In-depth characterization of particulate matter in a highly polluted urban environment at the foothills of Himalaya-Karakorum Region.

In recent years, the rising levels of atmospheric particulate matter (PM) have an impact on the earth's system, leading to undesirable consequences on various aspects like human health, visibility, and climate. The present work is carried out over an insufficiently studied but polluted urban area of Peshawar, which lies at the foothills of the famous Himalaya and Karakorum area, Northern Pakistan. The particulate matter with an aerodynamic diameter of less than 10 µm, i.e., PM10 are collected and analyzed for mineralogical, morphological, and chemical properties. Diverse techniques were used to examine the PM10 samples, for instance, Fourier transform infrared spectroscopy, x-ray diffraction, and scanning electron microscopy along with energy-dispersive x-ray spectroscopy, proton-induced x-ray emission, and an OC/EC carbon analyzer. The 24 h average PM10 mass concentration along with standard deviation was investigated to be 586.83 ± 217.70 µg/m3, which was around 13 times greater than the permissible limit of the world health organization (45 µg/m3) and 4 times the Pakistan national environmental quality standards for ambient PM10 (150 µg/m3). Minerals such as crystalline silicate, carbonate, asbestiform minerals, sulfate, and clay minerals were found using FTIR and XRD investigations. Microscopic examination revealed particles of various shapes, including angular, flaky, rod-like, crystalline, irregular, rounded, porous, chain, spherical, and agglomeration structures. This proved that the particles had geogenic, anthropogenic, and biological origins. The average value of organic carbon, elemental carbon, and total carbon is found to be 91.56 ± 43.17, 6.72 ± 1.99, and 102.41 ± 44.90 µg/m3, respectively. Water-soluble ions K+ and OC show a substantial association (R = 0.71). Prominent sources identified using Principle component analysis (PCA) are anthropogenic, crustal, industrial, and electronic combustion. This research paper identified the potential sources of PM10, which are vital for preparing an air quality management plan in the urban environment of Peshawar.

Revealing The Morphology of Ink and Aerosol Jet Printed Palladium-Silver Alloys Fabricated from Metal Organic Decomposition Inks.

Palladium films hold signicance due to their remarkable affinity for hydrogen diffusion, rendering them valauble for the seperation and purification of hydrogen in membrane reactors. However, palladium is expensive, and its films can become brittle after only a few cycles of hydrogen separation. Alloying with silver has been shown to overcome the problem of palladium embrittlement. Palladium-silver films have been produced via several methods but all have drawbacks, such as difficulties controlling the alloy composition. This study explores two promising jet printing methods: Inkjet and Aerosoljet. Both methods offer potential advantages such as direct patterning, which reduces waste, enables thin film production, and allows for the control of alloy composition. For the first time, palladium-silver alloys have been produced via inkjet printing using a palladium-silver metal organic decomposition (MOD) ink, which alloys at a temperature of 300 °C with nitrogen. Similarly, this study also demonstrates a pioneering approach for Aerosol Jet printing, showing the potential of a novel room-temperature method, for the deposition of palladium-silver MOD inks. This low temperature approach is considered an important development as palladium-silver MOD inks are originally designed for deposition on heated substrates.

Continuous Monitoring of Health and Mobility Indicators in Patients with Cardiovascular Disease: A Review of Recent Technologies.

Cardiovascular diseases kill 18 million people each year. Currently, a patient's health is assessed only during clinical visits, which are often infrequent and provide little information on the person's health during daily life. Advances in mobile health technologies have allowed for the continuous monitoring of indicators of health and mobility during daily life by wearable and other devices. The ability to obtain such longitudinal, clinically relevant measurements could enhance the prevention, detection and treatment of cardiovascular diseases. This review discusses the advantages and disadvantages of various methods for monitoring patients with cardiovascular disease during daily life using wearable devices. We specifically discuss three distinct monitoring domains: physical activity monitoring, indoor home monitoring and physiological parameter monitoring.

Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention-A Survey.

