Human Body Posture Recognition Approaches: A Review

Human body posture recognition has become the focus of many researchers in recent years. Recognition of body posture is used in various applications, including surveillance, security, and health monitoring. However, these systems that determine the body’s posture through video clips, images, or data from sensors have many challenges when used in the real world. This paper provides an important review of how most essential ‎ hardware technologies are ‎used in posture recognition systems‎. These systems capture and collect datasets through ‎accelerometer sensors or computer vision. In addition, this paper presents a comparison ‎study with state-of-the-art in terms of accuracy. We also present the advantages and ‎limitations of each system and suggest promising future ideas that can increase the ‎efficiency of the existing posture recognition system. Finally, the most common datasets ‎applied in these systems are described in detail. It aims to be a resource to help choose one of the methods in recognizing the posture of the human body and the techniques that suit each method. It analyzes more than 80 papers between 2015 and 202

Pupil Localisation and Eye Centre Estimation using Machine Learning and Computer Vision

Various methods have been used to estimate the pupil location within an image or a real-time video frame in many fields. However, these methods lack the performance specifically in low-resolution images and varying background conditions. We propose a coarse-to-fine pupil localisation method using a composite of machine learning and image processing algorithms. First, a pre-trained model is employed for the facial landmark identification to extract the desired eye-frames within the input image. We then use multi-stage convolution to find the optimal horizontal and vertical coordinates of the pupil within the identified eye-frames. For this purpose, we define an adaptive kernel to deal with the varying resolution and size of input images. Furthermore, a dynamic threshold is calculated recursively for reliable identification of the best-matched candidate. We evaluated our method using various statistical and standard metrics along-with a standardized distance metric we introduce first time in this study. Proposed method outperforms previous works in terms of accuracy and reliability when benchmarked on multiple standard datasets. The work has diverse artificial intelligence and industrial applications including human computer interfaces, emotion recognition, psychological profiling, healthcare and automated deception detection

Polylithiated (OLi2) functionalized graphane as a potential hydrogen storage material

Hydrogen storage capacity, stability, bonding mechanism and the electronic structure of polylithiated molecules (OLi2) functionalized graphane (CH) has been studied by means of first principle density functional theory (DFT). Molecular dynamics (MD) have confirmed the stability, while Bader charge analysis describe the bonding mechanism of OLi2 with CH. The binding energy of OLi2 on CH sheet has been found to be large enough to ensure its uniform distribution without any clustering. It has been found that each OLi2 unit can adsorb up to six H2 molecules resulting into a storage capacity of 12.90 wt% with adsorption energies within the range of practical H2 storage application.Comment: 11 pages, 4 figures, 1 table, Phys. Chem. Chem. Phys. (submitted

Smart Home Systems Security

© 2018 IEEE. Due to the increase of the Smart Home System market, it has become important to outline and understand the direction and progress needed to ensure that, as Smart Home Systems become more common, the security and functionality of these systems. This research sheds light on what has been done in the field and Smart Home System owners feel currently about the systems they already have, the reasons behind using it as well as what could be done differently to improve its security. The results are presented from feedback received from the questionnaire to provide knowledge and understanding of how a Smart Home System can be improved, and what the main paths of future progress in this area. The ultimate aims of this work are to identify the risks associated with Smart Home Systems and investigate how the risks can be mitigated

Strain induced lithium functionalized graphane as a high capacity hydrogen storage material

Strain effects on the stability, electronic structure, and hydrogen storage capacity of lithium-doped graphane (CHLi) have been investigated by stateof-the art first principle density functional theory (DFT). Molecular dynamics MD) simulations have confirmed the stability of Li on graphane sheet when it is subject to 10% of tensile strain. Under biaxial asymmetric strain, the binding energy of Li of graphane (CH) sheet increases by 52% with respect to its bulk's cohesive energy. With 25% doping concentration of Li on CH sheet,the gravimetric density of hydrogen storage is found to reach up to 12.12wt%. The adsorption energies of H2 are found to be within the range of practical H2 storage applications.Comment: 13 pages, 7 figures, 1 table, Applied Physics Letters (Under Review

