Vehicle Engine Classification Using of Laser Vibrometry Feature Extraction

Used as a non-invasive and remote sensor, the laser Doppler vibrometer (LDV) has been used in many different applications, such as inspection of aircrafts, bridge and structure and remote voice acquisition. However, using LDV as a vehicle surveillance device has not been feasible due to the lack of systematic investigations on its behavioral properties. In this thesis, the LDV data from different vehicles are examined and features are extracted. A tone-pitch indexing (TPI) scheme is developed to classify different vehicles by exploiting the engine’s periodic vibrations that are transferred throughout the vehicle’s body. Using the TPI with a two-layer feed-forward 20 intermediate-nodes neural network to classify vehicles’ engine, the results are encouraging as they can consistently achieve accuracies over 96%. However, the TPI required a length of 1.25 seconds of vibration, which is a drawback of the TPI, as vehicles generally are moving whence the 1.25 second signals are unavailable. Based on the success of TPI, a new normalized tone-pitch indexing (nTPI) scheme is further developed, using the engine’s periodic vibrations, and shortened the time period from 1.25 seconds to a reasonable 0.2 seconds. Keywords: LDV, Machine Learning, Neural network, Deep learning, Vehicle classificatio

A Hybrid Kinematic-Acoustic System for Automated Activity Detection of Construction Equipment

Automatically recognizing and tracking construction equipment activities is the first step towards performance monitoring of a job site. Recognizing equipment activities helps construction managers to detect the equipment downtime/idle time in a real-time framework, estimate the productivity rate of each equipment based on its progress, and efficiently evaluate the cycle time of each activity. Thus, it leads to project cost reduction and time schedule improvement. Previous studies on this topic have been based on single sources of data (e.g., kinematic, audio, video signals) for automated activity-detection purposes. However, relying on only one source of data is not appropriate, as the selected data source may not be applicable under certain conditions and fails to provide accurate results. To tackle this issue, the authors propose a hybrid system for recognizing multiple activities of construction equipment. The system integrates two major sources of data-audio and kinematic-through implementing a robust data fusion procedure. The presented system includes recording audio and kinematic signals, preprocessing data, extracting several features, as well as dimension reduction, feature fusion, equipment activity classification using Support Vector Machines (SVM), and smoothing labels. The proposed system was implemented in several case studies (i.e., ten different types and equipment models operating at various construction job sites) and the results indicate that a hybrid system is capable of providing up to 20% more accurate results, compared to cases using individual sources of data

Image Prior and Posterior Conditional Probability Representation for Efficient Damage Assessment

It is important to quantify Damage Assessment (DA) for Human Assistance and Disaster Response (HADR) applications. In this paper, to achieve efficient and scalable DA in HADR, an image prior and posterior conditional probability (IP2CP) is developed as an effective computational imaging representation. Equipped with the IP2CP representation, the matching pre- and post-disaster images are effectively encoded into one image that is then processed using deep learning approaches to determine the damage levels. Two scenarios of crucial importance for the practical use of DA in HADR applications are examined: pixel-wise semantic segmentation and patch-based contrastive learning-based global damage classification. Results achieved by IP2CP in both scenarios demonstrate promising performances, showing that our IP2CP-based methods within the deep learning framework can effectively achieve data and computational efficiency, which is of utmost importance for the DA in HADR applications.Comment: 6 pages, 2 figure

PnP Maxtools: Autonomous Parameter Control in MaxMSP Utilizing MIR Algorithms

This research presents a new approach to computer automation through the implementation of novel real-time music information retrieval algorithms developed for this project. It documents the development of the PnP.Maxtools package, a set of open source objects designed within the popular programming environment MaxMSP. The package is a set of pre/post processing filters, objective and subjective timbral descriptors, audio effects, and other objects that are designed to be used together to compose music or improvise without the use of external controllers or hardware. The PnP.Maxtools package objects are designed to be used quickly and easily using a `plug and play\u27 style with as few initial arguments needed as possible. The PnP.Maxtools package is designed to take incoming audio from a microphone, analyze it, and use the analysis to control an audio effect on the incoming signal in real-time. In this way, the audio content has a real musical and analogous relationship with the resulting musical transformations while the control parameters become more multifaceted and better able to serve the needs of artists. The term Reflexive Automation is presented that describes this unsupervised relationship between the content of the sound being analyzed and the analogous and automatic control over a specific musical parameter. A set of compositions are also presented that demonstrate ideal usage of the object categories for creating reflexive systems and achieving fully autonomous control over musical parameters