Method of optimization of the fundamental matrix by technique speeded up robust features application of different stress images

<span>The purpose of determining the fundamental matrix (F) is to define the epipolar geometry and to relate two 2D images of the same scene or video series to find the 3D scenes. The problem we address in this work is the estimation of the localization error and the processing time. We start by comparing the following feature extraction techniques: Harris, features from accelerated segment test (FAST), scale invariant feature transform (SIFT) and speed-up robust features (SURF) with respect to the number of detected points and correct matches by different changes in images. Then, we merged the best chosen by the objective function, which groups the descriptors by different regions in order to calculate ‘F’. Then, we applied the standardized eight-point algorithm which also automatically eliminates the outliers to find the optimal solution ‘F’. The test of our optimization approach is applied on the real images with different scene variations. Our simulation results provided good results in terms of accuracy and the computation time of ‘F’ does not exceed 900 ms, as well as the projection error of maximum 1 pixel, regardless of the modification.</span>

Sound Source Separation Mechanisms of Different Deep Networks Explained from the Perspective of Auditory Perception

Thanks to the development of deep learning, various sound source separation networks have been proposed and made significant progress. However, the study on the underlying separation mechanisms is still in its infancy. In this study, deep networks are explained from the perspective of auditory perception mechanisms. For separating two arbitrary sound sources from monaural recordings, three different networks with different parameters are trained and achieve excellent performances. The networks’ output can obtain an average scale-invariant signal-to-distortion ratio improvement (SI-SDRi) higher than 10 dB, comparable with the human performance to separate natural sources. More importantly, the most intuitive principle—proximity—is explored through simultaneous and sequential organization experiments. Results show that regardless of network structures and parameters, the proximity principle is learned spontaneously by all networks. If components are proximate in frequency or time, they are not easily separated by networks. Moreover, the frequency resolution at low frequencies is better than at high frequencies. These behavior characteristics of all three networks are highly consistent with those of the human auditory system, which implies that the learned proximity principle is not accidental, but the optimal strategy selected by networks and humans when facing the same task. The emergence of the auditory-like separation mechanisms provides the possibility to develop a universal system that can be adapted to all sources and scenes.

Induced microearthquakes predict permeability creation in the brittle crust

Fracturing controls rates of mass, chemical and energy cycling within the crust. We use observed locations and magnitudes of microearthquakes (MEQs) to illuminate the evolving architecture of fractures reactivated and created in the otherwise opaque subsurface. We quantitatively link seismic moments of laboratory MEQs to the creation of porosity and permeability at field scale. MEQ magnitudes scale to the slipping patch size of remanent fractures reactivated in shear - with scale-invariant roughnesses defining permeability evolution across nine decades of spatial volumes – from centimeter to decameter scale. This physics-inspired seismicity-permeability linkage enables hybrid machine learning (ML) to constrain in-situ permeability evolution at verifiable field-scales (~10 m). The ML model is trained on early injection and MEQ data to predict the dynamic evolution of permeability from MEQ magnitudes and locations, alone. The resulting permeability maps define and quantify flow paths verified against ground truths of permeability.

Predicting the Future with a Scale-Invariant Temporal Memory for the Past

In recent years, it has become clear that the brain maintains a temporal memory of recent events stretching far into the past. This letter presents a neutrally inspired algorithm to use a scale-invariant temporal representation of the past to predict a scale-invariant future. The result is a scale-invariant estimate of future events as a function of the time at which they are expected to occur. The algorithm is time-local, with credit assigned to the present event by observing how it affects the prediction of the future. To illustrate the potential utility of this approach, we test the model on simultaneous renewal processes with different timescales. The algorithm scales well on these problems despite the fact that the number of states needed to describe them as a Markov process grows exponentially.

Cornering the universal shape of fluctuations

Understanding the fluctuations of observables is one of the main goals in science, be it theoretical or experimental, quantum or classical. We investigate such fluctuations in a subregion of the full system, focusing on geometries with sharp corners. We report that the angle dependence is super-universal: up to a numerical prefactor, this function does not depend on anything, provided the system under study is uniform, isotropic, and correlations do not decay too slowly. The prefactor contains important physical information: we show in particular that it gives access to the long-wavelength limit of the structure factor. We exemplify our findings with fractional quantum Hall states, topological insulators, scale invariant quantum critical theories, and metals. We suggest experimental tests, and anticipate that our findings can be generalized to other spatial dimensions or geometries. In addition, we highlight the similarities of the fluctuation shape dependence with findings relating to quantum entanglement measures.

Advancement in Physical Education Teaching Using Improved Energy Efficient Scalable Routing Algorithm-Based Wireless Network

Physical education (PE) is a crucial topic in higher coaching that individually points motor abilities in health-enhancing activities. Conventional PE in institutions struggles to pique graduates’ attentiveness in sports, proceeding in low task involvements rates, and incapacity to exercise the body. Innovative teaching concepts and methodologies, coaching techniques and procedures, and coaching assessment techniques in physical education are all accompanied to developing the physical education study hall climate and successfully boosting physical education efficacy. Each element of regular living, especially education, is being influenced by wireless internet innovations. We will provide extra help to students by predicting academic endurance or dropout. We can improve the wireless platform’s potential utility in sports applications and change the character of PE, including visualization and repetition by incorporating it into PE teaching. Based on the concept of wireless network technology, this paper proposes an Improved Energy Efficient Scalable Routing Algorithm (IEESRA) for physical education advancement. Initially, the physical education dataset is preprocessed using normalization. The aspects are removed using the scale-invariant feature transform (SIFT) method. The data is transferred using a wireless network using Improved Energy Efficient Scalable Routing Algorithm (IEESRA). The classification is done using random forest (RF) classifier. The results of the analysis reveal that wireless network-based PE may increase graduates’ strength, speed, and qualities providing a more important reference and reference for enhancing the success of PE. The proposed strategy has the potential to enhance actual attention to PE teaching to 90% with raising students’ engagement to 70%.

Scale Invariant Turbulence and Gibbs Free Energy in the Atmosphere

A method of calculating the Gibbs Free Energy (Exergy) for the Earth’s atmosphere using statistical multifractality — scale invariance - is described, and examples given of its application to the stratosphere, including a methodology for extension to aerosol particles. The role of organic molecules in determining the radiative transfer characteristics of aerosols is pointed out. These methods are discussed in the context of the atmosphere as an open system far from chemical and physical equilibrium, and used to urge caution in deploying “solar radiation management”.

Fractal dimension analogous scale-invariant derivative of Hirsch’s index

We propose a scale-invariant derivative of the h-index as “h-dimension”, which is analogous to the fractal dimension of the h-index for institutional performance analysis. The design of h-dimension comes from the self-similar characteristics of the citation structure. We applied this h-dimension to data of 134 Japanese national universities and research institutes, and found well-performing medium-sized research institutes, where we identified multiple organizations related to natural disasters. This result is reasonable considering that Japan is frequently hit by earthquakes, typhoons, volcanoes and other natural disasters. However, these characteristic institutes are screened by larger universities if we depend on the existing h-index. The scale-invariant property of the proposed method helps to understand the nature of academic activities, which must promote fair and objective evaluation of research activities to maximize intellectual, and eventually economic opportunity.