The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

The pupil recognition method is helpful in many real-time systems, including ophthalmology testing devices, wheelchair assistance, and so on. The pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size, occlusion of eyelids, and eyelashes. Deep Convolutional Neural Networks (DCNN) are being used in pupil recognition systems and have shown promising results in terms of accuracy. To improve accuracy and cope with larger datasets, this research work proposes BOC (BAT Optimized CNN)-IrisNet, which consists of optimizing input weights and hidden layers of DCNN using the evolutionary BAT algorithm to efficiently find the human eye pupil region. The proposed method is based on very deep architecture and many tricks from recently developed popular CNNs. Experiment results show that the BOC-IrisNet proposal can efficiently model iris microstructures and provides a stable discriminating iris representation that is lightweight, easy to implement, and of cutting-edge accuracy. Finally, the region-based black box method for determining pupil center coordinates was introduced. The proposed architecture was tested using various IRIS databases, including the CASIA (Chinese academy of the scientific research institute of automation) Iris V4 dataset, which has 99.5% sensitivity and 99.75% accuracy, and the IIT (Indian Institute of Technology) Delhi dataset, which has 99.35% specificity and MMU (Multimedia University) 99.45% accuracy, which is higher than the existing architectures.     

Effect of SiC Weight Percentage and Sintering Duration on Microstructural and Mechanical Behaviour of Al6061/SiC Composites Produced by Powder Metallurgy Technique

Silicon - Aluminum-based metal matrix composites (MMC) are very popularly used in the aircraft, automotive, and armament industries because of their high young’s modulus, specific strength,...     

Microstructure,Mechanical Properties,and Corrosion Behavior of Co-Based Stellite 6 Multilayer Overlays Deposited on ASTM A36 Steel by Gas Metal Arc Welding Process

Hardfacing is an effective method for restoring the damaged part. Herein, cobalt-based superalloy Stellite-6 is deposited on A36 substrate using gas metal arc welding process. The mechanical properties, microstructure, and corrosion behavior of Stellite-6 overlays on A36 substrate are examined. Defect-free hard overlays are obtained, and a dilution ratio of 19.9% is achieved with weaving technique. X-ray diffraction analysis reveals the presence of M₇C₃, M₂₃C₆, M₆C, and martensite in the Stellite-6 hard overlays. The microstructure of the deposit contains martensite, carbides, and interdendritic precipitates while the mean hardness is 471 HV_0.5. Tensile testing with specimens extracted on the longitudinal (LD) and transverse (TD) orientation of the stellite-6 deposit shows a strength above 950 MPa along with the brittle nature of fracture. Grain growth is epitaxial and shows dendritic morphologies. The higher fraction of high-angle grain boundaries improves the tensile strength of the deposit and interface region. The localized corrosion behavior of stellite-6 hard overlays is found to be excellent, and the corrosion analysis exhibits a corrosion rate of 0.10 mils per year (mpy). The outcomes of the study highlight the feasibility of Stellite-6 hard overlays for extending the service life of A36 steel in chloride environments.     

Stable Graphene Membranes for Selective Ion Transport and Emerging Contaminants Removal in Water

Carbon-based materials, such as graphene oxide and reduced graphene oxide membranes have been recently used to fabricate ultrathin, high-flux, and energy-efficient membranes for ionic and molecular sieving in aqueous solution. However, these membranes appeared rather unstable during long-term operation in water with a tendency to swell over time. Membranes produced from pristine, stable, layered graphene materials may overcome these limitations while providing high-level performance. In this paper, an efficient and “green” strategy is proposed to fabricate µm-thick, graphene-based laminates by liquid phase exfoliation in Cyrene and vacuum filtration on a PVDF support. The membranes appear structurally robust and mechanically stable, even after 90 days of operation in water. In ion transport studies, the membranes show size selection (>3.3 Å) and anion-selectivity via the positively charged nanochannels forming the graphene laminate. In antibiotic (tetracycline) diffusion studies under dynamic conditions, the membrane achieve rejection rates higher than 95%. Sizable antibacterial properties are demonstrated in contact method tests with Staphylococcus aureus and Escherichia coli bacteria. Overall, these “green” graphene-based membranes represent a viable option for future water management applications.     

Effect of SiC Weight Percentage on Tribological Characteristics of Al7075/SiC Composites

Silicon - In aerospace and automobile industries, because of high strength and excellent anti-wear properties, aluminium silicon carbide composites are widely used. Hence, the current work...