An Enhanced Multi-frequency Distorted Born Iterative Method for Ultrasound Tomography based on Fundamental Tone and Overtones

Conventional Distorted Born Iterative Method (DBIM) using single frequency has low resolution and is prone to creating images with high-contrast subjects. We propose a productive frequency combination method to better result in tomographic ultrasound imaging based on the multi-frequency technique. This study uses the natural mechanism of emitting oscillators' frequencies and uses these frequencies for imaging in iterations. We use a fundamental tone (i.e., the starting frequency f0) for the first iteration in DBIM, then consecutively use its overtones for the next ones. The digital simulation scenarios are tested with other multi-frequency approaches to prove our method's feasibility. We performed 57 different simulation scenarios on the use of multi-frequency information for the DBIM method. As a result, the proposed method for the smallest normalization error (RRE = 0.757). The proposed method's imaging time is not significantly longer than the way of using single frequency information.

Deblending based on the frequency-structure weighted threshold: The case of the slope-constrained f-k transform

In the process of separating blended data, conventional methods based on sparse inversion assume that the primary source is coherent and the secondary source is randomized. The L1-norm, the commonly used regularization term, uses a global threshold to process the sparse spectrum in the transform domain; however, when the threshold is relatively high, more high-frequency information from the primary source will be lost. For this reason, we analyze the generation principle of blended data based on the convolution theory and then conclude that the blended data is only randomly distributed in the spatial domain. Taking the slope-constrained frequency-wavenumber ( f- k) transform as an example, we propose a frequency-dependent threshold, which reduces the high-frequency loss during the deblending process. Then we propose to use a structure weighted threshold in which the energy from the primary source is concentrated along the wavenumber direction. The combination of frequency and structure-weighted thresholds effectively improves the deblending performance. Model and field data show that the proposed frequency-structure weighted threshold has better frequency preservation than the global threshold. The weighted threshold can better retain the high-frequency information of the primary source, and the similarity between other frequency-band data and the unblended data has been improved.

The Influence of Noise in the Super-Resolution Reconstruction of Structured Illumination Microscopy

As one of the mainstream super-resolution imaging technologies, structured illumination microscopy (SIM) is popular for its fast imaging speed and simple optical path structure. Spectrum separation is a key step in the reconstruction of super-resolution images. However, in the process of imaging, the unavoidable noise will seriously affect the accuracy of frequency spectrum separation. This paper carries out a simulation study on the influence of noise in the process of frequency spectrum separation. The results show that although noise can cause distortion of low-frequency information in frequency spectrum separation results, it has little influence on high-frequency information. Therefore, a super-resolution image reconstruction method is proposed to effectively suppress the influence of noise. Both simulation and experimental results are shown the method can suppress the influence of noise without losing the details of super-resolution.

A Framework of Structural Damage Detection for Civil Structures Using Fast Fourier Transform and Deep Convolutional Neural Networks

In the field of structural health monitoring (SHM), vibration-based structural damage detection is an important technology to ensure the safety of civil structures. By taking advantage of deep learning, this study introduces a data-driven structural damage detection method that combines deep convolutional neural networks (DCNN) and fast Fourier transform (FFT). In this method, the structural vibration data are fed into FFT method to acquire frequency information reflecting structural conditions. Then, DCNN is utilized to automatically extract damage features from frequency information to identify structural damage conditions. To verify the effectiveness of the proposed method, FFT-DCNN is carried out on a three-story building structure and ASCE benchmark. The experimental result shows that the proposed method achieves high accuracy, compared with classic machine-learning algorithms such as support vector machine (SVM), random forest (RF), K-Nearest Neighbor (KNN), and eXtreme Gradient boosting (xgboost).

Why consumers drink natural wine? Consumer perception and information about natural wine

Similar to other foods, the concept of natural wine is much debated due to the lack of a clear and regulated definition, leading to a proliferation of heterogeneous norms and standards proposed from different natural wine associations at national levels. The current study explored the aspects which mediate individuals’ information and perception of natural wine, and the rationale behind natural wine consumption behavior among Italian (n = 501) and Spanish (n = 527) regular wine consumers. The results reveal a quite low self-reported degree of perceived information by Italian respondents and slightly higher levels among Spanish ones. The key drivers of natural wine consumption in both countries are wine consumption frequency, information, and natural product interest. In contrast, higher wine involvement levels decrease natural wine consumption frequency in both Italy and Spain. The findings also show that different perceptions lead to diverse motivations, suggesting the need for more homogeneous standards to mitigate the level of information asymmetry currently on the market.

Recovering Geometric Information with Learned Texture Perturbations

Regularization is used to avoid overfitting when training a neural network; unfortunately, this reduces the attainable level of detail hindering the ability to capture high-frequency information present in the training data. Even though various approaches may be used to re-introduce high-frequency detail, it typically does not match the training data and is often not time coherent. In the case of network inferred cloth, these sentiments manifest themselves via either a lack of detailed wrinkles or unnaturally appearing and/or time incoherent surrogate wrinkles. Thus, we propose a general strategy whereby high-frequency information is procedurally embedded into low-frequency data so that when the latter is smeared out by the network the former still retains its high-frequency detail. We illustrate this approach by learning texture coordinates which when smeared do not in turn smear out the high-frequency detail in the texture itself but merely smoothly distort it. Notably, we prescribe perturbed texture coordinates that are subsequently used to correct the over-smoothed appearance of inferred cloth, and correcting the appearance from multiple camera views naturally recovers lost geometric information.