters relating to the spread of the virus, while the second one is a non-parametric model based on the Fourier decomposition method (FDM), fitted on the available data. The study is performed for various countries, but detailed results are provided for the India, Italy, and United States of America (USA). The turnaround dates for the trend of infected cases are estimated. The end-dates are also predicted and are found to agree well with a very popular study based on the classic susceptible-infected-recovered (SIR) model. Worldwide, the total number of expected cases and deaths are 12.7 × 106 and 5.27 × 105, respectively, predicted with data as of 06-06-2020 and 95% confidence intervals. The proposed study produces promising results with the potential to serve as a good complement to existing methods for continuous predictive monitoring of the COVID-19 pandemic.