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Motion estimation (ME) being a fundamental process of crowd behavior analysis experienced real challenges at high densities due to visual ambiguities and occlusion problems etc. Various surveys rep...

In recent years there has been an increase in the enrollment of Muslim international students in higher education institutions in China. This research provides a brief overview of the issues that international Muslim students face during their campus life, such as adopting a new culture, lack of understanding from the broader university community, poor cultural or religious-responsive education, lack of accommodation for religious practices, and social isolation. This qualitative study also highlights some information about the unique needs of the Muslim students on campus and identifies areas for improvement. To overcome these issues, specific practical suggestions are given to the university administration, faculty, and staff to meet the needs of Muslim students, not only academically, but also socially and culturally.

Purpose The purpose of this paper is to investigate the relationship between knowledge sharing and project portfolio success in Chinese construction firms. It also examines how and under what conditions knowledge sharing contributes to project portfolio success. Design/methodology/approach The authors collected questionnaire survey from 178 construction enterprises in China that manage project portfolio as a main form of their business. Hierarchical multiple regression was used to test the proposed framework. Findings The empirical results indicate that all three types of knowledge sharing (knowledge sharing within the project, knowledge sharing among projects and knowledge sharing within the organization) are positively related to project portfolio success. Portfolio management quality (PMQ) conduits the impact of knowledge sharing on project portfolio success. Furthermore, portfolio interdependency moderates the main effect. Originality/value This research is among the first to explicitly differentiate three types of knowledge sharing existing in project portfolios. Also, this research contributes to the literature on the antecedents of project portfolio success by showing the effect of knowledge sharing. Furthermore, this study empirically contributes to portfolio management research by clarifying the critical role of PMQ and portfolio interdependency in the mechanism of knowledge sharing and project portfolio success.

This case is based on the entrepreneurial venture of MBA students from Mehran University of Engineering and Technology, named Ali Gohar (CEO) and Mubashsar Qureshi (CTO). Both studied at Cadet College and remembered the struggle they faced when they were looking for pretest preparations for the university admissions. That’s when they partnered to build the Unipedia, an easy way for struggling students to find quality aptitude test preparation at the low prices in comparison to coaching centres. This case is a brief about the online aptitude test preparation services in Pakistan. It will be a quick understanding of the industry dynamics and as to how the customer functions in this industry.

Diabetes mellitus (DM) is a metabolic syndrome that is spreading like an epidemic throughout the world without any differentiation of races and ethnic groups and has become the cause of death worldwide. It is characterized by high levels of glucose in the blood and has different types classified on the basis of varying pathophysiology. Type 1 diabetes or insulin-dependent diabetes is characterized by insulin insufficiency due to autoimmune dysfunction. Type 2 diabetes or non-insulin-dependent diabetes results from the combination of resistance to insulin action or/and inadequate insulin secretion. Gestational diabetes (GDM) is defined as hyperglycemia due to insulin resistance during pregnancy. Other types include the monogenic type of DM such as neonatal diabetes mellitus (NDM), maturity-onset diabetes of young (MODY), and diabetes in metabolic syndrome. Diabetes is diagnosed by criteria given by American Diabetes Association (ADA) for different tests like fasting plasma glucose test and hemoglobin A1c test. It is characterized by polydipsia, polyphagia, hyperglycemia, and glucosuria. Diabetes mellitus is managed through medications but many studies have proven that consumption of particular foods leads to decreased glucose levels in diabetic patients. Seeds like sunflower and flax seeds have a role in the reduction of glucose levels and can be used to treat type 2 diabetes. The bioactive components in these seeds like chlorogenic acid in sunflower seeds and secoisolariciresinol diglucosoid are involved in the treatment of insulin resistance or insulin production. In different studies, different amounts of these seed extracts were consumed by rats and humans and it resulted in better glycemic control, which provides information that these seeds have anti-diabetic properties.

This work reports the hydrothermal synthesis of silver decorated sandwiched-like vanadium pentoxide (Ag–V2O5) nanosheets and their investigation for sensing of ethanol, acetone, n-propylamine and ammonia vapors at room temperature. Sandwich-type architectured (MI–MIO–MIIO) Ag–Ag2O–V2O5 nanosheets have been synthesized here. Structure, morphology and oxidation states of the material were analyzed using X-rays diffraction, FESEM and XPS studies, respectively. Gas sensing characteristics of the sensors based on Ag–V2O5 nanosheets were evaluated both in static and dynamic modes. It was figured out that the behavior of the sensing material is of p-type semiconducting nature with a high sensitivity value (≈22), fast response speed (in seconds) and high selectivity toward ethanol at room temperature which has been explained on the basis of semiconductors models. High sensitivity, quick response and significant selectivity of the sensors confirm their potential applications for environmental, security, food preservation and other industrial purposes.

