This paper proposes a new method for image segmentation and extraction using nonlinear cellular networks. Flexible segmentation of complicated natural scene images is achieved by using resistive-fuse networks, and each segmented regions is extracted by nonlinear oscillator networks. We also propose a nonlinear cellular network circuit implementing both resistive-fuse and oscillator dynamics by using pulse-modulation techniques. The basic operation of the nonlinear network circuit is confirmed by SPICE simulation. Moreover, the 1010-pixel image segmentation and extraction are demonstrated by high-speed circuit simulation.

The purpose of this study is to make available a fall risk assessment for stroke patients during walking using an accelerometer. We assessed gait parameters, normalized root mean squared acceleration (NRMSA) and berg balance scale (BBS) values. Walking dynamics were better reflected in terms of the risk of falls during walking by NRMSA compared to the BBS.

This paper proposes a nonlinear oscillator network model for gray-level image segmentation suitable for massively parallel VLSI implementation. The model performs image segmentation in parallel using nonlinear analog dynamics. Because of the limited calculation precision in VLSI implementation, it is important to estimate the calculation precision required for proper operation. By numerical simulation, the necessary precision is estimated to be 5 bits. We propose a nonlinear oscillator network circuit using the pulse modulation approach suitable for an analog-digital merged circuit architecture. The basic operations of the nonlinear oscillator circuit and the connection weight circuit are confirmed by SPICE circuit simulation. The circuit simulation results also demonstrate that image segmentation can be performed within the order of 100 µs.