Principal Independent Component Analysis Research Articles

Performance Evaluation of Various Pre-Processing Techniques for R-Peak Detection in ECG Signal

ABSTRACT In recorded Electrocardiogram (ECG) signal, clinical information is masked by several noises and distortion resulting in low signal-to-noise-ratio (SNR). In this situation, an efficient pre-processing technique is required to improve SNR for efficient analysis of ECG signals. In this research article, performance of five pre-processing techniques viz. digital bandpass filter (DBPF), wavelet transform (WT), independent principal component analysis (IPCA), savitzky golay digital FIR filter (SGDFF) and fractional wavelet transform (FrWT) have been evaluated and compared for their effects on the efficiency of R-peak detection. FrWT has been utilized for pre-processing of ECG signal for the first time in this paper. A FrWT-based technique is also proposed using Yule Walker autoregressive modeling (YWARM) and Principal Component Analysis (PCA) for feature extraction and R-peak detection, respectively. YWARM is selected due to its more stable output for long time recorded ECG signal than existing techniques, whereas PCA is selected to get optimal dimensional feature vectors out of higher dimensional feature vectors. The proposed technique has been evaluated and compared with others on the basis of various performance parameters; SNR, mean squared error (MSE), sensitivity (SE), accuracy (Acc), and positive predictive value (PPV). The proposed technique yielded interesting results among all the methods; 34.37 dB of output SNR, 0.026% of MSE, 99.98% of SE, 99.97% of Acc and 99.99% of PPV on real time ECG database (RT DB) and 24.81 dB of output SNR, 0.099% of MSE, 99.96% of SE, 99.93% of Acc, and 99.97% of PPV on MIT-BIH Arrhythmia database (MIT-BIH Arr DB).

The prevalence and risk factors of hypokalemia among pregnant women in rural Eastern Cape South Africa

Background. Hypokalaemia is a rare disorder among healthy pregnant women. Life-threatening muscle and cardiac malfunction may develop if it remains untreated. Objectives. This study was carried out to estimate the prevalence and risk factors of hypokalaemia among pregnant women in rural Eastern Cape (EC) Province, South Africa (SA), and to establish whether geophagia, a common practice, increases the risk. Methods. This cross-sectional analytical study included 188 participants with geophagia and 233 participants without geophagia enrolled at Mthatha Gateway Clinic, EC, SA. Data included sociodemographic characteristics, magnitude of geophagia, dietary patterns and serum potassium levels. The χ2 test for categorical variables, analysis of variance to compare means, multivariate logistic regression for independent risk factors and principal component analysis for latent variable patterns that were associated with hypokalaemia were carried out. P <0.05 was considered statistically significant. Results. Hypokalaemia among pregnant women in rural EC was five times higher than expected. Geophagia accounted for only 15% of the observed cases. The risk of hypokalaemia was higher among primigravidas aged <25 years with low meat, fruit and vegetable consumption who practised geophagia, with concurrent excessive cola or caffeinated soft-drink consumption. Conclusion. Hypokalaemia is disproportionally prevalent among pregnant women in the rural EC. Young age, primigravida, geophagia, diet deficiency in meat, vegetables and fruits and excessive consumption of soft drinks increased the risk of hypokalaemia. The association of geophagia with low meat, vegetable and fruit consumption may indicate an underlying iron deficiency, necessitating further investigation.

Fusion of deep-learned and hand-crafted features for cancelable recognition systems

The recent years have witnessed a dramatic shift in the way of biometric identification, authentication, and security processes. Among the essential challenges that face these processes are the online verification and authentication. These challenges lie in the complexity of such processes, the necessity of the personal real-time identifiable information, and the methodology to capture temporal information. In this paper, we present an integrated biometric recognition method to jointly recognize face, iris, palm print, fingerprint and ear biometrics. The proposed method is based on the integration of the extracted deep-learned features together with the hand-crafted ones by using a fusion network. Also, we propose a novel convolutional neural network (CNN)-based model for deep feature extraction. In addition, several techniques are exploited to extract the hand-crafted features such as histogram of oriented gradients (HOG), oriented rotated brief (ORB), local binary patterns (LBPs), scale-invariant feature transform (SIFT), and speeded-up robust features (SURF). Furthermore, for dimensional consistency between the combined features, the dimensions of the hand-crafted features are reduced using independent component analysis (ICA) or principal component analysis (PCA). The core of this paper is the template protection via a cancelable biometric scheme without significantly affecting the recognition performance. Specifically, we have used the bio-convolving approach to enhance the user’s privacy and ensure the robustness against spoof attacks. Additionally, various CNN hyper-parameters with their impact on the proposed model performance are studied. Our experiments on various datasets revealed that the proposed method achieves 96.69%, 95.59%, 97.34%, 96.11% and 99.22% recognition accuracies for face, iris, fingerprint, palm print and ear recognition, respectively.

