Micro-doppler-based in-home aided and unaided walking recognition with multiple radar and sonar systems

Published in IET Radar, Sonar and Navigation. Online first 21/06/2016.The potential for using micro-Doppler signatures as a basis for distinguishing between aided and unaided gaits is considered in this study for the purpose of characterising normal elderly gait and assessment of patient recovery. In particular, five different classes of mobility are considered: normal unaided walking, walking with a limp, walking using a cane or tripod, walking with a walker, and using a wheelchair. This presents a challenging classification problem as the differences in micro-Doppler for these activities can be quite slight. Within this context, the performance of four different radar and sonar systems – a 40 kHz sonar, a 5.8 GHz wireless pulsed Doppler radar mote, a 10 GHz X-band continuous wave (CW) radar, and a 24 GHz CW radar – is evaluated using a broad range of features. Performance improvements using feature selection is addressed as well as the impact on performance of sensor placement and potential occlusion due to household objects. Results show that nearly 80% correct classification can be achieved with 10 s observations from the 24 GHz CW radar, whereas 86% performance can be achieved with 5 s observations of sonar

Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review

Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but they now work closely with us to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of the global food supply chain, and there is increasing consumer interest in organically fed and humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitat. This review sets the goal to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm and wild animal welfare. We use the notion of \emph{animal welfare} in broad terms, to review the technologies for assessing whether animals are healthy, free of pain and suffering, and also positively stimulated in their environment. Also the notion of \emph{smart computing and sensing} is used in broad terms, to refer to computing and sensing systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange and analysis. We review smart technologies for domestic animals, indoor and outdoor animal farming, as well as animals in the wild and zoos. The findings of this review are expected to motivate future research and contribute to data, information and communication management as well as policy for animal welfare

Recommendations for environmental baseline monitoring in areas of shale gas development

Environmental monitoring plays a key role in risk assessment and management of industrial operations where there is the potential for the release of contaminants to the environment (i.e. air and water) or for structural damage (i.e. seismicity). The shale-gas industry is one such industry. It is also new to the UK and so specific environmental regulation and other controls have been introduced only recently. Associated with this is a need to carry out monitoring to demonstrate that the management measures to minimise the risk to the environment are being effective. While much of the monitoring required is common to other industries and potentially polluting activities, there are a number of requirements specific to shale gas and to what is a new and undeveloped industry. This report presents recommendations for environmental monitoring associated with shale-gas activities and in particular the monitoring required to inform risk assessment and establish the pre-existing environmental conditions at a site and surrounding area. This baseline monitoring is essential to provide robust data and criteria for detecting any future adverse environmental changes caused by the shale-gas operations. Monitoring is therefore required throughout the lifecycle of a shale gas operation. During this lifecycle, the objectives of the monitoring will change, from baseline characterisation to operational and post-operational monitoring. Monitoring requirements will also change. This report focusses on good practice in baseline monitoring and places it in the context of the longer-term environmental monitoring programme, recognising the need to transition from the baseline condition and to establish criteria for detecting any changes within the regulatory framework. The core suite of environmental monitoring activities currently required to support regulatory compliance, i.e. meet environmental and other permit conditions, encompasses monitoring of seismicity, water quality (groundwater and surface water) and air quality. Recommendations for each of these are included in this report. Additionally, recommendations for a number of other types of environmental monitoring are included – radon in air, soil gas and ground motion (subsidence/uplift). These are not associated directly with regulatory compliance but can provide information to support interpretation of statutory monitoring results. They are also considered important for public reassurance. Health impacts arising from radon and damage caused by ground motion are both issues of public concern in relation to shale gas

