Detectors

Human Activity Recognition (HAR) plays a crucial role in various applications, including healthcare, fitness tracking, security, and smart environments, by enabling the automatic classification of human actions based on sensor and visual data. This dissertation presents a comprehensive exploration of HAR utilizing machine learning, sensor-based data, and Fusion approaches. HAR involves classifying human activities over time by analyzing data from sensors such as accelerometers and gyroscopes. Recent advancements in computational technology and sensor availability have driven significant progress in this field, enabling the integration of these sensors into smartphones and other devices. The first study outlines the foundational aspects of HAR and reviews existing literature, highlighting the importance of machine learning applications in healthcare, athletics, and personal use. In the second study, the focus shifts to addressing challenges in handling large-scale, variable, and noisy sensor data for HAR systems. The research applies machine learning algorithms to the KU-HAR dataset, revealing that the LightGBM classifier outperforms others in key performance metrics such as accuracy, precision, recall, and F1 score. This study underscores the continued relevance of optimizing machine learning techniques for improved HAR systems. The study highlights the potential for future research to explore more advanced fusion techniques to fully leverage different data modalities for HAR. The third study focuses on overcoming common challenges in HAR research, such as varying smartphone models and sensor configurations, by employing data fusion techniques.

The recent uptick in senseless shootings in otherwise quiet and relatively safe environments is powerful evidence of the need, now more than ever, to reduce these occurrences. Artificial intelligence (AI) can play a significant role in deterring individuals from attempting these acts of violence. The installation of audio sensors can assist in the proper surveillance of surroundings linked to public safety, which is the first step toward AI-driven surveillance. With the increasing popularity of machine learning (ML) processes, systems are being developed and optimized to assist personnel in highly dangerous situations. In addition to saving innocent lives, supporting the capture of the responsible criminals is part of the AI algorithm that can be hosted in acoustic gunshot detection systems (AGDSs). Although there has been some speculation that these AGDSs produce a higher false positive rate (FPR) than reported in their specifications, optimizing the dataset used for the model’s training and testing will enhance its performance.
This dissertation proposes a new gunshot-like sound database that can be incorporated into a dataset for improved training and testing of a ML gunshot detection model. Reduction of the sample bias (that is, a bias in ML caused by an incomplete database) is achievable. The Mel frequency cepstral coefficient (MFCC) feature extraction process was utilized in this research. The uniform manifold and projection (UMAP) algorithm revealed that the MFCCs of this newly created database were the closest sounds to a gunshot sound, as compared to other gunshot-like sounds reported in literature. The UMAP algorithm reinforced the outcome derived from the calculation of the distances of the centroids of various gunshot-like sounds in MFCCs’ clusters. Further research was conducted into the feature reduction aspect of the gunshot detection ML model. Reducing a feature set to a minimum, while also maintaining a high accuracy rate, is a key parameter of a highly efficient model. Therefore, it is necessary for field deployed ML applications to be computationally light weight and highly efficient. Building on the discoveries of this research can lead to the development of highly efficient gunshot detection models.

Tests in a cyclic chamber and in real atmospheric conditions resulted in the development of an improved corrosion coulometer sensor. First tests showed that it responded well in a reproduced environment but not satisfactorily in a real one, although it seemed to present a good correlation with the weather observations. However, these tests allowed a small time step data analysis of atmospheric corrosion and therefore an improved knowledge of this process. Also discussed are the possible ways of retrieving the corrosion coulometer data wirelessly, thus allowing a real-time analysis of atmospheric corrosion on steel structures. Ideas are proposed for improving both the sensor and the electronic package to make the system an efficient monitor of atmospheric corrosion.

This thesis presents a method for modeling navigation sensors used on ocean systems and particularly on Autonomous Underwater Vehicles (AUV). An extended Kalman filter was previously designed for the implementation of the Inertial Navigation System (INS) making use of Inertial Measurement Unit (IMU), a magnetic compass, a GPS/DGPS system and a Doppler Velocity Log (DVL). Emphasis is put on characterizing the static sensor error model. A "best-fit ARMA model" based on the Aikake Information Criterion (AIC), Whiteness test and graphical analyses were used for the model identification. Model orders and parameters were successfully estimated for compass heading, GPS position and IMU static measurements. Static DVL measurements could not be collected and require another approach. The variability of the models between different measurement data sets suggests online error model estimation.