Image fusion using hybrid methods in multimodality medical images.

An image fusion based on multimodal medical images renders a considerable enhancement in the quality of fused images. An effective image fusion technique produces output images by preserving all the viable and prominent information gathered from the source images without any introduction of flaws or unnecessary distortions. This review paper intends to bring out the process of image fusion, its utilization in the medical domain, merits, and demerits and reviews the perspective of multimodal medical image fusion. It also discusses the involvement of various medical entities like medical resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). The usefulness of such modalities is presented, suggesting plausible hybrid modality combinations which could greatly enhance image fusion. This review also discusses innovative dispositions in the medical image fusion techniques for the achievement of incisively desired, quality images focused on fusion with wavelet transform and use of independent component analysis (ICA) and principal component analysis (PCA) techniques for the purpose denoising and data dimension reductions. Additionally, the future-prospects of an ideal technique for medical image fusion through the utilization of various medical modalities have been also discussed in this review paper. Graphical abstract.

A Robust System for Detection of Artifacts from EEG Brain Recordings

Epilepsy is a chronic disorder and has the propensity of two or more brain. Analysis of EEG is the primary method for the diagnosis of epilepsy. Contamination of eye movement and blink artifacts presence in EEG data becomes more complicated to the doctors during the diagnosis period. Earlier detection of these artifacts gives a significant advantage of refining the Epilepsy identification process. In this regard, a robust subspace detection method is applied to detect the target signal in noise with possible interference-artifacts, then a dimensionality reduction model, with the combination of fast Independent and Robust Principal Component Analysis (FICA and rPCA) is implemented for identification of artifacts from EEG brain recordings. To perform this the proposed detection method uses synthetic data and artifact contaminated data. The extracted target subspace signal is considered as the input for rPCA and FICA. The ROC analysis is developed as a standard methodology to quantify the detectors' ability to correctly distinguish the target of interest (artifacts) from the background noise in the system.

A novel method for identifying electrocardiograms using an independent component analysis and principal component analysis network

Electrocardiograms (ECGs) have been extensively utilized for diagnosing cardiovascular abnormalities. However, due to the mixed noise and the subtle differences between ECGs, it is generally arduous to spot the ECG abnormalities with satisfactory efficiency with the naked eye. To address these issues, we proposed a novel automatic system for diagnosing arrhythmia. In this paper, several independent component analysis and principal component analysis networks (ICA-PCANets) were developed as the ECG feature extraction methods. To verify their effectiveness, linear support vector machine (SVM), K-nearest neighbors (KNN) and random forest (RF) were adopted as the classifier models in this work. Among them, the combination of ICA-PCANet and linear SVM achieved the highest accuracies of 98.01%, 98.63%, and 91.77% by classifying 2 classes, 5 classes (AAMI standard), and 14 detailed categories, respectively, on the MIT-BIH database. Based on the above comprehensive performances, the proposed system can be applied to clinical monitoring of heart conditions.

Performance of rotation forest ensemble classifier and feature extractor in predicting protein interactions using amino acid sequences

BackgroundThere are two significant problems associated with predicting protein-protein interactions using the sequences of amino acids. The first problem is representing each sequence as a feature vector, and the second is designing a model that can identify the protein interactions. Thus, effective feature extraction methods can lead to improved model performance. In this study, we used two types of feature extraction methods—global encoding and pseudo-substitution matrix representation (PseudoSMR)—to represent the sequences of amino acids in human proteins and Human Immunodeficiency Virus type 1 (HIV-1) to address the classification problem of predicting protein-protein interactions. We also compared principal component analysis (PCA) with independent principal component analysis (IPCA) as methods for transforming Rotation Forest.ResultsThe results show that using global encoding and PseudoSMR as a feature extraction method successfully represents the amino acid sequence for the Rotation Forest classifier with PCA or with IPCA. This can be seen from the comparison of the results of evaluation metrics, which were >73% across the six different parameters. The accuracy of both methods was >74%. The results for the other model performance criteria, such as sensitivity, specificity, precision, and F1-score, were all >73%. The data used in this study can be accessed using the following link: https://www.dsc.ui.ac.id/research/amino-acid-pred/.ConclusionsBoth global encoding and PseudoSMR can successfully represent the sequences of amino acids. Rotation Forest (PCA) performed better than Rotation Forest (IPCA) in terms of predicting protein-protein interactions between HIV-1 and human proteins. Both the Rotation Forest (PCA) classifier and the Rotation Forest IPCA classifier performed better than other classifiers, such as Gradient Boosting, K-Nearest Neighbor, Logistic Regression, Random Forest, and Support Vector Machine (SVM). Rotation Forest (PCA) and Rotation Forest (IPCA) have accuracy, sensitivity, specificity, precision, and F1-score values >70% while the other classifiers have values <70%.

RESEARCH ON DESIGNING COMPOSITE TECHNIQUES FOR OBTAINING THE 3D HYBRID COMPOSITES WITH CONDUCTIVE AND SEMICONDUCTIVE PROPERTIES FOR SENSORS AND ACTUATORS

In the paper are presented several aspects concerning the experimental preparation for sensors and actuators by using the factorial scheme based on independent and dependent variables and principal component analysis. The leading technologies envisaged are the classical ones (padding, coating, and printing) and advanced technologies such as RF plasma, microwave, and 3D printing. PCA is a statistical procedure well known by researchers and is based on orthogonal transformation of the variables possible correlated into a set of variable linearly uncorrelated (PC). The resulting vectors are a linear combination of the variables and contain x observation and represent an uncorrelated orthogonal set. Besides, in this paper are presented several technological flows for obtaining conductive or semiconductive 3D composite materials by using the standard and advanced technologies above mentioned

R-Peak Detection Using Chaos Analysis in Standard and Real Time ECG Databases

ObjectivesTimely and accurate R-peak detection is very important for analyzing electrocardiogram (ECG) signal in critical conditions. The main obstacle in observing the correct relation between underlying physiology and features is that there is no specific method to select the features that are needed for diagnosis of a particular heart disease. Therefore, choice of an advanced feature extraction technique is a major concern especially due to non-linear nature of the ECG signal. Material and methodsIn this study, physioNet (standard) and real-time ECG records have been used. During recording, ECG signal is affected by various noises/interferences which create further challenges in ECG signal analysis. Hence, it requires an effective pre-processing, advanced feature extraction and detection techniques. In this paper, independent principal component analysis (IPCA) is used for pre-processing, since it possesses good characteristic of both principal component analysis (PCA) and independent component analysis (ICA). Due to non-linear nature of ECG signals, chaos analysis is applied in feature extraction stage for different ECG databases. The monitoring and wide description of chaotic patterns of heartbeats are prime concerns for cardiologists. Chaos analysis has been used by estimating different attractors against various time delay dimensions. Correct R-peak detection is useful in diagnosing cardiac diseases and performance of the proposed methodology has been evaluated in terms of sensitivity (Se), positive predictivity (PP), and detection error rate (DER) for both PhysioNet (PN DB) and real-time (RT DB) databases. Results-case-I: Without pre-processingIn this case, R-peaks have been detected using chaos analysis+PCA. The proposed method yields Se of 99.91%, PP of 99.93%, and DER of 0.163% for PN DB and Se of 99.77%, PP of 99.83%, and DER of 0.387% for RT DB. Case-II: With pre-processingIn this case, R-peaks have been detected using IPCA+chaos analysis+PCA. The proposed method yields Se of 99.95%, PP of 99.96%, and DER of 0.093% for PN DB and Se of 99.96%, PP of 99.97%, and DER of 0.055% for RT DB. ConclusionThe proposed technique outperforms the other existing works on various selected evaluation parameters even without pre-processing. Hence, the proposed technique has successfully demonstrated its ability to discriminate different types of heartbeats in most of the critical situations. Therefore, there are strong merits in using chaos analysis as a feature extraction method to reduce the incidence of false diagnosis.