In this chapter, we considered the effects of illumination and window sizes on the focus measures for accurate calculation of depth map. We showed that the illumination effects can directly result in incorrect estimation of depth map if proper window size is not used for computation. We used two well established focus measures, i.e., Sum of Modified Laplacian and Gray Level Variance. We proved that larger window size results in two major errors. One is the introduction of blurring which results in smoothing of the object hence giving false impression of 3D smoothing in depth map. Second is the wrong extraction of frame numbers for depth map corresponding to the sharpest pixel values in the sequence of the images. Hence, it is suggested that smaller window size should be used with the upper bound of 5x5 on the size of the window. Hence, without pre-processing for image enhancement and without use of proper window size, it is not possible to obtain the accurate depth map for 3D shape recovery. It is worth noting that the problem defined in this chapter is not limited to Shape From Focus only. Rather most of the image processing techniques (especially 3D image recovery algorithms) based on window processing are marred with this problem, i.e., usage of large window size. Hence, this chapter provides guidance for research in this direction too. In addition, we have presented a focus measure based on robustness in the presence of noise. We tested and compared this focus measure using simulated cone images, real cone images and slanted planar object images. The results show that this focus measure tends to perform better than the traditional focus measures when the noise is present in the images. We have shown the performance of various focus measures with three different types of noise, i.e., Gaussian, Shot and Speckle noise. The various focus measures used for comparison include Sum of Modified Laplacian (SML), Gray Level Variance (GLV), Tenenbaum and M2 focus measures which clearly indicate that the optical focus measure is equally good for images without noise and at the same time, it shows much enhanced performance in comparison to others in the presence of noise. It can be argued that some noise removal filter can be used before processing with the focus measure. However, as shown, the result of the proposed focus measure (FMO) is better even in the absence of noise. Further, FMO does not require noise removal filter because noise removal property is inherent within this technique. Lastly, we know that different types of noise removal filter are employed for different types of noise, e.g., median filter for shot noise, Weiner filter for Gaussian noise etc. Hence, some knowledge of noise is required before hand for the application of such filters. We used RMSE and Correlation metric measures to compare the performance of the earlier focus measures with our optical focus measure. The results clearly indicate that the RMSE values are lowest while the correlation values are the highest for the presented focus measure when compared with the SML, GLV, Tenenbaum and M2 focus measures at almost all the noise levels for all objects. It is concluded from the results that the best performance is shown by FMO followed by GLV, Tenenbaum, M2 and SML.

Forensic science is a discipline that has the capacity to be merged with every promise to offer a solution to a criminal investigation to help the legal system. Epigenetics is a branch of genetics that deals with the study of environmental interaction with the genome. The emerging field of forensic epigenetics has a variety of applications in criminal investigations. It provides an addictive tool to solve criminal activity-related issues. In this review, we have showcased the brief use of epigenetics to sort out forensic-related issues and help the legal system. We have briefly taken a snapshot of genetics, epigenetics, and application of epigenetics in forensic science that how it could offer solutions to queries of forensic nature. Also, potential future developments in the field and their probable impact have been anticipated. It has many potential applications which are still to be explored. It complements forensic science at many potential levels.

Stroop color-word test (STW) is a reliable method that has been used in laboratory to induce stress to the subject. This paper describes the development of Stroop color-word Test which was implemented based on five sections of test condition: i. Introductory Section (IS), ii. Resting Section (RS), iii. Congruent Section (CS), iv. Incongruent Section 1 (ICS-1), and v. Incongruent Section 2 (ICS-2). This test condition was identified from normal state until the highest stress state. Psychology experiment and MATLAB software were used in implementing the stress test. Electroencephalogram (EEG) signal of six healthy subjects was recorded while conducting this test. Power Spectral Density (PSD) was computed and analyzed. Results found that PSD can differentiate the level of stress from normal until the highest stress state. As such, it can be used as computer-assisted tool for stress detection.

Traditional methods focus on low-level handcrafted features representations and it is difficult to design a comprehensive classification algorithm for remote sensing scene classification problems. Recently, convolutional neural networks (CNNs) have obtained remarkable performance outcomes, setting several remote sensing benchmarks. Furthermore, direct applications of UAV remote sensing images that use deep convolutional networks are extremely challenging given high input data dimensionality with relatively small amounts of available labelled data. We, therefore, propose a CNN approach to scene classification that architecturally incorporates sparse coding (SC) technique for dimension reduction to minimize overfitting. Outcomes were compared with principal component analysis (PCA) and global average pooling (GAP) alternatives that use fully connected layer(s) in pre-trained CNN architecture(s) to minimize overfitting. SC was used to encode deep features extracted from the last convolutional layer of pre-trained CNN models by using different features maps in which deep features had been converted into low-dimensional SC features. These same sparse-coded features were concatenated by means of different pooling techniques to obtain global image features for scene classification. The proposed algorithm outperformed current state-of-the-art algorithms based on handcrafted features. When using our own UAV-based dataset and existing datasets, it was also exceptionally efficient computationally when learning data representations, producing a 93.64% accuracy rate..

Background Most patients with ankylosing spondylitis are diagnosed in their reproductive years and pregnancy can pose a challenge for the physician. Referral for preconception counselling, monitoring and management of these patients is best done in a multidisciplinary specialist clinic. To date, literature does not associate AS with adverse effects on pregnancy outcome. Patients can have unchanged, improved or active disease during pregnancy, however, postpartum flare is common. Methods We conducted a retrospective audit looking at pregnancy outcomes in patients referred to the High Risk Antenatal Clinic (HR-ANC) in our Trust between January 2014 and September 2019. Results A total of 18 patients with 22 pregnancies were found on our electronic system. Analysis showed an average number of visits to the clinic of 3.4 visits per patient with a median of 4. Only 45% had some form of preconception counselling prior to referral. 41% of the pregnancies were not exposed to any regular medications prior to conception. All other patients had their medications stopped after conception. 59% of pregnancies had medical induction of labour, 23% had a caesarian section. Median delivery was 39 weeks. The average birth weight was 3,022 grams, median 3,205 grams. 59% of the patients had antenatal disease flare and 45% had a postpartum flare. 32% of pregnancies did not have postpartum follow up. Conclusion Appropriate and thorough counselling should be offered to all patients with emphasis on disease course and management, safety of medications and neonatal vaccinations. Patients should be aware of a high chance of having active disease during pregnancy and the postpartum period. The rate of caesarian section in our patient cohort was not increased compared to the local rates in low risk pregnancies. These findings of disease activity in the antenatal and postpartum periods are consistent with published data. Good monitoring in a multidisciplinary clinic can lead to a successful outcome. Based on our findings, we are setting up a formal preconception counselling clinic for patients with AS with the following aims: 1. Family planning, including contraception advice. 2. Advice regarding medication use in the preconception, antenatal and postpartum periods. 3. Advice regarding disease course and expectations. 4. Advice regarding neonatal vaccinations, especially in patients on biological treatment. 5. Hot line for unplanned pregnancy and advice regarding medications and referral to the HR-ANC. Disclosures M. Khudadah None. S. Jansz None. A. Saeed None. M. Bickerstaff None.

