Volume 14, Nº 2 (2024)

Intelligent Transportation Systems (ITS) have attracted the attention of developing nations because of their potential to enhance mobility and road safety, two issues that have become increasingly pressing in recent years. Information and Communication Technology (ICT) has accentuated every industry, and the automobile industry is no exception, allowing vehicles to communicate among themselves and the surrounding infrastructure for information exchange using Vehicle-to-Everything (V2X) communication. Vehicle-to-vehicle (V2V), vehicle- to-infrastructure (V2I), vehicle-to-pedestrian (V2P), and vehicle-to-cloud (V2C) connections are made possible through the use of state-of-the-art networking technology. With EVehicles at the forefront, the vision of an ITS expands further. ITS is anticipated to offer wireless network-based services including vehicle occupant entertainment, traffic flow optimization, and accident prevention. Most nations have been trying to standardize the ITS architecture, and the European Union (EU) has been at the forefront by developing and standardizing the ETSI (European Telecommunications Standards Institute) architecture. As the most mature ITS architecture, this paper thoroughly explains the ETSI architecture in a single document for both the researcher's and newcomers' ease. Additionally, the challenges and issues pertinent to adopting and implementing the ITS ecosystem have also been discussed in detail.

Background:Facial electromyography (fEMG) records muscular activities from the facial muscles, which provides details regarding facial muscle stimulation patterns in experimentation.

Objectives:The Principal Component Analysis (PCA) is mostly implemented, whereas the actual or unprocessed initial fEMG data are rendered into low-spatial units with minimizing the level of data repetition.

Methods:Facial EMG signal was acquired by using the instrument BIOPAC MP150. Four electrodes were fixed on the face of each participant for capturing the four different emotions like happiness, anger, sad and fear. Two electrodes were placed on arm for grounding purposes.

Results:The aim of this research paper is to propagate the functioning of PCA in synchrony with the subjective fEMG analysis and to give a thorough apprehension of the advanced PCA in the areas of machine learning. It describes its arithmetical characteristics, while PCA is estimated by implying the covariance matrix. Datasets which are larger in size are progressively universal and their interpretation often becomes complex or tough. So, it is necessary to minimize the number of variables and elucidate linear compositions of the data to explicate it on a huge number of variables with a relevant approach. Therefore, Principal Component Analysis (PCA) is applied because it is an unsupervised training method that utilizes advanced statistical concept to minimize the dimensionality of huge datasets.

Conclusion:This work is furthermore inclined toward the analysis of fEMG signals acquired for four different facial expressions using Analysis of Variance (ANOVA) to provide clarity on the variation of features.

Background:Centralized Radio Access Network (C-RAN) is the most promising network architecture for next-generation communication networks. It meets the need for flexibility on fronthaul as well as large bandwidth on backhaul of the network. All along, scheduling is very important for the transmission of information in an organized manner. C-RAN has not been studied with the scheduling domain strategies yet in the literature.

Objective:In this work, packet transmission duration, overall transmission time, wait time, and fairness index parameters have been calculated and analysed for C-RAN architecture for two different scheduling domains. The total transmission cycle time parameter is calculated for the three upper functional split options of C-RAN. The overall transmission time is a parameter calculated for the entire uplink channel.

Methods:To implement the network scenario, extensive scripting is done on MATLAB Editor for single scheduling domain (SSD) and multi-scheduling domain (MSD) for three higher functional split options of C-RAN. The data traffic generated in the network is considered random.

Results:A closer examination of results reveals the advantages and disadvantages of both algorithms, as well as trade-offs between them.

Conclusion:For quicker data transmission, SSD should be preferred whereas MSD should be preferred if multiple users want to access resources simultaneously. Lower functional split options of C-RAN require less transmission cycle time. The MSD technique is fairer than SSD.