A Comparison of ECG Signal Pre-processing Using FrFT, FrWT and IPCA for Improved Analysis

ObjectiveElectrocardiogram (ECG) is a diagnostic tool for recording electrical activities of the human heart non-invasively. It is detected by electrodes placed on the surface of the skin in a conductive medium. In medical applications, ECG is used by cardiologists to observe heart anomalies (cardiovascular diseases) such as abnormal heart rhythms, heart attacks, effects of drug dosage on subject's heart and knowledge of previous heart attacks. Recorded ECG signal is generally corrupted by various types of noise/distortion such as cardiac (isoelectric interval, prolonged depolarization and atrial flutter) or extra cardiac (respiration, changes in electrode position, muscle contraction and power line noise). These factors hide the useful information and alter the signal characteristic due to low Signal-to-Noise Ratio (SNR). In such situations, any failure to judge the ECG signal correctly may result in a delay in the treatment and harm a subject (patient) health. Therefore, appropriate pre-processing technique is necessary to improve SNR to facilitate better treatment to the subject. Effects of different pre-processing techniques on ECG signal analysis (based on R-peaks detection) are compared using various Figures of Merit (FoM) such as sensitivity (Se), accuracy (Acc) and detection error rate (DER) along with SNR. MethodsIn this research article, a new fractional wavelet transform (FrWT) has been proposed as a pre-processing technique in order to overcome the disadvantages of other existing commonly used techniques viz. wavelet transform (WT) and the fractional Fourier transform (FrFT). The proposed FrWT technique possesses the properties of multiresolution analysis and represents signal in the fractional domain which consists of representation in terms of rotation of signals in the time–frequency plane. In the literature, ECG signal analysis has been improvised using statistical pre-processing techniques such as principal component analysis (PCA), and independent component analysis (ICA). However, both PCA and ICA are prone to suffer from slight alterations in either signal or noise, unless the basis functions are prepared with a worldwide set of ECG. Independent Principal Component Analysis (IPCA) has been used to overcome this shortcoming of PCA and ICA. Therefore, in this paper three techniques viz. FrFT, FrWT and IPCA are selected for comparison in pre-processing of ECG signals. ResultsThe selected methods have been evaluated on the basis of SNR, Se, Acc and DER of the detected ECG beats. FrWT yields the best results among all the methods considered in this paper; 34.37dB output SNR, 99.98% Se, 99.96% Acc, and 0.036% DER. These results indicate the quality of biology-related information retained from the pre-processed ECG signals for identifying different heart abnormalities. ConclusionCorrect analysis of the acquired ECG signal is the main challenge for cardiologist due to involvement of various types of noises (high and low frequency). Twenty two real time ECG records have been evaluated based on various FoM such as SNR, Se, Acc and DER for the proposed FrWT and existing FrFT and IPCA preprocessing techniques. Acquired real-time ECG database in normal and disease situations is used for the purpose. The values of FoMs indicate high SNR and better detection of R-peaks in a ECG signal which is important for the diagnosis of cardiovascular disease. The proposed FrWT outperforms all other techniques and holds both analytical attributes of the actual ECG signal and alterations in the amplitudes of various ECG waveforms adequately. It also provides signal portrayals in the time-fractional-frequency plane with low computational complexity enabling their use practically for versatile applications.

Statistical analysis of wind-induced pressure fields and PIV measurements on two buildings

This study investigated the aerodynamic characteristics of two tall buildings through an independent component analysis (ICA) and principal component analysis (PCA) of wind-induced pressure fields. The pressure patterns identified by ICA and PCA were compared using the wind pressures on two building models measured in a series of wind tunnel tests under five cases consisting of different gaps between two buildings. To further investigate the flow patterns, particle image velocimetry was employed to measure the instantaneous wind flow patterns. The ICA results in the 1st, 2nd, and 4th modes indicated different pressure distributions on the inside building surfaces for different gaps, while the 3rd mode indicated a suction phenomenon on the outside and leeward surfaces; the PCA results indicated that the gap only influenced the pressure on the inside surfaces in the first four modes. Only the first PCA mode had a higher correlation with the original data than the ICA modes, and the ICA modes generally had higher correlations than the other modes. In wind forces, the 1st and 3rd PCA modes provided similar information, whereas all the ICA modes provided different information. Overall, ICA provided more diverse information than PCA, which yielded rather limited and homogenous information.

