Efficient power control framework for small-cell heterogeneous networks

Heterogeneous networks are rapidly emerging as one of the key enablers of beyond fifth-generation (5G) wireless networks. It is gradually becoming clear to the network operators that existing cellular networks may not be able to support the traffic demands of the future. Thus, there is an upsurge in the interest of efficiently deploying small-cell networks for accommodating a growing number of user equipment (UEs). This work further extends the state-of-the-art by proposing an optimization framework for reducing the power consumption of small-cell base stations (BSs). Specifically, a novel algorithm has been proposed which dynamically switches off the redundant small-cell BSs based on the traffic demands of the network. Due to the dynamicity of the formulated problem, a new UE admission control policy has been presented when the problem becomes infeasible to solve. To validate the effectiveness of the proposed solution, the simulation results are compared with conventional techniques. It is shown that the proposed power control solution outperforms the conventional approaches both in terms of accommodating more UEs and reducing power consumption.publishe

Interference Analysis for Vehicle-to-Vehicle Communications at 28 GHz

High capacity and ultra-reliable vehicular communication are going to be important aspects of beyond 5G communication networks. However, the vehicular communication problem becomes complex at a large scale when vehicles are roaming on the road, while simultaneously communicating with each other. Moreover, at higher frequencies (like 28 GHz), the dynamics of vehicular communication completely shift towards unpredictability and low-reliability. These factors may result in high packet error and a large amount of interference, resulting in regular disruptions in communications. A thorough understanding of performance variations is the key to moving towards the next generation of vehicular networks. With this intent, this article aims to provide a comprehensive interference analysis, wherein the closed-form expressions of packet error probability (PEP) and ergodic capacity are derived. Using the expression of the PEP, diversity analysis is provided which unveils the impact of channel nonlinearities on the performance of interference-constrained vehicular networks. The insights provided here are expected to pave the way for reliable and high capacity vehicular communication networks

Design of a CSK-CDMA Based Indoor Visible Light Communication Transceiver using Raspberry Pi and LabVIEW

Visible Light Communication (VLC) has drastically drawn the attention of both academia and industry as it can offer simultaneous lighting and data communication in an indoor environment. Additionally, VLC also tender a viable means to assuage the radio spectrum crunch. However, the data rate of the VLC system is choked because of the limited modulation bandwidth of Light Emitting Diode (LED), baseband modules, and intersymbol interference (ISI). In this article, an indoor VLC based software-defined radio (SDR) is designed and implemented that make use of Color Shift Keying (CSK) modulation, Code Division Multiple Access (CDMA) technique, and Raspberry Pi (RPi) to enabled the ISI free high data rate communication. The SDR is designed in LabVIEW software interfaced with the MATLAB and tested for text transmission. Numerous experiments were conducted on SDR at different alignments of transmitter and receiver. Our findings through experimentation showed that the SDR delivers an improved data rate of 2.645Mbps. Over and above, MATLAB based simulation packages are also conceived that validate the effectiveness of the proposed CSK-CDMA based VLC system. The bit-error-rate (BER) results of the proposed system are compared with the traditional CSK-OOK based VLC system. The results are quite impressive and show remarkable coding gain

A Data Mining Technique to Improve Configuration Prioritization Framework for Component-based Systems: An Empirical Study

In the current application development strategies, families of products are developed with personalized configurations to increase stakeholders’ satisfaction. Product lines have the ability to address several requirements due to their reusability and configuration properties. The structuring and prioritizing of configuration requirements facilitate the development processes, whereas it increases the conflicts and inadequacies. This results in increasing human effort, reducing user satisfaction, and failing to accommodate a continuous evolution in configuration requirements. To address these challenges, we propose a framework for managing the prioritization process considering heterogeneous stakeholders priority semantically. Features are analyzed, and mined configuration priority using the data mining method based on frequently accessed and changed configurations. Firstly, priority is identified based on heterogeneous stakeholder’s perspectives using three factors functional, experiential, and expressive values. Secondly, the mined configuration is based on frequently accessed or changed configuration frequency to identify the new priority for reducing failures or errors among configuration interaction. We evaluated the performance of the proposed framework with the help of an experimental study and by comparing it with analytical hierarchical prioritization (AHP) and Clustering. The results indicate a significant increase (more than 90 percent) in the precision and the recall value of the proposed framework, for all selected cases