Railway networks systems are by design open and accessible to people, but this presents challenges in the prevention of events such as terrorism, trespass, and suicide fatalities. With the rapid advancement of machine learning, numerous computer vision methods have been developed in closed-circuit television (CCTV) surveillance systems for the purposes of managing public spaces. These methods are built based on multiple types of sensors and are designed to automatically detect static objects and unexpected events, monitor people, and prevent potential dangers. This survey focuses on recently developed CCTV surveillance methods for rail networks, discusses the challenges they face, their advantages and disadvantages and a vision for future railway surveillance systems. State-of-the-art methods for object detection and behaviour recognition applied to rail network surveillance systems are introduced, and the ethics of handling personal data and the use of automated systems are also considered.

A Multimodal Neuroprosthetic Interface to Record, Modulate and Classify Electrophysiological Biomarkers Relevant to Neuropsychiatric Disorders.

Most mental disorders, such as addictive diseases or schizophrenia, are characterized by impaired cognitive function and behavior control originating from disturbances within prefrontal neural networks. Their often chronic reoccurring nature and the lack of efficient therapies necessitate the development of new treatment strategies. Brain-computer interfaces, equipped with multiple sensing and stimulation abilities, offer a new toolbox whose suitability for diagnosis and therapy of mental disorders has not yet been explored. This study, therefore, aimed to develop a biocompatible and multimodal neuroprosthesis to measure and modulate prefrontal neurophysiological features of neuropsychiatric symptoms. We used a 3D-printing technology to rapidly prototype customized bioelectronic implants through robot-controlled deposition of soft silicones and a conductive platinum ink. We implanted the device epidurally above the medial prefrontal cortex of rats and obtained auditory event-related brain potentials in treatment-naïve animals, after alcohol administration and following neuromodulation through implant-driven electrical brain stimulation and cortical delivery of the anti-relapse medication naltrexone. Towards smart neuroprosthetic interfaces, we furthermore developed machine learning algorithms to autonomously classify treatment effects within the neural recordings. The neuroprosthesis successfully captured neural activity patterns reflecting intact stimulus processing and alcohol-induced neural depression. Moreover, implant-driven electrical and pharmacological stimulation enabled successful enhancement of neural activity. A machine learning approach based on stepwise linear discriminant analysis was able to deal with sparsity in the data and distinguished treatments with high accuracy. Our work demonstrates the feasibility of multimodal bioelectronic systems to monitor, modulate and identify healthy and affected brain states with potential use in a personalized and optimized therapy of neuropsychiatric disorders.

Deep ConvNet: Non-Random Weight Initialization for Repeatable Determinism, Examined with FSGM.

A repeatable and deterministic non-random weight initialization method in convolutional layers of neural networks examined with the Fast Gradient Sign Method (FSGM). Using the FSGM approach as a technique to measure the initialization effect with controlled distortions in transferred learning, varying the dataset numerical similarity. The focus is on convolutional layers with induced earlier learning through the use of striped forms for image classification. Which provided a higher performing accuracy in the first epoch, with improvements of between 3-5% in a well known benchmark model, and also ~10% in a color image dataset (MTARSI2), using a dissimilar model architecture. The proposed method is robust to limit optimization approaches like Glorot/Xavier and He initialization. Arguably the approach is within a new category of weight initialization methods, as a number sequence substitution of random numbers, without a tether to the dataset. When examined under the FGSM approach with transferred learning, the proposed method when used with higher distortions (numerically dissimilar datasets), is less compromised against the original cross-validation dataset, at ~31% accuracy instead of ~9%. This is an indication of higher retention of the original fitting in transferred learning.

Trust-based fault detection and robust fault-tolerant control of uncertain cyber-physical systems against time-delay injection attacks.