Situational interest is widely explored in the psychology and education domains. It is proven to have positive effect on learning and academic achievement. Nonetheless, not much attention is given for assessing the feasibility of detecting this interest in natural classroom physiologically. Therefore, this study investigates the possibility of detecting situational interest using Electroencephalogram (EEG) in classroom. After preprocessing of EEG data, they were decomposed using Empirical Mode Decomposition (EMD). The resulted Intrinsic Mode Functions (IMFs) were ranked based on their significance using T-test and Receiver Operator Characteristics (ROC) in descending order. A matrix was constructed for all participants using the best six features from four EEG channels. These selected features were fed into Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers with 10 cross validation. While SVM achieved high accuracy of 93.3% and 87.5% for two data sets using features from the four EEG channels, KNN classifier achieved high accuracy of 87.5% and 86.7% in the same datasets using single EEG channel. It is found that gamma and delta bands can be used successfully to detect situational interest of students during learning in classrooms. Furthermore, data of single EEG channel - F3 in this study- was efficient to detect student’s situational interest in simultaneous recording of EEG in classroom.

The discrimination of four simple limb motor imagery movements for brain-computer interface (BCI) applications is still challenging. This is because most of the movement imaginations have close spatial representations on the motor cortex area. Nevertheless, due to its potential applications in significant areas including BCI, solutions need to be formulated to overcome the task discrimination issues faced when a motor imagery movement approach is utilized. Feature extraction is one of the most important steps in any BCI system; as such, enhancement to the existing methods has been incorporated in this work. For this, we propose four-class movement imaginations of the right hand, left hand, right foot, and left foot, and develop feature extraction methods utilizing discrete wavelet transform (DWT) and empirical mode decomposition (EMD); in both methods, artificial neural network (ANN) was used as a classifier. Based on the processed electroencephalography (EEG) data recorded from eleven subjects, it can be seen that EMD features outperform DWT features; the average accuracy achieved by the EMD features is 90.02%, and 84.77% using the DWT features. EMD even performs better than DWT in discriminating the most challenging tasks involving the right foot and left foot imageries, whose EEG data were derived from the same Cz node of the motor cortex.

Neuroscientists have investigated the functionality of the brain in detail and achieved remarkable results but this area still need further research. Functional magnetic resonance imaging (fMRI) is considered as the most reliable and accurate technique to decode the human brain activity, on the other hand electroencephalography (EEG) is a portable and low cost solution in brain research. The purpose of this study is to find whether EEG can be used to decode the brain activity patterns like fMRI. In fMRI, data from a very specific brain region is enough to decode the brain activity patterns due to the quality of data. On the other hand, EEG can measure the rapid changes in neuronal activity patterns due to its higher temporal resolution i.e., in msec. These rapid changes mostly occur in different brain regions. In this study, multivariate pattern analysis (MVPA) is used both for EEG and fMRI data analysis and the information is extracted from distributed activation patterns of the brain. The significant information among different classes is extracted using two sample t test in both data sets. Finally, the classification analysis is done using the support vector machine. A fair comparison of both data sets is done using the same analysis techniques, moreover simultaneously collected data of EEG and fMRI is used for this comparison. The final analysis is done with the data of eight participants; the average result of all conditions are found which is 65.7% for EEG data set and 64.1% for fMRI data set. It concludes that EEG is capable of doing brain decoding with the data from multiple brain regions. In other words, decoding accuracy with EEG MVPA is as good as fMRI MVPA and is above chance level.

The choice of an electroencephalogram (EEG) reference has fundamental importance and could be critical during clinical decision-making because an impure EEG reference could falsify the clinical measurements and subsequent inferences. In this research, the suitability of three EEG references was compared while classifying depressed and healthy brains using a machine-learning (ML)-based validation method. In this research, the EEG data of 30 unipolar depressed subjects and 30 age-matched healthy controls were recorded. The EEG data were analyzed in three different EEG references, the link-ear reference (LE), average reference (AR), and reference electrode standardization technique (REST). The EEG-based functional connectivity (FC) was computed. Also, the graph-based measures, such as the distances between nodes, minimum spanning tree, and maximum flow between the nodes for each channel pair, were calculated. An ML scheme provided a mechanism to compare the performances of the extracted features that involved a general framework such as the feature extraction (graph-based theoretic measures), feature selection, classification, and validation. For comparison purposes, the performance metrics such as the classification accuracies, sensitivities, specificities, and F scores were computed. When comparing the three references, the diagnostic accuracy showed better performances during the REST, while the LE and AR showed less discrimination between the two groups. Based on the results, it can be concluded that the choice of appropriate reference is critical during the clinical scenario. The REST reference is recommended for future applications of EEG-based diagnosis of mental illnesses.

Extracting the background from a video in the presence of various moving patterns is the focus of several background-initialization approaches. To model the scene background using rank-one matrices, this paper proposes a background-initialization technique that relies on the singular-value decomposition (SVD) of spatiotemporally extracted slices from the video tensor. The proposed method is referred to as spatiotemporal slice-based SVD (SS-SVD). To determine the SVD components that best model the background, a depth analysis of the computation of the left/right singular vectors and singular values is performed, and the relationship with tensor-tube fibers is determined. The analysis proves that a rank-1 matrix extracted from the first left and right singular vectors and singular value represents an efficient model of the scene background. The performance of the proposed SS-SVD method is evaluated using 93 complex video sequences of different challenges, and the method is compared with state-of-the-art tensor/matrix completion-based methods, statistical-based methods, search-based methods, and labeling-based methods. The results not only show better performance over most of the tested challenges, but also demonstrate the capability of the proposed technique to solve the background-initialization problem in a less computational time and with fewer frames.

