Road surface quality is essential for improving driving experience and reducing traffic accidents. Traditional road condition monitoring systems are limited in their temporal (speed) spatial (coverage) responses needed maintaining overall quality. Several alternative have been proposed that utilize sensors mounted on vehicles. In particular, with the ubiquitous use of smartphones navigation, smartphone-based assessment has emerged as a promising new approach. this paper, we propose to...

Diabetes is a large healthcare burden worldwide. There substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals important to design targeted prevention strategies. In this paper, we present automatic tool uses machine learning techniques predict the development type 2 diabetes mellitus (T2DM). Data generated from oral glucose tolerance test (OGTT) was used develop predictive model based on...

Brain cancer is a life-threatening disease requiring close attention. Early and accurate diagnosis using non-invasive medical imaging critical for successful treatment patient survival. However, manual by radiologist experts time-consuming has limitations in processing large datasets efficiently. Therefore, efficient systems capable of analyzing vast amounts data early tumor detection are urgently needed. Deep learning (DL) with deep convolutional neural networks (DCNNs) emerges as promising...

Abstract Partial parallel imaging (PPI) techniques using array coils and multichannel receivers have become an effective approach to achieving fast magnetic resonance (MRI). This article presents a Matlab toolbox called PULSAR (Parallel Utilizing Localized Surface‐coil Acquisition Reconstruction) that can simulate the data acquisition image reconstruction, analyze performance of five common PPI techniques. sensitivity functions rectangular loop quasi‐static model based on Biot‐Savart's Law...

Spectral variability remains a challenging problem for target detection and classification in hyperspectral (HS) imagery. In this letter, we have applied the nonlinear support vector data description (SVDD) to perform full-pixel detection. Using pure signature first-order Markov model, developed novel pattern recognition algorithm train an SVDD characterize class. We inserted signatures into urban HS scene with varying levels of spectral explore performance proposed detector different...

Multispectral autofluorescence lifetime imaging (maFLIM) can be used to clinically image a plurality of metabolic and biochemical biomarkers oral epithelial dysplasia cancer. This study tested the hypothesis that maFLIM-derived in machine-learning (ML) models discriminate dysplastic cancerous from healthy tissue. Clinical widefield maFLIM endoscopy lesions was performed at two clinical centers. Endoscopic images 34 patients acquired one centers were optimize ML for automated discrimination A...

Purpose To provide visualization of the brachytherapy seeds and differentiation with natural structures in MRI by taking advantage their high magnetic susceptibility to generate positive‐contrast images. Methods The method is based on mapping using an equivalent short‐TE sequence a kernel deconvolution algorithm regularized L 1 minimization. An appealing aspect that signals from surrounding areas where signal noise ratio (SNR) sufficiently are used derive seeds, even though SNR immediate...

Compressed sensing (CS) is a promising method to speed up MRI. Because most clinical MRI scanners are equipped with multichannel receive systems, integrating CS systems may not only shorten the scan time but also provide improved image quality. However, significant computation required perform reconstruction, whose complexity scaled by number of channels. In this article, we propose reconstruction procedure that uses ubiquitously available multicore central processing unit accelerate from...

Solar power generation continues to grow as a dominant renewable energy source. Soiling because of dust accumulation is one the important challenges facing large-scale solar photovoltaic (PV) plant operations, particularly, in arid regions. The assessment and mitigation soiling effects on large arrays PV panels are often limited by area coverage how data can be collected. To overcome this limitation, article, we propose method directly use images quantify panels. A prototype lab system image...

Abstract MR spectroscopic (MRS) images from a large volume of brain can be obtained using 3D echo‐planar imaging (3D‐EPSI) sequence. However, routine applications 3D‐EPSI are still limited by long scan time. In this communication, new approach termed “spectral phase‐corrected generalized autocalibrating partially parallel acquisitions” (SPC‐GRAPPA) is introduced for the reconstruction data to accelerate acquisition while preserving accuracy quantitation metabolites. SPC‐GRAPPA,...

Chemical exchange saturation transfer (CEST) is an emerging MRI contrast mechanism that capable of noninvasively imaging dilute CEST agents and local properties such as pH temperature, augmenting the routine methods. However, includes a long RF pulse followed by fast image readout, which associated with high specific absorption rate limited spatial resolution. In addition, echo planar (EPI)-based readout prone to distortion, particularly severe at field. To address these limitations, we...

This paper presents an analysis of the mutual information (MI) metric in rigid-body registration two digital images, particular, local fluctuations MI value due to interpolation. In contrast existing work this area, starts with hypothetical continuous based on which both sampling and interpolation effects are analyzed. indicates that "ideal" interpolator may not be able completely suppress undesirable minima if effect is negligible. Several preprocessing methods discussed for reducing effects.

Compressed Sensing (CS) is a recently emerged technique for reconstructing signals from data sampled under the Nyquist rate. It takes advantage of signal sparsity in transformed domain to reconstruct high-resolution reduced data. This paper presents CS imaging method dynamic magnetic resonance imaging. Specifically, difference operator applied temporal frames enhance spatial reconstruction. The new algorithm was assessed using simulated and in-vivo result shows that can obtain higher...

Both parallel Magnetic Resonance Imaging (pMRI) and Compressed Sensing (CS) can significantly reduce imaging time in MRI, the former by utilizing multiple channel receivers latter sparsity of MR images a transformed domain. In this work, pMRI CS are integrated to take advantages sensitivity information from coils characteristics images. Specifically, is used as regularization method for inverse problem raised based on L1 norm Total Variation (TV) term. We test new with set 8-channel, in-vivo...

To accelerate iterative reconstructions of compressed sensing (CS) MRI from 3D multichannel data using graphics processing units (GPUs).The sparsity signals and parallel array receivers can reduce the acquisition requirements. However, CS acquired an system may take a significantly long time, especially for large number channels. This paper presents efficient method CS-MRI reconstruction GPUs. In this method, were simultaneously processed in channel-by-channel fashion on GPU, which...

A pilot plant campaign has been conducted by Sherritt Technologies in support of Eldorado Gold Corporation's evaluation pressure oxidation (POX) technology for the treatment refractory gold concentrates from Certej deposit Romania. Preliminary batch tests indicated that extraction oxidised subsequent cyanide leaching was adversely affected presence organic carbon POX feed when chloride solution at an elevated level. As such, process conditions tested were selected to either counteract...

This study aims to develop an accelerated susceptibility-based positive contrast MR imaging method for visualizing compatible metallic devices. A modified fast spin echo sequence is used accelerate data acquisition. Each readout gradient in the slightly shifted by a short distance T shift. Phase changes accumulated within shift are then calculate susceptibility map using kernel deconvolution algorithm with regularized ℓ1 minimization. To evaluate proposed method, three phantom experiments...