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    Construction of 3D Soil Moisture Maps in Agricultural Fields by Using Wireless Sensor Communication
    (Gazi Üniversitesi, 2021) Koyuncu, Hakan; Gündüz, Burak; Koyuncu, Baki
    Over-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.
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    Handwritten Character Recognition by using Convolutional Deep Neural Network; Review
    (İstanbul Gelişim Üniversitesi Yayınları / Istanbul Gelisim University Press, 2019-03-29) Koyuncu, Baki; Koyuncu, Hakan
    Abstract - 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).
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    MIMO-OFDM/OCDM low-complexity equalization under a doubly dispersive channel in wireless sensor networks
    (SAGE PUBLICATIONS INC, 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA, 2020) Solyman, Ahmad Amin Ahmad; Attar, Hani; Khosravi, Mohammad R.; Koyuncu, Baki
    In this article, three novel systems for wireless sensor networks based on Alamouti decoding were investigated and then compared, which are Alamouti space-time block coding multiple-input single-output/multiple-input multiple-output multicarrier modulation (MCM) system, extended orthogonal space-time block coding multiple-input single-output MCM system, and multiple-input multiple-output system. Moreover, the proposed work is applied over multiple-input multiple-output systems rather than the conventional single-antenna orthogonal chirp division multiplexing systems, based on the discrete fractional cosine transform orthogonal chirp division multiplexing system to mitigate the effect of frequency-selective and time-varying channels, using low-complexity equalizers, specifically by ignoring the intercarrier interference coming from faraway subcarriers and using the LSMR iteration algorithm to decrease the equalization complexity, mainly with long orthogonal chirp division multiplexing symbols, such as the TV symbols. The block diagrams for the proposed systems are provided to simplify the theoretical analysis by making it easier to follow. Simulation results confirm that the proposed multiple-input multiple-output and multiple-input single-output orthogonal chirp division multiplexing systems outperform the conventional multiple-input multiple-output and multiple-input single-output orthogonal frequency division multiplexing systems. Finally, the results show that orthogonal chirp division multiplexing exhibited a better channel energy behavior than classical orthogonal frequency division multiplexing, thus improving the system performance and allowing the system to decrease the equalization complexity.
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    NXTGeUH: Lorawan based next generation ubiquitous healthcare system for vital signs monitoring falls detection
    (Institute of Electrical and Electronics Engineers Inc., 2018) Patel, Warish D.; Pandya, Sharnil; Koyuncu, Baki; Ramani, Bhupendra; Bhaskar, Sourabh; Ghayvat, Hemant
    The challenge for deployment of low-cost and high-speed ubiquitous Smart Health services has prompted us to propose new framework design for providing excellent healthcare to humankind. So, there exists a very high demand for developing an Internet of Medical Things (IoMT) based Ubiquitous Real-Time LoRa (Long Range) Healthcare System using Convolutional Neural Networks (CNN) to agree if a sequence of frames contains a person falling. To model the video motion and make the system scenario sovereign, in this research, we use optical flow images as input to the networks. Right now hospital and home falls are a noteworthy medical services concern overall on account of the aging populace. Current observational information, vital signs and falls history give the necessary data identified with the patient's physiology, and movement information give an additional utensil in falls risk evaluation. The proposed framework utilizes Real-Time Vital signs monitoring and emergency alert message to caregivers or doctors. In this context, we introduce "LoRaWAN based Next Generation Ubiquitous Healthcare System (NXTGeUH), an intelligent middleware platform. In addition, this proposed method is evaluated with different public hospital datasets achieving the state-of-The-Art outcomes in all aspects. © 2018 IEEE.