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

In this paper, we investigated the relationships between EEG and semantic long-term memory retrieval using spectral analysis and functional connectivity. We employed an EEG data set from a previous study, which was recorded from 34 healthy participants during a memory retrieval process. The data set consists of EEG collected during two tasks, a learning task, in which participants studied animated learning material, and a memory recall task, in which they recalled the learned information. We analyzed both EEG signals and behavioral responses. We conducted spectral analyses for the absolute power and relative power of EEG frequency bands, EEG coherence, absolute phase delay, and EEG amplitude asymmetry features. The delta-band EEG and theta-band absolute power were negatively correlated with task performance at frontal regions, while the alpha-band activity was positively correlated in frontal regions and negatively correlated in parietal and temporal sites. High-beta in the frontal lobe was positively correlated with memory recall score. EEG coherence was negatively correlated in the delta band at the fronto-parietal and fronto-central sites, and positively correlated in the gamma band at widespread regions, including all lobes. EEG phase delay showed the highest degree of connectivity in the delta and alpha bands at frontal sites, and was negatively correlated with memory recall score. The negative correlation in phase delay demonstrated the synchronization and positive correlation reflected the de-synchronization of electrode pairs during memory recollection in the delta and alpha frequencies. We concluded that neuronal networks producing low EEG frequencies at frontal regions and those producing high frequencies in widespread regions are active during the memory recollection process. Thus, semantic memory storage involves widespread neuronal networks across the brain.

Background The abnormal alcohol consumption could cause toxicity and could alter the human brain’s structure and function, termed as alcohol used disorder (AUD). Unfortunately, the conventional screening methods for AUD patients are subjective and manual. Hence, to perform automatic screening of AUD patients, objective methods are needed. The electroencephalographic (EEG) data have been utilized to study the differences of brain signals between alcoholics and healthy controls that could further developed as an automatic screening tool for alcoholics. Method In this work, resting-state EEG-derived features were utilized as input data to the proposed feature selection and classification method. The aim was to perform automatic classification of AUD patients and healthy controls. The validation of the proposed method involved real-EEG data acquired from 30 AUD patients and 30 age-matched healthy controls. The resting-state EEG-derived features such as synchronization likelihood (SL) were computed involving 19 scalp locations resulted into 513 features. Furthermore, the features were rank-ordered to select the most discriminant features involving a rank-based feature selection method according to a criterion, i.e., receiver operating characteristics (ROC). Consequently, a reduced set of most discriminant features was identified and utilized further during classification of AUD patients and healthy controls. In this study, three different classification models such as Support Vector Machine (SVM), Naive Bayesian (NB), and Logistic Regression (LR) were used. Results The study resulted into SVM classification accuracy = 98%, sensitivity = 99.9%, specificity = 95%, and f-measure = 0.97; LR classification accuracy = 91.7%, sensitivity = 86.66%, specificity = 96.6%, and f-measure = 0.90; NB classification accuracy = 93.6%, sensitivity = 100%, specificity = 87.9%, and f-measure = 0.95. Conclusion The SL features could be utilized as objective markers to screen the AUD patients and healthy controls.

This work offers an approach to aerial image classification for use in remote sensing object recognition, image processing and computer vision. Sparse coding (SC) is used to classify unmanned-aerial-vehicle (UAV) and satellite images because SC representation can generalize a large dataset and improve the detection of distinctive features by reducing calculation time for feature matching and classification. Features from images are extracted based on the following descriptors: (a) Scale Invariant Feature Transform; (b) Histogram of Oriented Gradients; and (c) Local Binary Patterns. SC representation and local image features are combined to represent global features for classification. Features are deployed in a sparse model to store descriptor features using extant dictionaries such as (a) the Discrete Cosine Transform and (b) the Discrete Wavelet Transform. An additional two dictionaries are proposed as developed for the present work: (c) the Discrete Ridgelet Transform (DRT) and (d) the Discrete Tchebichef Transform. The DRT dictionary is constructed by using the Ricker wavelet function to generate finite Ridgelet transforms as basis elements for a hybrid dictionary. Different pooling methods have also been employed to convert sparse-coded features into a feature matrix. Various machine learning algorithms are then applied to the feature matrix to classify objects contained in UAV and satellite imagery data. Experimental results show that the SC model secured better accuracy rates for extracted discriminative features contained in remote sensing images. The authors concluded that the proposed SC technique and proposed dictionaries provided feasible solutions for image classification and object recognition.

Background Simultaneous electroencephalography (EEG) and functional magnetic resonance image (fMRI) acquisitions provide better insight into brain dynamics. Some artefacts due to simultaneous acquisition pose a threat to the quality of the data. One such problematic artefact is the ballistocardiogram (BCG) artefact. Methods We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact. Results The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals. Comparison with existing methods Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy. Conclusions The proposed method does not require any reference signal, prior information or assumption to extract the BCG artefact. It will be very useful in circumstances where the reference signal is not available.

This research studies the impact of the imagination of movements and associated feedbacks on the modulation of sensorimotor electroencephalographic (EEG) rhythms, for the online controls of a brain-computer interface (BCI). Nine subjects with no physical or mental impairments were selected. The number of sessions was five: one calibration and four feedback sessions. A computer screen’s cursor movement was controlled in one dimension using EEG-based four-class BCI involving motor imagery tasks of moving the right hand, the left hand, both hands, or both feet. Our findings reveal that the visual feedback applied during motor imagery movement modulates sensorimotor EEG rhythms clearly in the mu and beta bands. The analyses of event-related desynchronization/synchronization (ERD/ERS) suggest significant differences between brain activities in the calibration and feedback sessions; large ERDs during the online feedback sessions compared to that in the calibration session have been observed. The increasing ERDs in the online feedback session were noticed over the mu1 (8–10 Hz) and upper beta (18–24 Hz) rhythms, resulting in the cursor control success rate at 73.3%.