Cloud enhancement of NOAA multispectral images by using independent component analysis and principal component analysis for sustainable systems

Multispectral (MS) images, which display spectral and spatial information, play a vital role in remote sensing applications for analysing objects. This paper focuses on the spatial and spectral enhancement of cloud objects through the fusion technique. The complete information is represented in limited bands through independent component analysis and principal component analysis. The objects of MS images do not have the same quality in all individual bands. Instead of a selection of individual bands, the complete information about input image obtained by limited bands with skew- and kurtosis-based ICA transforms, it is suitable for non-Gaussian application in the satellite image. In preprocessing, the spatial and spectral information is lost due to the low resolution of individual bands. The information is recovered from the high-resolution first principal component of the MS image. The classification was performed using k-means clustering, and accurate values were obtained with the proposed method. This method gives a good tradeoff between the spectral and spatial information and superior accuracy results compared with other enhancement and fusion methods.

Multimodal Neuroimaging: Basic Concepts and Classification of Neuropsychiatric Diseases.

Neuroimaging techniques are widely used in neuroscience to visualize neural activity, to improve our understanding of brain mechanisms, and to identify biomarkers-especially for psychiatric diseases; however, each neuroimaging technique has several limitations. These limitations led to the development of multimodal neuroimaging (MN), which combines data obtained from multiple neuroimaging techniques, such as electroencephalography, functional magnetic resonance imaging, and yields more detailed information about brain dynamics. There are several types of MN, including visual inspection, data integration, and data fusion. This literature review aimed to provide a brief summary and basic information about MN techniques (data fusion approaches in particular) and classification approaches. Data fusion approaches are generally categorized as asymmetric and symmetric. The present review focused exclusively on studies based on symmetric data fusion methods (data-driven methods), such as independent component analysis and principal component analysis. Machine learning techniques have recently been introduced for use in identifying diseases and biomarkers of disease. The machine learning technique most widely used by neuroscientists is classification-especially support vector machine classification. Several studies differentiated patients with psychiatric diseases and healthy controls with using combined datasets. The common conclusion among these studies is that the prediction of diseases increases when combining data via MN techniques; however, there remain a few challenges associated with MN, such as sample size. Perhaps in the future N-way fusion can be used to combine multiple neuroimaging techniques or nonimaging predictors (eg, cognitive ability) to overcome the limitations of MN.

Separation of Subcutaneous Fat From Muscle in Surface Electrical Impedance Myography Measurements Using Model Component Analysis

Electrical impedance myography (EIM) is a relatively new technique to assess neuromuscular disorders (NMD). Although the application of EIM using surface electrodes (sEIM) has been adopted by the neurology community in recent years to evaluate NMD status, sEIM's sensitivity as a biomarker of skeletal muscle condition is impacted by subcutaneous fat (SF) tissue. Here, we develop a method that is able to remove the contribution of SF from sEIM data. We evaluate independent component analysis (ICA) and principal component analysis (PCA) for this purpose. Then, we introduce the so-called model component analysis (MCA). All methods are validated with numerical simulations using impedivity data from SF and muscle tissues. The methods are then tested with measurements performed in diseased individuals ( n=3). Simulations demonstrate that MCA is the most accurate method at separating the impedivity of SF and muscle tissues with the accuracy being 99.2%, followed by ICA with 51.4%, and finally PCA with 38.5%. Experimental results from sEIM data measured on the triceps brachii of patients are consistent with muscle grayscale level values obtained using ultrasound imaging. MCA can be used to separate the impedivity of SF and muscle tissues from sEIM data, thus increasing the sensitivity to detect changes in the muscle. MCA can make the sEIM technique a better diagnostic tool and biomarker of disease progression and response to therapy by removing the confounding effect of SF tissue in NMD patients with excess subcutaneous fat tissue for any reason.

