# Deep Machine Learning for Path Length Characterization Using Acoustic Diffraction
## 1 Introduction
This makes manual analysis of the diffraction patterns a difficult task as human eyes cannot make clear determinations. In this research, we aim to determine whether a machine can extract the necessary features to determine the depth. Depth is an important feature because it tells us the elasticity of the anomaly as the acoustic path length is the product of the elastic modulus and the physical length. In this study we assess the feasibility of assessing acoustic diffraction data by creating a model which aims to make determinations in both the Fresnel range and Fraunhofer range. To combat the previously introduced challenges of analyzing acoustic imagery, the transfer function phase and transfer function amplitude of the signal are analyzed. This solves the issue of low reflectivity commonly experienced in acoustic imagery. Using this data, the machine learning algorithms compensate for low spatial resolution so that common image processing techniques can be applied to the acoustic data. This enables the machine learning algorithm to locate the anomaly and extract more detailed information which characterizes the acoustic path length of the anomaly. The preliminary experiment is performed on thin metallic specimens with induced anomalies of several dimensions, as described in Section 2. Designing the model in such a way allows more control over the diffractive properties and acoustic path lengths of the ground truth data that the model will train on, thus allowing a controlled analysis of the model's performance with known data to be performed. After the desired performance is achieved by this preliminary model, transfer learning can then be used to adapt the model for use with biofilm specimens. The results from this preliminary experiment lend weight to using this technique to distinguish characteristics of biological materials such as biofilms. Two areas in which this would be particularly useful are in determining the presence and thickness of a biofilm and in determining the density of a biofilm. One complication is that the elastic constant of a cell and that of water are similar, making it difficult to distinguish constituent cells from the surrounding fluid. This results in low acoustic contrast and reflectivity [10] in the imagery. This preliminary research, however, has shown that a CNN will be able to make determinations even in low-contrast acoustic imagery, so this should not be problematic for a machine learning approach. The body of this article is organized into the following sections: introduction, preprocessing, materials and methods, results, discussion, and conclusions. The current section details the problems with biofilm detection, which motivate the current study as well as the theory which drives the model development. The next section, Section 2, demonstrates the setup of the experiment, the data collection process, the data preprocessing, and the model design. Section 3 details the data analysis used to assess the applicability of each model, the performance of the models during training, and the performance of the models when used to predict acoustic path length on an additional dataset. Section 4 examines the performance of each model as it relates to the theoretical concepts introduced in the current section as well as the analysis performed in Section 3. The final section, Section 5, wraps the discussion up with conclusions that can be drawn from Section 4 and recommendations for future work to address some of the shortcomings of the model discussed in that section.