International audience; Video-based heartbeat rate measurement is a rapidly growing application in remote health monitoring. Video-based heartbeat rate measuring methods operate mainly by estimating photoplethysmography or ballistocardiography signals. These methods operate by estimating the microscopic color change in the face or by estimating the microscopic rigid motion of the head/facial skin. However, the robustness to motion artifacts caused by illumination variance and motion variance of the subject poses main challenge. We present a video-based heartbeat rate measuring framework to overcome these problems by using the principle of ballistocardiography. In this paper, we proposed a ballistocardiography model based on Newtons third law of force and dynamics of harmonic oscillation. We formulate a framework based on the ballistocardiography model to measure the rigid involuntary head motion caused by the ejection of the blood from the heart. Our proposed framework operates by estimating the motion of multivariate feature points to estimate the heartbeat rate autonomously. We evaluated our proposed framework along with existing video-based heartbeat rate measuring methods with three databases, namely; MAHNOB HCI database, human-computer interaction database, and driver health monitoring database. Our proposed framework outperformed existing methods by reporting a low mean error rate of 4.34 bpm with a standard deviation of 3.14 bpm, root mean square error of 5.29 with a high Pearson correlation coefficient of 0.91. The proposed method also operated robustly in the human-computer interaction database and driver health monitoring database by overcoming the issues related to illumination and motion variance.

Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and early diagnosis for depression could become challenging. This paper proposed a machine learning framework involving EEG-derived synchronization likelihood (SL) features as input data for automatic diagnosis of MDD. It was hypothesized that EEG-based SL features could discriminate MDD patients and healthy controls with an acceptable accuracy better than measures such as interhemispheric coherence and mutual information. In this work, classification models such as support vector machine (SVM), logistic regression (LR) and Naïve Bayesian (NB) were employed to model relationship between the EEG features and the study groups (MDD patient and healthy controls) and ultimately achieved discrimination of study participants. The results indicated that the classification rates were better than chance. More specifically, the study resulted into SVM classification accuracy = 98%, sensitivity = 99.9%, specificity = 95% and f-measure = 0.97; LR classification accuracy = 91.7%, sensitivity = 86.66%, specificity = 96.6% and f-measure = 0.90; NB classification accuracy = 93.6%, sensitivity = 100%, specificity = 87.9% and f-measure = 0.95. In conclusion, SL could be a promising method for diagnosing depression. The findings could be generalized to develop a robust CAD-based tool that may help for clinical purposes.

Aerial scene classification purposes to automatically label aerial images with specific semantic categories. However, cataloguing presents a fundamental problem for high-resolution remote-sensing imagery HRRS. Recent developments include several approaches and numerous algorithms address the task. This article proposes a convolutional neural network CNN approach that utilizes sparse coding for scene classification applicable for HRRS unmanned aerial vehicle UAV and satellite imagery. The article has two major sections: the first describes the extraction of dense multiscale features multiple scales from the last convolutional layer of a pre-trained CNN models; the second describes the encoding of extracted features into global image features via sparse coding to achieve scene classification. The authors compared experimental outcomes with existing techniques such as Scale-Invariant Feature Transform and demonstrated that features from pre-trained CNNs generalized well with HRRS datasets and were more expressive than low-and mid-level features, exhibiting an overall 90.3% accuracy rate for scene classification compared to 85.4% achieved by SIFT with sparse coding. Thus, the proposed CNN-based sparse coding approach obtained a robust performance that holds promising potential for future applications in satellite and UAV imaging.

Fires or electrical hazards and accidents can occur if vegetation is not controlled or cleared around overhead power lines, resulting in serious risks to people and property and significant costs to the community. There are numerous blackouts due to interfering the trees with the power transmission lines in hilly and urban areas. Power distribution companies are facing a challenge to monitor the vegetation to avoid blackouts and flash-over threats. Recently, several methods have been developed for vegetation monitoring; however, existing methods are either not accurate or could not provide better disparity map in the textureless region. Moreover, are not able to handle depth discontinuity in stereo thus are not able to find a feasible solution in the smooth areas to compute the disparity map. This study presents a cost-effective framework based on UAV and satellite Stereo images to monitor the trees and vegetation, which provide better disparity. We present a novel approach based on the fusion of the convolutional neural network (CNN) and sparse representation that handled textureless region, depth discontinuity and smooth region to produce better disparity map that further used for threat estimation using height and distance of vegetation/trees near power lines and poles. Extensive experimental evaluation on real time powerline monitoring showed considerable imporvemnt in vegetation threat estimation with accuracy of 90.3% in comparison to graph-cut, dynamic programming, belief propagation, and area-based methods.

For crowd analytics and surveillance systems, motion estimation is an essential first step. Lots of crowd motion estimation algorithms have been presented in the last years comprising pedestrian motion. However, algorithms based on optical flow and background subtraction have numerous limitations such as the complexity of the computation in the presence of high dense crowd and sudden motion changes. Therefore, a novel estimation algorithm is proposed to measure the motion of crowd with less computational complexity and satisfy the real time requirements. The proposed algorithm is based on block-based matching, particle advection, and social force model. By the block-based matching, the motion is estimated in each frame, and the corresponding motion field is created. The particle advection process provides more information about the behavior of pedestrians groups, their tracked trajectories and the boundary of each group segment. Relying on the social force model, a predicted direction of the motion vectors (MV) could be measured significantly. Subsequently, the block-based technique is combined with the social force model to obtain the accurate motion vector with the less possible number of search points. The experimental results indicate that the proposed method achieves high performance by reducing the search points, particularly when many collision situations or obstacles exist in the scenes. Considering the reduction in the computational complexity, the quality of degradation is very low. In all cases, average PSNR degradation of the proposed algorithm is only 0.09.

