Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification.

Automatic machine classification of concrete structural defects in images poses significant challenges because of multitude of problems arising from the surface texture, such as presence of stains, holes, colors, poster remains, graffiti, marking and painting, along with uncontrolled weather conditions and illuminations. In this paper, we propose an interleaved deep artifacts-aware attention mechanism (iDAAM) to classify multi-target multi-class and single-class defects from structural defect images. Our novel architecture is composed of interleaved fine-grained dense modules (FGDM) and concurrent dual attention modules (CDAM) to extract local discriminative features from concrete defect images. FGDM helps to aggregate multi-layer robust information with wide range of scales to describe visually-similar overlapping defects. On the other hand, CDAM selects multiple representations of highly localized overlapping defect features and encodes the crucial spatial regions from discriminative channels to address variations in texture, viewing angle, shape and size of overlapping defect classes. Within iDAAM, FGDM and CDAM are interleaved to extract salient discriminative features from multiple scales by constructing an end-to-end trainable network without any preprocessing steps, making the process fully automatic. Experimental results and extensive ablation studies on three publicly available large concrete defect datasets show that our proposed approach outperforms the current state-of-the-art methodologies

Deep Regularized Discriminative Network

Traditional linear discriminant analysis (LDA) approach discards the eigenvalues which are very small or equivalent to zero, but quite often eigenvectors corresponding to zero eigenvalues are the important dimensions for discriminant analysis. We propose an objective function which would utilize both the principal as well as nullspace eigenvalues and simultaneously inherit the class separability information onto its latent space representation. The idea is to build a convolutional neural network (CNN) and perform the regularized discriminant analysis on top of this and train it in an end-to-end fashion. The backpropagation is performed with a suitable optimizer to update the parameters so that the whole CNN approach minimizes the within class variance and maximizes the total class variance information suitable for both multi-class and binary class classification problems. Experimental results on four databases for multiple computer vision classification tasks show the efficacy of our proposed approach as compared to other popular methods

MacularNet: Towards Fully Automated Attention-Based Deep CNN for Macular Disease Classification

AbstractIn this work, we propose an attention-based deep convolutional neural network (CNN) model as an assistive computer-aided tool to classify common types of macular diseases: age-related macular degeneration, diabetic macular edema, diabetic retinopathy, choroidal neovascularization, macular hole, and central serous retinopathy from normal macular conditions with the help of scans from optical coherence tomography (OCT) imaging. Our proposed architecture unifies refined deep pre-trained models using transfer learning with limited training data and a deformation-aware attention mechanism encoding crucial morphological variations appearing in the deformation of retinal layers, detachments from the subsequent layers, presence of fluid-filled regions, geographic atrophy, scars, cysts, drusen, to achieve superior macular imaging classification performance. The proposed attention module facilitates the base network to automatically focus on the salient features arising due to the macular structural abnormalities while suppressing the irrelevant (or no cues) regions. The superiority of our proposed method lies in the fact that it does not require any pre-processing steps such as retinal flattening, denoising, and selection of a region of interest making it fully automatic and end-to-end trainable. Additionally, it requires a reduced number of network model parameters while achieving higher diagnostic performance. Extensive experimental results, analysis on four datasets along with the ablation studies show that the proposed architecture achieves state-of-the-art performance.</jats:p

Surfactant Assisted Synthesis and Characterization of High Surface Area Mesoporous Nanocrystalline Pure, Eu3+ and Sm3+ doped Ceria for Selected Applications

CeO2 is one of the most interesting oxides industrially because it has been widely used as a catalyst, metal polishing agent, three-way automotive catalytic converters for purification of exhaust gases, oxygen ion conductor in solid oxide fuel cells, oxidative coupling of methane and water-gas shift reaction, oxygen sensors, and so forth for long periods of time. Recently, CeO2 nanoparticles has also emerged as a fascinating and lucrative material for environmental remediation application as photocatalyst for degradation of toxic pollutants. The key for most of the above mentioned applications of CeO2 based materials is its extraordinary ability to release or uptake oxygen by shifting some Ce4+ to Ce3+ ions. Better catalytic performances of CeO2 have been reported in the presence of Ce3+ and oxygen vacancy defects, which are potentially potent surface sites for catalysis. The present work is undertaken on multigram synthesis of high content of Ce3+, high surface area and high quality mesoporous pure CeO2 as well as Sm3+, and Eu3+ doped CeO2 using cheaper metal inorganic precursor. The XRD results showed that even as-prepared material has cubic fluorite structure of CeO2 with no crystalline impurity phase. Thereby, confirming the ability of the present aqueous based synthetic approach to prepare mesoporous crystalline CeO2 nanoparticles at a lower temperature of 100°C. All the nanopowder exhibited strong absorption in the UV region and good transmittance in the visible region. Sm3+ and Eu3+ doped CeO2 nanopowder showed enhanced photoluminescence in the red and orange region. Mesoporous Sm3+ doped CeO2 sample could effectively photodegrade all types of cationic, anionic and nonionic dyes under natural sunlight irradiation. These high surface area mesoporous materials exhibited notable adsorption and effective removal of Cr(VI) from aqueous solutions at room temperature and without any adjustment of pH. Mesoporous Sm3+ doped CeO2 samples also exhibited excellent autocatalytical properties. The presence of increased surface hydroxyl group, mesoporosity, and surface defects have contributed towards an improved activity of mesoporous CeO2, which appears to be potential candidates for optical, environmental and biomedical applications