Communication is an essential part of human life. We humans use speech (words) to convey information to each other. Vocal tract characteristics in a human voice help identify a speaker. However, human speech signals are language independent and information is speaker dependent. With advanced technology, humans can use her/his voice as biometric authentication, which is unique for individuals. One can use their voice in different applications such as, adding an extra lawyer for security in online banking etc. The main aim of the paper is to identify the uniqueness of a voice, which should be independent of a speakers' language.
The objective of this paper is to introduce a novel methodology of playing a strategic video game by applying several statistical modeling techniques to meet the required troop quality metric. The focus is on how to build a powerful troop which can both attack the remote, stronger castles and could also defend the main castles when attacking troops were far away. K-Means Clustering computing was utilized to cluster the nearby castles to form a joint alliance which was able to communicate and support each other against any mighty attacking from nearby Kingdoms. Principal Component Analysis (PCA) was utilized to study 7 troop characteristics among 40+ troop types available. The PCA Eigenvalue and Eigenvector analysis could effectively provide the insights of troop improvement strategy based on the PCA patterns and each principal component. Established the Transfer Function Sensitivity Analysis through Custom Profile to conduct the troop design optimization. Based on provided Kingdom's expansion strategy, the optimal troop design was identified. The design optimization algorithm and the optimal design were fully aligned or predicted by PCA eigen analysis. This PCA-based algorithm could maximize the Return of Investment (RDI) on playing strategic video game. The same PCA modeling technique could be extended to most modern Business Management World.
The costs of research and development (R&D) activities represent a large part of the product costs. These activities require new methodologies to keep up with the development of the business environment and the factories of the future (FoF). One of the most significant tools used in this domain is a Virtual Reality (VR) in a manufacturing environment which called Virtual Manufacturing (VM). This paper aims to examine the impact of (VM) on mitigating risks and costs management of R&D activities which support the FoF. As well as, the study performed a technique for order preference by similarity to ideal solution (TOPSIS) to determine the importance of improving R&D activities in FoF. There are many criteria used to assess the impacts of (VM) on costs management of R&D activities and decrease the risks in FoF. The experimental results of this work showed that the full application of (VM) technique in R&D activities leads to almost 99 % performance improvement whereas the partial application of VM leads to improvement by 68.7 %. Thus, this paper is considered one of the best contributions which provide an evaluation of impact of VM on R&D activities to meet changing customer requirements. In addition, it can serve as a valuable reference in academic research which is interested in sustainable business.
In present time there are numerous institutions around the globe, in order to attract the youth every institute claims to be best. But while choosing a right institution it proves to be a big hardship due to lack of relevant and trustable information. In this paper, we aim to set up a multilevel fusible transparent recommendation and ranking system module based on analysis of integrated datasets collected from students and staff members. The purpose is to ensure the fulfilment of academic and co-curricular parameters for the overall development as well as to assist students and teachers to predict a best fit institution with the help of data mining techniques. On the other hand, we believe to promote the fairness among the institutional network and promotes healthy competition.
The continuous rapid growth of Internet of Things (IoT) devices has presented a new model in fourth generation and beyond cellular networks. This continuous growth and the increasing demand in the provision of high transmission rate, delay sensitive and spectrum efficient cellular networks have made the development of fifth generation (5G) networks a reality. The design of the end-to-end 5G networks anticipated the need for terrestrial radio access networks to be complemented by its satellite counterpart in order to ensure that 5G services are provided seamlessly. These 5G services include IoT communications. This has necessitated the need for 5G satellite radio access networks to provide access to IoT devices that are located in remote and rural areas. This type of IoT devices are termed Internet of Remote Things (IoRT) devices. While this remain an appealing solution, many radio resource management issues including packet scheduling for IoRT communications in 5G satellite networks remains undefined. Hence, this paper aims to propose a new dynamic packet scheduling algorithm that will be appropriate for mixed traffic type IoRT communications in 5G satellite networks. The performance evaluation of the proposed packet scheduler is conducted through simulations, using delay, spectral efficiency, throughput and fairness index as the performance indices.