Control systems need to be able to operate under uncertainty and especially under attacks. To address such challenges, this paper formulates the solution of robust control for uncertain systems under time-varying and unknown time-delay attacks in cyber-physical systems (CPSs). A novel control method able to deal with thwart time-delay attacks on closed-loop control systems is proposed. Using a descriptor model and an appropriate Lyapunov functional, sufficient conditions for closed-loop stability are derived based on linear matrix inequalities (LMIs). A design procedure is proposed to obtain an optimal state feedback control gain such that the uncertain system can be resistant under an injection time-delay attack with variable delay. Furthermore, various fault detection frameworks are proposed by following the dynamics of the measured data at the system's input and output using statistical analysis such as correlation analysis and K-L (Kullback-Leibler) divergence criteria to detect attack's existence and to prevent possible instability. Finally, an example is provided to evaluate the proposed design method's effectiveness.

Weighted Transfer Learning of Dynamic Time Warped Data for Motor Imagery based Brain Computer Interfaces.

A large amount of calibration data is typically needed to train an electroencephalogram (EEG)-based brain-computer interfaces (BCI) due to the non-stationary nature of EEG data. This paper proposes a novel weighted transfer learning algorithm using a dynamic time warping (DTW) based alignment method to alleviate this need by using data from other subjects. DTW-based alignment is first applied to reduce the temporal variations between a specific subject data and the transfer learning data from other subjects. Next, similarity is measured using Kullback Leibler divergence (KL) between the DTW aligned data and the specific subject data. The other subjects' data are then weighted based on their KL similarity to each trials of the specific subject data. This weighted data from other subjects are then used to train the BCI model of the specific subject. An experiment was performed on publicly available BCI Competition IV dataset 2a. The proposed algorithm yielded an average improvement of 9% compared to a subject-specific BCI model trained with 4 trials, and the results yielded an average improvement of 10% compared to naive transfer learning. Overall, the proposed DTW-aligned KL weighted transfer learning algorithm show promise to alleviate the need of large amount of calibration data by using only a short calibration data.

Dynamic time warping-based transfer learning for improving common spatial patterns in brain-computer interface.

OBJECTIVE: Common spatial patterns (CSP) is a prominent feature extraction algorithm in motor imagery (MI)-based brain-computer interfaces (BCIs). However, CSP is computed using sample-based covariance-matrix estimation. Hence, its performance deteriorates if the number of training trials is small. To address this problem, this paper proposes a novel regularized covariance matrix estimation framework for CSP (i.e. DTW-RCSP) based on dynamic time warping (DTW) and transfer learning. APPROACH: The proposed framework combines the subject-specific covariance matrix ([Formula: see text]) estimated using the available trials from the new subject, with a novel DTW-based transferred covariance matrix ([Formula: see text]) estimated using previous subjects' trials. In the proposed [Formula: see text], the available labelled trials from the previous subjects are temporally aligned to the average of the available trials of the new subject from the same class using DTW. This alignment aims to reduce temporal variations and non-stationarities between previous subjects' trials and the available trials from the new subjects. Moreover, to tackle the problem of regularization parameter selection when only a few trials are available for training, an online method is proposed, where the best regularization parameter is selected based on the confidence scores of the trained classifier on the upcoming first few labelled testing trials. MAIN RESULTS: The proposed framework is evaluated on two datasets against two baseline algorithms. The obtained results reveal that DTW-RCSP significantly outperformed the baseline algorithms at various testing scenarios, particularly, when only a few trials are available for training. SIGNIFICANCE: Impressively, our results show that successful BCI interactions could be achieved with a calibration session as small as only one trial per class.

Wildlife surveillance using deep learning methods.

