Nov 21
The IUP Journal of Telecommunications
Focus
This issue contains four papers covering 5G data storage strategies, interference mitigation techniques in wireless system, IoT-enabled wireless systems and usage of machine learning in video image diagnostic and decision-making application.
Wireless network creates a data storage architecture for structured and unstructured data as a central repository to work as a truly distributed system for easy access to emerging 5G networks. The Unstructured Data Storage Function (UDSF) is used as data storage architecture and offers services for data storage, manipulation and retrieval to every Network Function (NF) within the 5G Service-Based Architecture (SBA). The first paper, "Unstructured Data Storage Function for 5G Core Network", by K S Keerthi and Bhagya R, describes the implementation of unstructured data storage function and its role in network functionality. The UDSF achieves stateless access and uses a mobility function, which facilitates higher reliability and provides superior load distribution in the network.
The second paper, "Design and Simulation of Interference Mitigation Algorithm for an L-Band Digital Aeronautical Communications System", by Pushpa Lakshmi Jayaramaiah and K Saraswathi, proposes design of an L-Band Digital Aeronautical Communications System (LDACS) using 2 ? 2 Multiple Input Multiple Output (MIMO) system and reports a simulation analysis involving pulse blanking technique to mitigate the interference in the designed system. The simulation results under the proposed operating condition reduce the interference and confirm an improvement in the Bit Error Rate (BER) performance.
In the present network system, the IoT is emerging rapidly and becoming a part of worldwide machine networked systems suitable for information transmission and control purposes. The third paper, "IoT-Based Smart Alarm Clock", by P Deepali and K Ramudu, proposes a design of an IoT-based smart alarm clock to be operated and controlled from remote. The alarm clock is built using Node MicroController Unit (NodeMCU), where alarm time can be set using a webpage. The alarm is intelligent, collects various useful information from the IoT device, sets into the alarm and updates the information at the observer end.
The last paper, "Recognizing Suspicious Activities in Examination Scenario Using Machine Learning Techniques", by Agwi Uche Celestine, Ogwueleka Francisca N and Irhebhude Martins Ekata, discusses a video diagnostic-based system which identifies any suspicious activity during video observation. The proposed model demonstrated excellent performance, indicating that the system can adequately complement the efforts of examining and supervising the video remotely.
Consulting Editor
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| Article | Price (₹) | ||
| Unstructured Data Storage Function for 5G Core Network |
100
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| Design and Simulation of Interference Mitigation Algorithm for an L-Band Digital Aeronautical Communications System |
100
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| IoT-Based Smart Alarm Clock |
100
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| Recognizing Suspicious Activities in Examination Scenario Using Machine Learning Techniques |
100
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Unstructured Data Storage Function for 5G Core Network
The 3rd Generation Partnership Project (3GPP) created a data storage architecture to create a truly distributed system in 5G, where structured data and unstructured data can be stored at central repositories. The Unstructured Data Storage Function (UDSF) will be used as data storage architecture and offers services for data storage, manipulation and retrieval to every Network Function (NF) within the 5G Service-Based Architecture (SBA) (Ahmed et al., 2018). In this paper, the implementation of UDSF has been explained. The UDSF can be used by any NFs to store local data within the centralized repository. The UDSF will be used for achieving a fully stateless AMF (Access and Mobility Function) that achieves higher reliability and load distribution in the network.
Design and Simulation of Interference Mitigation Algorithm for an L-Band Digital Aeronautical Communications System
The L-Band Digital Aeronautical Communications System (LDACS) 1 is an alternative to the current Very High Frequency (VHF) technology which meets the needs of upcoming demands. LDACS1, an Orthogonal Frequency Division Multiplexing (OFDM), operates in the L-Band between 960 and 1167 MHz frequencies installed in the middle of two Distance Measuring Equipment (DME) channels with 500 kHz spectral gap. In this paper?LDACS1 OFDM and LDACS1 2 ? 2 Multiple Input Multiple Output (MIMO) are designed and simulated without any interference. The DME interference is introduced only to the LDACS1 OFDM, and significant degradation in the performance is observed. The pulse blanking technique helps in lowering the interference in the LDACS1 OFDM system, resulting in a performance close to the performance in the interference-free case. The Bit Error Rate (BER) performances for the conditions with and without interference and pulse blanking are compared. An improvement in the BER performance is seen with respect to design of LDACS1 2 ? 2 MIMO system.
IoT-Based Smart Alarm Clock
Internet of Things (IoT) is an emerging and evergreen technology that has created worldwide networked machines and also devices that can help in exchanging communication. As the real-time applications have been increasing day-by-day, the need for smart connections has also increased. Challenging smart connectivity, IoT-based smart alarm clocks have been designed in this paper. For decades, alarm clocks have been in use, but as the technology progressed, mobile phones came into existence, and people find it easier to set alarms on mobiles. Among electronics hobbyists, alarm projects have always been in great demand. IoT alarm clock is built using Node MicroController Unit (NodeMCU), where alarm time can be set using a webpage without Internet connection.
Recognizing Suspicious Activities in Examination Scenario Using Machine Learning Techniques
Monitoring the activities of examinees during examination is very challenging. The paper recognizes and classifies activities of examinees as suspicious or normal during examination using machine learning techniques. The processing and analysis of image data follows a typical sequence of distinct steps referred to as the vision pipeline. Data was acquired with a surveillance camera and frames extracted from the videos. Preprocessing activities include selecting the required frames from frame sequences, and cropping and segmenting foreground/background object. Video conversion to frame was accomplished with MATLAB scripts, while segmentation of image frames was achieved with GrabCut algorithm. Shape/pose features were extracted from objects using Histogram of Oriented Gradient (HOG) and Regionprop algorithms, and represented in feature vectors that were fed into Support Vector Machine (SVM) classifier. Holdout validation technique was used for the classifier training and tested from the given datasets. 70% of the dataset was used for training, while 30% was used for testing. The model gave an accuracy of 98.1% and 100%, respectively, for each examination scenario. The model accuracy was visualized in confusion matrix and the Receiver Operating Characteristic (ROC). MATLAB software was used as the simulation environment. The model demonstrated excellent performance, indicating that the system can adequately complement the efforts of invigilators in examination invigilation.