Modified divisive clustering useful for quantitative analysis of remotely sensed data

Efficient sparse subspace clustering by nearest neighbour filtering

The inclusion of the Basset (history) term in the equation of motion of a sphere in creeping flow increases substantially the computational time and memory requirements for a numerical solution of the trajectory of the sphere. A transformation of the equation of motion of a sphere in a fluid is obtained, in which the velocity of the sphere does not appear in the history integral. Although the transformed equation is a second-order ordinary differential equation (ODE), it is explicit in the dependent variable and presents considerable advantages in reduced computational time and memory requirements.

An efficient nonparametric, hierarchical, symbolic agglomerative clustering procedure based on the mutual nearest neighbourhood concept is proposed for classifying remotely sensed multispectral data. The procedure utilized a data reduction technique and an innovative symbolic concept to minimize the memory and computational time requirements. A new non-metric similarity measure and a novel method of formulation of composite symbolic objects are proposed to enrich the performance of the algorithm. A Mean Difference Index (MDI) concept for identifying the optimal number of classes was used. Experiments were conducted on IRS (Indian Remote Sensing) satellite data to authenticate the efficacy of the procedure.

Abstract. Image segmentation, unlike most conventional approaches, assesses image pixel information in relation to image contextual information to extract congruent image objects. A comprehensive review of literature during the research conceptualization phase revealed that while a lot of work has been done into the optimization of the segmentation computational time, there was a conspicuous research gap in the area of computational time modelling and pre-analytical projections.The focus of this research was, therefore, to model the computational time requirements of segmentation as a function of variability in image spatial resolutions and pixel counts. The employed methodology integrated structure from motion (SFM), segmentation, and regression modelling into a multiphase workflow to process UAV-acquired imagery into an orthomosaic, which underwent segmentation at multiple resolutions. The segmentation computational time was regressed against the image pixel count and spatial resolution.The results showed that the image pixel count was the main determinant of the computational time, expressing a statistically strong linear relationship. However, when the regressors were tested individually, the spatial resolution showed an exponential relationship with computational time which, however, exhibited saturation. It can be inferred that image spatial resolution is not the key determinant of segmentation computational time but the pixel count of the image.This research recommends the adoption of statistically viable models to thoroughly assess computational time requirements for image segmentation and a pre-analytics estimation of the time requirements such that image quality – image processing time adjustments can be made before rather than after analyses have commenced.

Appearance-based localization is increasingly used for loop closure detection in metric SLAM systems. Since it relies only upon the appearance-based similarity between images from two locations, it can perform loop closure regardless of accumulated metric error. However, the computation time and memory requirements of current appearance-based methods scale linearly not only with the size of the environment but also with the operation time of the platform. These properties impose severe restrictions on longterm autonomy for mobile robots, as loop closure performance will inevitably degrade with increased operation time. We present a set of improvements to the appearance-based SLAM algorithm CAT-SLAM to constrain computation scaling and memory usage with minimal degradation in performance over time. The appearance-based comparison stage is accelerated by exploiting properties of the particle observation update, and nodes in the continuous trajectory map are removed according to minimal information loss criteria. We demonstrate constant time and space loop closure detection in a large urban environment with recall performance exceeding FAB-MAP by a factor of 3 at 100% precision, and investigate the minimum computational and memory requirements for maintaining mapping performance.

Optimal analysis for optimal design of cyclic symmetric structures subject to frequency constraints

