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Basnet, Barun,Bang, Junho Hindawi Limited 2018 Journal of sensors Vol.2018 No.-
<P>The application of sensors and information and communication technology (ICT) in agriculture has played a vital role in improving agricultural production and the value chain. Recently, the use of data analytics has shifted agriculture from input-intensive to knowledge-intensive as a large amount of agricultural data can be stored, shared, and analyzed to create information. In this paper, we have reviewed existing sensors and data analytics techniques used in different areas of agriculture. We have classified agriculture into five categories and reviewed the state-of-the-art technology in practice and ongoing research in each of these areas. Also, we have presented a case study of Korean scenario compared with other developed nations and addressed some of the issues associated with it. Finally, we have discussed current and future challenges and provided our views on how such issues can be addressed.</P>
Design of LP/BP filter with VDTA-Gm Floating Inductor
Barun Basnet,Junho Bang,Jeho Song 한국산학기술학회 2014 SmartCR Vol.4 No.5
This paper presents a voltage differencing transconductance amplifier (VDTA)-Gm floating inductor and its application to the design of a low pass (LP) filter and a band pass (BP) filter. This VDTA-Gm floating inductor uses only one VDTA and one Gm. Through small signal analysis, the total impedance of the VDTA-Gm floating inductor is calculated and matched with the impedance of a passive floating inductor. Then the active floating inductor is employed to build LP and BP filters of 1 MHz center frequency each. Finally, the characteristics of the filters are analyzed through Hspice simulation results.
An Smart Greenhouse Automation System Applying Moving Average Algorithm
Barun Basnet(바스넷버룬),Injae Lee(이인재),Myungjun Noh(노명준),Hyunjun Chun(천현준),Aman Jaffari(자파르아만),Junho Bang(방준호) 대한전기학회 2016 전기학회논문지 Vol.65 No.10
Automation of greenhouses has proved to be extremely helpful in maximizing crop yields and minimizing labor costs. The optimum conditions for cultivating plants are regularly maintained by the use of programmed sensors and actuators with constant monitoring of the system. In this paper, we have designed a prototype of a smart greenhouse using Arduino microcontroller, simple yet improved in feedbacks and algorithms. Only three important microclimatic parameters namely moisture level, temperature and light are taken into consideration for the design of the system. Signals acquired from the sensors are first isolated and filtered to reduce noise before it is processed by Arduino. With the help of LabVIEW program, Time domain analysis and Fast Fourier Transform (FFT) of the acquired signals are done to analyze the waveform. Especially, for smoothing the outlying data digitally, Moving average algorithm is designed. With the implement of this algorithm, variations in the sensed data which could occur from rapidly changing environment or imprecise sensors, could be largely smoothed and stable output could be created. Also, actuators are controlled with constant feedbacks to ensure desired conditions are always met. Lastly, data is constantly acquired by the use of Data Acquisition Hardware and can be viewed through PC or Smart devices for monitoring purposes.
A Gaussian Approach in Stabilizing Outputs of Electrical Control Systems
Barun Basnet(바스넷버룬),Jun-ho Bang(방준호),In-ho Ryu(유인호),Tae-hyeong Kim(김태형) 대한전기학회 2018 전기학회논문지 Vol.67 No.11
Sensor readings always have a certain degree of randomness and fuzziness due to its intrinsic property, other electronic devices in the circuitry, wires and the rapidly changing environment. In an electrical control system, such readings will bring instability in the system and other undesired events especially if the signal hovers around the threshold. This paper proposes a Gaussian-based statistical approach in stabilizing the output through sampling the sensor data and automatic tuning the threshold to the range of multiple standard deviations. It takes advantage of the Central limit theorem and its properties assuming that a large number of sensor data samples will eventually converge to a Gaussian distribution. Experimental results demonstrate the effectiveness of the proposed algorithm in completely stabilizing the outputs over known filtering algorithms like Exponential smoothing and Kalman Filter.