Future OFDM-based Communication Systems Towards 6G and Beyond: Machine Learning Approaches

The vision towards 6G and beyond communication systems demands higher rate transmission, massive amount of data processing, and low latency communication. Orthogonal Frequency Division Modulation (OFDM) has been adopted in the current 5G networks and has become one of the potential candidates for the future communication systems. Although OFDM offers many benefits including high spectrum efficiency and high robustness against the multipath fading channels, it has major challenges such as frequency offset and high Peak to Power Ratio (PAPR). In 5G communication network, there is a significant increase in the number of sensors and other low-power devices where users or devices may create large amount of connection and dynamic data processing. In order to deal with the increasingly complex communication network, Machine Learning (ML) has been increasingly utilised to create intelligent and more efficient communication network. This paper discusses challenges and the impacts of embedding ML in OFDM-based communication systems.

Automatic Temperature Control System on Smart Poultry Farm Using PID Method

Chicken farmers in Indonesia are facing a problem as a result of the country's harsh weather conditions. Poultry species are very susceptible to temperature and humidity fluctuations. As a result, an intelligent poultry farm is necessary to intelligently adjust the temperature in the chicken coop. A smart poultry farm is a concept in which farmers may automatically manage the temperature in the chicken coop, thereby improving the livestock's quality of life. The purpose of this research is to develop a chicken coop prototype that focuses on temperature control systems on smart poultry farms via the PID control approach. The PID control method is expected to allow the temperature control system to adapt to the temperature within the cage, thereby assisting chicken farmers in their tasks. The sensor utilized is a DHT22 sensor with a calibration accuracy of 96.88 percent. The PID response was found to be satisfactory for the system with Kp = 10, Ki = 0, and KD = 0.1, and the time necessary for the system to reach the specified temperature was 121 seconds with a 1.03 % inaccuracy.

Reinventing The Future Online Education Using Emerging Technologies

The pandemic of Coronavirus Disease 2019 (COVID-19) has forced the teaching and learning activities to be conducted remotely. Before the pandemic, many academic institutions had offered online distance learning for selected courses. However, in practice, most of these programs were delivered as blended learning program instead of a full-fledged distance learning program. Distance learning programs faced challenges and limitations in terms of communication, integrity, and interactions compared to the traditional face-to-face teaching and learning method. Despite the challenges and limitations in distance teaching and learnings, academic staff are expected to accomplish the same (or better) outcomes than the traditional face-to-face teaching and learning. Hence, distance learning method was not popular to many academic staff and students before the pandemic time. In order to improve the quality of the full distance learning delivery, emerging technologies and more interactive platforms are being developed rapidly. This article discusses the emerging technologies and strategies to make full distance learning or remote education competitive compared to the traditional teaching and learning method. The future potential teaching and learning technology, i.e., digital twins, is also briefly presented.

Chest X-Ray Classification of Lung Diseases Using Deep Learning

Chest X-ray images can be used to detect lung diseases such as COVID-19, viral pneumonia, and tuberculosis (TB). These diseases have similar patterns and diagnoses, making it difficult for clinicians and radiologists to differentiate between them. This paper uses convolutional neural networks (CNNs) to diagnose lung disease using chest X-ray images obtained from online sources. The classification task is separated into three and four classes, with COVID-19, normal, TB, and viral pneumonia, while the three-class problem excludes the normal lung. During testing, AlexNet and ResNet-18 gave promising results, scoring more than 95% accuracy.

Analysis of Effectiveness in the Utilization and Control of Electronic Waste (E-Waste) in Indonesia

In Indonesia, E-waste continues to grow rapidly, along with the increasing use of electronic goods such as telecommunications devices, households, offices, etc. Although it can be recycled, only a small portion can be done, and the recycling process is still under minimal control. Most E-waste is categorized as hazardous and toxic material waste. E-waste has a very high hazard impact if it is not recycled properly and correctly, such as polluting, damaging, and endangering the environment. This article uses forecasting of e-waste growth and canalization e-waste in Indonesia. The first data was obtained from EWasteRJ, a social community engaged in e-waste collection. The second data is obtained from questionnaires distributed to 110 respondents, focusing on knowledge and ways of handling E-waste. Using statistical analysis on both data shows that the amount of E-waste in Indonesia continues to increase every year, and public awareness of the dangers of E-waste is increasing.