Relief-F is a nonparametric, nearest-neighbor machine learning method that has been successfully used to identify relevant variables that may interact in complex multivariate models to explain phenotypic variation. While several tools have been developed for assessing differential expression in sequence-based transcriptomics, the detection of statistical interactions between transcripts has received less attention in the area of RNA-seq analysis. We describe a new extension and assessment of Relief-F for feature selection in RNA-seq data. The ReliefSeq implementation adapts the number of nearest neighbors (k) for each gene to optimize the Relief-F test statistics (importance scores) for finding both main effects and interactions. We compare this gene-wise adaptive-k (gwak) Relief-F method with standard RNA-seq feature selection tools, such as DESeq and edgeR, and with the popular machine learning method Random Forests. We demonstrate performance on a panel of simulated data that have a range of distributional properties reflected in real mRNA-seq data including multiple transcripts with varying sizes of main effects and interaction effects. For simulated main effects, gwak-Relief-F feature selection performs comparably to standard tools DESeq and edgeR for ranking relevant transcripts. For gene-gene interactions, gwak-Relief-F outperforms all comparison methods at ranking relevant genes in all but the highest fold change/highest signal situations where it performs similarly. The gwak-Relief-F algorithm outperforms Random Forests for detecting relevant genes in all simulation experiments. In addition, Relief-F is comparable to the other methods based on computational time. We also apply ReliefSeq to an RNA-Seq study of smallpox vaccine to identify gene expression changes between vaccinia virus-stimulated and unstimulated samples. ReliefSeq is an attractive tool for inclusion in the suite of tools used for analysis of mRNA-Seq data; it has power to detect both main effects and interaction effects. Software Availability: http://insilico.utulsa.edu/ReliefSeq.php.

Medical imaging data is now extremely abundant due to over two decades of digitisation of imaging protocols and data storage formats. However, clean, well-curated data, that is amenable to machine learning, is relatively scarce, and AI developers are paradoxically data starved. Imaging and clinical data is also heterogeneous, often unstructured and unlabelled, whereas current supervised and semi-supervised machine learning techniques rely on homogeneous and carefully annotated data. While imaging biobanks contain small volumes of well-curated data, it is the leveraging of ‘big data’ from the front-line of healthcare that is the focus of many machine learning developers hoping to train and validate computer vision algorithms. The quest for sufficiently large volumes of clean data that can be used for training, validation and testing involves several hurdles, namely ethics and consent, security, the assessment of data quality, ground truth data labelling, bias reduction, reusability and generalisability. In this chapter we propose a new medical imaging data readiness (MIDaR) scale. The MIDaR scale is designed to objectively clarify data quality for both researchers seeking imaging data and clinical providers aiming to share their data. It is hoped that the MIDaR scale will be used globally during collaborative academic and business conversations, so that everyone can more easily understand and quickly appraise the relevant stages of data readiness for machine learning in relation to their AI development projects. We believe that the MIDaR scale could become essential in the design, planning and management of AI medical imaging projects, and significantly increase chances of success.

A Domain Decomposition (DD) method for solving time-harmonic aeroacoustic problems is presented. The computational domain is decomposed into subdomains, and the aerodynamic-aeroacoustic boundary-value problem is solved independently for each subdomain. On the artificially introduced subdomain boundaries impedance-type transmission conditions are imposed, which assure the uniqueness of the solution. The Finite Element method is used to discretize the subdomain problems. A direct solver (LU factorization) is used to solve the subdomain systems of equations and an iterative scheme updates the transmission conditions to recover the global solution. This DD method is applied to solve the classical problem of a thin airfoil in a transverse gust. The problem is formulated in terms of the unsteady pressure, and exact non-reflecting conditions are imposed on the outer computational boundary. The accuracy of the numerical scheme is validated by comparison with existing solutions for both the near-field unsteady pressure and the farfiled radiated sound. The convergence as well as the computational time and memory requirements of the present method are investigated. It is shown that by combining the subdomain direct solvers with global iterations, this DD method significantly reduces both the computational time and the memory requirements. The present algorithm is particularly suited for parallel computing.

An automated hybrid approach using clustering and nature inspired optimization technique for improved tumor and tissue segmentation in magnetic resonance brain images

Full waveform inversion (FWI) is a powerful tool to quantify Earth properties of the subsurface from seismic data, and has become a major area of research. Because of very high computational cost, FWI has so far been used for either 2D full elastic or 3D acoustic media. Here we propose an efficient way to perform 3D elastic FWI for time-lapse seismic data using grid injection method, which allows for efficient calculation of synthetic seismograms after model alterations within a localized area. The technique allows for significant reductions in computational time and memory requirements without any significant loss in accuracy.

