A complete VARMA modelling methodology based on scalar components

Abstract. This article proposes an extension to scalar component methodology for the identification and estimation of VARMA models. The complete methodology determines the exact positions of all free parameters in any VARMA model with a predetermined embedded scalar component structure. This leads to an exactly identified system of equations that is estimated using full information maximum likelihood.     

Visual tracking using convolutional features with sparse coding

Artificial Intelligence Review - Visual object tracking has become one of the most active research topics in computer vision, and it has been applied in several commercial...     

The rare caribbean spongeLeucosolenia canariensis: Phospholipid fatty acids and sterols

The phospholipid fatty acid composition of the Calcarean spongeLeucosolenia canariensis was studied, and no Δ^5,9 fatty acids were detected. These results are in contrast to the phospholipids from sponges belonging to the class Demospongiae where Δ5,9 fatty acids are predominant. Odd branched-chain fatty acids between 17 and 19 carbons accounted for 26% of the total fatty acids ofL. canariensis, while straight-chain fatty acids between 16 and 22 carbons accounted for 61% of the total fatty acid composition. The sterol composition ofL. canariensis is also reported, and only Δ^5,7,22 sterols were observed.     

Speech enhancement with an adaptive Wiener filter

This paper proposes an adaptive Wiener filtering method for speech enhancement. This method depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. It is implemented in the time domain rather than in the frequency domain to accommodate for the time-varying nature of the speech signals. The proposed method is compared to the traditional frequency-domain Wiener filtering, spectral subtraction and wavelet denoising methods using different speech quality metrics. The simulation results reveal the superiority of the proposed Wiener filtering method in the case of Additive White Gaussian Noise (AWGN) as well as colored noise.     

An efficient singular value decomposition algorithm for digital audio watermarking

The singular value decomposition (SVD) mathematical technique is utilized, in this paper, for audio watermarking in time and transform domains. Firstly, the audio signal in time or an appropriate transform domain is transformed to a 2-D format. The SVD algorithm is applied on this 2-D matrix, and an image watermark is added to the matrix of singular values (SVs) with a small weight, to guarantee the possible extraction of the watermark without introducing harmful distortions to the audio signal. The transformation of the audio signal between the 1-D and 2-D formats is performed in the well-known lexicographic ordering method used in image processing. A comparison study is presented in the paper between the time and transform domains as possible hosting media for watermark embedding. Experimental results are in favor of watermark embedding in the time domain if the distortion level in the audio signal is to be kept as low as possible with a high detection probability. The proposed algorithm is utilized also for embedding chaotic encrypted watermarks to increase the level of security. Experimental results show that watermarks embedded with the proposed algorithm can survive several attacks. A segment-by-segment implementation of the proposed SVD audio watermarking algorithm is also presented to enhance the detectability of the watermark in the presence of severe attacks.     

Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

Fullerene: biomedical engineers get to revisit an old friend

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The synthesis of nano-sized ZSM-5 zeolite by dry gel conversion method and investigating the effects of experimental parameters by Taguchi experimental design

In this study ZSM-5 Nano particles with high crystallinity were synthesized using a dry gel conversion technique. An L9 orthogonal array of the Taguchi method was applied to investigate the effect of synthesis parameters, such as crystallization time, gel drying temperature, molar composition of template (TPAOH) and water content in the crystallization stage on crystallinity and particle size of the ZSM-5 catalyst. The products were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM). Beside short crystallization time, the particle sizes were considerably smaller in comparison with those prepared using the hydrothermal method. The results showed that the particle size and crystallinity increased with increasing water content and crystallization time. The effects of gel drying temperature and molar composition of template were found to be more complex, however. Comparing to hydrothermal method, ZSM-5 samples synthesized with the dry gel conversion exhibited higher selectivity to gasoline than other hydrocarbons.