Herein, a new space-time block coding technique is proposed for a MIMO 2 × 2 multiple-input multiple output (MIMO) system to minimize the bit error rate (BER) in Rayleigh fading channels with reduced decoding complexity using ZF and MMSE linear detection techniques. The main objective is to improve the service quality of wireless communication systems and optimize the number of antennas used in base stations and terminals. The idea is to exploit the correlation product technique between both information symbols to transmit per space-time block code and their own orthogonal Walsh-Hadamard sequences to ensure orthogonality between both symbol vectors and create a full-rate orthogonal STBC code. Using 16 quadrature amplitude modulation and the quasi-static Rayleigh channel model in the MATLAB environment, the simulation results show that the proposed space-time block code performs better than the Alamouti code in terms of BER performance in the 2 × 2 MIMO system for both cases of linear decoding ZF and MMSE.

The new 5G radio technology (NR) can provide ultra-reliable low latency communications. The supporting 5G network infrastructure will move away from the previous point-to-point network architecture to a service-based architecture. 5G can provide three new things, i.e., wider channels, lower latency and more bandwidth. These will allow 5G to support three main types of connected services, including enhanced mobile broadband, mission-critical communications, and the massive Internet of Things (IoT). In 2015, the 5th generation (5G) mobile communication was officially approved by the International Telecommunication Union (ITU) as IMT-2020. Since then, 3GPP, the international organization responsible for 5G standards, is actively developing specifications for 5G technologies. 3GPP Release 15 provides the first full set of 5G standards, and the evolution and expansion of 5G are now being standardized in Release 16 and 17, respectively. This paper provides an overview of 3GPP 5G technologies and key services.

In this research, we create a network infrastructure based on a service-oriented infrastructure (SOI) for the virtualization technology and integrate it with a cloud technology that applies the cloud integration management platform (CIMP) concept. In CIMP, the server and storage will be separated. The server will be adopted for virtualization while the storage will be used by students and teachers to store data. As long they save their data in the storage module, every time, everywhere, and on every device, they can access their data. This research will implement the design of the network infrastructure and be applied to the remote practical learning system in the laboratory. Students and teachers will ultimately adopt this network infrastructure for remote practice using their respective devices without physically meeting in the laboratory. In the future, if the implementation phase is successful, then in addition to laboratory environments, it can be implemented in all learning activities at our campus.

The reinforcement learning algorithm has proven its potential in solving sequential decision-making problems under uncertainties, such as finding paths to route data packets in wireless sensor networks. With reinforcement learning, the computation of the optimum path requires careful definition of the so-called reward function, which is defined as a linear function that aggregates multiple objective functions into a single objective to compute a numerical value (reward) to be maximized. In a typical defined linear reward function, the multiple objectives to be optimized are integrated in the form of a weighted sum with fixed weighting factors for all learning agents. This study proposes a reinforcement learning -based routing protocol for wireless sensor network, where different learning agents prioritize different objective goals by assigning weighting factors to the aggregated objectives of the reward function. We assign appropriate weighting factors to the objectives in the reward function of a sensor node according to its hop-count distance to the sink node. We expect this approach to enhance the effectiveness of multi-objective reinforcement learning for wireless sensor networks with a balanced trade-off among competing parameters. Furthermore, we propose SDN (Software Defined Networks) architecture with multiple controllers for constant network monitoring to allow learning agents to adapt according to the dynamics of the network conditions. Simulation results show that our proposed scheme enhances the performance of wireless sensor network under varied conditions, such as the node density and traffic intensity, with a good trade-off among competing performance metrics.

Coronary heart disease (CHD) is a comorbidity of COVID-19; therefore, routine early diagnosis is crucial. A large number of examination attributes in the context of diagnosing CHD is a distinct obstacle during the pandemic when the number of health service users is significant. The development of a precise machine learning model for diagnosis with a minimum number of examination attributes can allow examinations and healthcare actions to be undertaken quickly. This study proposes a CHD diagnosis model based on feature selection, data balancing, and ensemble-based classification methods. In the feature selection stage, a hybrid SVM-GA combined with fast correlation-based filter (FCBF) is used. The proposed system achieved an accuracy of 94.60% and area under the curve (AUC) of 97.5% when tested on the z-Alizadeh Sani dataset and used only 8 of 54 inspection attributes. In terms of performance, the proposed model can be placed in the very good category.

With the development of technology, the procedures for making systems are altered. Jobs that were initially difficult with technology have become easier. Likewise with work as a developer. In this era, it takes work that can be done quickly, precisely, and accurately. Inspired by the Copernicus' theory that the sun is the center of the solar system, this research aims to create a plugin that is integrated with the Unity game engine so that it can create dungeons quickly and dynamically using the orbital dungeon generation method. In general, games that have several dungeons are role playing games genre with the roguelike subgenre, where games of this type are widely adopted by well-known games. This method is expected to be employed by developers, especially game developers, to simplify and speed up the creation of games using dungeons, because the complexity of the system that is made can be adjusted according to needs.

Fertilizers have long been used to increase crop yields; however, farmers are still having difficulties in managing fertilizers for growing crops as well as economic problems. The conventional method of soil sampling and laboratory analysis to determine soil pH is time consuming and costly; therefore, a portable pH sensor is developed to characterize spatial or temporal variability within fields via rapid and dense data acquisition. The portable pH sensor comprises an electrode unit, a portable console, and a USB connector. The soil water content (SWC) and electrical conductivity (EC) affect the electrical resistance of soil. An artificial test soil is performed to evaluate the effect of SWC and EC on soil pH. The test results show that stable pH measurements are achieved at SWCs greater than 20 mL (16.3%). Regardless of the SWC, the electric potential difference (EPD) remains at 2.5 g of NaCl. As the EC increases in the soil samples, the EPD increases.

Breast ultrasonic reading is critical as a primary screening test for the early diagnosis of breast cancer. However, breast ultrasound examinations show significant differences in diagnosis based on the difference in image quality according to the ultrasonic equipment, experience, and proficiency of the examiner. Accordingly, studies are being actively conducted to analyze the texture characteristics of normal breast tissue, positive tumors, and malignant tumors using breast ultrasonography and to use them for computer-assisted diagnosis. In this study, breast ultrasonography was conducted to select 247 ultrasound images of 71 normal breast tissues, 87 fibroadenomas among benign tumors, and 89 malignant tumors. The selected images were calculated using a statistical method with 21 feature parameters extracted using the gray level co-occurrence matrix algorithm, and classified as normal breast tissue, benign tumor, and malignancy. In addition, we proposed five feature parameters that are available for computer-aided diagnosis of breast cancer classification. The average classification rate for normal breast tissue, benign tumors, and malignant tumors, using this feature parameter, was 82.8%.

With the mark of the fourth industrial revolution, the smart factory is evolving into a new future manufacturing plant. As a human-machine-interactive tool, augmented reality (AR) helps workers acquire the proficiency needed in smart factories. The valuable data displayed on the AR device must be delivered intuitively to users. Current AR applications used in smart factories lack user movement calibration, and visual fiducial markers for position correction are detected only nearby. This paper demonstrates a marker-based object detection using perspective projection to adjust augmented content while maintaining the user's original perspective with displacement. A new angle, location, and scaling values for the AR content can be calculated by comparing equivalent marker positions in two images. Two experiments were conducted to verify the implementation of the algorithm and its practicality in the smart factory. The markers were well-detected in both experiments, and the applicability in smart factories was verified by presenting appropriate displacement values for AR contents according to various movements.