Latitude, longitude, and beyond:mining mobile objects' behavior

Rapid advancements in Micro-Electro-Mechanical Systems (MEMS), and wireless communications, have resulted in a surge in data generation. Mobility data is one of the various forms of data, which are ubiquitously collected by different location sensing devices. Extensive knowledge about the behavior of humans and wildlife is buried in raw mobility data. This knowledge can be used for realizing numerous viable applications ranging from wildlife movement analysis, to various location-based recommendation systems, urban planning, and disaster relief. With respect to what mentioned above, in this thesis, we mainly focus on providing data analytics for understanding the behavior and interaction of mobile entities (humans and animals). To this end, the main research question to be addressed is: How can behaviors and interactions of mobile entities be determined from mobility data acquired by (mobile) wireless sensor nodes in an accurate and efficient manner? To answer the above-mentioned question, both application requirements and technological constraints are considered in this thesis. On the one hand, applications requirements call for accurate data analytics to uncover hidden information about individual behavior and social interaction of mobile entities, and to deal with the uncertainties in mobility data. Technological constraints, on the other hand, require these data analytics to be efficient in terms of their energy consumption and to have low memory footprint, and processing complexity

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

Intensity based interrogation of optical fibre sensors for industrial automation and intrusion detection systems

In this study, the use of optical fibre sensors for intrusion detection and industrial automation systems has been demonstrated, with a particular focus on low cost, intensity-based, interrogation techniques. The use of optical fibre sensors for intrusion detection systems to secure residential, commercial, and industrial premises against potential security breaches has been extensively reviewed in this thesis. Fibre Bragg grating (FBG) sensing is one form of optical fibre sensing that has been underutilised in applications such as in-ground, in-fence, and window and door monitoring, and addressing that opportunity has been a major goal of this thesis. Both security and industrial sensor systems must include some centralised intelligence (electronic controller) and ideally both automation and security sensor systems would be controlled and monitored by the same centralised system. Optical fibre sensor systems that could be used for either application have been designed, developed, and tested in this study, and optoelectronic interfaces for integrating these sensors with electronic controllers have been demonstrated. The versatility of FBG sensors means that they are also ideal for certain mainstream industrial applications. Two novel transducers have been developed in this work; a highly sensitive low pressure FBG diaphragm transducer and a FBG load cell transducer. Both have been designed to allow interrogation of the optical signal could occur within the housing of the individual sensors themselves. This is achieved in a simple and low cost manner that enables the output of the transducers to be easily connected to standard electronic controllers, such as programmable logic controllers. Furthermore, some of the nonlinear characteristics of FBG sensors have been explored with the aim of developing transducers that are inherently decoupled from strain and temperature interference. One of the major advantages of optical fibre sensors is their ability to be both time division and wavelength division multiplexed. The intensity-based interrogation techniques used here complement this attribute and are a major consideration when developing the transducers and optoelectronic circuits. A time division multiplexing technique, using transmit-reflect detection and incorporating a dual bus, has also been developed. This system architecture enables all the different optical fibre transducers on the network to have the same Bragg wavelength and hence the number of spare replacement transducers required is minimal. Moreover, sensors can be replaced in an online control system without disrupting the network. In addition, by analysing both the transmitted and reflected signals, problems associated with optical power fluctuations are eliminated and the intensity of the sensor signals is increased through differential amplification. Overall, the research addresses the limitations of conventional electrical sensors, such as susceptibility to corrosive damage in wet and corrosive environments, and risk of causing an explosion in hazardous environments, as well as the limitations of current stand-alone optical fibre sensor systems. This thesis supports more alert, reliable, affordable, and coordinated, control and monitoring systems in an on-line environment

FOOTSTEP DETECTION AND CLASSIFICATION USING DISTRIBUTED MICROPHONES

ABSTRACT This paper addresses footstep detection and classification with multiple microphones distributed on the floor. We propose to introduce geometrical features such as position and velocity of a sound source for classification which is estimated by amplitude-based localization. It does not require precise inter-microphone time synchronization unlike a conventional microphone array technique. To classify various types of sound events, we introduce four types of features, i.e., time-domain, spectral and Cepstral features in addition to the geometrical features. We constructed a prototype system for footstep detection and classification based on the proposed ideas with eight microphones aligned in a 2-by-4 grid manner. Preliminary classification experiments showed that classification accuracy for four types of sound sources such as a walking footstep, running footstep, handclap, and utterance maintains over 70% even when the signal-to-noise ratio is low, like 0 dB. We also confirmed two advantages with the proposed footstep detection and classification. One is that the proposed features can be applied to classification of other sound sources besides footsteps. The other is that the use of a multichannel approach further improves noise-robustness by selecting the best microphone among the microphones, and providing geometrical information on a sound source

The Smart Stone Network: Design and Protocols

The Smart Stone Protocol (SSP) has been developed to achieve rapid synchronization in a wireless sensor network, establish Time Division Multiple Access (TDMA)communication slots, and perform distributed sensing with global shared awareness. The SSP achieves a synchronization precision of 50μs among receivers. The sender is synchronized to the receivers using a novel scheme to identify the closest comparable times on the sender and receiver. The protocol is tightly related to events that occur in the mote hardware, and is designed to operate on resource constrained wireless sensor motes. Robust TDMA communication slots are set up based on the achieved synchronization, and an innovative algorithm is employed to maintain synchronization without sending any additional synchronization bytes. To test and validate the protocol, Smart Stones have been custom designed using commercial off-the-shelf (COTS) components, and the SSP has been successfully demonstrated on the Smart Stone Network performing an acoustic sensing application

Latest research trends in gait analysis using wearable sensors and machine learning: a systematic review

Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications