Product beamforming and nested array in tandem for enhanced sonar performance

Multidimensional Systems and Signal Processing - High-performance sonar systems mostly combine a large sensor array and an efficient beamformer for enhancing the overall detection capabilities and...     

Biocompatible antimicrobial cotton fibres for healthcare industries: a biogenic approach for synthesis of bio‐organic‐coated silver nanoparticles

Cotton fibres coated with biogenically fabricated silver nanoparticles (SNPs) are most sought material because of their enhanced activity and biocompatibility. After successful synthesis of SNPs on cotton fibres using leaf extract of Vitex negundo Linn, the fibres were studied using diffuse reflectance spectroscopy, scanning electron microscopy, nanoparticle tracking analysis, energy dispersive X‐ray, and inductively coupled plasma atomic emission spectrometry. The characterisation revealed uniformly distributed spherical agglomerates of SNPs having individual particle size around 50 nm with the deposition load of 423 μg of silver per gram of cotton. Antimicrobial assay of cotton–SNPs fibres showed effective performance against pathogenic bacteria and fungi. The method is biogenic, environmentally benign, rapid, and cost‐effective, producing highly biocompatible antimicrobial coating required for the healthcare industry.Inspec keywords: cotton, health care, nanoparticles, coatings, silver, fibres, nanofabrication, scanning electron microscopy, X‐ray chemical analysis, atomic emission spectroscopy, plasma applications, microorganisms, biotechnologyOther keywords: biocompatible antimicrobial cotton fibre coating, healthcare industry, bioorganic‐coated silver nanoparticle synthesis, biogenically fabricated silver nanoparticle, SNP, leaf extraction, Vitex negundo Linn, diffuse reflectance spectroscopy, scanning electron microscopy, nanoparticle tracking analysis, energy dispersive X‐ray spectrometry, inductively coupled plasma atomic emission spectrometry, uniformly distributed spherical agglomerate, antimicrobial assay, pathogenic bacteria, fungi, Ag     

Manifold Learning Using Linear Local Tangent Space Alignment (LLTSA) Algorithm for Noise Removal in Wavelet Filtered Vibration Signal

A denoising procedure is proposed to remove both out-band and in-band noise for extraction of weak bursts in signal obtained from defective bearing. Energy of continuous wavelet scalogram is computed and the band having higher energy is selected to remove the out-band noise. Signals of selected band are brought together to form a high-dimensional waveform feature space. Further, low dimensional waveform manifold is formed using linear local tangent space alignment (LLTSA) algorithm to remove in-band noise. A criterion, entitled as frequency factor is also proposed to determine the optimum neighbour size of LLTSA. The two complicated conditions are chosen to demonstrate the effectiveness of the technique in the extraction of bursts in the noisy situations. A significant improvement in the signal to noise ratio is observed when in-band noise is removed using manifold learning by LLTSA algorithm. The experimental result reveals the success of the proposed denoising procedure in extraction of defect features, even in the case of noisy condition.     

Context sensitive trust based geographic opportunistic routing in mobile ad hoc networks

Sādhanā - Position based opportunistic routing (POR) is a stateless, robust, and reliable geographic routing protocol in Mobile AdHoc NETwork (MANET). The opportunistic routing embraces...     

Automatic text classification using BPLion-neural network and semantic word processing

Text mining has become a major research topic in which text classification is the important task for finding the relevant information from the new document. Accordingly, this paper presents a semantic word processing technique for text categorization that utilizes semantic keywords, instead of using independent features of the keywords in the documents. Hence, the dimensionality of the search space can be reduced. Here, the Back Propagation Lion algorithm (BP Lion algorithm) is also proposed to overcome the problem in updating the neuron weight. The proposed text classification methodology is experimented over two data sets, namely, 20 Newsgroup and Reuter. The performance of the proposed BPLion is analysed, in terms of sensitivity, specificity, and accuracy, and compared with the performance of the existing works. The result shows that the proposed BPLion algorithm and semantic processing methodology classifies the documents with less training time and more classification accuracy of 90.9%.     

An improved anti-forensics JPEG compression using Least Cuckoo Search algorithm

The lossy nature of the JPEG compression leaves traces which are utilized by the forensic agents to identify the local tampering in the image. In this paper, a tricky anti-forensic method has been proposed to remove the traces left by the JPEG compression in both the spatial domain and discrete cosine transform domain. A novel Least Cuckoo Search algorithm is devised in the proposed anti-forensic compression scheme. Moreover, a new fitness function called histogram deviation is formulated in the optimization algorithm. The experimentation of the proposed anti-forensic compression scheme is performed over uncompressed images from UCID database. The performance of the proposed method is evaluated, and it is compared with the existing methods using PSNR, MSE and classification accuracy as measures. The experimentation ensued with promising results, i.e. accuracy of 0.97, PSNR of 44.34?dB, and MSE of 0.1789 which prove the efficacy of the proposed method.     

Acoustic modeling problem for automatic speech recognition system: conventional methods (Part I)

In automatic speech recognition (ASR) systems, the speech signal is captured and parameterized at front end and evaluated at back end using the statistical framework of hidden Markov model (HMM). The performance of these systems depend critically on both the type of models used and the methods adopted for signal analysis. Researchers have proposed a variety of modifications and extensions for HMM based acoustic models to overcome their limitations. In this review, we summarize most of the research work related to HMM-ASR which has been carried out during the last three decades. We present all these approaches under three categories, namely conventional methods, refinements and advancements of HMM. The review is presented in two parts (papers): (i) An overview of conventional methods for acoustic phonetic modeling, (ii) Refinements and advancements of acoustic models. Part I explores the architecture and working of the standard HMM with its limitations. It also covers different modeling units, language models and decoders. Part II presents a review on the advances and refinements of the conventional HMM techniques along with the current challenges and performance issues related to ASR.     

Artificial intelligence applications in Permanent Magnet Brushless DC motor drives

Permanent Magnet Brushless DC (PMBLDC) machines are more popular due its simple structure and low cost. Improvements in permanent magnetic materials and power electronic devices have resulted in reliable, cost effective PMBLDC drives, for many applications. Advances in artificial intelligent applications like neural network, fuzzy logic, Genetic algorithm etc. have made tremendous impact on electric motor drives. The brushless DC motor is a multivariable and non-linear system. In conventional PMBLDC drives speed and position sensing of brushless DC motors require high degree of accuracy. Unfortunately, traditional methods of control require detailed modelling of all the motor parameters to achieve this. The Intelligent control techniques like, fuzzy logic control/Neural network control etc. uses heuristic input–output relations to deal with vague and complex situations. This paper presents a literature survey on the intelligent control techniques for PMBLDC motor drives. Various AI techniques for PMBLDC motor drives are described. Attempt is made to provide a guideline and quick reference for the researchers and practicing engineers those are working in the area of PMBLDC motor drives.     

Flank wear detection of cutting tool inserts in turning operation: application of nonlinear time series analysis

It has been established that turning process on a lathe exhibits low dimensional chaos. This study reports the results of nonlinear time series analysis applied to sensor signals captured real time. The purpose of this chaos analysis is to differentiate three levels of flank wears on cutting tool inserts—fresh, partially worn and fully worn—utilizing the single value index extracted from the reconstructed chaotic attractor; the correlation dimension. The analysis reveals distinguishable dynamics of cutting characterized by different values for the dimension of the attractor when different quality tool inserts are used. This dependence can be effectively utilized as one of the indicators in tool condition monitoring in a lathe. This paper presents the experimental results and shows that tool vibration signals can transmit tool wear conditions reliably.