Although clinical percussion remains one of the most widespread traditional noninvasive methods for diagnosing pulmonary disease, the available analysis of physical characteristics of the percussion sound using modern signal processing techniques is still quite limited. The majority of existing literature on the subject reports either time-domain or spectral analysis methods. However, Fourier analysis, which represents the signal as a sum of infinite periodic harmonics, is not naturally suited for decomposition of short and aperiodic percussion signals. Broadening of the spectral peaks due to damping leads to their overlapping and masking of the lower amplitude peaks, which could be important for the fine-level signal classification. In this study, an attempt is made to automatically decompose percussion signals into a sum of exponentially damped harmonics, which in this case form a more natural basis than Fourier harmonics and thus allow for a more robust representation of the signal in the parametric space. The damped harmonic decomposition of percussion signals recorded on healthy volunteers in clinical setting is performed using the matrix pencil method, which proves to be quite robust in the presence of noise and well suited for the task.

Polyethylene (PE) pipes are widely used in gas distribution. Their joints are prone to various flaws and are the most problematic part of the pipeline, so the infrastructure industry requires an effective inspection technique. Butt-fusion (BF) is the most common method of joining PE pipes. In this research, we investigated the applicability of machine learning (ML) to automate the ultrasonic inspection of PE pipe BF joints. Flawless and defective joints were fabricated. A-scan signals were collected from each group of samples using a customized chord transducer, with the aim of developing and assessing the viability of ML approaches to the problem of joint classification. We compared several ML approaches to the problem and found that convolutional neural networks were most performant, classifying signals with an F1 score of 0.874 in a four-class problem (identifying defect presence and type) and of 0.912 in binary classification (defect presence/absence only). Our results show that an ultrasonic chord-type transducer approach can effectively resolve flawless samples versus those with coarse contaminants or cold fusions and that an ML approach can be used to effectively assess these ultrasonic signals. Our findings can be used to develop a portable, efficient, user-friendly, and inexpensive device for in-field joint inspections.

Objectives: The purpose of this study was to characterize human breast cancer tissues by the measurement of microacoustic properties.Methods: We investigated eight breast cancer patients using acoustic microscopy. For each patient, seven blocks of tumor tissue were collected from seven different positions around a tumor mass. Frozen sections (10 micrometer, µm) of human breast cancer tissues without staining and fixation were examined in a scanning acoustic microscope with focused transducers at 80 and 200 MHz. Hematoxylin and Eosin (H&E) stained sections from the same frozen breast cancer tissues were imaged by optical microscopy for comparison.Results: The results of acoustic imaging showed that acoustic attenuation and sound speed in cancer cell-rich tissue regions were significantly decreased compared with the surrounding tissue regions, where most components are normal cells/tissues, such as fibroblasts, connective tissue and lymphocytes. Our observation also showed that the ultrasonic properties were influenced by arrangements of cells and tissue patterns.Conclusions: Our data demonstrate that attenuation and sound speed imaging can provide biomechanical information of the tumor and normal tissues. The results also demonstrate the potential of acoustic microscopy as an auxiliary method for operative detection and localization of cancer affected regions.

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