Super-resolution generative adversarial network based on the dual dimension attention mechanism for biometric image super-resolution

There exist many types of intelligent security sensors in the environment of the Internet of Things (IoT) and cloud computing. Among them, the sensor for biometrics is one of the most important types. Biometric sensors capture the physiological or behavioral features of a person, which can be further processed with cloud computing to verify or identify the user. However, a low-resolution (LR) biometrics image causes the loss of feature details and reduces the recognition rate hugely. Moreover, the lack of resolution negatively affects the performance of image-based biometric technology. From a practical perspective, most of the IoT devices suffer from hardware constraints and the low-cost equipment may not be able to meet various requirements, particularly for image resolution, because it asks for additional storage to store high-resolution (HR) images, and a high bandwidth to transmit the HR image. Therefore, how to achieve high accuracy for the biometric system without using expensive and high-cost image sensors is an interesting and valuable issue in the field of intelligent security sensors. In this paper, we proposed DDA-SRGAN, which is a generative adversarial network (GAN)-based super-resolution (SR) framework using the dual-dimension attention mechanism. The proposed model can be trained to discover the regions of interest (ROI) automatically in the LR images without any given prior knowledge. The experiments were performed on the CASIA-Thousand-v4 and the CelebA datasets. The experimental results show that the proposed method is able to learn the details of features in crucial regions and achieve better performance in most cases

Coverless image steganography using morphed face recognition based on convolutional neural network

In recent years, information security has become a prime issue of worldwide concern. To improve the validity and proficiency of the image data hiding approach, a piece of state-of-the-art secret information hiding transmission scheme based on morphed face recognition is proposed. In our proposed data hiding approach, a group of morphed face images is produced from an arranged small-scale face image dataset. Then, a morphed face image which is encoded with a secret message is sent to the receiver. The receiver uses powerful and robust deep learning models to recover the secret message by recognizing the parents of the morphed face images. Furthermore, we design two novel Convolutional Neural Network (CNN) architectures (e.g. MFR-Net V1 and MFR-Net V2) to perform morphed face recognition and achieved the highest accuracy compared with existing networks. Additionally, the experimental results show that the proposed schema has higher retrieval capacity and accuracy and it provides better robustness

A Comparative Study of Thermal Performance of Different Nanofluids: An Analytic Approach

The purpose of this study was to determine an exact solution for the fluid flow and heat transfer of laminar steady magnetohydrodynamics (MHD) nanofluid flow over a stretching/shrinking surface. Appropriate similarity transformations were used to transform the governing partial differential equations into coupled nonlinear ordinary differential equations. The current study showed good correspondence with previously published work. The solution was deduced from the solution of the flow field and temperature field. Furthermore, the dimensionless skin friction coefficient and Nusselt number were derived. The solution of the temperature field was deduced in terms of the generalized Laguerre polynomial. The value of the generalized Laguerre polynomial was calculated using the “LaguerreL” command in MuPAD. The impact of different physical parameters of the symmetry on the thermal performance, including the nanoparticle volume fraction parameter, magnetic parameter, mass suction/injection parameter and stretching/shrinking parameter, is discussed in detail for different nanoparticles. Furthermore, the effect of nanoparticle type on the fluid velocity component, temperature distribution, skin friction coefficient and Nusselt number was studied in detail. Four different nanoparticles were considered in this study. This work reveals that the nanoparticles within the base fluid have the potential to increase the heat transfer ability of many liquids. The results indicate that silver and titanium oxide nanoparticles had the largest and lowest skin friction coefficients, respectively, in the shrinking surface case, exhibiting opposite behavior in the stretching surface case among all the nanoparticles considered. The results also indicate that silver and titanium oxide nanoparticles had the largest and lowest Nusselt numbers, respectively, for both the stretching and the shrinking surface cases. It is suggested silver nanoparticles are not used for optimum heat transfer

A Comparative Study of Thermal Performance of Different Nanofluids: An Analytic Approach