Wildlife conservation and the management of human-wildlife conflicts require cost-effective methods of monitoring wild animal behavior. Still and video camera surveillance can generate enormous quantities of data, which is laborious and expensive to screen for the species of interest. In the present study, we describe a state-of-the-art, deep learning approach for automatically identifying and isolating species-specific activity from still images and video data.We used a dataset consisting of 8,368 images of wild and domestic animals in farm buildings, and we developed an approach firstly to distinguish badgers from other species (binary classification) and secondly to distinguish each of six animal species (multiclassification). We focused on binary classification of badgers first because such a tool would be relevant to efforts to manage Mycobacterium bovis (the cause of bovine tuberculosis) transmission between badgers and cattle.We used two deep learning frameworks for automatic image recognition. They achieved high accuracies, in the order of 98.05% for binary classification and 90.32% for multiclassification. Based on the deep learning framework, a detection process was also developed for identifying animals of interest in video footage, which to our knowledge is the first application for this purpose.The algorithms developed here have wide applications in wildlife monitoring where large quantities of visual data require screening for certain species.

Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks.

The key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. The model dynamics are crucially dependent on the parameter values which are often estimated from observations. Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. The related inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability, and checking, and optimum experiment design, sensitivity analysis, and bifurcation analysis. The aim of this review paper is to examine the developments in literature to understand what BRN models are commonly used, and for what inference tasks and inference methods. The initial collection of about 700 documents concerning estimation problems in BRNs excluding books and textbooks in computational biology and chemistry were screened to select over 270 research papers and 20 graduate research theses. The paper selection was facilitated by text mining scripts to automate the search for relevant keywords and terms. The outcomes are presented in tables revealing the levels of interest in different inference tasks and methods for given models in the literature as well as the research trends are uncovered. Our findings indicate that many combinations of models, tasks and methods are still relatively unexplored, and there are many new research opportunities to explore combinations that have not been considered-perhaps for good reasons. The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics, and state space representations whereas the most common tasks are the parameter inference and model identification. The most common methods in literature are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The new research problems which cannot be directly deduced from the text mining data are also discussed.

Traffic Estimation for Large Urban Road Network with High Missing Data Ratio.

Intelligent transportation systems require the knowledge of current and forecasted traffic states for effective control of road networks. The actual traffic state has to be estimated as the existing sensors does not capture the needed state. Sensor measurements often contain missing or incomplete data as a result of communication issues, faulty sensors or cost leading to incomplete monitoring of the entire road network. This missing data poses challenges to traffic estimation approaches. In this work, a robust spatio-temporal traffic imputation approach capable of withstanding high missing data rate is presented. A particle based approach with Kriging interpolation is proposed. The performance of the particle based Kriging interpolation for different missing data ratios was investigated for a large road network comprising 1000 segments. Results indicate that the effect of missing data in a large road network can be mitigated by the Kriging interpolation within the particle filter framework.

Robust Non-Rigid Feature Matching for Image Registration Using Geometry Preserving.

In this paper, a robust non-rigid feature matching approach for image registration with geometry constraints is proposed. The non-rigid feature matching approach is formulated as a maximum likelihood (ML) estimation problem. The feature points of one image are represented by Gaussian mixture model (GMM) centroids, and are fitted to the feature points of the other image by moving coherently to encode the global structure. To preserve the local geometry of these feature points, two local structure descriptors of the connectivity matrix and Laplacian coordinate are constructed. The expectation maximization (EM) algorithm is applied to solve this ML problem. Experimental results demonstrate that the proposed approach has better performance than current state-of-the-art methods.

Weighted Transfer Learning for Improving Motor Imagery-Based Brain-Computer Interface.

One of the major limitations of motor imagery (MI)-based brain-computer interface (BCI) is its long calibration time. Due to between sessions/subjects variations in the properties of brain signals, typically, a large amount of training data needs to be collected at the beginning of each session to calibrate the parameters of the BCI system for the target user. In this paper, we propose a novel transfer learning approach on the classification domain to reduce the calibration time without sacrificing the classification accuracy of MI-BCI. Thus, when only few subject-specific trials are available for training, the estimation of the classification parameters is improved by incorporating previously recorded data from other users. For this purpose, a regularization parameter is added to the objective function of the classifier to make the classification parameters as close as possible to the classification parameters of the previous users who have feature spaces similar to that of the target subject. In this paper, a new similarity measure based on the Kullback-Leibler divergence (KL) is used to measure the similarity between two feature spaces obtained using subject-specific common spatial patterns (CSP). The proposed transfer learning approach is applied on the logistic regression classifier and evaluated using three datasets. The results showed that compared with the subject-specific classifier, the proposed weighted transfer learning classifier improved the classification results, particularly when few subject-specific trials were available for training (p < 0.05). Importantly, this improvement was more pronounced for users with medium and poor accuracy. Moreover, the statistical results showed that the proposed weighted transfer learning classifier performed significantly better than the considered comparable baseline algorithms.

