The translucent and yellow gummy latex of mangosteen by using the VFSS Measurement

The vibration frequency base on strain gage sensor (VFSS) has proposed to predict an internal translucent and yellow gummy latex in mangosteen fruit, this measurement were used nondestructive method by vibrate on 25,30,35 and 40Hz. The VFSS were obtained an evaluation of feature extraction base on time and frequency domain, which can classify by two scatter plot. From the experimental results, the first day (day1), WAMP and RMS is the best feature comparing with the other feature, there have percentage accuracy higher than the other day. From this result, this method can obtain the high classification accuracy. Keywords: Vibration Fruit base on Strain gage Sensor (VFSS), feature extraction, yellow gummy latex and translucent

The translucent and yellow gummy latex of mangosteen by using autoregressive coefficient method

A nondestructive measurement to predict an internal translucent disorder and yellow gummy latex in mangosteen fruit has proposed by using Vibration Frequency base on Strain gage Sensor (VFSS). This measurement were used vibrate with frequency  0 – 50 Hz The VFSS of 100 mangosteen samples were obtained an evaluation of various existed VFSS signal features base on time and frequency domains. From the experimental results, Auto-regressive (AR) coefficient was suggested to use as a feature for the VFSS measurement. We will be obtained the classification accuracy on good sample and device the sample into two groups.   Keywords: Vibration Fruit base on Strain gage Sensor (VFSS), Auto-regressive (AR) coefficient, feature extraction, yellow gummy latex and translucent

Development a heat-pulse sapflow sensor to continuously record water use in fruit trees

The prototype of a heat-pulse sapflow sensor (PSU-TTSF) was developed for continuous recording of sapflow. The efficiency of the measurement of PSU-TTSF was evaluated by comparing with the Greenspan Sapflow Sensor (a commercial equipment). The 10-year old longkong trees were used as the test plants. The results showed that both equipments could be used for continuously automated records. The accuracy of the measurement was evaluated, and it was found that the sap flow values measured by PSU-TTSF exhibited high relationship with those values measured by Greenspan Sapflow Sensors. The sap flow measured by PSUTTSF tended to be lower, and the difference was approximately 16%. To reduce the error of measurement, the method of installing PSU-TTSF probe set needs to be improved by using a drill guide

Efficient feature for classification of eye movements using electrooculography signals

Electrooculography (EOG) signal is widely and successfully used to detect activities of human eye. The advantages of the EOG-based interface over other conventional interfaces have been presented in the last two decades; however, due to a lot of information in EOG signals, the extraction of useful features should be done before the classification task. In this study, an efficient feature extracted from two directional EOG signals: vertical and horizontal signals has been presented and evaluated. There are the maximum peak and valley amplitude values, the maximum peak and valley position values, and slope, which are derived from both vertical and horizontal signals. In the experiments, EOG signals obtained from five healthy subjects with ten directional eye movements were employed: up, down, right, left, up-right, up-left, down-right down-left clockwise and counterclockwise. The mean feature values and their standard deviations have been reported. The difference between the mean values of the proposed feature from different eye movements can be clearly seen. Using the scatter plot, the differences in features can be also clearly observed. Results show that classification accuracy can approach 100% with a simple distinction feature rule. The proposed features can be useful for various advanced human-computer interface applications in future researches

A system for improving fall detection performance using critical phase fall signal and a neural network

We present a system for improving fall detection performance using a short time min-max feature based on the specificsignatures of critical phase fall signal and a neural network as a classifier. Two subject groups were tested: Group A involvingfalls and activities by young subjects; Group B testing falls by young and activities by elderly subjects. The performance wasevaluated by comparing the short time min-max with a maximum peak feature using a feed-forward backpropagation networkwith two-fold cross validation. The results, obtained from 672 sequences, show that the proposed method offers a betterperformance for both subject groups. Group B’s performance is higher than Group A’s. The best performances are 98.2%sensitivity and 99.3% specificity for Group A, and 99.4% sensitivity and 100% specificity for Group B. The proposed systemuses one sensor for a body’s position, without a fixed threshold for 100% sensitivity or specificity and without additionalprocessing of posture after a fall

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