Some biological effects on plant nematodes and their hosts of the organo-phosphorus compound thionazin

Imperial Users onl

More than Gates: The Physical and Invisible Barriers to Gated Communities and Their Consequences on the Broader Community

From the Washington University Senior Honors Thesis Abstracts (WUSHTA), 2017. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Sciences; Lindsey Paunovich, Editor; Helen Human, Programs Manager and Assistant Dean in the College of Arts and Sciences Mentor: Carol Camp Yeake

Evaluating the impact of malicious spoofing attacks on Bluetooth low energy based occupancy detection systems

Occupancy detection of a building has a wide range of applications. Areas such as emergency management, home automation and building energy management can benefit from the knowledge of occupants' locations to provide better results and improve their efficiency. Bluetooth Low Energy (BLE) beacons installed inside a building are able to provide information on an occupant's location. Since, however, their operation is based on broadcasting advertisements, they are vulnerable to network security breaches. In this work, we evaluate the effect of two types of spoofing attacks on a BLE based occupancy detection system. The system is composed of BLE beacons installed inside the building, a mobile application installed on occupants’ mobile phones and a remote control server. Occupancy detection is performed by a classifier installed on the remote server. We use our real-world experimental results to evaluate the impact of these attacks on the system's operation, particularly in terms of the accuracy with which it can provide location information

Occupancy detection for building emergency management using BLE beacons

Being able to reliable estimate the occupancy of areas inside a building can prove beneficial for managing an emergency situation, as it allows for more efficient allocation of resources such as emergency personnel. In indoor environments, however, occupancy detection can be a very challenging task. A solution to this can be provided by the use of Bluetooth Low Energy (BLE) beacons installed in the building. In this work we evaluate the performance of a BLE based occupancy detection system geared towards emergency situations that take place inside buildings. The system is composed of BLE beacons installed inside the building, a mobile application installed on occupants' mobile phones and a remote control server. Our approach does not require any processing to take place on the occupants' mobile phones, since the occupancy detection is based on a classifier installed on the remote server. Our real-world experiments indicated that the system can provide high classification accuracy for different numbers of installed beacons and occupant movement patterns

Bluetooth low energy based occupancy detection for emergency management

A reliable estimation of an area’s occupancy can be beneficial to a large variety of applications, and especially in relation to emergency management. For example, it can help detect areas of priority and assign emergency personnel in an efficient manner. However, occupancy detection can be a major challenge in indoor environments. A recent technology that can prove very useful in that respect is Bluetooth Low Energy (BLE), which is able to provide the location of a user using information from beacons installed in a building. Here, we evaluate BLE as the primary means of occupancy estimation in an indoor environment, using a prototype system composed of BLE beacons, a mobile application and a server. We employ three machine learning approaches (k-nearest neighbours, logistic regression and support vector machines) to determine the presence of occupants inside specific areas of an office space and we evaluate our approach in two independent experimental settings. Our experimental results indicate that combining BLE with machine learning is certainly promising as the basis for occupancy estimation

A framework of integrating knowledge of human factors to facilitate HMI and collaboration in intelligent manufacturing

Recent developments in the field of intelligent manufacturing have led to increased levels of automation and robotic operators becoming commonplace within manufacturing processes. However, the human component of such systems remains prevalent, resulting in significant disturbance and uncertainty. Consequently, semi-automated processes are difficult to optimise. This paper studies the relationships between robotic and human operators to develop the understanding of how the human influence affects these production processes, and proposes a framework to integrate and implement knowledge of such factors, with the aim of improving Human-Machine-Interaction, facilitating bi-directional collaboration, and increasing productivity and quality, supported by an example case-study

Location-enhanced activity recognition in indoor environments using off the shelf smart watch technology and BLE beacons

Activity recognition in indoor spaces benefits context awareness and improves the efficiency of applications related to personalised health monitoring, building energy management, security and safety. The majority of activity recognition frameworks, however, employ a network of specialised building sensors or a network of body-worn sensors. As this approach suffers with respect to practicality, we propose the use of commercial off-the-shelf devices. In this work, we design and evaluate an activity recognition system composed of a smart watch, which is enhanced with location information coming from Bluetooth Low Energy (BLE) beacons. We evaluate the performance of this approach for a variety of activities performed in an indoor laboratory environment, using four supervised machine learning algorithms. Our experimental results indicate that our location-enhanced activity recognition system is able to reach a classification accuracy ranging from 92% to 100%, while without location information classification accuracy it can drop to as low as 50% in some cases, depending on the window size chosen for data segmentation

An agent-based reinforcement learning approach to improve human-robot-interaction in manufacturing

This work is aimed at the understanding and application of several emerging technologies as they relate to improving the interactions which occur between robotic operators and their human colleagues across a range of manufacturing processes. These interactions are problematic, as variation in performance of human beings remains one of the largest sources of disturbances within such systems, with potentially significant implications for productivity if it continues unmitigated. The problem remains for the most part unaddressed, despite these interactions becoming increasingly prevalent as the rate of adoption of automation technologies increases. By reconciling multiple areas encompassed by the wider domain of intelligent manufacturing, the presented work identifies a methodology and a set of software tools which leverage the strengths of neural-network-based reinforcement learning to develop intelligent software agents capable of adaptable behaviour in response to observed environmental changes. The methodology further focuses on developing representative simulation models for these interactions following a pattern of generalisation, to effectively represent both human and robotic elements, and facilitate implementation. By learning through their interaction with the simulated manufacturing environment, these agents can determine an appropriate policy, by which to autonomously adjust their operating parameters, as a response to changes in their human colleagues. This adaptability is demonstrated to enable the intelligent agents to determine an action policy which results in less observed idle time, along with improved leanness and overall productivity, over multiple scenarios. The findings of the work suggest that software agents that make use of a reinforcement based learning approach are well suited to the task of enabling robotic adaptability in such a way, and the developed methodology provides a platform for further development and exploration, along with numerous insights into the effective development of these agents