Human behavior analysis has become a critical area of research in computer vision and artificial intelligence research community. In recent years, video surveillance systems of crowd scenes have witnessed an increased demand in different applications, such as safety, security, entertainment, and personal mental health. Although many methods have been proposed, certain limitations exist, and many unresolved issues remain open. In this paper, we proposed a novel spatio-temporal sparse coding representation, based on sparse coded features with $k$ -means singular value decomposition for robust classification of crowd behaviors. Extensive experiments have shown that dictionary learning method with sparsely coded features captured vital structures of video scenes and yielded discriminant descriptors for classifications than conventional bag-of-visual-features. Relying on the measurable features of crowd scenes and motion characteristics, we can represent different attributes of the crowd scenes. Experiments on hundreds of video scenes were carried out on publicly available datasets. Quantitative evaluation indicates that the proposed model display superior accuracy, precision, and recall in classifying human behaviors with linear support vector machine when compared with the state-of-the-art methods. The proposed method is conceptually simple and easy to train: thereby achieving an accuracy of 93.50%, a precision of 93.40%, and a recall of 95.96%.

Assessing cognitive load during a learning phase is important, as it assists to understand the complexity of the learning task. It can help in balancing the cognitive load of postlearning and during the actual task. Here, we used electroencephalography (EEG) to assess cognitive load in multimedia learning task. EEG data were collected from 34 human participants at baseline and a multimedia learning state. The analysis was based on feature extraction and partial directed coherence (PDC). Results revealed that the EEG frequency bands and activated brain regions that contribute to cognitive load differed depending on the learning state. We concluded that cognitive load during multimedia learning can be assessed using feature extraction and measures of effective connectivity (PDC).

Recently, Electroencephalogram (EEG)-based computer-aided (CAD) techniques have shown their promise as decision-making tools to diagnose major depressive disorder (MDD) or simply depression. Although the research results have motivated the use of CAD techniques to help assist psychiatrists in clinics yet their clinical translation has been less clear and remains a research topic. In this paper, a proposed machine learning (ML) scheme was tested and validated with resting-state EEG data involving 33 MDD patients and 30 healthy controls. The EEG-derived measures such as power of different EEG frequency bands and EEG alpha interhemispheric asymmetry were investigated as input features to the proposed ML scheme to discriminate the MDD patients and healthy controls, and to prove their feasibility for diagnosing depression. The acquired EEG data were subjected to noise removal and feature extraction. As a result, a data matrix was constructed by the columns-wise concatenation of the extracted features. Furthermore, the z-score standardization was performed to standardize each column of the data matrix according to its mean and variance. The data matrix may have redundant and irrelevant features; therefore, to determine the most significant features, a weight was assigned to each feature based on its ability to separate the target classes according to the criterion, i.e., receiver operating characteristics (roc). Hence, only the most significant features were used for testing and training the classifier models: Logistic regression (LR), Support vector machine (SVM), and Naive Bayesian (NB). Finally, the classifier models were validated with 10-fold cross-validation that has provided the performance metrics such as test accuracy, sensitivity, and specificity. As a result of the investigations, most significant features such as EEG signal power and EEG alpha interhemispheric asymmetry from the brain areas such as frontal, temporal, parietal and occipital were found significant. In addition, the proposed ML framework proved automatic identification of aberrant EEG patterns specific to disease conditions and provide high classification results i.e., LR classifier (accuracy = 97.6%, sensitivity = 96.66%, specificity = 98.5%), NB classification (accuracy = 96.8%, sensitivity = 96.6%, specificity = 97.02%), and SVM (accuracy = 98.4%, sensitivity = 96.66%, specificity = 100%). In conclusion, the proposed ML scheme along with the EEG signal power and EEG alpha interhemispheric asymmetry are proved suitable as clinical diagnostic tools for MDD.

Mental stress has become a social issue and could become a cause of functional disability during routine work. In addition, chronic stress could implicate several psychophysiological disorders. For example, stress increases the likelihood of depression, stroke, heart attack, and cardiac arrest. The latest neuroscience reveals that the human brain is the primary target of mental stress, because the perception of the human brain determines a situation that is threatening and stressful. In this context, an objective measure for identifying the levels of stress while considering the human brain could considerably improve the associated harmful effects. Therefore, in this paper, a machine learning (ML) framework involving electroencephalogram (EEG) signal analysis of stressed participants is proposed. In the experimental setting, stress was induced by adopting a well-known experimental paradigm based on the montreal imaging stress task. The induction of stress was validated by the task performance and subjective feedback. The proposed ML framework involved EEG feature extraction, feature selection (receiver operating characteristic curve, t-test and the Bhattacharya distance), classification (logistic regression, support vector machine and naïve Bayes classifiers) and tenfold cross validation. The results showed that the proposed framework produced 94.6% accuracy for two-level identification of stress and 83.4% accuracy for multiple level identification. In conclusion, the proposed EEG-based ML framework has the potential to quantify stress objectively into multiple levels. The proposed method could help in developing a computer-aided diagnostic tool for stress detection.

Human brain has a complex structure with the billions of neurons, so it is a difficult and challenging task to predict the behavior of human brain. Different methods and classifiers are used to measure and classify the brain activities with higher accuracy and reliability. In this paper, instead of using mostly used classifier (support vector machine), prediction of the brain activity is done by estimating the match score densities. This method is based on likelihood ratio test which helps in finding the optimal combination of match scores. The distributions of match scores are modeled for different classes based on density score fusion in which the densities of different classes are estimated from the training data set and match scores are found by fusing the estimated densities with the testing data. The fusion is done with the data extracted from distributed activation patterns using multivariate pattern analysis (MVPA) against a visual task. MVPA is an intense strategy which helps in better understanding of the human brain. The match score-based technique is used in different biometric systems but never been used for the prediction of brain activity. In order to test the performance of proposed method, prediction accuracy is compared with the support vector machine using two data sets of different modalities, one is electroencephalography (EEG) and the other is functional magnetic resonance imaging (fMRI). The results show that the proposed method predicts the novel data with improved accuracy of 66.1% and 69.3% compared with support vector machine which have 64.15% and 65.7% for fMRI and EEG data sets, respectively.