The purpose of this study was to determine an exact solution for the fluid flow and heat transfer of laminar steady magnetohydrodynamics (MHD) nanofluid flow over a stretching/shrinking surface. Appropriate similarity transformations were used to transform the governing partial differential equations into coupled nonlinear ordinary differential equations. The current study showed good correspondence with previously published work. The solution was deduced from the solution of the flow field and temperature field. Furthermore, the dimensionless skin friction coefficient and Nusselt number were derived. The solution of the temperature field was deduced in terms of the generalized Laguerre polynomial. The value of the generalized Laguerre polynomial was calculated using the “LaguerreL” command in MuPAD. The impact of different physical parameters of the symmetry on the thermal performance, including the nanoparticle volume fraction parameter, magnetic parameter, mass suction/injection parameter and stretching/shrinking parameter, is discussed in detail for different nanoparticles. Furthermore, the effect of nanoparticle type on the fluid velocity component, temperature distribution, skin friction coefficient and Nusselt number was studied in detail. Four different nanoparticles were considered in this study. This work reveals that the nanoparticles within the base fluid have the potential to increase the heat transfer ability of many liquids. The results indicate that silver and titanium oxide nanoparticles had the largest and lowest skin friction coefficients, respectively, in the shrinking surface case, exhibiting opposite behavior in the stretching surface case among all the nanoparticles considered. The results also indicate that silver and titanium oxide nanoparticles had the largest and lowest Nusselt numbers, respectively, for both the stretching and the shrinking surface cases. It is suggested silver nanoparticles are not used for optimum heat transfer

Large scale image dataset construction using distributed crawling with hadoop YARN

© 2018 IEEE. with the rapid advancement in the internet, we are now living in the era of big data. The image data over the web has the potential to assist in the development of sophisticated and robust models and algorithms to interact with images and multimedia data. Images Data sets are widely used in image processing tasks and analyses. They are in use in various fields including Artificial Intelligence, Data extraction and collection, Computer Vision, Research studies and education. In this research work, we are going to propose a system that crawls the web in a systematic manner using Hadoop MapReduce technique to collect images from millions of web pages on the web. With Celebrity images just a use case, we would be able to search for and retrieve any image tagged with some specific terms. The system uses some simple techniques to reduce noisy images like thumbnails and icons. The proposed system is based on Apache Hadoop and Apache Nutch, an open source web crawler. A customized crawl is run through Apache Nutch in a Hadoop Cluster that searches images for one or more categories on the web and retrieves their links. Next, HIPI, Hadoop Image Processing Interface is used to download the images and create datasets for an individual category or a dataset of multiple categories

An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification

Optical character recognition is gaining immense importance in the domain of deep learning. With each passing day, handwritten digits (0–9) data are increasing rapidly, and plenty of research has been conducted thus far. However, there is still a need to develop a robust model that can fetch useful information and investigate self-build handwritten digit data efficiently and effectively. The convolutional neural network (CNN) models incorporating a sigmoid activation function with a large number of derivatives have low efficiency in terms of feature extraction. Here, we designed a novel CNN model integrated with the extreme learning machine (ELM) algorithm. In this model, the sigmoid activation function is upgraded as the rectified linear unit (ReLU) activation function, and the CNN unit along with the ReLU activation function are used as a feature extractor. The ELM unit works as the image classifier, which makes the perfect symmetry for handwritten digit recognition. A deeplearning4j (DL4J) framework-based CNN-ELM model was developed and trained using the Modified National Institute of Standards and Technology (MNIST) database. Validation of the model was performed through self-build handwritten digits and USPS test datasets. Furthermore, we observed the variation of accuracies by adding various hidden layers in the architecture. Results reveal that the CNN-ELM-DL4J approach outperforms the conventional CNN models in terms of accuracy and computational time

Classification of Body Constitution Based on TCM Philosophy and Deep Learning

There is a growing demand for alternative or complementary medicine in health care disciplines that uses a non-invasive instrument to evaluate the health status of various organs inside the human body. In this regard, we proposed a real-time, non-invasive, and painless technique to assess an individual’s health condition. Our approach is based on the combination of iridology and the philosophy of traditional Chinese medicine (TCM). The iridology chart presents perfect symmetry between the left and right eyes, and such a unique representation reveals the body constitution based on TCM philosophy, which classifies the aforementioned body constitution into a combination of nine categories to describe the varieties of genomic traits. In addition, we applied a deep-learning method along with the combination of iridology and TCM to predict the possible physiological or psychological strength or weakness of the subjects and give advice to them about how to take care of their health according to the body constitution assessment. We used several pre-trained convolutional neural networks (CNNs, or ConvNet), such as a residual neural network (ResNet50), InceptionV3, and dense convolutional network (DenseNet201), to classify the body constitution using iris images. In the experiments, the CASIA-Iris-Thousand database was used to perform this task. The experimental results showed that the proposed iris-based health assessment method achieved an 82.9% accuracy

