Rotation Invariant Parallel Signal Processing Using a Diffractive Phase Element for Image Compression

We propose a new rotation invariant correlator using dimensionality reduction. A diffractive phase element is used to focus image data into a line which serves as input for a conventional correlator. The diffractive element sums information over each radius of the scene image and projects the result onto one point of a line located at a certain distance behind the image. The method is flexible, to a large extent, and might include parallel pattern recognition and classification as well as further geometrical invariance. Although the new technique is inspired from circular harmonic decomposition, it does not suffer from energy loss. A theoretical analysis, as well as examples, are given.

IBEX Ribbon Separation Using Spherical Harmonic Decomposition of the Globally Distributed Flux

Remote imaging of plasmas in the heliosphere and very local interstellar medium is possible with energetic neutral atoms (ENAs), created through the charge exchange of protons with interstellar neutral atoms. ENA observations collected by the Interstellar Boundary Explorer (IBEX) revealed two distinctive sources. One source is the globally distributed flux (GDF), which extends over the entire sky and varies over large spatial scales. The other source encompasses only a narrow circular band in the sky and is called the IBEX ribbon. Here, we utilize the observed difference in spatial scales of these two ENA sources to separate them. We find that linear combinations of spherical harmonics up to degree ℓ max = 3 can reproduce most of the ENA fluxes observed outside the ribbon region. We use these combinations to model the GDF and the difference between the observed fluxes and the GDF yields estimation of the ribbon emission. The separated ribbon responds with a longer time delay to the solar wind changes than the GDF, suggesting a more distant source of the ribbon ENAs. Moreover, we locate the direction of the maximum plasma pressure based on the GDF. This direction is 17°.2 ± 0°.5 away from the upwind direction within the plane containing the interstellar flow and interstellar magnetic field vectors. This deflection is consistent with the expected position of the maximum external pressure at the heliopause. The maps with separated ribbon and GDF are posted concurrently with this paper and can be used to further study these two sources.

Mechanical assessment of defects in welded joints: morphological classification and data augmentation

We develop a methodology for classifying defects based on their morphology and induced mechanical response. The proposed approach is fairly general and relies on morphological operators (Angulo and Meyer in 9th international symposium on mathematical morphology and its applications to signal and image processing, pp. 226-237, 2009) and spherical harmonic decomposition as a way to characterize the geometry of the pores, and on the Grassman distance evaluated on FFT-based computations (Willot in C. R., Méc. 343(3):232–245, 2015), for the predicted elastic response. We implement and detail our approach on a set of trapped gas pores observed in X-ray tomography of welded joints, that significantly alter the mechanical reliability of these materials (Lacourt et al. in Int. J. Numer. Methods Eng. 121(11):2581–2599, 2020). The space of morphological and mechanical responses is first partitioned into clusters using the “k-medoids” criterion and associated distance functions. Second, we use multiple-layer perceptron neural networks to associate a defect and corresponding morphological representation to its mechanical response. It is found that the method provides accurate mechanical predictions if the training data contains a sufficient number of defects representing each mechanical class. To do so, we supplement the original set of defects by data augmentation techniques. Artificially-generated pore shapes are obtained using the spherical harmonic decomposition and a singular value decomposition performed on the pores signed distance transform. We discuss possible applications of the present method, and how medoids and their associated mechanical response may be used to provide a natural basis for reduced-order models and hyper-reduction techniques, in which the mechanical effects of defects and structures are decorrelated (Ryckelynck et al. in C. R., Méc. 348(10–11):911–935, 2020).

Machine learning for computationally efficient electrical loads estimation in consumer washing machines

Estimating the wear of the single electrical parts of a home appliance without resorting to a large number of sensors is desirable for ensuring a proper level of maintenance by the manufacturers. Deep learning techniques can be effective tools for such estimation from relatively poor measurements, but their computational demands must be carefully considered, for the actual deployment. In this work, we employ one-dimensional Convolutional Neural Networks and Long Short-Term Memory networks to infer the status of some electrical components of different models of washing machines, from the electrical signals measured at the plug. These tools are trained and tested on a large dataset (502 washing cycles $$\approx$$ ≈ 1000 h) collected from four different washing machines and are carefully designed in order to comply with the memory constraints imposed by available hardware selected for a real implementation. The approach is end-to-end; i.e., it does not require any feature extraction, except the harmonic decomposition of the electrical signals, and thus it can be easily generalized to other appliances.

Sequential dual sided SPIF using low order geometry reconstruction

Harmonic decomposition is an analytical technique that is able to express a manifold surface as the sum of a number of simple surface harmonic components. By reconstructing the initial geometry using a reduced number of components, a similar surface is obtained with a lower level of geometric detail. Because small features are filtered out and the resulting surface lies equal parts above and below the original surface, a tailored multi-step SPIF (Single Point Incremental Forming) processing strategy can be devised. This sequential SPIF strategy uses three processing passes to form a workpiece. The first step is a regular SPIF operation using a conventional toolpath strategy to form the reduced geometry. Two finishing steps are then needed, one from the same side to form the smaller features that lies deeper than the reduced geometry and one backwards pass from the other side of the sheet. To add features that need to be shallower than the reduced geometry, the part is flipped around. The used sequence of these finishing steps and the toolpath strategy used significantly influence the final part accuracy and surface quality. The advantages and disadvantages of four of these combined strategies are examined and compared to regular SPIF.