• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.17 no.1
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• pp.197-207
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• 1987

This study was undertaken to investigate the relationships between film and processing solution at different processing temperatures. Three kinds of periapical film were used for this study. They included EP-2l film, DF-58, and A film Each film was processed by automatic film processor with RD-Ⅲ X-dol 90, and A processing solutions at 68° 74° 80° 86° and 92°F. Film density was measured with the densitometer, and base plus fog density, film relative speed, film contrast, and subject contrast were evaluated. The following results were obtained; 1. As the processing temperature was increased, base plus density was increased. Inadequate base plus fog densities were obtained with three films in combination with three processing solutions at 92°F. 2. Lowest base plus fog densities were obtained with A film, followed in ascending order by EP-21, and DF-58 film in combination with A or RD-Ⅲ processing solutions. The sequence of base plus fog densities was in ascending order by EP-21, A, and DF-58 film in combination with X-dol 90 processing solution. 3. The sequence of film relative speed values was in ascending order of EP-21, A, and DF-58 film in combination with A and RD-Ⅲ processing solutions, respectively. 4. As the processing temperature was increased, film contrast values was increased. The sequence of film contrast values was in descending order solution. The sequence of film contrast values was in descending order of EP-2l, DF-58, and A film in combination with RD-Ⅲ, X-dol 90 processing solution at 80°F. 5. As the processing temperature was increased, subject contrast was increased. The sequence of subject contrast was in descending order of A, X-dol 90, and RD-Ⅲ processing solution in combination with three films at 80°F. The sequence of subject contrast was in descending order of EP-21, A, and DF-58 film in combination with A processing solution at different processing temperatures.

• Journal of Sensor Science and Technology
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• v.6 no.2
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• pp.95-105
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• 1997

A new open-loop signal processing technique of digital phase tracking is known to have a Potential to solve the problems in the open-loop processor such as limited dynamic range, dependence on the optical intensity fluctuations, and dependence on gain fluctuations of signal path. But new problems with digital phase tracking must be solved before it can be a useful signal processing method. In this paper, barriers to the success of the digital phase tracking such as harmonics content, phase difference, amplitude variations of the phase modulation(PM) signal, bandwidth limit of the signal path, and the implementation of the mixer, are pointed out and their effects on the performance of the signal processor are analyzed to calculate the requirements of the signal processor for $1{\mu}rad$-grade FOG.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.2
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• pp.103-111
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• 2019

The IoT-driven large-scaled systems consist of connected things with on-chip executable embedded software. These light-weighted embedded things have limited hardware space, especially small size of on-chip flash memory. In addition, on-chip embedded software in flash memory is not easy to update in runtime to equip with latest services in IoT-driven applications. It is becoming important to develop light-weighted IoT devices with various software in the limited on-chip flash memory. The remote instruction execution in cloud via IoT connectivity enables to provide high performance software execution with unlimited software instruction in cloud and low-power streaming of instruction execution in IoT edge devices. In this paper, we propose a Cloud-IoT asymmetric structure for providing high performance instruction execution in cloud, still low power code executable thing in light-weighted IoT edge environment using remote instruction execution. We propose a simulated approach to determine efficient partitioning of software runtime in cloud and IoT edge. We evaluated the instruction cloudification using remote instruction by determining the execution time by the proposed structure. The cloud-connected instruction set simulator is newly introduced to emulate the behavior of the processor. Experimental results of the cloud-IoT connected software execution using remote instruction showed the feasibility of cloudification of on-chip code flash memory. The simulation environment for cloud-connected code execution successfully emulates architectural operations of on-chip flash memory in cloud so that the various software services in IoT can be accelerated and performed in low-power by cloudification of remote instruction execution. The execution time of the program is reduced by 50% and the memory space is reduced by 24% when the cloud-connected code execution is used.

• Journal of IKEEE
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• v.18 no.4
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• pp.485-494
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• 2014

Traffic Sign Recognition(TSR) is an important element in an Advanced Driver Assistance System(ADAS). However, many studies related to TSR approaches only in normal daytime environment because a sign's unique color doesn't appear in poor environment such as night time, snow, rain or fog. In this paper, we propose a new TSR algorithm based on machine learning for daytime as well as poor environment. In poor environment, traditional methods which use RGB color region doesn't show good performance. So we extracted sign characteristics using HoG extraction, and detected signs using a Support Vector Machine(SVM). The detected sign is recognized by a decision tree based on 25 reference points in a Normalized RGB system. The detection rate of the proposed system is 96.4% and the recognition rate is 94% when applied in poor environment. The testing was performed on an Intel i5 processor at 3.4 GHz using Full HD resolution images. As a result, the proposed algorithm shows that machine learning based detection and recognition methods can efficiently be used for TSR algorithm even in poor driving environment.