Earticle

Energy has been a major concern in almost all networks, especially the consumption and optimized utilization of energy. Lots of work has been experimented and proposed in the past with relevant evidences for fault detection too. However, researchers have yet to find the absolute solution to routing protocol for pre - “fault tolerance and prevention” using energy efficient awareness. In this paper, we have designed a protocol, if in case of failure which selects succeeding node in energy efficient manner; and if in case of its failure, sensitively restores the connectivity of the neighbors of a cluster around that node.

The global population is increasing rapidly because of increasing urbanization and such increasing urbanization directs the up-growing need of urban safety and preventions. This urbanization is also responsible for two things that is increased job opportunities and increased the crime rates. In this era technology has gone far more forward in a positive way. By making use of these technologies such as machine learning, artificial intelligence and big data we presented an approach through which crime pattern analysis is done. We have used apache spark (scala-programming) and machine learning algorithm for predictive crime pattern analysis. The data that we have used is a real-world data set based on Chicago city of United State of America. Our main goal of work is to define a predictive crime analysis which shows top crime patterns related to the top community areas of Chicago city.

In this paper, we analyzed and simulated performance enhancement scheme of electric-vehicle (EV) charging system based on power line communication (PLC). For reliable EV charging system, impulsive noise which is generated by random on-off pattern of various electric devices in EVs should be mitigated efficiently. Impulsive noise can deteriorate the frame synchronization performance and bit error probability. So, in this paper, we propose a simple frame synchronization scheme based on repeated orthogonal Kasami sequence (ROKS). From the simulation results, it is confirmed that the results of the paper can be applied to design of various applications for in-home, in-plane, in-ship, and in-vehicle scenarios.

In this paper, a microstrip bandstop filter (BSF) is designed and simulated for X-band wireless communication system. It consists of open stubs (parallel line with T-shaped) coupled with high impedance of stepped impedance resonator (SIR) at 7.5 GHz for X-band system applications. All the stubs are optimized to have the stopband characteristics. The layout pattern is simulated using Sonnet EM tool on Teflon substrate with a dielectric constant of 2.54 and thickness of 0.54 mm. The main features of this work are smaller size, low cost, and controllable bandwidth. The bandwidth can be controlled by adjusting the parallel line open stubs connected with open T-stub. The 3dB bandwidth is achieved as 0.3 GHz at 7.5 GHz with good frequency responses. The overall physical dimension of the designed BSF is 23.62 mm by 8.0 mm excluding reference lines.

The adoption of new technology has been under debate since the 1970s. Over the decades, many theories and models have proposed to address the consumer adoption issues; one of them is the Technology Acceptance Model (TAM). The focus of this research article is to highlight the significance of the TAM model in explaining system adoption and usage in Internet banking and Mobile banking. This research paper will provide insight by providing a comprehensive review of the literature in the context of the TAM model its extensions, development, and shortcomings. This research study contributes to the existing literature; and will be beneficial for future researchers interested in Internet banking and Mobile banking research comprehending the TAM model.

The acceptance and use of information technology (IT) and information system (IS) innovations have been a key apprehension for research and practice. Since the emergence of Unified Theory of Acceptance and Use of Technology (UTAUT), it has been used by a large number of studies for the adoption and acceptance of different technologies (e.g. m-commerce), and hence it seems to have turned into a prevalent theoretical choice in the field of information technology (IT)/information system (IS) adoption and diffusion. With the progress of this theory and finding the future research directions, this paper presents the critical review on the studies of the UTAUT and UTAUT2 model by focusing on the findings of basic constructs of these two models to predict Behavioral Intention (BI). Results from previous studies of UTAUT and UTAUT2 reveals that constructs of these two models contributed to Behavioral Intention to adopt and use m-commerce and its applications.