Hypothesis Testing Using Spatially Dependent Heavy-Tailed Multisensor Data

The detection of spatially dependent heavy-tailed signals is considered in this dissertation. While the central limit theorem, and its implication of asymptotic normality of interacting random processes, is generally useful for the theoretical characterization of a wide variety of natural and man-made signals, sensor data from many different applications, in fact, are characterized by non-Gaussian distributions. A common characteristic observed in non-Gaussian data is the presence of heavy-tails or fat tails. For such data, the probability density function (p.d.f.) of extreme values decay at a slower-than-exponential rate, implying that extreme events occur with greater probability. When these events are observed simultaneously by several sensors, their observations are also spatially dependent. In this dissertation, we develop the theory of detection for such data, obtained through heterogeneous sensors. In order to validate our theoretical results and proposed algorithms, we collect and analyze the behavior of indoor footstep data using a linear array of seismic sensors. We characterize the inter-sensor dependence using copula theory. Copulas are parametric functions which bind univariate p.d.f. s, to generate a valid joint p.d.f. We model the heavy-tailed data using the class of alpha-stable distributions. We consider a two-sided test in the Neyman-Pearson framework and present an asymptotic analysis of the generalized likelihood test (GLRT). Both, nested and non-nested models are considered in the analysis. We also use a likelihood maximization-based copula selection scheme as an integral part of the detection process. Since many types of copula functions are available in the literature, selecting the appropriate copula becomes an important component of the detection problem. The performance of the proposed scheme is evaluated numerically on simulated data, as well as using indoor seismic data. With appropriately selected models, our results demonstrate that a high probability of detection can be achieved for false alarm probabilities of the order of 10^-4. These results, using dependent alpha-stable signals, are presented for a two-sensor case. We identify the computational challenges associated with dependent alpha-stable modeling and propose alternative schemes to extend the detector design to a multisensor (multivariate) setting. We use a hierarchical tree based approach, called vines, to model the multivariate copulas, i.e., model the spatial dependence between multiple sensors. The performance of the proposed detectors under the vine-based scheme are evaluated on the indoor footstep data, and significant improvement is observed when compared against the case when only two sensors are deployed. Some open research issues are identified and discussed

APPLICATIONS OF MACHINE LEARNING AND COMPUTER VISION FOR SMART INFRASTRUCTURE MANAGEMENT IN CIVIL ENGINEERING

Machine Learning and Computer Vision are the two technologies that have innovative applications in diverse fields, including engineering, medicines, agriculture, astronomy, sports, education etc. The idea of enabling machines to make human like decisions is not a recent one. It dates to the early 1900s when analogies were drawn out between neurons in a human brain and capability of a machine to function like humans. However, major advances in the specifics of this theory were not until 1950s when the first experiments were conducted to determine if machines can support artificial intelligence. As computation powers increased, in the form of parallel computing and GPU computing, the time required for training the algorithms decreased significantly. Machine Learning is now used in almost every day to day activities. This research demonstrates the use of machine learning and computer vision for smart infrastructure management. This research’s contribution includes two case studies – a) Occupancy detection using vibration sensors and machine learning and b) Traffic detection, tracking, classification and counting on Memorial Bridge in Portsmouth, NH using computer vision and machine learning. Each case study, includes controlled experiments with a verification data set. Both the studies yielded results that validated the approach of using machine learning and computer vision. Both case studies present a scenario where in machine learning is applied to a civil engineering challenge to create a more objective basis for decision-making. This work also includes a summary of the current state-of-the -practice of machine learning in Civil Engineering and the suggested steps to advance its application in civil engineering based on this research in order to use the technology more effectively

Algorithms leveraging smartphone sensing for analyzing explosion events

The increasing frequency of explosive disasters throughout the world in recent years have created a clear need for the systems to monitor for them continuously to improve the post-disaster emergency events such as rescue and recovery operations. Disasters both man-made and natural are unfortunate and not preferred, however monitoring them may be a lifesaving phenomenon in emergency scenarios. Dedicated sensors deployed in the public places and their associated networks to monitor such events may be inadequate and must be complemented for making the monitoring more pervasive and effective. In the recent past, modern smartphones with significant processing, networking and storage capabilities have become a rich source of mobile infrastructure empowering participatory sensing to address many problems in the area of pervasive computing. In the work presented in this dissertation, smartphone sensed data during disastrous scenarios is extensively studied, analyzed and algorithms were built for participatory sensing to address the problems, specifically in the context of Explosion -- Events which are of interest to the current study. This work presents description of the systems for assisting people by detecting, ranging and estimating intensity of the explosion events leveraging multi-modal smartphone sensors. This work also presents various challenges and opportunities in utilizing the capabilities of the sensors in smartphone for building such systems along with practical applications, limitations and future directions --Abstract, page iii

2016 Scholarly Productivity Report

https://scholarsmine.mst.edu/care-scholarly_productivity_reports/1003/thumbnail.jp