• Progress in Superconductivity
• /
• v.8 no.2
• /
• pp.181-185
• /
• 2007

For more than two decades Nb trilayer ($Nb/Al_2O_3/Nb$) process has been serving as the most stable fabrication process of the Josephson junction integrated circuits. Fast development of semiconductor fabrication technology has been possible with the recent advancement of the fabrication equipments. In this work, we took an advantage of advanced fabrication equipments in developing a superconducting Arithmetic Logic Unit (ALU) by using Nb trilayers. The ALU is a core element of a computer processor that performs arithmetic and logic operations on the operands in computer instruction words. We used DC magnetron sputtering technique for metal depositions and RF sputtering technique for $SiO_2$ depositions. Various dry etching techniques were used to define the Josephson junction areas and film pattering processes. Our Nb films were stress free and showed the $T{_c}'s$ of about 9 K. To enhance the step coverage of Nb films we used reverse bias powered DC magnetron sputtering technique. The fabricated 1-bit, 2-bit, and 4-bit ALU circuits were tested at a few kilo-hertz clock frequency as well as a few tens giga-hertz clock frequency, respectively. Our 1-bit ALU operated correctly at up to 40 GHz clock frequency, and the 4-bit ALU operated at up to 5 GHz clock frequency.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.23 no.12
• /
• pp.1388-1398
• /
• 2012

SAR(Synthetic Aperture Radar) systems can provide images of wide coverage in day, night, and all-weather conditions. However wideband SAR systems are known to be vulnerable to interferences from other devices operating at in-band or adjacent spectrums and this may lead to image corruptions. In this paper, a SAR point target simulator is developed that provides performance analysis on image distortion caused by interferences from other devices. Interference signals are generated based on the experimental data observed from acquired SAR raw data. Simulation results include typical SAR performance measures such as spatial resolution, peak to sidelobe ratio and integrated sidelobe ratio. Finally, SAR target simulations are performed and shown to correspond to the image corruptions found in real SAR missions affected by RF interferences.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.11 no.4
• /
• pp.846-851
• /
• 2007

The wireless sensor network is one of the most practical and cost-effective solutions for monitoring systems covering wild and wide area such as wildfire monitoring. However, the RF distance between sensor nodes is very short due to the need of low power consumption of the sensor node, so the number of sensor nodes to be deployed in the target area is more than tens of thousands. In this paper, we design and analyze the deployment issues as well as re-deployment problem occurred when the battery is exhausted. We also propose the needs and solutions for coverage problem in dynamic deployment. By the experimental evaluations, we analyze the packet success ratio between sensor nodes under various environments such as obstacles and variable distances.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_3
• /
• pp.925-938
• /
• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.