An explicit point spread function (PSF) evaluator in the frequency domain is described for an ultrasonic transducer operating in the pulse-echo mode. The PSF evaluator employs the patch element model for transducer field determination and scattered field assessment from a small but finite "point" reflector. The PSF for a planar transducer in a medium has been evaluated in the near and the far field. The computed PSFs were used to deconvolve and restore surface images, obtained experimentally, of a single hole and a five-hole cluster in an Al calibration block. A calibration plot is arrived at for estimating, without the need for deconvolution, the actual diameters of circular reflectors from apparent diameters obtained experimentally for a single-medium imaging configuration. The PSF, when the transducer and the point reflector are in two media separated by a planar interface, was evaluated in the near and far field. The computed PSFs were used to deconvolve and restore subsurface images, obtained experimentally, of flat bottom holes (FBHs) in an Al calibration block. We show that the PSF, in the presence of a planar interface, can be obtained from a single-medium PSF model using an effective single-medium path length concept. The PSFs and modulation transfer functions (MTFs) are evaluated for spherical focused and annular transducers and compared with those for the planar transducer. We identify imaging distances to get better-resolved images when using planar, spherical focused, and annular transducers.

Ultrasonic imaging using a computed point spread function

The 2006 ultrasonic benchmark problem involves pulse‐echo angle beam scanning of a notch located on an inclined planar back surface. The response from a side‐drilled hole is to be used as a reference. The models are to simulate (a) the peak‐to‐peak B‐scan P‐ and SV‐ responses of the slots normalized by the appropriate SDH response and (b) the maximum peak‐to‐peak corner response of the slots (either mode‐converted or not). At CNDE, several simulation tools are being developed to assess/predict UT response for various geometries. The Finite‐Difference‐Time‐Difference (FDTD) scheme is one such simulation tool that has been under development in 1D, 2D and 3D. The FDTD is an explicit time domain tool that can simulate pulse propagation characteristics in acoustic/elastic media. The computational domain is limited by implementing Perfectly Matched Layers (PMLs) at the domain boundaries. We present the results of calculations based on 2D FDTD to determine the response of rectangular shaped surface‐breaking defe...

The 2006 Ultrasonic Benchmark Problem — FDTD Simulations in 2D

Breast cancer (BC) remains a significant health threat, with no long-term cure currently available. Early detection is crucial, yet mammography interpretation is hindered by high false positives and negatives. With BC incidence projected to surpass lung cancer, improving early detection methods is vital. Thermography, using high-resolution infrared cameras, offers promise, especially when combined with artificial intelligence (AI). This work presents an attention-based convolutional neural network for segmentation, providing increased speed and precision in BC detection and classification. The system enhances images and performs cancer segmentation with explainable AI. We propose a transformer-attention-based convolutional architecture (UNet) for fault identification and employ Gradient-weighted Class Activation Mapping (Grad-CAM) to analyze areas of bias and weakness in the UNet architecture with IRT images. The superiority of our proposed framework is confirmed when compared with existing deep learning frameworks.

Breast Cancer Segmentation using Attention-based Convolutional Network and Explainable AI

The 2004 ultrasonic benchmark problem requires models to predict, given a reference pulse waveform, the pulse echo response of cylindrical voids of various radii located in an elastic solid for various incidence angles of a transducer immersed in water. We present the results of calculations based on the patch element model, recently developed at CNDE, to determine the response of an SDH in aluminum for specific oblique incidence angles. Patch element model calculations for a scan across the SDH, involving a range of oblique incidence angles, are also presented. Measured pulse‐echo scans involving the SDH response under oblique incidence conditions are reported. In addition, through transmission measurements involving a pinducer as a receiver and an immersion planar probe as a transmitter under oblique incidence conditions are also reported in a defect‐free Aluminum block. These pinducer‐based measurements on a defect‐free block are utilised to characterize the fields at the chosen depth. Comparisons are made between predictions and measurements for the pulse‐echo response of a SDH.

The 2004 Ultrasonic Benchmark Problem - SDH Response Under Oblique Incidence: Measurements and Patch Element Model Calculations

Monitoring plant health is crucial for maintaining agricultural productivity and food safety. Disruptions in the plant's normal state, caused by diseases, often interfere with essential plant activities, and timely detection of these diseases can significantly mitigate crop loss. In this study, we propose a deep learning-based approach for efficient detection of plant diseases using drone-captured imagery. A comprehensive database of various plant species, exhibiting numerous diseases, was compiled from the Internet and utilized as the training and test dataset. A Convolutional Neural Network (CNN), renowned for its performance in image classification tasks, was employed as our primary predictive model. The CNN model, trained on this rich dataset, demonstrated superior proficiency in crop disease categorization and detection, even under challenging imaging conditions. For field implementation, we deployed a prototype drone model equipped with a high-resolution camera for live monitoring of extensive agricultural fields. The captured images served as the input for our trained model, enabling real-time identification of healthy and diseased plants. Our approach promises an efficient and scalable solution for improving crop health monitoring systems.

Detection of healthy and diseased crops in drone captured images using Deep Learning

The determination of material symmetries and principle plane orientations of anisotropic plates, whose planes of symmetries are not known apriori, were calculated using a Genetic Algorithm (GA) based blind inversion method. The ultrasonic phase velocity profiles were used as input data to the inversion. The assumption of a general anisotropy was imposed during the start of each blind inversion. The multi‐parameter solution space of the Genetic Algorithm was exploited to identify the “statistically significant” solution sets of elastic moduli in the geometric coordinate system of the plate, by thresholding the coefficients‐of‐variation (Cv). Using these “statistically significant” elastic moduli, the unknown material symmetry and the principle planes (angles between the geometrical coordinates and the material symmetry coordinates) were evaluated using the method proposed by Cowin and Mehrabadi. This procedure was verified using simulated ultrasonic velocity data sets on material with orthotropic symmetry. Experimental validation was also performed on unidirectional Graphite Epoxy [0]7s fiber reinforced composite plate.

J. Vishnu Vardhan

Papers

The 2004 Ultrasonic Benchmark Problem - SDH Response Under Oblique Incidence: Measurements and Patch Element Model Calculations

Detection of healthy and diseased crops in drone captured images using Deep Learning

Material Symmetries from Ultrasonic Velocity Measurements: A Genetic Algorithm Based Blind Inversion Method

Breast Cancer Segmentation using Attention-based Convolutional Network and Explainable AI