Self-Supervised Learning Framework toward State-of-the-Art Iris Image Segmentation

Iris segmentation plays a pivotal role in the iris recognition system. The deep learning technique developed in recent years has gradually been applied to iris recognition techniques. As we all know, applying deep learning techniques requires a large number of data sets with high-quality manual labels. The larger the amount of data, the better the algorithm performs. In this paper, we propose a self-supervised framework utilizing the pix2pix conditional adversarial network for generating unlimited diversified iris images. Then, the generated iris images are used to train the iris segmentation network to achieve state-of-the-art performance. We also propose an algorithm to generate iris masks based on 11 tunable parameters, which can be generated randomly. Such a framework can generate an unlimited amount of photo-realistic training data for down-stream tasks. Experimental results demonstrate that the proposed framework achieved promising results in all commonly used metrics. The proposed framework can be easily generalized to any object segmentation task with a simple fine-tuning of the mask generation algorithm

Development and in vitro evaluation of κ-carrageenan based polymeric hybrid nanocomposite scaffolds for bone tissue engineering

The excellent biocompatible and osteogenesis characteristics of porous scaffolds play a vital role in bone regeneration. In this study, we have synthesized polymeric hybrid nanocomposites via free-radical polymerization from carrageenan/acrylic-acid/graphene/hydroxyapatite. Porous hybrid nanocomposite scaffolds were fabricated through a freeze-drying method to mimic the structural and chemical composition of natural bone. Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and water contact-angle studies were carried-out for functional groups, surface morphology and hydrophilicity of the materials, followed by biodegradation and swelling analysis. The cell viability, cell culture and proliferation were evaluated against mouse pre-osteoblast (MC3T3-E1) cell lines using neutral red dye assay. The cell adherence and proliferation studies were determined by SEM. Physical characterization including optimum porosity and pore size (49.75% and 0.41 × 103 μm2), mechanical properties (compression strength 8.87 MPa and elastic modulus 442.63 MPa), swelling (70.20% at 27 °C and 77.21% at 37 °C) and biodegradation (23.8%) were performed. The results indicated CG-g-AAc-3 with a high optical density and better cell viability. Hence, CG-g-AAc-3 was found to be more efficient for bone regeneration with potential applications in fractured bone regeneration

Smart and pH-sensitive rGO/arabinoxylan/chitosan composite for wound dressing: in-vitro drug delivery, antibacterial activity, and biological activities

Carbohydrate polymers are biological macromolecules that have sparked a lot of interest in wound healing due to their outstanding antibacterial properties and sustained drug release. Arabinoxylan (ARX), Chitosan (CS), and reduced graphene oxide (rGO) sheets were combined and crosslinked using tetraethyl orthosilicate (TEOS) as a crosslinker to fabricate composite hydrogels and assess their potential in wound dressing for skin wound healing. Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), atomic force microscopy (AFM), transmission electron microscopy (TEM), and biological assays were used to evaluate the composite hydrogels. FTIR validated the effective fabrication of the composite hydrogels. The rough morphologies of the composite hydrogels were revealed by SEM and AFM (as evident from the Ra values). ATC-4 was discovered to have the roughest surface. TEM revealed strong homogeneous anchoring of the rGO to the polymer matrix. However, with higher amount of rGO agglomeration was detected. The % swelling at various pHs (1−13) revealed that the hydrogels were pH-sensitive. The controlled release profile for the antibacterial drug (Silver sulfadiazine) evaluated at various pH values (4.5, 6.8, and 7.4) in PBS solution and 37 °C using the Franz diffusion method revealed maximal drug release at pH 7.4 and 37 °C. The antibacterial efficacy of the composite hydrogels against pathogens that cause serious skin diseases varied. The MC3T3-E1 cell adhered, proliferated, and differentiated well on the composite hydrogels. MC3T3-E1 cell also illustrated excellent viability (91%) and proper cylindrical morphologies on the composite hydrogels. Hence, the composite hydrogels based on ARX, CS, and rGO are promising biomaterials for treating and caring for skin wounds