A noise reduction technique of speech signal using ICA and spectral analysis

This paper discusses the new method on noise reduction exploiting the combined effects of wavelet decomposition, ICA and spectral analysis on noisy speech. The input noisy speech is wavelet decomposed into two signals. Wavelet entropy is computed based on the modified probability density function for the signal derived from the approximation coefficients during wavelet decomposition. By proper entropy comparison, the starting frame is detected. Between the two signals obtained from the wavelet decomposition, one is speech combined with noise and another one is noise alone. These two signals are analysed in independent component analysis (ICA) domain, in order to generate an enhanced speech. Zero-crossing rate is computed and used to discriminate between speech and noise. Then, spectral analysis is performed on the noise prior to starting frame and noisy speech. Elimination of noise frequencies in the noisy speech leads to noise reduced speech. Subjective analysis and experimental results show the considerable noise reduction capability of the proposed algorithm.     

Applicability of sub graph centrality to improve data accessibility among peers in MANETs

Data sharing plays an essential role in many of the mobile ad-hoc network (MANET) applications that exhibits collaborative behavior. In such applications, replication is used as a foremost and fundamental technique to improve data availability. However, due to the dynamic nature of the network, data replication becomes more intricate in MANET. To alleviate this problem, we have proposed a mechanism which not only enhances data accessibility, replicates data in a minimum number of nodes, relocates shared data on the prediction of mobility of replica holder and in addition, data can be accessed by any node in a minimum number of hops. In our approach, we have prefaced mathematical concept known as minimum dominating set and sub graph centrality principle to decide the number of replicas both in static and dynamic environment. Simulation results when compared with the existing mechanisms shows that the response time or data access delay is reduced, client can access the data from the server in a minimum number of hops, and consequently the number of forwarded messages to access the data are greatly reduced thus making our network energy efficient.     

Investigations on the photocatalytic activity of sol–gel derived plain and Fe/Nb-doped titania coatings on glass substrates

Plain and doped (Fe³⁺ and Nb⁵⁺) titania coatings were deposited by dip coating on soda lime glass substrates using titania sol synthesized by sol–gel route in combination with commercial nanoparticle dispersions. The dopant concentrations were fixed at 0.07 wt% and the coatings heat treated at 400 °C were characterized with respect to their thickness, phase composition, hydrophilicity/hydrophobicity and microstructure. Photocatalytic activity testing was carried out on the coatings by following up the degradation of methylene blue dye for up to 4 h at 1 h time intervals after exposure to sunlight. The effect of a trivalent and pentavalent doping of Ti⁴⁺ sites on photocatalytic activity of TiO₂ was investigated.     

Catalytic activity of gold supported on ZnO tetrapods for the preferential oxidation of carbon monoxide under hydrogen rich conditions

It is reported that 3 nm gold nanoparticles deposited on ZnO tetrapods show high activity for the selective oxidation of carbon monoxide in hydrogen rich streams; the catalytic activity of this system is at least twice as high as the values hither to observed on any conventional support for this reaction.     

RMCARTAM For DDoS Attack Mitigation in SDN Using Machine Learning

The impact of a Distributed Denial of Service (DDoS) attack on Software Defined Networks (SDN) is briefly analyzed. Many approaches to detecting DDoS attacks exist, varying on the feature being considered and the method used. Still, the methods have a deficiency in the performance of detecting DDoS attacks and mitigating them. To improve the performance of SDN, an efficient Real-time Multi-Constrained Adaptive Replication and Traffic Approximation Model (RMCARTAM) is sketched in this article. The RMCARTAM considers different parameters or constraints in running different controllers responsible for handling incoming packets. The model is designed with multiple controllers to handle network traffic but can turn the controllers according to requirements. The multi-constraint adaptive replication model monitors different features of network traffic like rate of packet reception, class-based packet reception and target-specific reception. According to these features, the method estimates the Replication Turning Weight (RTW) based on which triggering controllers are performed. Similarly, the method applies Traffic Approximation (TA) in the detection of DDoS attacks. The detection of a DDoS attack is performed by approximating the incoming traffic to any service and using various features like hop count, payload, service frequency, and malformed frequency to compute various support measures on bandwidth access, data support, frequency support, malformed support, route support, and so on. Using all these support measures, the method computes the value of legitimate weight to conclude the behavior of any source in identifying the malicious node. Identified node details are used in the mitigation of DDoS attacks. The method stimulates the network performance by reducing the power factor by switching the controller according to different factors, which also reduces the cost. In the same way, the proposed model improves the accuracy of detecting DDoS attacks by estimating the features of incoming traffic in different corners.     

Boron Deteriorates the Thermal Stability of Nanostructured Silicon

Silicon - Guided by Darling et al. model for nanostructure stability, Boron was chosen for stabilizing nanostructured silicon. The Si-4 at.% B alloy was synthesized by high energy ball...