It is important to reduce the power consumption of complementary metal oxide semiconductor (CMOS) logic circuits, especially those used in mobile devices. A CMOS logic circuit consists of metal-oxide-semiconductor field-effect transistors (MOSFETs), which consume electrical power dynamically when they charge and discharge load capacitance that is connected to their output. Load capacitance mainly exists in wiring or buses, and transitions between logic 0 and logic 1 cause these charges and discharges. Many methods have been proposed to reduce these transitions. One novel method (called segmentation coding) has recently been proposed that reduces power consumption of CMOS buses carrying band-limited signals, such as audio data. It improves performance by employing dedicated encoders for the upper and lower bits of transmitted data, in which the transition characteristics of band-limited signals are utilized. However, it uses a conventional majority voting circuit in the encoder for lower bits, and the circuit uses many adders to count the number of 1s to calculate the Hamming distance between the transmitted data. This paper proposes segmentation coding with pseudo-majority voting. The proposed pseudo-majority voting circuit counts the number of 1s with fewer circuit resources than the conventional circuit by further utilizing the transition characteristics of band-limited signals. The effectiveness of the proposed method was demonstrated through computer simulations and experiments.

This paper presents a vision-based human interface system that enables a user to move a target object in a 3D CG world by moving his hand. The system can interpret hand motions both in a frame fixed in the world and a frame attached to the user. If the latter is chosen, the user can move the object forward by moving his hand forward even if he has changed his body position. In addition, the user does not have to keep in mind that his hand is in the camera field of view. The active camera system tracks the user to keep him in its field of view. Moreover, the system does not need any camera calibration. The key for the realization of the system with such features is vision algorithms based on the multiple view affine invariance theory. We demon-strate an experimental system as well as the vision algorithms. Human operation experiments show the usefulness of the system.