Parameterized principal component analysis

When modeling multivariate data, one might have an extra parameter of contextual information that could be used to treat some observations as more similar to others. For example, images of faces can vary by age, and one would expect the face of a 40 year old to be more similar to the face of a 30 year old than to a baby face. We introduce a novel manifold approximation method, parameterized principal component analysis (PPCA) that models data with linear subspaces that change continuously according to the extra parameter of contextual information (e.g. age), instead of ad-hoc atlases. Special care has been taken in the loss function and the optimization method to encourage smoothly changing subspaces across the parameter values. The approach ensures that each observation’s projection will share information with observations that have similar parameter values, but not with observations that have large parameter differences. We tested PPCA on artificial data based on known, smooth functions of an added parameter, as well as on two real datasets with different types of parameters. We compared PPCA to PCA, sparse PCA and to independent principal component analysis (IPCA), an atlas based method that groups observations by their parameter values and projects each group using PCA with no sharing of information between groups. PPCA recovers the known functions with less error and projects the datasets’ test set observations with consistently less reconstruction error than IPCA does. In some cases where the manifold is truly nonlinear, PCA outperforms all the other manifold approximation methods compared.

A robust wavelet based decomposition of facial images to improve recognition accuracy in standard appearance based statistical face recognition methods

In traditional appearance based face recognition approaches, the facial images in standard face databases are used as it is for testing standard statistical face recognition algorithms. Due to the camera angle, position and lightening conditions, the facial image captured contains occlusion. Human pose, facial expressions and alignment of face with respect to camera axis also result in occluded image. Sometimes the background of the face also covered during the facial image capturing. We proposed an approach, called wavelet based facial image decomposition (WBFD) and in this approach the facial images are decomposed using two levels wavelet transform. In the faces obtained after two level wavelet decomposition, the occlusion and background images are suppressed. The face images from Yale database are used for our experiments. In order to test the performance of proposed WBFD approach, the face images obtained from WBFD approach, resized Yale face and Yale face from the original Yale face database are tested with five standard statistical appearance based face recognition algorithms such as eigen face, Fischer discriminant analysis, kernel principal component analysis, independent component analysis and 2D principal component analysis. The experimental results obtained show that, the proposed WBFD approach gives high recognition accuracy and require minimum recognition time in seconds for all the five face recognition methods.

Sonophore-enhanced nanoemulsions for optoacoustic imaging of cancer.

Optoacoustic imaging offers the promise of high spatial resolution and, at the same time, penetration depths well beyond the conventional optical imaging technologies, advantages that would be favorable for a variety of clinical applications. However, similar to optical fluorescence imaging, exogenous contrast agents, known as sonophores, need to be developed for molecularly targeted optoacoustic imaging. Despite numerous optoacoustic contrast agents that have been reported, there is a need for more rational design of sonophores. Here, using a library screening approach, we systematically identified and evaluated twelve commercially available near-infrared (690-900 nm) and highly absorbing dyes for multi-spectral optoacoustic tomography (MSOT). In order to achieve more accurate spectral deconvolution and precise data quantification, we sought five practical mathematical methods, namely direct classical least squares based on UV-Vis (UV/Vis-DCLS) or optoacoustic (OA-DCLS) spectra, non-negative LS (NN-LS), independent component analysis (ICA) and principal component analysis (PCA). We found that OA-DCLS is the most suitable method, allowing easy implementation and sufficient accuracy for routine analysis. Here, we demonstrate for the first time that our biocompatible nanoemulsions (NEs), in combination with near-infrared and highly absorbing dyes, enable non-invasive in vivo MSOT detection of tumors. Specifically, we found that NE-IRDye QC1 offers excellent optoacoustic performance and detection compared to related near-infrared NEs. We demonstrate that when loaded with low fluorescent or dark quencher dyes, NEs represent a flexible and new class of exogenous sonophores suitable for non-invasive pre-clinical optoacoustic imaging.

