A three-dimensional broadband underwater acoustic concentrator

We propose a three-dimensional (3D) omnidirectional underwater acoustic concentrator based on the concept of acoustic prison, which can realize a substantial enhancement of underwater sound signals in broadband ranges. This device mainly employs the non-resonant multiple reflection characteristics of the semi-enclosed geometric space, so it has a wide working frequency bandwidth. Compared with the previous reported concentrators based on transform acoustics mechanism, the structure is more simple, and most importantly, it can realize omnidirectional signal enhancement in 3D space. Moreover, the working frequency band of this acoustic concentrator depends on the size of the concentrator, so it can be changed directly through a size scaling, which is convenient for engineering applications. In general, the designed underwater acoustic concentrator has the advantages of simple structure, scalability and large bandwidth of working frequency, and high signal gain. It has potential application values in underwater target detection and other aspects.

From space to movement: the walk as an intellectual dynamic in Diderot's La Promenade du sceptique

Diderot's Promenade du sceptique has sometimes been criticised for a somewhat systematic use of allegory, associated with a tripartite division of space summarising and tracing, between the thorns of devotion, the flowers of worldly life, and the chestnut trees of philosophy, the three paths of life available to men. The work's device is nevertheless much more complex and elaborate, and is affected by a diffuse scepticism from within. Open rather than closed, the space and the places of the walk in La Promenade du sceptique are misleadingly the support of an analogical understanding of the world. The writing of the walk becomes a metaphor for the intellectual dynamic that doubts and searches. Movement in its temporal dimension, as a concrete experience, challenges the conception of a geometric space, mocks deism as well as idealism, and opens the way to the experimental method and the materialist hypothesis.

Dynamically corrected gates from geometric space curves

Quantum information technologies demand highly accurate control over quantum systems. Achieving this requires control techniques that perform well despite the presence of decohering noise and other adverse effects. Here, we review a general technique for designing control fields that dynamically correct errors while performing operations using a close relationship between quantum evolution and geometric space curves. This approach provides access to the global solution space of control fields that accomplish a given task, facilitating the design of experimentally feasible gate operations for a wide variety of applications.

Optical Flow-Based Image Registration In Flair MRI

<div>Medical imaging is one of the most common areas of computer vision research and algorithm development. FLAIR-MRI is particularly useful in highlighting damaged and necrotic tissue in brain images due to high contrast and resolution. Image registration is a method of warping images to the same geometric space to quantify tissue changes with accuracy. With advances in deep-learning via convolutional neural networks, complex problems can now move closer to some semblance of a solution with purpose-built and domain specific models. To overcome the non-learnable nature of current registration algorithms, ideas are adapted from video processing solutions of calculating optical flow between temporally spaced frames using unsupervised CNN-based methods to warp moving medical images to a fixed image space. The proposed total network loss combines pixelwise photometric differences, flow smoothness, and intensity correlation. Registration accuracy of the proposed and four other registration algorithms is measured by examining tissue integrity, pixelwise</div><div>alignment, orientation, and global intensity similarity. The results, tested on two large FLAIRMRI datasets consisting of 700 and 4000 brain volumes, show that the optical-flow registration technique is able to obtain maximal alignment while maintaining structural tissue integrity.</div>

Optical Flow-Based Image Registration In Flair MRI

<div>Medical imaging is one of the most common areas of computer vision research and algorithm development. FLAIR-MRI is particularly useful in highlighting damaged and necrotic tissue in brain images due to high contrast and resolution. Image registration is a method of warping images to the same geometric space to quantify tissue changes with accuracy. With advances in deep-learning via convolutional neural networks, complex problems can now move closer to some semblance of a solution with purpose-built and domain specific models. To overcome the non-learnable nature of current registration algorithms, ideas are adapted from video processing solutions of calculating optical flow between temporally spaced frames using unsupervised CNN-based methods to warp moving medical images to a fixed image space. The proposed total network loss combines pixelwise photometric differences, flow smoothness, and intensity correlation. Registration accuracy of the proposed and four other registration algorithms is measured by examining tissue integrity, pixelwise</div><div>alignment, orientation, and global intensity similarity. The results, tested on two large FLAIRMRI datasets consisting of 700 and 4000 brain volumes, show that the optical-flow registration technique is able to obtain maximal alignment while maintaining structural tissue integrity.</div>

SICEM: A Generation Approach of Band Combination for Hyperspectral Imagery Reconstitution Based on Space and Information Analyses

A band selection algorithm named space and information comprehensive evaluation model (SICEM) is proposed in this paper, which reconstitutes the hyperspectral imagery by building an optimal subset to replace the original spectrum. SICEM reduces the dimensions while keeping the vital information of an image, and these are accomplished through two phases. Specifically, the improved fast density peaks clustering (I-FDPC) algorithm is employed to pick out the scattered bands in geometric space to generate a candidate set U at first. Then, we conduct pruning in U through iterative information analysis until the target set Ω is built. In this phase, we need to calculate comprehensive information score (CIS) for every member in U after assigning weights to the amount of information (AoI) and correlation. In each iteration, the band with highest score is selected into Ω , and the ones highly related to it will be removed out of U via a threshold. Compared with the four state-of-the-art unsupervised algorithms on real-world HSI datasets (IndianP and PaviaU), we find that SICEM has strong ability to form an optimal reduced-dimension combination with low correlation and rich information and it performs well in discrete band distribution, accuracy, consistency, and stability.

Measurements and geometry

The paper is aimed at demonstrating the points of contact between measurements and geometry, which is done by modelling the main elements of the measurement process by the elements of geometry. It is shown that the basic equation for measurements can be established from the expression of projective metric and represents its particular case. Commonly occurring groups of functional transformations of the measured value are listed. Nearly all of them are projective transformations, which have invariants and are useful if greater accuracy of measurements is desired. Some examples are given to demonstrate that real measurement transformations can be dealt with via fractional-linear approximations. It is shown that basic metrological and geometrical categories are related, and a concept of seeing a multitude of physical values as elements of an abstract geometric space is introduced. A system of units can be reasonably used as the basis of this space. Two tensors are introduced in the space. One of them (the affinor) describes the interactions within the physical object, the other (the metric tensor) establishes the summation rule on account of the random nature of components.