Development and Characterisation of Nanoclays from Indian Clays

Indian clays are known for their smecticity. One such clay sample collected from Bhuj (Gujarat)was characterised and modified by successive sedimentation processes for different time intervals.The non-plastic components of clay, viz., quartz, illite, iron oxide, CaO, MgO, and organic matterwere removed in different steps, as the heavy impurities in the clay-water suspensions, settledown during sedimentation. The free iron oxide present in clay suspension was reduced bygiving sodium citrate-bicarbonate-dithionite treatment and iron content was further reducedfrom 12Œ15 per cent to 5Œ7 per cent respectively. The organic matter was removed by sodiumacetate-H2O2 treatment. The modified clay so obtained was characterised by thermal analysis,FTIR, and XRD,  SEM and TEM. The cation exchange capacities of original and modified clayswere determined both by methylene blue method and ammonium acetate method. The cationex change capacity is found to enhance from 120Œ130 meq/100 g to 135Œ145 meq/100 g. Usingthe above procedure, 92 per cent smecticity was obtained. Organo philisation of purified clay(smectite) was carried out by intercalation with alkyl ammonium salt. The  XRD  analysis show edenhancement of interlamellar spacing from 1.294 nm to 2.855 nm.Defence Science Journal, 2008, 58(4), pp.517-524, DOI:http://dx.doi.org/10.14429/dsj.58.167

CONSTRUCTION OF ASYMMETRICAL RESPONSE SURFACE DESIGNS

The paper proposes several methods for constructing both rotatable and non-rotatable asymmetrical response surface designs. The idea of modified rotatable design is introduced. In most of the experiments conducted by the experimenter it is not necessary that all the factors under study may have equal number of levels The methods proposed will have wider use under these circumstances

Dissimilarity Based Contrastive Divergence for Anomaly Detection

This paper describes training of a Re- stricted Boltzmann Machine(RBM) using dissimilarity-based contrastive divergence to obtain an anomaly detector. We go over the merits of the method over other approaches and describe the method's usefulness to ob- tain a generative model

MAST: Multiscale Audio Spectrogram Transformers

We present Multiscale Audio Spectrogram Transformer (MAST) for audio classification, which brings the concept of multiscale feature hierarchies to the Audio Spectrogram Transformer (AST). Given an input audio spectrogram we first patchify and project it into an initial temporal resolution and embedding dimension, post which the multiple stages in MAST progressively expand the embedding dimension while reducing the temporal resolution of the input. We use a pyramid structure that allows early layers of MAST operating at a high temporal resolution but low embedding space to model simple low-level acoustic information and deeper temporally coarse layers to model high-level acoustic information with high-dimensional embeddings. We also extend our approach to present a new Self-Supervised Learning (SSL) method called SS-MAST, which calculates a symmetric contrastive loss between latent representations from a student and a teacher encoder. In practice, MAST significantly outperforms AST by an average accuracy of 3.4% across 8 speech and non-speech tasks from the LAPE Benchmark. Moreover, SS-MAST achieves an absolute average improvement of 2.6% over SSAST for both AST and MAST encoders. We make all our codes available on GitHub at the time of publication.Comment: Submitted ICASSP 202

Formation of Silicon Carbide Whiskers from Organic Precursors Via Sol-Gel Method

Silicon Carbide (SiC) was synthesized by carbothermal reduction of silica precursor and carbon precursor. The silica precursor was obtained from tetraethoxysilane. Sucrose was used as carbon source. Tetraethoxysilane (TEOS) was hydrolyzed in acidic water (pH = 2). The molar ratio of TEOS-H2O-EtOH was taken as 1:8:2 in the sol-gel processing. Hydrolysed silica sol was polymerized with sucrose to incorporate carbon precursor into the silica network. The gel thus obtained was dried in an oven at 70 oC and at 100 oC. The solid mass obtained on drying was heat treated at 1000 °C in nitrogen atmosphere to obtain the black glass. It was characterized by FTIR, SEM and TGA. The black glass was further heated to 1500 oC in argon to yield silicon carbide. this resulted in formation of β-SiC whiskers

Studies on synthesis and Reduction of Graphene Oxide from Natural Graphite by using Chemical Method