Internet-of-Video Things Based Real-Time Traffic Flow Characterization

Real-world traffic flow parameters are fundamental for devising smart mobility solutions. Though numerous solutions (intrusive and non-intrusive sensors) have been proposed, however, these have serious limitations under heterogeneous and congested traffic conditions. To overcome these limitations, a low-cost real-time Internet-of-Video-Things solution has been proposed. The sensor node (fabricated using Raspberry Pi 3B, Pi cameral and power bank) has the capability to stream 2 Mbps MJPEG video of 640x480 resolution and 20 frames per second (fps). The Camlytics traffic analysis software installed on a Dell desktop is employed for traffic flow characterization. The proposed solution was field-tested with vehicle detection rate of 85.3%. The novelty of the proposed system is that in addition to vehicle count, it has the capability to measure speed, density, time headway, time-space diagram and trajectories. Obtained results can be employed for road network planning, designing and management

Analysing the impact of post-pandemic factors on entrepreneurial intentions: The enduring significance of self-efficacy in student planned behaviour.

After the pandemic, there has still been an increased interest in examining university students’ entrepreneurial goals. In this study, we looked at the practicality and validity of using self-efficacy to broaden the theory of planned behavior (TPB) in assessing students’ intent to be entrepreneurs. Additionally, we looked at how students’ geographic location and gender affected their plans to start their businesses. Following the epidemic, we analyzed data obtained from a number of university students in both urban and rural regions of India using PLS-SEM and ANN methods. Our study confirmed the pivotal role that university students’ self-efficacy had in their entrepreneurial goals. The results of multi-group analysis (MGA) reported the insignificant moderating role of gender for the students’ entrepreneurial intentions. Still, they found a statistically significant difference in their said behavior control for entrepreneurial intentions regarding location. Based on their perceived behavioral control, the findings also suggest that youths in rural areas had lower entrepreneurial inclinations than urban students. The study indicated that considering the importance of student self-efficacy, universities should focus on improving students’ skill sets and problem-solving mindsets while constructing education courses

Architecture for "Other". China's eclectic pragmatism in developing countries within the framework of the Belt and Road initiative

L'abstract è presente nell'allegato / the abstract is in the attachmen

A New Approach to Information Extraction in User-Centric E-Recruitment Systems

In modern society, people are heavily reliant on information available online through various channels, such as websites, social media, and web portals. Examples include searching for product prices, news, weather, and jobs. This paper focuses on an area of information extraction in e-recruitment, or job searching, which is increasingly used by a large population of users in across the world. Given the enormous volume of information related to job descriptions and users’ profiles, it is complicated to appropriately match a user’s profile with a job description, and vice versa. Existing information extraction techniques are unable to extract contextual entities. Thus, they fall short of extracting domain-specific information entities and consequently affect the matching of the user profile with the job description. The work presented in this paper aims to extract entities from job descriptions using a domain-specific dictionary. The extracted information entities are enriched with knowledge using Linked Open Data. Furthermore, job context information is expanded using a job description domain ontology based on the contextual and knowledge information. The proposed approach appropriately matches users’ profiles/queries and job descriptions. The proposed approach is tested using various experiments on data from real life jobs’ portals. The results show that the proposed approach enriches extracted data from job descriptions, and can help users to find more relevant jobs

A game player expertise level classification system using electroencephalography (EEG)

The success and wider adaptability of smart phones has given a new dimension to the gaming industry. Due to the wide spectrum of video games, the success of a particular game depends on how efficiently it is able to capture the end users' attention. This leads to the need to analyse the cognitive aspects of the end user, that is the game player, during game play. A direct window to see how an end user responds to a stimuli is to look at their brain activity. In this study, electroencephalography (EEG) is used to record human brain activity during game play. A commercially available EEG headset is used for this purpose giving fourteen channels of recorded EEG brain activity. The aim is to classify a player as expert or novice using the brain activity as the player indulges in the game play. Three different machine learning classifiers have been used to train and test the system. Among the classifiers, naive Bayes has outperformed others with an accuracy of 88%, when data from all fourteen EEG channels are used. Furthermore, the activity observed on electrodes is statistically analysed and mapped for brain visualizations. The analysis has shown that out of the available fourteen channels, only four channels in the frontal and occipital brain regions show significant activity. Features of these four channels are then used, and the performance parameters of the four-channel classification are compared to the results of the fourteen-channel classification. It has been observed that support vector machine and the naive Bayes give good classification accuracy and processing time, well suited for real-time applications