International audience; Driving distraction is a topic of great interest in the transport safety-research community, because it is now a primary cause of road accidents. A recent report has revealed that distraction is more alarming than previously thought, and a suitable measurement to effectively detect distraction is required. Most agree that driving distraction actually comprises the simultaneous interaction of two or more types of distraction. The purpose of this paper is, therefore, to determine the promising method for measuring visual cognitive distraction. We discuss the five common measurement methods for visual and cognitive driving distraction, which include driving performance, driver physical measures, driver biological measures, subjective reports, and hybrid measures. Hybrid measurement of driver's physical measures (e.g., eye movement) and driver's biological measures (e.g., electroencephalogram) is better than other methods at detecting types of visual cognitive distraction. This new perspective on measurement methods will help the field of transport safety to determine the best means of detecting and measuring the effect of visual cognitive distraction.

Screening alcohol use disorder (AUD) patients has been challenging due to the subjectivity involved in the process. Hence, robust and objective methods are needed to automate the screening of AUD patients. In this paper, a machine learning method is proposed that utilized resting-state electroencephalography (EEG)-derived features as input data to classify the AUD patients and healthy controls and to perform automatic screening of AUD patients. In this context, the EEG data were recorded during 5 min of eyes closed and 5 min of eyes open conditions. For this purpose, 30 AUD patients and 15 aged-matched healthy controls were recruited. After preprocessing the EEG data, EEG features such as inter-hemispheric coherences and spectral power for EEG delta, theta, alpha, beta and gamma bands were computed involving 19 scalp locations. The selection of most discriminant features was performed with a rank-based feature selection method assigning a weight value to each feature according to a criterion, i.e., receiver operating characteristics curve. For example, a feature with large weight was considered more relevant to the target labels than a feature with less weight. Therefore, a reduced set of most discriminant features was identified and further be utilized during classification of AUD patients and healthy controls. As results, the inter-hemispheric coherences between the brain regions were found significantly different between the study groups and provided high classification efficiency (Accuracy = 80.8, sensitivity = 82.5, and specificity = 80, F-Measure = 0.78). In addition, the power computed in different EEG bands were found significant and provided an overall classification efficiency as (Accuracy = 86.6, sensitivity = 95, specificity = 82.5, and F-Measure = 0.88). Further, the integration of these EEG feature resulted into even higher results (Accuracy = 89.3 %, sensitivity = 88.5 %, specificity = 91 %, and F-Measure = 0.90). Based on the results, it is concluded that the EEG data (integration of the theta, beta, and gamma power and inter-hemispheric coherence) could be utilized as objective markers to screen the AUD patients and healthy controls.

Wireless Capsule Endoscopy (WCE) is becoming an extensively accepted method used by physicians to diagnose diseases which affect small intestine. During the WCE examination of patients, a series of images are acquired to generate a video footage. Inspecting a WCE video is laborious and time-consuming that results in a limited application of WCE. Therefore, it would be extremely advantageous to develop an intelligent algorithm to inspect the WCE images. This research aims to develop an algorithm to enhance wireless capsule endoscopy images and analyses them to detect Ulcer located in small intestine. This algorithm of capsule images can be clinically very useful as it can discriminate between normal and abnormal frame. It also provides correlative information for huge set of data. We have proposed color detection method in RGB color space as it is a key technique of color image interpretation and recognition, as well as suitable for Ulcer detection in WCE images. Support Vector Machine (SVM) used to classify frames according to findings. Analysis is done on basis of pixel and frame information to check whether it is detected correctly or not. © 2016 American Scientific Publishers. All rights reserved.

Memory plays an important role in human life. Memory can be divided into two categories, i.e., long term memory and short term memory (STM). STM or working memory (WM) stores information for a short span of time and it is used for information manipulations and fast response activities. WM is generally involved in the higher cognitive functions of the brain. Different studies have been carried out by researchers to understand the WM process. Most of these studies were based on neuroimaging modalities like fMRI, EEG, MEG etc., which use standalone processes. Each neuroimaging modality has some pros and cons. For example, EEG gives high temporal resolution but poor spatial resolution. On the other hand, the fMRI results have a high spatial resolution but poor temporal resolution. For a more in depth understanding and insight of what is happening inside the human brain during the WM process or during cognitive tasks, high spatial as well as high temporal resolution is desirable. Over the past decade, researchers have been working to combine different modalities to achieve a high spatial and temporal resolution at the same time. Developments of MRI compatible EEG equipment in recent times have enabled researchers to combine EEG-fMRI successfully. The research publications in simultaneous EEG-fMRI have been increasing tremendously. This review is focused on the WM research involving simultaneous EEG-fMRI data acquisition and analysis. We have covered the simultaneous EEG-fMRI application in WM and data processing. Also, it adds to potential fusion methods which can be used for simultaneous EEG-fMRI for WM and cognitive tasks.

Electroencephalography EEG is widely used in variety of research and clinical applications which includes the localization of active brain sources. Brain source localization provides useful information to understand the brain's behavior and cognitive analysis. Various source localization algorithms have been developed to determine the exact locations of the active brain sources due to which electromagnetic activity is generated in brain. These algorithms are based on digital filtering, 3D imaging, array signal processing and Bayesian approaches. According to the spatial resolution provided, the algorithms are categorized as either low resolution methods or high resolution methods. In this research study, EEG data is collected by providing visual stimulus to healthy subjects. FDM is used for head modelling to solve forward problem. The low-resolution brain electromagnetic tomography LORETA and standardized LORETA sLORETA have been used as inverse modelling methods to localize the active regions in the brain during the stimulus provided. The results are produced in the form of MRI images. The tables are also provided to describe the intensity levels for estimated current level for the inverse methods used. The higher current value or intensity level shows the higher electromagnetic activity for a particular source at certain time instant. Thus, the results obtained demonstrate that standardized method which is based on second order Laplacian sLORETA in conjunction with finite difference method FDM as head modelling technique outperforms other methods in terms of source estimation as it has higher current level and thus, current density J for an area as compared to others.

