Radiological analysis for radioactivity depth distribution in activated concrete using gamma-ray spectrometry.

In this research, in-situ measurement methods to analyze the radioactivity depth distribution were developed by measuring 152Eu emitted from activated concrete used the Peak to Compton (PTC) method using a high-purity germanium (HPGe) detector. The experimental results of various radioactivity depth distributions agree within 3% relative error with the results of an MCNP simulation. The correlation between the values obtained with the PTC method and the radioactivity depth distribution was derived. In order to use the PTC method in the field, the impact of the material composition, surface area, and density change of the measurement target was evaluated by an MCNP simulation. The developed method was applied to activated concrete in cyclotron facilities and the results were compared with the sample analysis, revealing a relative error of less than 20%. The results of this research will be useful in quickly and accurately evaluating the radioactivity depth distribution of activated concrete. Further study should be carried out in order to enable analysis of various forms of radioactivity depth distributions of activated structures in nuclear facilities.

Tooth brushing pattern classification using three-axis accelerometer and magnetic sensor for smart toothbrush.

The concept of intelligent toothbrush, capable of monitoring brushing motion, orientation through the grip axis, during toothbrushing was suggested in our previous study. In this study, we describe a tooth brushing pattern classification algorithm using three-axis accelerometer and three-axis magnetic sensor. We have found that inappropriate tooth brushing pattern showed specific moving patterns. In order to trace the position and orientation of toothbrush in a mouth, we need to know absolute coordinate information of toothbrush. By applying tilt-compensated azimuth (heading) calculation algorithm, which is generally used in small telematics devices, we could find the inclination and orientation information of toothbrush. To assess the feasibility of the proposed algorithm, 8 brushing patterns were preformed by 6 individual healthy subjects. The proposed algorithm showed the detection ratio of 98%. This study showed that the proposed monitoring system was conceived to aid dental care personnel in patient education and instruction in oral hygiene regarding brushing style.

Using neural network to recognize human emotions from heart rate variability and skin resistance.

The purpose of this paper is to estimate emotions using Neural network and the changes in activities of autonomic nervous system(ANS). Since ANS cannot be controlled artificially, we presumed that the changes in emotions would be reflected to the changes in ANS. In order to observe those changes, we provided the subjects with some video clips which can induce a variety of emotions and measured the changes in ANS, especially in Heart Rate Variability(HRV) and in Galvanic Skin Response(GSR). With those analyzed results from the experiments, we established an algorithm based on Neural network, finally we could reach the estimating rate of 80.2%

A real time QRS detection using delay-coordinate mapping for the microcontroller implementation.

In this article, we propose a new algorithm using the characteristics of reconstructed phase portraits by delay-coordinate mapping utilizing lag rotundity for a real-time detection of QRS complexes in ECG signals. In reconstructing phase portrait the mapping parameters, time delay, and mapping dimension play important roles in shaping of portraits drawn in a new dimensional space. Experimentally, the optimal mapping time delay for detection of QRS complexes turned out to be 20 ms. To explore the meaning of this time delay and the proper mapping dimension, we applied a fill factor, mutual information, and autocorrelation function algorithm that were generally used to analyze the chaotic characteristics of sampled signals. From these results, we could find the fact that the performance of our proposed algorithms relied mainly on the geometrical property such as an area of the reconstructed phase portrait. For the real application, we applied our algorithm for designing a small cardiac event recorder. This system was to record patients' ECG and R-R intervals for 1 h to investigate HRV characteristics of the patients who had vasovagal syncope symptom and for the evaluation, we implemented our algorithm in C language and applied to MIT/BIH arrhythmia database of 48 subjects. Our proposed algorithm achieved a 99.58% detection rate of QRS complexes.