A Review of Point Set Registration: From Pairwise Registration to Groupwise Registration.

This paper presents a comprehensive literature review on point set registration. The state-of-the-art modeling methods and algorithms for point set registration are discussed and summarized. Special attention is paid to methods for pairwise registration and groupwise registration. Some of the most prominent representative methods are selected to conduct qualitative and quantitative experiments. From the experiments we have conducted on 2D and 3D data, CPD-GL pairwise registration algorithm and JRMPC groupwise registration algorithm seem to outperform their rivals both in accuracy and computational complexity. Furthermore, future research directions and avenues in the area are identified.

Data Space Adaptation for Multiclass Motor Imagery-based BCI.

Various adaptation techniques have been proposed to address the non-stationarity issue faced by electroencephalogram (EEG)-based brain-computer interfaces (BCIs). However, most of these adaptation techniques are only suitable for binary-class BCIs. This paper proposes a supervised multiclass data space adaptation technique (MDSA) to transform the test data using a linear transformation such that the distribution difference between the multiclass train and test data is minimized. The results of using the proposed MDSA on BCI Competition IV dataset 2a improved the classification accuracy by an average of 4.3% when 20 trials per class were used from the test session to estimate adaptation transformation. The results also showed that the proposed MDSA algorithm outperformed the multi pooled mean linear discrimination (MPMLDA) technique with as few as 10 trials per class used for calculating the transformation matrix. Hence the results showed the effectiveness of the proposed MDSA algorithm in addressing non-stationarity issue for multiclass EEG-based BCI.

Learning Methods for Dynamic Topic Modeling in Automated Behavior Analysis.

Semisupervised and unsupervised systems provide operators with invaluable support and can tremendously reduce the operators' load. In the light of the necessity to process large volumes of video data and provide autonomous decisions, this paper proposes new learning algorithms for activity analysis in video. The activities and behaviors are described by a dynamic topic model. Two novel learning algorithms based on the expectation maximization approach and variational Bayes inference are proposed. Theoretical derivations of the posterior estimates of model parameters are given. The designed learning algorithms are compared with the Gibbs sampling inference scheme introduced earlier in the literature. A detailed comparison of the learning algorithms is presented on real video data. We also propose an anomaly localization procedure, elegantly embedded in the topic modeling framework. It is shown that the developed learning algorithms can achieve 95% success rate. The proposed framework can be applied to a number of areas, including transportation systems, security, and surveillance.

Estimation of joint angle based on surface electromyogram signals recorded at different load levels.

To control upper-limb exoskeletons and prostheses, surface electromyogram (sEMG) is widely used for estimation of joint angles. However, the variations in the load carried by the user can substantially change the recorded sEMG and consequently degrade the accuracy of joint angle estimation. In this paper, we aim to deal with this problem by training classification models using a pool of sEMG data recorded from all different loads. The classification models are trained as either subject-specific or subject-independent, and their results are compared with the performance of classification models that have information about the carried load. To evaluate the proposed system, the sEMG signals are recorded during elbow flexion and extension from three participants at four different loads (i.e. 1, 2, 4 and 6 Kg) and six different angles (i.e. 0, 30, 60, 90, 120, 150 degrees). The results show while the loads were assumed unknown and the applied training data was relatively small, the proposed joint angle estimation model performed significantly above the chance level in both the subject-specific and subject-independent models. However, transferring from known to unknown load in the subject-specific classifiers leads to 20% to 32% loss in the average accuracy.