With the development of personal electronic equipment, the use of a smartphone with a tri-axial accelerometer to detect human physical activity is becoming popular. In this paper, we propose a new feature based on FFT for activity recognition from tri-axial acceleration signals. To improve the classification performance, two fusion methods, minimal distance optimization (MDO) and variance contribution ranking (VCR), are proposed. The new proposed feature achieves a recognition rate of 92.41%, which outperforms six traditional time- or frequency-domain features. Furthermore, the proposed fusion methods effectively improve the recognition rates. In particular, the average accuracy based on class fusion VCR (CFVCR) is 97.01%, which results in an improvement in accuracy of 4.14% compared with the results without any fusion. Experiments confirm the effectiveness of the new proposed feature and fusion methods.

We propose a new method of progressive transmission of continuous tone images using multi-level error diffusion method. Assuming that the pixels are ordered and the error is diffused to later pixels, multi-level error-diffused images are resolved into a multiple number of bit planes. In an image with 8 bits per pixel, the number of the bit planes that we construct is 9, and the 2-level, 3-level, 5-level,, error-diffused images are produced by a successive use of the bit planes. The original image is finally achieved precisely.