National Institute of Technology, Silchar

About: National Institute of Technology, Silchar is a education organization based out in Silchar, Assam, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 1934 authors who have published 4219 publications receiving 41149 citations. The organization is also known as: NIT Silchar.

Ethylene glycol mediated synthesis of SnS quantum dots and their application towards degradation of eosin yellow and brilliant green dyes under solar irradiation

Abstract: SnS (tin sulfide) quantum dots (QDs) were synthesized by a chemical coprecipitation method using ethylene glycol as a solvent and capping agent and thiourea as a sulfur source at a temperature of 160 °C, 4 h. The as synthesized SnS QDs were characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), UV-Vis-NIR spectroscopy and FT-Raman spectroscopy. XRD patterns show the formation of single phase SnS QDs with rhombohedral structure. Ethylene glycol mediated synthesis resulted 2.5–3 nm SnS QDs. The UV-Vis-NIR optical absorption spectra of the SnS QDs displayed that the SnS QDs possess an absorption profile across the whole visible-light and near-infrared region. The direct band gap and indirect band gap energy for SnS QDs are found to be 1.17 eV and 1.11 eV, respectively. FT-Raman spectra of SnS QDs demonstrate vibrational modes at 73, 97, 162, 188, 222 cm−1. The Brunauer–Emmett–Teller (BET) surface area of SnS QDs was found to be 5.63 m2 g−1. SnS QDs showed powerful photodegradation activity towards degradation of eosin yellow and brilliant green dyes under sunlight. The enhanced photocatalytic activity of SnS QDs is attributed to improved visible light absorption and efficient separation of photogenerated charge carriers. In addition, the quenching effects of different quenchers suggest that superoxide radicals play a major role in the photodegradation process.

Mammography classification using modified hybrid SVM-KNN

Abstract: Today leading cause of cancer deaths for women is the Breast cancer. For early and accurate detection of breast cancer, mammography is found to be the most reliable and effective technique. In this context, computer aided diagnosis of breast cancer from mammograms is gaining high importance and priority for many researchers. In this paper machine learning based mammogram classification using modified hybrid SVM-KNN is proposed. The idea is to map the feature points to kernel space using kernel and find the K nearest neighbors among the training dataset for a given test data point. Doing this we narrow down the search for support vectors. Mammogram images are preprocessed and region of interest is extracted using Fuzzy C Means clustering and Active Counter technique. GLCM (grey level covariance matrix) based texture features are extracted from segmented ROI. These features are used to train modified hybrid SVM-KNN classifier proposed by authors. The trained classifier is used to classify breast tissues in normal/abnormal classes and further abnormal class into benign/malignant. Proposed technique is experimented on two standard MIAS and DDSM databases. The proposed classifier reports classification accuracy of 100 % for DDSM and 94% for MIAS database for benign and malignant class. Results are compared with SVM, KNN and Random Forest classifiers.

Static Structural and Modal Analysis of Gas Turbine Blade

Abstract: Gas turbine is one of the most versatile items of turbo machinery nowadays. It is used in different modes such as power generation, oil and gas, process plants, aviation, domestic and related small industries. This paper is based on the problems concerning blade profile selection, material selection and turbine rotor blade vibration that seriously impact the induced stress-deformation and structural functioning of developmental gas turbine engine. In this paper for generating specific power by rotating blade at specific RPM, blade profile and material has been decided by static structural analysis. Gas turbine rotating blade RPM is decided by Modal Analysis so that the natural frequency of blade should not match with the excitation frequency. For the above blade profile has been modeled in SOLIDWORKS and analysis has been done in ANSYS WORKBENCH 14. Existing NACA6409 profile has been selected as base model and then it is modified by bending it through 72.5° and 145°. Hence these three different blade profiles have been analyzed for three different materials viz. Super Alloy X, Nimonic 80A and Inconel 625 at three different speed viz. 20000, 40000 and 60000RPM. It is found that NACA6409 with 72.5° bent gives best result for all material at all speed. Among all the material Inconel 625 gives best result. Hence Blade of Inconel 625 having 72.5° bent profile is the best combination for all RPM.