No abstract

No abstract

Abstract-In this paper, we present the design and implementation of real-time sound localization based on 0.13 µm CMOS process. Time delay of arrival (TDOA) estimation was used to obtain the direction of the sound signal. The sound localization chip consists of four modules: data buffering, short-term energy calculation, cross correlation, and azimuth calculation. Our chip achieved real-time processing speed with full range (360°) using three microphones. Additionally, we developed a dedicated sound localization circuit (DSLC) system for measuring the accuracy of the sound localization chip. The DSLC system revealed that our chip gave reasonably accurate results in an experiment that was carried out in a noisy and reverberant environment. In addition, the performance of our chip was compared with those of other chip designs.

Robots used in industry rely on complex and time-consuming programming, such as a teaching panel and offline programming. Different intuitive programming methods have been proposed to overcome this drawback. A method using a force/torque sensor is practical and has been used in several organizations. However, problems, such as precision, have to be overcome in this method. This paper presents a method using an exoskeleton device that can overcome the problems based on robot teaching. Its usefulness is demonstrated compared to a method using a force/torque sensor.

Noise removal in image processing is required in a variety of fields such as object tracking, stereo vision and medical image reconstruction. To obtain accurate results, various video pre-processing is required. We propose a hardware architecture using FPGA to improve the processing speed with the Total Variation algorithm for noise removing images. In the proposed system, we can process images with a resolution of 640ⅹ 480. We remove noise from the input noisy image, after 10 cycles of operations. In the first step, we obtain the right, bottom and center pixel values and the differences to obtain. In the second step, we add pixels to the center of the operation parameters, and the difference between the values are obtained central pixel. The operation parameters and the difference of the values of the surrounding pixels are reflected in the following operations. We repeat this process 10 times to remove the noise in the image. The noise removal performance is better than prior results, but the operation is complex and requires considerable computing power. We implemented the proposed system in hardware that requires high computing power for real-time processing with these processes. The processing delay is 0.8ms. We designed pipeline architecture to delay the operation. The proposed system can operate on image resolution of 640 ⅹ480 with a speed of 250Mhz.