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.

     

Impacts of user-selfishness on cooperative content caching in social wireless networks

Cooperative caching can be an effective mechanism for reducing electronic content provisioning cost in Social Wireless Networks (SWNETs). These networks are formed by a collection of mobile data enabled devices physically gathering in settings such as university campus, malls, airport and other public places. In this paper, we first propose an optimal collaborative object caching policy in order to minimize the object provisioning cost in SWNETs with homogenous user requests and a peer-rebate model for promoting collaboration. Then using an analytical model we study the impacts of user selfishness on the provisioning cost and the earned rebate when certain nodes in an SWNET selfishly deviate from the optimal policy. User selfishness is motivated in order to either increase individually earned rebate or to reduce content provisioning cost. The analytical model is validated by experimental results from simulated SWNETs using the network simulator ns2.     

Theoretical considerations in the optimization of surface waves on a planar structure

The problem of optimum excitation of surface waves on a grounded dielectric slab by means of slots in the ground plane is considered. By adopting a two-dimensional (2-D) model, analysis lead to closed forms for the power launched as surface waves and power leaked as radiation. Input admittance of a single slot source and mutual admittance between two slots are derived and utilized to design a three element Yagi array of slots to achieve a prescribed ratio of forward to backward surface wave power. As a development of the 2-D model, we allow finite extent of slot excitation by assuming a Gaussian E-field distribution across the slot. The effect of the Gaussian width on the excited surface wave power is studied. The analysis is relevant to the study of surface waves on printed circuits. Specifically, it applies to the implementation of power combiners based on quasioptical slab beam that have been recently introduced in the literature for use in the millimeter wave band.     

Off‐grid DoA estimation method of wideband signals

In this paper, an off‐grid direction of arrival (DoA) estimation method is proposed for wideband signals. This method is based on the sparse representation (SR) of the array covariance matrix. Similar to the time domain DoA estimation methods, the correlation function of the sources was assumed to be the same and known. A new measurement vector is obtained using the lower‐left triangular elements of the covariance matrix. The DoAs are estimated by quantizing the entire range of continuous angle space into discrete grid points. However, the exact DoAs may be located between two grid points; therefore, this estimation has errors. The accuracy of DoA estimation is improved by the minimization of the difference between the new measurement vector and its estimated values. Simulation results revealed that the proposed method can enhance the DoA estimation accuracy of wideband signals.     

Outage Probability Analysis of Cooperative Diversity Networks Over Weibull and Weibull-Lognormal Channels

This paper presents the analysis of outage probability for a cooperative diversity wireless network using amplify-and-forward relays over independent non-identical distributed Weibull and Weibull-lognormal fading channels for single as well as multiple relays. To reach that end, a closed-from expression for the moment-generating function of the total signal-to-noise-ratio (SNR) at the destination is derived in terms of the tabulated Meijer’s G-function. Since it is hard to determine the exact probability distribution function of the SNR, a tight lower bound approximation is proposed. Simulation results are presented that show that the outage probability lower bound tends to be tight at high SNR values thus verifying the analytical results. The results also show the potential gain of relaying on the outage probability.     

Thermally induced structural changes and optical properties of tin dioxide nanoparticles synthesized by a conventional precipitation method

Tin dioxide (SnO₂) nanoparticles were synthesized by a conventional precipitation method using the reaction between tin chloride pentahydrate and ammonia solutions. The obtained powders were calcined at varied temperatures from 300 to 1050 °C, and then characterized by using thermogravimetric analysis, differential thermal analysis and Fourier transformation infrared spectroscopy. The average crystallite size, determined by x-ray diffraction, was found to be in the range of 3.45–23.5 nm. The analysis exhibited a tetragonal phase. The activation energy of crystal growth was calculated and found to be 12.12 kJ/mol. The microstructure of nanoparticles was examined by high resolution transmission electron microscopy. Optical properties were investigated by a UV–vis absorption spectrophotometer. The calculated optical band gap lies between 4.75–4.25 eV as a result of increasing the calcination temperatures and crystallite size.     

Novel Tight Closed-Form Bounds for the Symbol Error Rate of EGC and MRC Diversity Receivers Employing Linear Modulations Over \alpha -\mu Fading

In this paper, we propose novel lower and upper bounds on the average symbol error rate (SER) of the dual-branch maximal-ratio combining and equal-gain combining diversity receivers assuming independent branches. \(M\) -ary pulse amplitude modulation and \(M\) -ary phase shift keying schemes are employed and operation over the \(\alpha -\mu \) fading channel is assumed. The proposed bounds are given in closed form and are very simple to calculate as they are composed of a double finite summation of basic functions that are readily available in the commercial software packages. Furthermore, the proposed bounds are valid for any combination of the parameters \(\alpha \) and \(\mu \) as well as \(M\) . Numerical results presented show that the proposed bounds are very tight when compared to the exact SER obtained via performing the exact integrations numerically making them an attractive much simpler alternative for SER evaluation studies.     

An expert egg grading system based on machine vision and artificial intelligence techniques

The main purpose of this research was design and development of an intelligent system based on combined fuzzy logic and machine vision techniques for grading of egg using parameters such as defects and size of eggs. The detected defects were internal blood spots, cracks and breakages of eggshell. The Hue-Saturation-Value (HSV) color space was found useful in obtaining visual features during Image Processing (IP) stage. The fuzzy inference system (FIS) was designed based on triangular and trapezoidal membership functions, fuzzy rules with logical operator of AND inference system of Mamdani and method of center average for defuzzifier. The evaluation results of IP algorithms showed that use of IP technique has good performance for defects and size detection. The Correct Classification rate (CCR) was 95% for size detection, 94.5% for crack detection and 98% for breakage detection. The overall accuracy FIS model in grading of the eggs was 95.4.