Remote monitoring of cardiorespiratory signals from a hovering unmanned aerial vehicle

BackgroundRemote physiological measurement might be very useful for biomedical diagnostics and monitoring. This study presents an efficient method for remotely measuring heart rate and respiratory rate from video captured by a hovering unmanned aerial vehicle (UVA). The proposed method estimates heart rate and respiratory rate based on the acquired signals obtained from video-photoplethysmography that are synchronous with cardiorespiratory activity.MethodsSince the PPG signal is highly affected by the noise variations (illumination variations, subject’s motions and camera movement), we have used advanced signal processing techniques, including complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and canonical correlation analysis (CCA) to remove noise under these assumptions.ResultsTo evaluate the performance and effectiveness of the proposed method, a set of experiments were performed on 15 healthy volunteers in a front-facing position involving motion resulting from both the subject and the UAV under different scenarios and different lighting conditions.ConclusionThe experimental results demonstrated that the proposed system with and without the magnification process achieves robust and accurate readings and have significant correlations compared to a standard pulse oximeter and Piezo respiratory belt. Also, the squared correlation coefficient, root mean square error, and mean error rate yielded by the proposed method with and without the magnification process were significantly better than the state-of-the-art methodologies, including independent component analysis (ICA) and principal component analysis (PCA).

Skills Acquisition Shortfall: A Study of Professional Accounting Education

Previously, there was a paucity of empirical literature to illustrate the educators' performance of professional accounting institutions in Pakistan. Therefore, this research investigates the skills acquisition shortfall in professional accounting institutes of Pakistan. In this regard, independent sample t-test and principal component analysis is employed to draw statistical findings. Out of the 35 investigated skills, 24 skills were found to be undeveloped in students for employment purposes benchmarked against employers' expectations. Further, this research has proposed a skills acquisition framework comprising of 6 components of skills. These components of skills now could be viewed as a skills acquisition framework from professional accounting education perspective.

Regional background O&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt; and NO&amp;lt;sub&amp;gt;&amp;lt;i&amp;gt;x&amp;lt;/i&amp;gt;&amp;lt;/sub&amp;gt; in the Houston–Galveston–Brazoria (TX) region: a decadal-scale perspective

Abstract. Ozone (O3) in the lower troposphere is harmful to people and plants, particularly during summer, when photochemistry is most active and higher temperatures favor local chemistry. Local precursor emissions, such as those of volatile organic compounds (VOCs) and nitrogen oxides (NOx), together with their chemistry contribute to the O3 and NOx mixing ratios in the Houston–Galveston–Brazoria (HGB) region. In addition to local emissions, chemistry and transport, larger-scale factors also contribute to local O3 and NOx. These additional contributions (often referred to as regional background) are not well quantified within the HGB region, impeding more efficient controls on precursor emissions to achieve compliance with the National Ambient Air Quality Standards for O3. In this study, we estimate ground-level regional background O3 and NOx in the HGB region and quantify their decadal-scale trends.We use four different approaches based on principal component analysis (PCA) to quantify background O3 and NOx. Three of these approaches consist of independent PCA on both O3 and NOx for both 1 and 8 h levels to compare our results with previous studies and to highlight the effect of both temporal and spatial scales. In the fourth approach, we co-varied O3, NOx and meteorology.Our results show that the estimation of regional background O3 has less inherent uncertainty when it was constrained by NOx and meteorology, yielding a statistically significant temporal trend of −0.68 ± 0.27 ppb yr−1. Likewise, the estimation of regional background NOx trend constrained by O3 and meteorology was −0.04 ± 0.02 ppb yr−1 (upper bound) and −0.03 ± 0.01 ppb yr−1 (lower bound). Our best estimates of the 17-year average of season-scale background O3 and NOx were 46.72 ± 2.08 ppb and 6.80 ± 0.13 ppb (upper bound) or 4.45 ± 0.08 ppb (lower bound), respectively. Average background O3 is consistent with previous studies and between the approaches used in this study, although the approaches based on 8 h averages likely overestimate background O3 compared to the hourly median approach by 7–9 ppb. Similarly, the upper bound of average background NOx is consistent between approaches in this study (A–C) but overestimated compared to the hourly approach by 1 ppb, on average. We likely overestimate the upper-bound background NOx due to instrument overdetection of NOx and the 8 h averaging of NOx and meteorology coinciding with MDA8 O3.Regional background O3 and NOx in the HGB region both have declined over the past 2 decades. This decline became steadier after 2007, overlapping with the effects of controlling precursor emissions and a prevailing southeasterly–southerly flow.