The Internet of Things (IoT) applications has been developing greatly in recent years to solve communication problems, especially in rural areas. Within the IoT, the context-awareness paradigm, especially in precision agricultural practices, has come to a state of the planning of production time. As smart cities approach, the smart environment approach also increases its place in IoT applications and has dominated research in recent years in literature. In this study, soil and environmental information were collected in 17 km diameter in rural area with developed Long Range (LoRa) based context-aware platform. With the developed sensor and actuator control unit, soil moisture at 5 cm and 30 cm depth and soil surface temperature information were collected and the communication performance was investigated. During the study, the performance measurements of the developed Serial Peripheral Interface (SPI) enabled Long Range Wide Area Network (LoRaWAN) gateway were also performed.
The Internet of Things (IoT) paradigm is referring to the underlying constituents of the 4th Industrial Revolution that will also affect the use of the internet in industrial production in the future. More than 50 billion smart devices will be able to communicate with each other and internet services over the increasing network capabilities of wireless sensor networks nodes on IoT applications in the next ten years. One of the leading production areas using IoT within wireless sensor networks is precision agricultural practices. In this study, a new sensor node design, which includes ambient light and temperature sensors employing Bluetooth Low Energy (BLE) communication protocol, is used as an IoT application. Subsequent to this, sensor node power consumption and management cost was investigated. The experimental results show that the developed sensor node lifetime is about 8 years and the total cost of nodes and gateway model is under $50 per year per 0.1 hectare.
Covid-19 infection, which spread to the whole world in December 2019 and is still active, caused more than 250 thousand deaths in the world today. Researches on this subject have been focused on analyzing the genetic structure of the virus, developing vaccines, the course of the disease, and its source. In this study, RNA sequences belonging to the SARS-CoV-2 virus are transformed into gene motifs with two basic image processing algorithms and classified with the convolutional neural network (CNN) models. The CNN models achieved an average of 98% Area Under Curve(AUC) value was achieved in RNA sequences classified as Asia, Europe, America, and Oceania. The resulting artificial neural network model was used for phylogenetic analysis of the variant of the virus isolated in Turkey. The classification results reached were compared with gene alignment values in the GISAID database, where SARS-CoV-2 virus records are kept all over the world. Our experimental results have revealed that now the detection of the geographic distribution of the virus with the CNN models might serve as an efficient method.
Design of Experiment (DOE) is a widely used method for examining experiments especially in industrial production and robust design processes. This method is a set of statistical approaches in which mathematical models are developed through experimental testing to estimate possible outputs and given input values or parameters. The method aims to determine the main factors that affect the results with the smallest number of experimental studies. In this study, L16 (2 15 ) orthogonal array, which was used in the Taguchi parameter design was reconstructed with the Support Vector Machines learning model and the Pearson VII kernel function. With this model, array elements were successfully classified in 87.04%. The new and original array were compared and 3.8% difference was measured between their Signal to Noise (S / N) ratios in an exemplary experiment.
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