This work addresses the problem of localizing an impact in a dispersive medium (waveguide) using a network of vibration sensors (accelerometers), distributed at various locations in the waveguide, measuring (and detecting the arrival of) the impact-generated seismic wave. In particular, the last part of this document focuses on the problem of localizing footsteps using underfloor accelerometers. The author believes the outcomes of this work pave the way for realizing real-time indoor occupant tracking using underfloor accelerometers; a system that is tamper-proof and non-intrusive compared to occupant tracking systems that rely on video image processing. A dispersive waveguide (e.g., a floor) causes the impact-generated wave to distort with the traveled distance and renders conventional time of flight localization methods inaccurate. Therefore, this work focuses on laying the foundation of a new alternative approach to impact localization in dispersive waveguides. In this document, localization algorithms, including wave-signal detection and signal processing, are developed utilizing the fact that the generated wave's energy is attenuated with the traveled distance. The proposed localization algorithms were evaluated using simulations and experiments of hammer impacts, in addition to occupant tracking experiments. The experiments were carried out on an instrumented floor section inside a smart building. As will be explained in this document, energy-based localization will turn out to be computationally cheap and more accurate than conventional time of flight techniques. / PHD / When a person walks, each footstep impact generates a tiny floor-quake. The floor-quake sends a shock wave traveling along the floor, and causes the floor to vibrate. If these vibrations are sensed/measured at different locations in the floor, then the measurements can be used to estimate the individual footstep impact locations. Estimating the location of each footstep impact can then be utilized to track the walking path of the person. This dissertation proposes a novel footstep location estimation approach. The localization approach uses a group of underfloor vibration sensors, called accelerometers, to measure the footstep-generated floor vibration. Then, the sensor measurements are used to estimate footstep locations. Footstep location estimates are generated using the fact that the strength/energy of the generated wave is absorbed by the floor, and consequently the wave energy is attenuated with the traveled distance. The proposed footstep localization approach can be used to track occupants inside buildings, providing a tracking system that is non-intrusive compared to tracking occupants using a system of cameras and a video image-processing software.

Master of Science / Department of Computing and Information Sciences / Gurdip Singh / The Patient Monitoring System is a wireless sensor network application used for dynamically tracking a patient’s physical activity using 3D Accelerometer Sensors in the Sun Small Programmable Object Technology (SPOT) platform. The system is able to detect different postures of a person and recognize high-level actions performed by a patient by monitoring different pattern of postures. This activity can be monitored remotely from a nurse station or a handheld device. The monitoring system can be used for alerting the nurse station in a hospital, if a patient performs some abnormal action. In the proposed system, the Sun SPOTs are affixed on a person's chest, thigh, leg and arm. The application determines the posture of a person by sensing the acceleration and tilt values of the SPOT in the direction of X, Y and Z axis. Based on these values the application can determine the postures of a person such as Lying Down, Sitting, Standing, Walking, Bending, and Arm Moving. We provide user mechanisms to define high level actions such as “attempting to get up from Lying Down position”, in terms of patterns of lower-level posture sequences. The system detects these patterns from the posture sequences reported by the Sun SPOTs, and reports them at desired locations.

There has been an enormous growth in the usage of smartphones in recent times. Smartphones are not limited to communication purposes. It has various applications designed as per the daily requirements of humans such as web-searching, online shopping, bank transactions, games, etc. With the increase in the usage of the smartphone, the more useful information is captured and stored by it, which raises the question of security. The goal of this research is to develop two android applications. One is a sensor detector application and the second is a screen lock application. The first application will help the user to identify all the hidden sensors and working sensors on the mobile phone. This application even describes the features and usage of every sensor in detail. Using a graphical description of each sensor which depicts the behaviour of each sensor as per environment/movement. The second application is designed using a combination of two sensors. Screen lock applications contain two main factors. One is to work properly in all cases and efficiently do the functions that are required to do. The second is to maintain a smooth inner system interaction because in addition to locking the screen this application should make sure to hide the display of all the other applications without closing the process of these applications. With the increase in the usage of the smartphone, it becomes difficult for older generations to memorize the security pattern techniques and use them. This thesis develops a simple technique in the mobile authentication android application. The thesis is developed on the Android studio platform. The background functionality of the app is coded in java using android SDK tool and frontend of the application is designed using XML files. The GENYMOTION emulator and a mobile phone are used to test the output.

MEMS accelerometers are quickly approaching navigation grade performance and navigation market for MEMS accelerometer systems are expected to grow in the recent years. Compared to conventional accelerometers, these micromachined sensors are smaller and more durable but are generally worse in terms of noise and dynamic range performance. Since MEMS accelerometers are already dominant in the tactical and consumer electronics market, as they are in all modern smart phones today, there is significant demand for MEMS accelerometers that can reach navigation grade performance without significantly altering the developed process technologies. This research aims to improve the performance of previously fabricated and well-known MEMS capacitive closed loop &Sigma / &Delta / accelerometer systems to navigation grade performance levels. This goal will be achieved by reducing accelerometer noise level through significant changes in the system architecture and implementation of a new electronic interface readout ASIC. A flexible fourth order &Sigma / &Delta / modulator was chosen as the implementation of the electro-mechanical closed loop system, and the burden of noise shaping in the modulator was shifted from the mechanical sensor to the programmable electronic readout. A novel operational transconductance amplifier (OTA) was also designed for circuit implementation of the electronic interface readout. Design and fabrication of the readout was done in a standard 0.35 &micro / m CMOS technology. With the newly designed and fabricated readout, single-axis accelerometers were implemented and tested for performance levels in 1g range. The implemented system achieves 5.95 &micro / g/sqrt Hz, 6.4 &micro / g bias drift, 131.7 dB dynamic range and up to 37.2 g full scale range with previously fabricated dissolved epitaxial wafer process (DEWP) accelerometers in METU MEMS facilities. Compared to a previous implementation with the same accelerometer element reporting 153 &micro / g/sqrtHz, 50 &micro / g bias drift, 106.8 dB dynamic range and 33.5 g full scale range / this research reports a 25 fold improvement in noise, 24 dB improvement in dynamic range and removal of the deadzone region.