Control of Hybrid Diesel/PV/Battery/Ultra-Capacitor Systems for Future Shipboard Microgrids

In recent times, concerns over fossil fuel consumption and severe environmental pollution have grabbed attention in marine vessels. The fast development in solar technology and the significant reduction in cost over the past decade have allowed the integration of solar technology in marine vessels. However, the highly intermittent nature of photovoltaic (PV) modules might cause instability in shipboard microgrids. Moreover, the penetration is much more in the case of utilizing PV panels on ships due to the continuous movement. This paper, therefore, presents a frequency sharing approach to smooth the effect of the highly intermittent nature of PV panels integrated with the shipboard microgrids. A hybrid system based on an ultra-capacitor and a lithium-ion battery is developed such that high power and short term fluctuations are catered by an ultra-capacitor, whereas long duration and high energy density fluctuations are catered by the lithium-ion battery. Further, in order to cater for the fluctuations caused by weather or variation in sea states, a battery energy storage system (BESS) is utilized in parallel to the dc-link capacitor using a buck-boost converter. Hence, to verify the dynamic behavior of the proposed approach, the model is designed in MATLAB/SIMULINK. The simulation results illustrate that the proposed model helps to smooth the fluctuations and to stabilize the DC bus voltage

EEG-based multi-modal emotion recognition using bag of deep features: An optimal feature selection approach

Much attention has been paid to the recognition of human emotions with the help of electroencephalogram (EEG) signals based on machine learning technology. Recognizing emotions is a challenging task due to the non-linear property of the EEG signal. This paper presents an advanced signal processing method using the deep neural network (DNN) for emotion recognition based on EEG signals. The spectral and temporal components of the raw EEG signal are first retained in the 2D Spectrogram before the extraction of features. The pre-trained AlexNet model is used to extract the raw features from the 2D Spectrogram for each channel. To reduce the feature dimensionality, spatial, and temporal based, bag of deep features (BoDF) model is proposed. A series of vocabularies consisting of 10 cluster centers of each class is calculated using the k-means cluster algorithm. Lastly, the emotion of each subject is represented using the histogram of the vocabulary set collected from the raw-feature of a single channel. Features extracted from the proposed BoDF model have considerably smaller dimensions. The proposed model achieves better classification accuracy compared to the recently reported work when validated on SJTU SEED and DEAP data sets. For optimal classification performance, we use a support vector machine (SVM) and k-nearest neighbor (k-NN) to classify the extracted features for the different emotional states of the two data sets. The BoDF model achieves 93.8% accuracy in the SEED data set and 77.4% accuracy in the DEAP data set, which is more accurate compared to other state-of-the-art methods of human emotion recognition. - 2019 by the authors. Licensee MDPI, Basel, Switzerland.Funding: This research was funded by Higher Education Commission (HEC): Tdf/67/2017.Scopu

Low-Rank Multi-Channel Features for Robust Visual Object Tracking

Kernel correlation filters (KCF) demonstrate significant potential in visual object tracking by employing robust descriptors. Proper selection of color and texture features can provide robustness against appearance variations. However, the use of multiple descriptors would lead to a considerable feature dimension. In this paper, we propose a novel low-rank descriptor, that provides better precision and success rate in comparison to state-of-the-art trackers. We accomplished this by concatenating the magnitude component of the Overlapped Multi-oriented Tri-scale Local Binary Pattern (OMTLBP), Robustness-Driven Hybrid Descriptor (RDHD), Histogram of Oriented Gradients (HoG), and Color Naming (CN) features. We reduced the rank of our proposed multi-channel feature to diminish the computational complexity. We formulated the Support Vector Machine (SVM) model by utilizing the circulant matrix of our proposed feature vector in the kernel correlation filter. The use of discrete Fourier transform in the iterative learning of SVM reduced the computational complexity of our proposed visual tracking algorithm. Extensive experimental results on Visual Tracker Benchmark dataset show better accuracy in comparison to other state-of-the-art trackers

Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’ Response Against COVID Variants

The COVID-19 outbreak began in December 2019 and was declared a global health emergency by the World Health Organization. The four most dominating variants are Beta, Gamma, Delta, and Omicron. After the administration of vaccine doses, an eminent decline in new cases has been observed. The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies. However, strong variants like Delta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination. Therefore, it is indispensable to study, analyze and most importantly, predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons. In this regard, machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes. In this study, prediction of T-cells Epitopes’ response was conducted for vaccinated and unvaccinated people for Beta, Gamma, Delta, and Omicron variants. The dataset was divided into two classes, i.e., vaccinated and unvaccinated, and the predicted response of T-cell Epitopes was divided into three categories, i.e., Strong, Impaired, and Over-activated. For the aforementioned prediction purposes, a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers. Furthermore, the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach. Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error

Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques

Recent technological developments pave the path for deep learning-based techniques to be used in almost every domain of life. The precision of deep learning techniques make it possible for these to be used in the medical field for the classification and detection of various diseases. Recently, the coronavirus (COVID-19) pandemic has put a lot of pressure on the health system all around the world. The diagnosis of COVID-19 is possible by PCR testing and medical imagining. Since COVID-19 is highly contagious, diagnosis using chest X-ray is considered safe in various situations. In this study, a deep learning-based technique is proposed to classify COVID-19 infection from other non-COVID-19 infections. To classify COVID-19, three different pre-trained models named EfficientNetB1, NasNetMobile and MobileNetV2 are used. The augmented dataset is used for training deep learning models while two different training strategies have been used for classification. In this study, not only are the deep learning model fine-tuned but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. Moreover, the classification head is regularized to improve the performance. For the evaluation of the proposed techniques, several performance parameters are used to gauge the performance. EfficientNetB1 with regularized classification head outperforms the other models. The proposed technique successfully classifies four classes that include COVID-19, viral pneumonia, lung opacity, and normal, with an accuracy of 96.13%. The proposed technique shows superiority in terms of accuracy when compared with recent techniques present in the literature

Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks

Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream. Many researchers have been working on vision-based gesture recognition due to its various applications. This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network (3D-CNN) and a Long Short-Term Memory (LSTM) network. The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation. The 3D-CNN is used for the extraction of spectral and spatial features which are then given to the LSTM network through which classification is carried out. The proposed model is a light-weight architecture with only 3.7 million training parameters. The model has been evaluated on 15 classes from the 20BN-jester dataset available publicly. The model was trained on 2000 video-clips per class which were separated into 80% training and 20% validation sets. An accuracy of 99% and 97% was achieved on training and testing data, respectively. We further show that the combination of 3D-CNN with LSTM gives superior results as compared to MobileNetv2 + LSTM

A Deep Learning-Based Semantic Segmentation Architecture for Autonomous Driving Applications

In recent years, the development of smart transportation has accelerated research on semantic segmentation as it is one of the most important problems in this area. A large receptive field has always been the center of focus when designing convolutional neural networks for semantic segmentation. A majority of recent techniques have used maxpooling to increase the receptive field of a network at an expense of decreasing its spatial resolution. Although this idea has shown improved results in object detection applications, however, when it comes to semantic segmentation, a high spatial resolution also needs to be considered. To address this issue, a new deep learning model, the M-Net is proposed in this paper which satisfies both high spatial resolution and a large enough receptive field while keeping the size of the model to a minimum. The proposed network is based on an encoder-decoder architecture. The encoder uses atrous convolution to encode the features at full resolution, and instead of using heavy transposed convolution, the decoder consists of a multipath feature extraction module that can extract multiscale context information from the encoded features. The experimental results reported in the paper demonstrate the viability of the proposed scheme

The prevalence of HBV infection in the cohort of IDPs of war against terrorism in Malakand Division of Northern Pakistan

<p>Abstract</p> <p>Background</p> <p>Hepatitis B is an important public health problem in the Pakistani population and is the major cause of chronic hepatitis, cirrhosis, fibrosis and hepatocellular carcinoma. High prevalence of HBV infections has been observed especially in areas of low economic status. In spite of effective immunization programs, no significant change has been observed in the epidemiology of HBV in the rural areas of Pakistan (~67.5% of the total population) mainly due to lack of interest from government authorities and poor hygienic measures. The current study was aimed at estimating the prevalence and risk factors associated with HBV infection within internally displaced persons (IDPs) due to war against terrorism in the Malakand Division of Northern Pakistan.</p> <p>Methods</p> <p>Blood samples from 950 IDPs suspected with HBV infection (including both males and females) were collected and processed with commercial ELISA kits for HBsAg, Anti HBs, HBeAg, Anti HBe antibodies. The samples positive by ELISA were confirmed for HBV DNA by real-time PCR analysis.</p> <p>Results</p> <p>The overall prevalence of HBV observed was 21.05% of which 78.5% were males and 21.5% were females. Most confirmed HBV patients belong to the Malakand and Dir (lower) district. High-risk of infection was found in the older subjects 29.13% (46-60 years), while a lower incidence (11.97%) was observed in children aged <15 years. Lack of awareness, socioecomic conditions, sexual activities and sharing of razor blades, syringes and tattooing needles were the most common risk factors of HBV infection observed during the cohort of patients.</p> <p>Conclusion</p> <p>The present study, revealed for the first time a high degree of prevalence of HBV infection in rural areas of Northern Pakistan. The noticed prevalence is gender- and age-dependent that might be due to their high exposures to the common risk factors. To avoid the transmission of HBV infection proper awareness about the possible risk factors and extension of immunization to the rural areas are recommended.</p

Hepatitis B virus infection among different sex and age groups in Pakistani Punjab

<p>Abstract</p> <p>Background</p> <p>Hepatitis B virus (HBV) infection is a serious health problem in the developing countries including Pakistan. Various risk factors are responsible for the spread of this infectious disease. Prevalence of HBV infection in apparently suspected individual of Punjab province of Pakistan was analyzed during January 2008 to December 2010. Current study was aimed to investigate the epidemiology and risk factors of HBV infection.</p> <p>Methodology</p> <p>Four thousand eight hundred and ninety patients suffering from chronic liver disease were screened for the presence of HBV DNA using qualitative Real Time PCR methodology to confirm their status of infection. A predesigned standard questionnaire was filled for all the patients that included information about the possible risk factors.</p> <p>Results</p> <p>A total of 4890 ELISA positive patients were screened for Hepatitis B virus infection. Of these 3143 were positive for HBV, includes 68.15% males and 31.85% females. Male were observed to be more frequently infected as compared to the female with a positivity ratio of 2.14: 1. The rate of infection increases with the passage of time in the course of three years. Highest frequency of infection was found in the age of 21-30 was 34.93% followed by 23.83% in 31-40. Only (13.39%) were belonging to the age group 11-20 year. The rate of infection declines with increasing age as shown by age groups 41-50 (16.13%) and 51-60 (7.09%). While children aged 0-10 and very old >60 age groups were very less frequently 1.49% and 1.65% infected respectively. Important risk factors contributing to HBV spread include barber risk (23.60%), blood transfusion (4.04%), History of injection 26.19%, Reuse of syringes 26.60%, dental risk (11.20%) and surgical procedure (4.26%). Among the entire respondents trend sharing personal items was very common. History of injection, barber risk, surgery and dental procedure and reuse of syringes appear as major risk factors for the transmission.</p> <p>Conclusion</p> <p>Male were more frequently exposed to the risk factors as compared to female. Similarly the younger age group had high rate of infection as compared to the children's and the older age groups. Reuse of syringes', barber risk and History of injection were main risk identified during the present study. To lower HBV transmission rate Government should take aggressive steps towards massive awareness and vaccination programs to decrease the burden of HBV from the Punjab province of Pakistan.</p