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Seismic refraction is a conventional geophysical method for determining geological structures by making use of velocity differences of propagating waves in various rock types. Data interpretation to determine the structures are based on travel time and distance from the source to each receiver (geophone) presented in the form of a t-x graph. In addition to the many conventional methods, our paper evaluates here a Genetic Algorithm (GA) for the interpretation of seismic refraction data. The studied subsurface structures were 2-layer and 3-layer models with horizontal and dipping planar interfaces. Actual field tests were also used for the evaluation. Comparing the GA interpretation technique with the conventional Seismic Interpretation Program (SIP), the results showed that the GA can process seismic refraction data well and gives virtually exact solutions for the synthetic structures. On actual field data, the results are quite similar to those from SIP.

For using the surface electromyography (sEMG) in various applications, the process consists of three parts: an onset time detection for detecting the first point of movement signals, a feature extraction for extracting the signal attribution, and a feature classification for classifying the sEMG signals. The first and the most significant part that influences the accuracy of other parts is the onset time detection, particularly for automatic systems. In this paper, an automatic and simple algorithm for the real-time onset time detection is presented. There are two main processes in the proposed algorithm: a smoothing process for reducing the noise of the measured sEMG signals and an automatic threshold calculation process for determining the onset time. The results from the algorithm analysis demonstrate the performance of the proposed algorithm to detect the sEMG onset time in various smoothing-threshold equations. Our findings reveal that using a simple square integral as the smoothing-threshold equation with the given sEMG signals gives the best performance for the onset time detection. Additionally, our proposed algorithm is also implemented on a real hardware platform, namely NI myRIO. Using the real-time simulated sEMG data, the experimental results guarantee that the proposed algorithm can properly detect the onset time in the real-time manner. [ABSTRACT FROM AUTHOR]