Supervised deep networks have achieved promisingperformance on image denoising, by learning priors andnoise statistics plenty pairs of noisy and clean images. Unsupervised denoising are trained with only However, for an unseen corrupted image, both supervised andunsupervised ignore either its particular prior, the noise statistics, or both. That is, learned from external images inherently suffer a domain gap problem: very different between training test This problem becomes more clear when...

Extreme learning machine (ELM) is a algorithm for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. After input biases are chosen randomly, ELM can be simply considered linear system. However, time mainly spent on calculating Moore-Penrose inverse matrices matrix. This paper focuses effective computation ELM, several methods proposed. They reduced QR factorization with column Pivoting Geninv...

ABSTRACT Left ventricular ejection fraction (LVEF) is a key measure of heart pumping performance, playing pivotal role in the ongoing management and efficacy assessment cardiovascular disease treatments. By quantifying percentage blood that pumped out left ventricle with each heartbeat, LVEF provides invaluable insights into overall efficiency heart's function, enabling clinical professionals to make informed decisions regarding point care therapeutic strategies. However, accurate...

Using metallicities from the literature, combined with Revised Bologna Catalogue of photometric data for M31 clusters and cluster candidates [the latter which is most comprehensive catalogue currently available, including 337 confirmed globular (GCs) 688 GC candidates], we determine 443 reddening values intrinsic colours, 209 individual without spectroscopic observations. This, largest sample GCs presently then used to analyse metallicity distribution GCs, bimodal peaks at [Fe/H]≈−1.7 −0.7...

Blind image deconvolution involves two key objectives: 1) latent and 2) blur estimation. For estimation, we propose a fast algorithm, which uses an prior of nondimensional Gaussianity measure to enforce sparsity undetermined boundary condition methodology reduce artifacts. linear inverse problem with normalization nonnegative constraints must be solved. However, the constraint is ignored in many blind deblurring methods, mainly because it makes less tractable. In this paper, show that can...

In image deconvolution, various boundary conditions (BC) based deconvolution methods have been proposed to reduce artifacts. However, most of them are not considering the accuracy BC due computation limitation. this paper, we propose a framework, which considers convolution matrix as product partial and condition matrix. By computing adjoint matrix, can solve large linear system with conjugate gradient algorithm. With easily derive two efficient non-blind algorithms, treat borders repeated...

Iteratively reweighted least squares (IRLS) is one of the most effective methods to minimize l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> regularized linear inverse problem. Unfortunately, regularizer nonsmooth and nonconvex when 0 <; p 1. In spite its properties mainly due high computation cost, IRLS not widely used in image deconvolution reconstruction. this paper, we first derive method from perspective majorization minimization...

The ultrasound (US) screening of the infant hip is vital for early diagnosis developmental dysplasia (DDH). US DDH refers to measuring alpha and beta angles that quantify joint development. These two are calculated from key anatomical landmarks structures hip. However, this measurement process not trivial sonographers usually requires a thorough understanding complex structures. In study, we propose multi-task framework learn relationships among jointly automatically evaluate DDH. Our...

This article presents a data mining methodology for driving-condition monitoring via CAN-bus that is based on the general process. The approach applicable to many driving condition problems, and example of road type classification without use location information investigated. Location from Global Positioning Satellites related map are often not available (for business reasons), or cannot represent full dynamics conditions. In this work, Controller Area Network (CAN)-bus signals used instead...

At present, deep learning drives the rapid development of face recognition. However, in unconstrained scenario, change facial posture has a great impact on Moreover, current model still some shortcomings accuracy and robustness. The existing research formulated two methods to solve above problems. One method is train each pose separately. Then, fusion decision will be made. other make “frontal” faces image or feature level transform them into Based second idea, we propose profile frontal...

Supervised feature selection aims to find the signals that best predict a target variable. Typical approaches use measures of correlation or similarity, as seen in filter methods, predictive power learned models, wrapper methods. In both approaches, selected features often have high entropies and are not suitable for compression. This is particular drawback automotive domain where fast communication archival vehicle telemetry data increasingly important, especially with technologies such V2V...

We present optical photometry and spectra for the Type Ia supernova (SN Ia) 2007gi in nearby galaxy NGC 4036. SN is characterized by extremely high-velocity (HV) features of intermediate-mass elements (Si, Ca, S), with expansion velocities ($v_{\rm exp}$) approaching $\sim$15,500 km s$^{-1}$ near maximum brightness (compared to $\sim$10,600 SNe normal $v_{\rm exp}$). reached a $B$-band peak magnitude 13.25$\pm$0.04 mag decline rate $Δm_{15}(B)$(true) = 1.33$\pm$0.09 mag. The light curve...

Face detection is an important basic technique for face-related applications, such as face analysis, recognition, and reconstruction. Images in unconstrained scenes may contain many small-scale faces. The features that the detector can extract from faces are limited, which will cause missed greatly reduce precision of detection. Therefore, this study proposes a novel method to detect based on region-based fully convolutional network (R-FCN). First, we propose R-FCN framework with ability...

Hepatic echinococcosis is a parasitic disease. Ultrasound imaging crucially important tool for the diagnosis of this Based on ultrasonic manifestations, hepatic can be classified into many subtypes. However, subtyping nontrivial due to challenges complex sonographic textures and large intraclass small interclass differences. The purpose study develop computer-aided system based ultrasound images.We collected multicenter dataset containing 9112 images from 5028 patients who were diagnosed...

Driving is a safety critical task that requires high levels of attention and workload from the driver. Despite this, people often also perform secondary tasks such as eating or using mobile phone, which increase divert cognitive physical primary driving. If vehicle aware driver currently under workload, functionality can be changed in order to minimize any further demand. Traditionally, measurements have been performed intrusive means physiological sensors. Another approach may monitor...

Intelligent transportation systems often identify and make use of locations extracted from GPS trajectories to informed decisions. However, many the identified by existing are false positives, such as those in heavy traffic. Signals vehicle, speed seatbelt status, can be used these positives. In this paper, we (i) demonstrate utility Gradient-based Visit Extractor (GVE) automotive domain, (ii) propose a classification stage for removing positives location extraction process, (iii) evaluate...

Abstract Transfer learning uses knowledge learnt in source domains to aid predictions a target domain. When and are online, they susceptible concept drift, which may alter the mapping of between them. Drifts online environments can make additional information available each domain, necessitating continuing transfer both from vice versa. To address this, we introduce Bi-directional Online Learning (BOTL) framework, domain others. We two variants BOTL that incorporate model culling minimise...

Blind image deconvolution (BID) is a severely ill-posed problem which requires prior information on the latent to estimate blur kernel. In this paper, new observation that blurring always pushes gradient of local region toward its mean value introduced. And we formulate novel function measure distance between and value. A regularizer associated with means proposed. As it segment whole into small regions, propose an approximate method without any segmentation. Thanks simplicity algorithm fast...