The embodiments contemplate methods and devices for communicating an acoustic emission via an array of transducers and wirelessly communicating data via a transceiver, where the transceiver may be in communication with the array of transducers. Embodiments also contemplate providing power for the acoustic emission via a power source and providing information regarding a temperature sensor to the transceiver. The wirelessly communicated data may include the information regarding the temperature sensor. Embodiments also contemplate processing the wirelessly communicated data at a relatively remote location with respect to both the power source and the temperature sensor.

Transducer array imaging system

An ultrasonic array is a single transducer that contains a number of individually connected elements. Recent years have seen a dramatic increase in the use of ultrasonic arrays for non-destructive evaluation. Arrays offer great potential to increase inspection quality and reduce inspection time. Their main advantages are their increased flexibility over traditional single element transducer methods, meaning that one array can be used to perform a number of different inspections, and their ability to produce immediate images of the test structure. These advantages have led to the rapid uptake of arrays by the engineering industry. These industrial applications are underpinned by a wide range of published research which describes new piezoelectric materials, array geometries, modelling methods and inspection modalities. The aim of this paper is to bring together the most relevant published work on arrays for non-destructive evaluation applications, comment on the state-of the art and discuss future directions. There is also a significant body of published literature referring to use of arrays in the medical and sonar fields and the most relevant papers from these related areas are also reviewed. However, although there is much common ground, the use of arrays in non-destructive evaluation offers some distinctly different challenges to these other disciplines.

Ultrasonic arrays for non-destructive evaluation: A review

Synthetic Aperture Ultrasound Imaging

A survey on Visual-Based Localization: On the benefit of heterogeneous data

Multimedia content analysis is applied in different real-world computer vision applications, and digital images constitute a major part of multimedia data. In last few years, the complexity of multimedia contents, especially the images, has grown exponentially, and on daily basis, more than millions of images are uploaded at different archives such as Twitter, Facebook, and Instagram. To search for a relevant image from an archive is a challenging research problem for computer vision research community. Most of the search engines retrieve images on the basis of traditional text-based approaches that rely on captions and metadata. In the last two decades, extensive research is reported for content-based image retrieval (CBIR), image classification, and analysis. In CBIR and image classification-based models, high-level image visuals are represented in the form of feature vectors that consists of numerical values. The research shows that there is a significant gap between image feature representation and human visual understanding. Due to this reason, the research presented in this area is focused to reduce the semantic gap between the image feature representation and human visual understanding. In this paper, we aim to present a comprehensive review of the recent development in the area of CBIR and image representation. We analyzed the main aspects of various image retrieval and image representation models from low-level feature extraction to recent semantic deep-learning approaches. The important concepts and major research studies based on CBIR and image representation are discussed in detail, and future research directions are concluded to inspire further research in this area.

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Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review

Metric learning aims to measure the similarity among samples while using an optimal distance metric for learning tasks. Metric learning methods, which generally use a linear projection, are limited in solving real-world problems demonstrating non-linear characteristics. Kernel approaches are utilized in metric learning to address this problem. In recent years, deep metric learning, which provides a better solution for nonlinear data through activation functions, has attracted researchers’ attention in many different areas. This article aims to reveal the importance of deep metric learning and the problems dealt with in this field in the light of recent studies. As far as the research conducted in this field are concerned, most existing studies that are inspired by Siamese and Triplet networks are commonly used to correlate among samples while using shared weights in deep metric learning. The success of these networks is based on their capacity to understand the similarity relationship among samples. Moreover, sampling strategy, appropriate distance metric, and the structure of the network are the challenging factors for researchers to improve the performance of the network model. This article is considered to be important, as it is the first comprehensive study in which these factors are systematically analyzed and evaluated as a whole and supported by comparing the quantitative results of the methods.

Deep Metric Learning: A Survey

Multi-element synthetic aperture techniques employing subaperture processing over successive firing steps can produce good image quality with simple front-end hardware but are susceptible to motion and phase aberration artifacts. We explore correlation processing using fully common spatial frequencies of overlapping subapertures to adapt beamforming for motion and phase aberrations. Signals derived from the subset of elements representing common spatial frequencies exhibit significantly higher correlation coefficients than those from signals computed using the entire subaperture. In addition, the correlation coefficient decreases linearly with subaperture separation for complete subaperture signals, but remains nearly constant with subaperture separation if only common spatial frequencies are used. Adaptive multi-element synthetic aperture imaging with correlation processing using fully common spatial frequencies is tested on experimental RF data acquired from a diffuse scattering phantom using a 3.5 MHz, 128-element transducer array. The results indicate that common spatial frequencies can be used efficiently for correlation processing to correct motion and phase aberration for adaptive multi-element synthetic aperture imaging.

Adaptive multi-element synthetic aperture imaging with motion and phase aberration correction

Content based image retrieval with sparse representations and local feature descriptors : A comparative study

With the recent improvements in computation power and high scale datasets, many interesting studies have been presented based on discriminative models such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) for various classification problems. These models have achieved current state-of-the-art results in almost all applications of computer vision but not sufficient sampling out-of-data, understanding of data distribution. By pioneers of the deep learning community, generative adversarial training is defined as the most exciting topic of computer vision field nowadays. With the influence of these views and potential usages of generative models, many kinds of researches were conducted using generative models especially Generative Adversarial Network (GAN) and Autoencoder (AE) based models with an increasing trend. In this study, a comprehensive review of generative models with defining relations among them is presented for a better understanding of GANs and AEs by pointing the importance of generative models.

Recent Trends in Deep Generative Models: a Review

Correlation processing using fully common spatial frequencies of overlapping subapertures is explored for motion estimation between consecutive excitations in multi-element synthetic aperture imaging. Signals derived from the subset of elements representing common spatial frequencies exhibit significantly higher correlation coefficients than those from signals computed using the entire subaperture. In addition, the correlation coefficient decreases linearly with subaperture separation for complete subaperture signals, but remains nearly constant with subaperture separation if only common spatial frequencies are used. Correlation processing using fully common spatial frequencies is tested on experimental rf data acquired from a diffuse scattering phantom using a 3.5 MHz, 128-element transducer array. The results indicate that common spatial frequencies can be used for correlation processing to minimize motion artifacts in synthetic aperture imaging.

Hasan Sakir Bilge

Papers

Deep Metric Learning: A Survey

Adaptive multi-element synthetic aperture imaging with motion and phase aberration correction

Content based image retrieval with sparse representations and local feature descriptors : A comparative study

Recent Trends in Deep Generative Models: a Review

Motion estimation using common spatial frequencies in synthetic aperture imaging