Graphene is a material with rapidly growing interest. It consists of flat monolayer of carbon atoms tightly packed into a two-dimensional (2D) honeycomb lattice and is basic building block for all graphitic materials. Interest in Graphene is because of its excellent mechanical, electrical, thermal, optical properties and its very high specific surface area. Studies have been performed on wet oxidation of natural graphite by using Modified Hummers Method followed by exfoliation and reduction in order toВ  synthesise graphene from Graphite Oxide (GO). Acid route has been followed for oxidation whereas reduction has been carried out in water with hydrazine hydrate and Sodium Borohydrate. It results in to a material with characteristics that are comparable to those of pristine graphite. The reaction at every step has been characterized by using FTIR, TGA, XRD, Raman spectroscopy and surface area measurement

UNFUSED: UNsupervised Finetuning Using SElf supervised Distillation

In this paper, we introduce UnFuSeD, a novel approach to leverage self-supervised learning and reduce the need for large amounts of labeled data for audio classification. Unlike prior works, which directly fine-tune a self-supervised pre-trained encoder on a target dataset, we use the encoder to generate pseudo-labels for unsupervised fine-tuning before the actual fine-tuning step. We first train an encoder using a novel self-supervised learning algorithm (SSL) on an unlabeled audio dataset. Then, we use that encoder to generate pseudo-labels on our target task dataset via clustering the extracted representations. These pseudo-labels are then used to guide self-distillation on a randomly initialized model, which we call unsupervised fine-tuning. Finally, the resultant encoder is then fine-tuned on our target task dataset. Through UnFuSeD, we propose the first system that moves away from generic SSL paradigms in literature, which pre-train and fine-tune the same encoder, and present a novel self-distillation-based system to leverage SSL pre-training for low-resource audio classification. In practice, UnFuSeD achieves state-of-the-art results on the LAPE Benchmark, significantly outperforming all our baselines. Additionally, UnFuSeD allows us to achieve this at a 40% reduction in the number of parameters over the previous state-of-the-art system. We make all our codes publicly available.Comment: Under review at ICASSP 2023 SASB Worksho

Change Vector Analysis using Enhanced PCA and Inverse Triangular Function-based Thresholding

Change vector analysis is a very sophisticated method to evaluate land-use/land-cover changes meaningfully. By making proper choice of input data in the form of bands (for instance, red, NIR etc) or features (for instance, greenness, brightness, wetness etc), information about both the magnitude as well as the type/nature of changes can be extracted. However, improper selection of thresholds is always a hindrance to a good change detection algorithm. The paper has proposed an improved technique to select threshold appropriately by means of principal component difference and inverse triangular function. The changes have been represented using class-based circular wheel representation. Results have been shown to further testify the performance of proposed algorithm.Defence Science Journal, 2012, 62(4), pp.236-242, DOI:http://dx.doi.org/10.14429/dsj.62.107

SLICER: Learning universal audio representations using low-resource self-supervised pre-training

We present a new Self-Supervised Learning (SSL) approach to pre-train encoders on unlabeled audio data that reduces the need for large amounts of labeled data for audio and speech classification. Our primary aim is to learn audio representations that can generalize across a large variety of speech and non-speech tasks in a low-resource un-labeled audio pre-training setting. Inspired by the recent success of clustering and contrasting learning paradigms for SSL-based speech representation learning, we propose SLICER (Symmetrical Learning of Instance and Cluster-level Efficient Representations), which brings together the best of both clustering and contrasting learning paradigms. We use a symmetric loss between latent representations from student and teacher encoders and simultaneously solve instance and cluster-level contrastive learning tasks. We obtain cluster representations online by just projecting the input spectrogram into an output subspace with dimensions equal to the number of clusters. In addition, we propose a novel mel-spectrogram augmentation procedure, k-mix, based on mixup, which does not require labels and aids unsupervised representation learning for audio. Overall, SLICER achieves state-of-the-art results on the LAPE Benchmark \cite{9868132}, significantly outperforming DeLoRes-M and other prior approaches, which are pre-trained on $10\times$ larger of unsupervised data. We will make all our codes available on GitHub.Comment: Submitted to ICASSP 202