In this article, a fast and efficient hybrid method that combines the volume-surface integral equation (VSIE) and the modified surface integration (MSI) method is proposed to evaluate the electromagnetic (EM) performance of the large-/medium-sized radome-enclosed antennas (REAs). An approximate equivalence principle is presented to formulate the physical model for the EM radiation of the REA, and then, the full-wave VSIE for the antennas and the high-frequency MSI method for the dielectric radome are coupled with each other in an iterative manner. Since mutual interactions up to the second order among the antennas and the radome are considered, the method shows good accuracy in both the near- and far-field regions of the REA system. In addition, all the computation processes involved in the method, including the method of moments (MoM) solution of the VSIE, surface integrations (SIs) in the MSI, and ray tracing for determining the local transmission matrix for the dielectric radome, are accelerated by the multilevel fast multipole algorithm (MLFMA), which makes the method highly efficient in terms of both computational time and memory requirement. Numerical results show that the proposed hybrid method can predict the EM performance, e.g., the input impedance and radiation pattern, of the REA system with good accuracy compared with the full-wave simulations.

Introduction O VER the past decade there has been considerable progress in the development of numerical simulation techniques for unsteady transonic flow calculations. This activity was motivated by the need to supplement the expensive and time-consuming wind-tunnel investigations and flight tests with inexpensive, fast, and efficient computer simulation programs to accurately predict flutter boundaries and many other important aeroelastic phenomena in the transonic regime. Although the basic fluid motion in unsteady aerodynamics is governed by the Navier-Stokes (NS) equations, numerical simulations of the complete NS equations for general threedimensional flow problems are not yet sufficiently developed for flutter applications. Computational methods in threedimensional unsteady transonic flows concentrate mainly on the transonic small disturbance (TSD) equation or full potential (FP) equation. The major advantage of the simpler model using TSD or FP approach over those based on Euler and NS formulation is the large reduction in computational time and memory requirements. In Ref. 1, the TSD code UST3D was described. This code utilizes the time-linearization approach for solving the transonic small disturbance equation. It was demonstrated that the technique could be used with confidence for computations of three-dimensional unsteady transonic flows over an isolated thin wing. An improved version of the UST3D program was given in Ref. 2 and results from a validation study were presented. This transonic aerodynamics code was incorporated into the Institute for Aerospace Research (IAR) Flutter Analysis Program. This Note presents some results from an aerodynamic and flutter analysis of the AGARD 445.6 wing which was recommended by AGARD as a standard configuration for code validation.

Full waveform inversion (FWI) is a powerful tool to quantify Earth properties of the subsurface from seismic data, and has become a major area of research. Because of very high computational cost, FWI has so far been used for either 2D full elastic or 3D acoustic media. Here we propose an efficient way to perform time-lapse elastic FWI in 3D using grid injection method, which allows for efficient calculation of synthetic seismograms after model alterations within a localized area. The technique allows for significant reductions in computational time and memory requirements without noticeable losses in accuracy.

Realistic single-cell neuronal dynamics are typically obtained by solving models that involve solving a set of differential equations similar to the Hodgkin-Huxley (HH) system. However, realistic simulations of neuronal tissue dynamics —especially at the organ level, the brain— can become intractable due to an explosion in the number of equations to be solved simultaneously. Consequently, such efforts of modeling tissue- or organ-level systems require a lot of computational time and the need for large computational resources. Here, we propose to utilize a cellular automata (CA) model as an efficient way of modeling a large number of neurons reducing both the computational time and memory requirement. First, a first-order approximation of the response function of each HH neuron is obtained and used as the response-curve automaton rule. We then considered a system where an external input is in a few cells. We utilize a Moore neighborhood (both totalistic and outer-totalistic rules) for the CA system used. The resulting steady-state dynamics of a two-dimensional (2D) neuronal patch of size 1, 024 × 1, 024 cells can be classified into three classes: (1) Class 0–inactive, (2) Class 1–spiking, and (3) Class 2–oscillatory. We also present results for different quasi-3D configurations starting from the 2D lattice and show that this classification is robust. The numerical modeling approach can find applications in the analysis of neuronal dynamics in mesoscopic scales in the brain (patch or regional). The method is applied to compare the dynamical properties of the young and aged population of neurons. The resulting dynamics of the aged population shows higher average steady-state activity 〈a(t → ∞)〉 than the younger population. The average steady-state activity 〈a(t → ∞)〉 is significantly simplified when the aged population is subjected to external input. The result conforms to the empirical data with aged neurons exhibiting higher firing rates as well as the presence of firing activity for aged neurons stimulated with lower external current.