The recent innovation in technology led to huge advancement in Human-Computer Interface (HCI) systems and applications. Detection of brain activities is the vital element in these applications. This paper is employing Singular Value Decomposition (SVD) on EEG data acquired simultaneously from students in classroom to detect the changes of brain activities during learning process. Situational interest of subjects and the learning materials were evaluated through questionnaires. After preprocessing and segmentation of the data, SVD was applied on each segment separately. The 2-norms of the singular values were compared to the subject baseline and the overall result complied with the questionnaire result. Furthermore, feeding these features to Support Vector Machine (SVM) classifier achieved 83.3% accuracy in differentiating between high and low situationally interested students. It is therefore, suggested that SVD could be applied successfully to detect changes in students’ brain activities in classrooms.

Classification of resting and cognitive states has its importance in brain neuroscience for understating the underlying behaviors of cognition. The human brain is considered as a complex system having different mental states such as resting, active or cognitive states. It is a well-understood fact that the brain activity increases with the increased demand of cognition. In this paper, the cognitive and resting state classification based on EEG-based feature extraction and connectivity approaches are described. EEG-based connectivity approaches are a good discriminator for different mental states. EEG data were collected from 34 human participants at resting and during a learning state. After preprocessing, EEG-based feature extraction method and connectivity approach were implemented, and their results were classified. Results showed that the connectivity approach gave 79.90% accuracy while the highest accuracy achieved by feature extraction approach was 78.50%. It is concluded that EEG-based connectivity approach discriminates the resting and cognitive states more efficiently.

Classification of resting and cognitive states has its importance in brain neuroscience for understating the underlying behaviors of cognition. The human brain is considered as a complex system having different mental states such as resting, active or cognitive states. It is a well-understood fact that the brain activity increases with the increased demand of cognition. The deep learning algorithm based on Pre-trained convolutional neural network (CNN) networks have been used as a transfer learning for the classification of rest and cognitive states and also assessed the cognitive load using brain waves particularly alpha wave. EEG data were collected from 34 human participants at resting and during a learning state. The important EEG electrodes were selected based on the effective connectivity method and after preprocessing, EEG data has been segmented into equal number of segments using selected electrodes to investigate the deep temporal information for cognitive load assessment. The brain waves were extracted using discrete wavelet transform (DWT) for each segment and fed these segments to proposed model for classification and assessment of cognitive load. The results shows that alpha brain wave produced consistent behavior using for all cognition tasks based on pre-trained CNN models for classification and cognitive load assessment.

This study analyzes the learning styles of subjects based on their electroencephalo-graphy (EEG) signals. The goal is to identify how the EEG features of a visual learner differ from those of a non-visual learner. The idea is to measure the students' EEGs during the resting states (eyes open and eyes closed conditions) and when performing learning tasks. For this purpose, 34 healthy subjects are recruited. The subjects have no background knowledge of the animated learning content. The subjects are shown the animated learning content in a video format. The experiment consists of two sessions and each session comprises two parts: (1) Learning task: the subjects are shown the animated learning content for an 8-10 min duration. (2) Memory retrieval task The EEG signals are measured during the leaning task and memory retrieval task in two sessions. The retention time for the first session was 30 min, and 2 months for the second session. The analysis is performed for the EEG measured during the memory retrieval tasks. The study characterizes and differentiates the visual learners from the non-visual learners considering the extracted EEG features, such as the power spectral density (PSD), power spectral entropy (PSE), and discrete wavelet transform (DWT). The PSD and DWT features are analyzed. The EEG PSD and DWT features are computed for the recorded EEG in the alpha and gamma frequency bands over 128 scalp sites. The alpha and gamma frequency band for frontal, occipital, and parietal regions are analyzed as these regions are activated during learning. The extracted PSD and DWT features are then reduced to 8 and 15 optimum features using principal component analysis (PCA). The optimum features are then used as an input to the k-nearest neighbor (k-NN) classifier using the Mahalanobis distance metric, with 10-fold cross validation and support vector machine (SVM) classifier using linear kernel, with 10-fold cross validation. The classification results showed 97% and 94% accuracies rate for the first session and 96% and 93% accuracies for the second session in the alpha and gamma bands for the visual learners and non-visual learners, respectively, for k-NN classifier for PSD features and 68% and 100% accuracies rate for first session and 100% accuracies rate for second session for DWT features using k-NN classifier for the second session in the alpha and gamma band. For PSD features 97% and 96% accuracies rate for the first session, 100% and 95% accuracies rate for second session using SVM classifier and 79% and 82% accuracy for first session and 56% and 74% accuracy for second session for DWT features using SVM classifier. The results showed that the PSDs in the alpha and gamma bands represent distinct and stable EEG signatures for visual learners and non-visual learners during the retrieval of the learned contents.

Student's performance assessment tools had gone through a huge improvement throughout time and reached high levels of reliability and accuracy. According to several studies, situational interest measure can be used to assess and improve student's academic achievement. So far, there is a lack in defining objective measures for situational interest rather than depending on the subjective measures. In this paper, we propose the use of EMD-based feature to detect situational interest in classroom settings using EEG. Several subjective measures: Personal Interest (PI) test, Situational Interest (SI) test and knowledge tests were used to evaluate subjectively student's interest. EEG data were then preprocessed and decomposed using Empirical Mode Decomposition (EMD) and the resulted Intrinsic Mode Functions (IMFs) was ranked using t-test to select the most significant ones. Eight features out of the 64 features extracted, were then fed into KNN and SVM classifiers achieving high accuracy of 90% and 100% respectively using features from temporal regions.