SiO2@LaOF:Eu3+ core-shell functional nanomaterials for sensitive visualization of latent fingerprints and WLED applications.

For the first time, intense red color composite of SiO2@LaOF:Eu3+ core-shell nanostructures (NS) were fabricated via facile solvothermal method followed by thermal treatment. The obtained core-shell particles display better spherical shape and non-agglomeration with a narrow size distribution. Photoluminescence (PL) emission spectra exhibits intense peaks at ∼593 nm, 611 nm, 650 nm corresponds to 5D0 → 7FJ (J = 0, 1 and 2) Eu3+ transitions respectively. The spectral intensity parameters and Eu-O ligand behaviors are estimated by means of Judd-Ofelt (J-O) theory. CIE co-ordinates are found to be (x = 0.63, y = 0.36) which is very close to standard NTSC values (x = 0.67, y = 0.33). CCT value is ∼3475 K which is less than 5000 K, as a result this phosphor is suitable for warm light emitting diodes. The optimized core-shell SiO2 (coat III)@LaOF:Eu3+ (5 mol%) was used as a fluorescent labeling marker for the visualization of latent fingerprints on both porous and non-porous surfaces. Obtained fingerprints are highly sensitive and selective also no background hindrance which enables level-I to level-III fingerprint ridge characteristics. Observed results indicate that the significant improvement in luminescence of coreshell NS can be explored as a sensitive functional nanopowder for advanced forensic and solid state lightning applications.

A wavelet transform based feature extraction and classification of cardiac disorder.

This paper approaches an intellectual diagnosis system using hybrid approach of Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classification of Electrocardiogram (ECG) signals. This method is based on using Symlet Wavelet Transform for analyzing the ECG signals and extracting the parameters related to dangerous cardiac arrhythmias. In these particular parameters were used as input of ANFIS classifier, five most important types of ECG signals they are Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF), Pre-Ventricular Contraction (PVC), Ventricular Fibrillation (VF), and Ventricular Flutter (VFLU) Myocardial Ischemia. The inclusion of ANFIS in the complex investigating algorithms yields very interesting recognition and classification capabilities across a broad spectrum of biomedical engineering. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies. The results give importance to that the proposed ANFIS model illustrates potential advantage in classifying the ECG signals. The classification accuracy of 98.24 % is achieved.