Ceramic bushing for glass ≈ fibre production

Ceramic bushings of different geometries and sizes have been developed for drawing continuous glass fibres as reinforcements. Both pure alumina and stabilised zirconia have been used in fabricating such bushings. The characteristics of fibres drawn through these bushings are shown to be similar to those drawn through conventional platinum-rhodium ones. Alumina bushings have poor corrosion resistance properties with respect to E-glass but are satisfactory for A- and C- glass compositions. Stabilised zirconia bushings give superior performance with regard to E-glass.     

Thermoelectric behaviour of amorphous magnetic alloys

The Thermoelectric emfs of thermocouples formed by amorphous METGLAS 2826 (Fe₄₀Ni₄₀P₁₄B₆) and METGLAS 2826B (Fe₂₉Ni₄₉P₁₄B₆Si₂) with standard thermocouple wires like copper, chromel, alumel, etc., were measured as a function of temperature between −196° C and 30° C to assess their suitability as thermoelectric temperature sensors. Thermoelectric emfs generated by METGLAS 2826/Cu and METGLAS 2826B/Cu thermocouples at −196° C are about an order of magnitude smaller when compared to thermal emfs of a standard copper/constantan thermocouple at the same temperature.     

Spin-wave excitations in amorphous ferromagnets

Mössbauer measurements have been performed on a number of metallic glasses. The temperature dependence of average hyperfine or internal magnetic fieldH _eff(T) arises from long wavelength spin wave excitations in these glasses. Values ofB _3/2 andC _5/2 are in general much higher than those observed for crystalline ferromagnets indicating higher density of states for spin waves in amorphous ferromagnets.     

Retina disease prediction using modified convolutional neural network based on Inception-ResNet model with support vector machine classifier

Artificial intelligence and deep learning have aided ocular disease through experiments including automatic illness recognition from images of the iris, fundus, or retina. Automated diagnosis systems (ADSs) provide services for the benefit of humanity and are essential in the early detection of harmful diseases. In fact, early detection is essential to avoid total blindness. In real life, several diagnostic tests such as visual ocular tonometry, retinal exam, and acuity test are performed, but they are conclusively time demanding and stressful for the patient. To consume time and detect the retinal disease earlier, an efficient prediction method is designed. In this proposed model, the first process is data collection that consists of a retinal disease dataset for testing and training. The second process is pre-processing, which executes image resizing and noise filter for feature extraction. The third step is feature extraction, which extracts the image's form, size, color, and texture for classification with CNN based on Inception-ResNet V2. The classification process is done by using the SVM with the extracted features. The prediction of diseases is classified such as normal, cataract, glaucoma, and retinal disease. The suggested model's performance is assessed using performance indicators such as accuracy, error, sensitivity, precision, and so forth. The suggested model's accuracy, error, sensitivity, and precision are 0.96, 0.962, 0.964, and 0.04, respectively, higher than existing techniques such as VGG16, Mobilenet V1, ResNet, and AlexNet. Thus, the proposed model instantly predicts retinal disease.     

Keyword-based private searching on cloud data along with keyword association and dissociation using cuckoo filter

International Journal of Information Security - Outsourcing of data is a very common scenario in the present-day world and quite often we need to outsource confidential data whose privacy is of...     

VLSI Implementation of Reliable and Secure Face Recognition System

Wireless Personal Communications - Biometric authentications systems are being recognized as the better way of securing data or personal devices as they are unique. Face is considered the best way...     

A stochastic approximation approach to active queue management

Recently, a dynamic adaptive queue management with random dropping (AQMRD) scheme has been developed to capture the time-dependent variation of average queue size by incorporating the rate of change of average queue size as a parameter. A major issue with AQMRD is the choice of parameters. In this paper, a novel online stochastic approximation based optimization scheme is proposed to dynamically tune the parameters of AQMRD and which is also applicable for other active queue management (AQM) algorithms. Our optimization scheme significantly improves the throughput, average queue size, and loss-rate in relation to other AQM schemes.