Jadavpur University

About: Jadavpur University is a education organization based out in Kolkata, India. It is known for research contribution in the topics: Population & Fuzzy logic. The organization has 10856 authors who have published 27678 publications receiving 422069 citations. The organization is also known as: JU & Jadabpur University.

Effect of oxygen partial pressure on the electrical and optical properties of highly (200) oriented p-type Ni1−xO films by DC sputtering

Abstract: Thin films of NiO (bunsenite) with (200) preferential orientation were synthesized on glass substrates by direct current sputtering technique in Ar+O2 atmosphere. Nanostructural properties of the NiO films were investigated by X-ray diffraction and also by atomic force microscopic (AFM) studies. Electrical and optical properties of the deposited films were investigated as a function of different partial pressure of oxygen in the sputtering gas mixture during deposition. The films showed p-type electrical conduction and the conductivity depends on the partial pressure of oxygen. The electrical conductivity (σRT) was found to be .0615 S cm−1 for films deposited with 100% O2 and its value sharply decreased with the decrease the partial pressure of O2; for example σRT for 50% O2 was 6.139 × 10−5 S cm-1. The mechanism of the origin of p-type electrical conductivity in the NiO film is discussed from the viewpoint of nickel or oxygen vacancies, which generate holes and electrons respectively. X-ray photoelectron spectroscopic studies supported the above argument. Corresponding optical properties showed that the transparency decreases with increasing oxygen partial pressure and the bandgap also decreases.

Self-adaptive differential evolution for feature selection in hyperspectral image data

Abstract: Hyperspectral images are captured from hundreds of narrow and contiguous bands from the visible to infrared regions of electromagnetic spectrum. Each pixel of an image is represented by a vector where the components of the vector constitute the reflectance value of the surface for each of the bands. The length of the vector is equal to the number of bands. Due to the presence of large number of bands, classification of hyperspectral images becomes computation intensive. Moreover, higher correlation among neighboring bands increases the redundancy among them. As a result, feature selection becomes very essential for reducing the dimensionality. In the proposed work, an attempt has been made to develop a supervised feature selection technique guided by evolutionary algorithms. Self-adaptive differential evolution (SADE) is used for feature subset generation. Generated subsets are evaluated using a wrapper model where fuzzy k-nearest neighbor classifier is taken into consideration. Our proposed method also uses a feature ranking technique, ReliefF algorithm, for removing duplicate features. To demonstrate the effectiveness of the proposed method, investigation is carried out on three sets of data and the results are compared with four other evolutionary based state-of-the-art feature selection techniques. The proposed method shows promising results compared to others in terms of overall classification accuracy and Kappa coefficient.

Native Cellulose Microfiber-Based Hybrid Piezoelectric Generator for Mechanical Energy Harvesting Utility.

Abstract: A flexible hybrid piezoelectric generator (HPG) based on native cellulose microfiber (NCMF) and polydimethylsiloxane (PDMS) with multi wall carbon nanotubes (MWCNTs) as conducting filler is presented where the further chemical treatment of the cellulose and traditional electrical poling steps for piezoelectric voltage generation is avoided. It delivers a high electrical throughput that is an open circuit voltage of ∼30 V and power density ∼9.0 μW/cm3 under repeated hand punching. We demonstrate to power up various portable electronic units by HPG. Because cellulose is a biocompatible material, suggesting that HPG may have greater potential in biomedical applications such as implantable power source in human body.