The 3rd Generation Partnership Project (3GPP) specification of the fifth generation (5G) New Radio (NR) allows for a highly scalable and flexible radio access technology to cater to network operators with different requirements. Such scalability and flexibilities in network configurations inevitably translate to complications in the design and implementation of 5G-NR systems. Radio access in 5G-NR is much more complex and involved than its predecessor, 4G long term evolution (LTE) and LTE-Advanced technology. Therefore, the 5G-NR specifications turn out to be quite dense. Specifically, the specifications are concise, design motivations rarely explained, and the information can be convoluted or distributed across several documents. Moreover, there are several key design details associated with the access layer procedures for any given physical layer channel, which are often omitted in the specifications. For example, design motivation aspects of initial access channels or signal generation can be quite difficult to follow or understand in 5G-NR. In this paper, all the design details associated with initial access channels and signal generation in 5G-NR specifications are laid out. The contributions of the paper are three folds. First, the design details and justifications associated with both downlink and uplink access channels are presented along with signal generation details. Secondly, receiver design aspects of NR PRACH short formats are discussed. Lastly, PRACH receiver implementation aspects and performance reports from different network operators are presented and compared with 3GPP specified Radio Performance and Protocol aspect requirements1 for millimeter wave (mmW) access. The work in this paper is of significant value to researchers and system engineers looking to design and build initial access algorithms as part of 5G NR systems.

The global standards in the field of industrial automation are maintained in industries by completely digitizing their manufacturing process with industry 4.0 standard. Internet of Things (IoT) enables the conservation of cultural heritage with proper assistance on data management on the data collected from the sensors. However, energy efficient conservation is required to monitor the IoT sensors in order to deal with building a better infrastructure. In this paper, we develop a bio-inspired algorithm which can automate the entire furnace monitoring and controlling system in order to eliminate the human intervention involved in the physical process. The algorithm is blended as a web-based remote application for the better control of the tasks involved, energy utilized, and its subsequent log-report maintenance. The entire system employs Wi-Fi communication for data transfer from device to cloud where the stored data including temperature log, forth coming schedule, and process graphic are maintained by the proposed algorithm to predict the machine failure at an earlier stage. The real-time prototype system is supported by a heat treatment process that is completely automated using IoT to monitor and maintain the temperature during the production of metal casting process.

With increasing advancements in the field of telecommunication, the attainment of a higher data transfer rate is essentially a greater need to meet high-performance communication. The exploitation of the fuzzy system in the wireless telecommunication systems, especially in Fifth Generation Mobile Networks (or) 5G networks is a vital paradigm in telecommunication markets. A comprehensive survey is dealt in the paper, where it initially reviews the basic understanding of fuzzy systems over 5G telecommunication. The literature studies are collected from various repositories that include reference materials, Internet, and other books. The collection of articles is based on empirical or evidence-based from various peer-reviewed journals, conference proceedings, dissertations, and theses. Most of the existing soft computing models are streamlined to certain applications of 5G networking. Firstly, it is hence essential to provide the readers to find research gaps and new innovative models on wide varied applications of 5G. Secondly, it deals with the scenarios in which the fuzzy systems are developed under the 5G platform. Thirdly, it discusses the applicability of fuzzy logic systems on various 5G telecommunication applications. Finally, the paper derives the conclusions associated with various studies on the fuzzy systems that have been utilized for the improvement of 5G telecommunication systems.

Electrocardiograms (ECG) are extensively used for the diagnosis of cardiac arrhythmias. This paper investigates the use of machine learning classification algorithms for ECG analysis and arrhythmia detection. This is a crucial component of a conventional electronic health system, and it frequently necessitates ECG signal reduction for long-term data storage and remote transmission. Signal processing methods must be used to extract the function of the morphological properties of the ECG signal changing with time, which is difficult to discern in the typical visual depiction of the ECG signal. In biomedical research, signal processing and data analysis are commonly employed methodologies. This work proposes the use of an ECG arrhythmia classification method based on Fast Fourier Transform (FFT) for feature extraction and an improved AlexNet classifier to distinguish the difference between four types of arrhythmia conditions that were collected from records. The Convolutional Neural Network (CNN) algorithm’s results are compared to those of other algorithms, and the simulation results prove that the proposed technique is more effective for various parameters. The final results of the proposed system show that its ability to find deviations is 20% better than that of traditional systems.