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Öğe Construction of 3D Soil Moisture Maps in Agricultural Fields by Using Wireless Sensor Communication(Gazi Üniversitesi, 2021) Koyuncu, Hakan; Gündüz, Burak; Koyuncu, BakiOver-irrigation without considering the soil property reduce the product yield and variety in many agricultural areas. In this study, it is aimed to produce a more useful, and user-friendly 3D soil moisture detection system by using wireless communication across the agricultural areas. The deficiencies of agricultural land can be eliminated in terms of irrigation, product variety, and product yield. 3D moisture information obtained from the soil can be transferred to a database system and the farmers can use this system to cultivate across the correct fields. A capacitive soil moisture sensor is deployed as a sensor unit. Each sensor unit with its electronics is placed in a PVC pipe with a specific length. This PVC pipe is placed vertically in the soil with sensor electrodes contacting the soil. Moisture measurements are carried out across the agricultural area. The system provides 3D moisture maps of the soil at fixed depths. Each 3D map represents a subsurface moisture layer. The sensor units are calibrated by measuring the moisture in the water, corresponding to %100 moisture in the soil, and the moisture in dry air, corresponding to %0 moisture in the soil. A percentage moisture determination formula is developed between these two extreme levels for each sensor unit. Hence the benefit of the results will be the knowledge of % moisture values in-depth profile of the agricultural areas. Farmers will have comprehensive and real-time information about moisture data and this data will help them to grow better crops.Öğe Determination of Positioning Accuracies by Using Fingerprint Localisation and Artificial Neural Networks(VINCA INST NUCLEAR SCI, MIHAJLA PETROVICA-ALASA 12-14 VINCA, 11037 BELGRADE. POB 522, BELGRADE, 11001, SERBIA, 2019) Koyuncu, HakanFingerprint localisation technique is an effective positioning technique to determine the object locations by using radio signal strength, values in indoors. The technique is subject to big positioning errors due to challenging environmental conditions. In this paper, initially, a fingerprint localisation technique is deployed by using classical k-nearest neighborhood method to determine the unknown object locations. Additionally, several artificial neural networks, are employed, using fingerprint data, such as single-layer feed forward neural network multi-layer feed forward neural network, multi-layer back propagation neural network general regression neural network, and deep neural network to determine the same unknown object locations. Fingerprint database is built by received signal strength indicator measurement signatures across the grid locations. The construction and the adapted approach of different neural networks using the fingerprint data are described. The results of them are compared with the classical k-nearest neighborhood method and it was found that deep neural network was the best neural network technique providing the maximum positioning accuracies.Öğe Handwritten Character Recognition by using Convolutional Deep Neural Network; Review(İstanbul Gelişim Üniversitesi Yayınları / Istanbul Gelisim University Press, 2019-03-29) Koyuncu, Baki; Koyuncu, HakanAbstract - Handwritten character recognition is an important domain of research with implementation in varied fields. Past and recent works in this field focus on diverse languages to utilize the character recognition in automated data-entry applications. Studies in Deep Neural Network recognize the individual characters in the form of images. The reliance of each recognition, which is provided by the neural network as part of the ranking result, is one of the things used to customize the implementation to the request of the client. Convolutional deep neural network model is reviewed to recognize the handwritten characters in this study. This model, initially, learned a useful set of admittance by using local receptive areas and densely connected network layers are employed for the discernment task. Keywords Handwritten Character Recognition, Deep Neural Network (DNN), Deep Convolutional Neural Network (DCNN).Öğe A New Energy Efficient Multitier Deterministic Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks(MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2020) Koyuncu, Hakan; Tomar, Geetam S.; Sharma, DineshWireless Sensor Networks (WSNs) may be incorporated with thousands of small nodes. This gives them the capability to effectively sense, communicate, and compute parameters. However, the security and life span of a WSN node is a primary concern. This paper is focused on introducing a mathematical model of a modified Multitier Deterministic Energy-Efficient Clustering (DEC) based on novel election multi-tier random probability protocol for agricultural WSNs to enhance the life span of a WSN node along with a comparison of it with existing DEC protocol. In the proposed model, the selection of cluster heads, (CH), is done based on the energy drain pattern and location of the sensor nodes, which increased the lifespan of sensor nodes. In addition, several WSN probabilistic routing protocols to save energy throughout data transmissions like Low Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient Gathering in Sensor Information Systems (PEGASIS), DEC, and Stable Election Protocol (SEP) are explained. Moreover, it has been found that, after some mathematical modification in existing DEC routing, it will be capable to give a more positive result and reduce the energy drain in WSN nodes using a selective cluster head technique based on residual energy of sensor nodes. The DEC protocol is also compared with our proposed modified protocol for showing the energy-efficiency. The energy efficiency of clustering is associated with the field of energy sustainability of wireless sensor networks which is in the scope of Symmetry journal.