Forecasting Natural Events Using Axonal Delay

The ability to forecast natural phenomena relies on understanding causality. By definition this understanding must include a temporal component. In this paper, we consider the ability of an emerging class of neural network, which encode temporal information into the network, to perform the difficult task of Natural Event Forecasting. The Axonal Delay Network (ADN) models axonal delay in order to make predictions about sunspot activity, the Auroral Electrojet (AE) index and daily temperatures during a heatwave. The performance of this network is benchmarked against older types of neural networks; including the Multi-Layer Perceptron (MLP) network and Functional Link Neural Network (FLNN). The results indicate that the inherent temporal characteristics of the Axonal Delay Network make it well suited to the processing and prediction of natural phenomena

Dynamic Neural Network for Business and Market Analysis

© 2019, Springer Nature Switzerland AG. The problem of predicting nonlinear and nonstationary signals is complex since the physical law that controls them is unknown and it is complicated to be considered. In these cases, it is necessary to devise nonlinear models that imitate or learn the rules of behavior of the problem and can be developed based on historical data. For this reason, neural networks are useful tools to deal with this type of problem due to their nonlinearly and their capacity of generalizing. This paper aims at exploring various types of neural network architectures and study their performance with time series predictions. Predictions on two sets of data (of a very different nature) will be made using three neural networks including multilayer perceptrons, recurrent neural network and long-short term memory varying some important parameters: input neurons, epochs and the anticipation with which the predictions are made. Then, all results will be compared using standard metrics. As a conclusion, the influence of the type of series under study is more important than the parameters considered in what concerns the performance. The management of the memory in the networks is a key to its success in the prediction of S&P 500 and electrical power time series

Community fire prevention via population segmentation modelling

In this paper we examine the use of population segmentation modelling for targeting fire prevention to the needs of the community. A population segmentation approach based upon socio-economic characteristics data was developed to provide a deeper understanding of the fire risks associated with different social groups by a partnership consisting of a UK fire and rescue service, a National Health Service trust, a local council, and a police force. This approach supported more targeted and co-ordinated community fire prevention measures by the agencies involved. This approach was used to target those most at risk, and improve intra-agency co-ordination and collaboration between the agencies involved. The modelling enabled differences in terms of the risk of fire related injuries and fatalities between the population segments to be examined. Overall, the research examines how and why population segmentation was undertaken by the fire and rescue service studied, and how this was implemented and used operationally to support fire prevention activities. The project was funded by the UK Department of Communities and Local Government

Novel Framework for Outdoor Mobility Assistance and Auditory Display for Visually Impaired People

Outdoor mobility of Visually Impaired People (VIPs) has always been challenging due to the dynamically varying scenes and environmental states. Variety of systems have been introduced to assist VIPs’ mobility that include sensor mounted canes and use of machine intelligence. However, these systems are not reliable when used to navigate the VIPs in dynamically changing environments. The associated challenges are the robust sensing and avoiding diverse types of obstacles, dynamically modelling the changing environmental states (e.g. moving objects, road-works), and effective communication to interpret the environmental states and hazards. In this paper, we propose an intelligent wearable auditory display framework that will process real-time video and multi-sensor data streams to: a) identify the type of obstacles, b) recognize the surrounding scene/objects and corresponding attributes (e.g. geometry, size, shape, distance from user), c) automatically generate the descriptive information about the recognized obstacle/objects and attributes, d) produce accurate, precise and reliable spatial information and corresponding instructions in audio-visual form to assist and navigate VIPs safely with or without the assistance of traditional means

Genetic study in congenital heart defects

Background: Congenital heart diseases (CHD) are relatively common with a prevalence ranging from 3.7 to 17.5 per 1000 live births. Little is known about genetic link with respect to congenital heart disease. Iroquoise (Irx) homeobox genes have been widely studied and their expression in both developing and adult heart. Author tried to study the role of irx4 and irx5 genes in structural congenital heart disease, keeping the focus on study reported by Cheng Z et al.Methods: Author studied reported mutation site sequences in 25 various congenital heart disease patients and control healthy relatives of patients. It is a unique study and there has not been such a study reported in literature till date. Besides comparison with healthy related controls, author took cardiac tissue biopsy in patients while doing corrective cardiac surgery. However, blood samples were taken from controls due to ease of feasibility.Results: Although, there were no sequence variations in the studied exon regions, but author got a base pair sequence change at 6 bp intron region, which is near the exon splice site in irx4 gene. Besides two ASD patient’s male children (one child each) had ASD prompting us to believe some role of sex linkage. However later needs pedigree analysis and sex chromosome studies for further analysis.Conclusions: Gene sequence in the Kashmiri population is unique. There is possibility of role of irx genes in CHD. ASD might have sex linkage in some