A zero-crossing based amplifier whose power is scalable to a sampling frequency is presented. An inverter-based zero-crossing detector (ZCD) is proposed to consume no static power consumption compared with a conventional ZCD using a class-A based preamplifier. A common-mode feedback (CMFB) circuit is adopted to calibrate a variation of a ZCD threshold voltage due to supply voltage and temperature (VT) variations. In addition, the CMFB enables an only single transfer phase for high speed operation. An 11-bit pipelined successive approximation register (SAR) ADC was designed in a 65-nm CMOS technology and a total active area is 0.15 mm2. The post-layout transient noise simulation result shows the signal-to-noise-and-distortion ratio (SNDR) is 60.6 dB at 100 MS/s from a 1.2 V supply voltage. The proposed amplifier consumes 746 uA at 100 MS/s, 376 uA at 50 MS/s and 208 uA at 25 MS/s, respectively.

A new adaptive background calibration for timing and gain mismatches in a Time-Interleaved Analog-to-Digital Converter (TI-ADC) is presented. The proposed technique using a pseudo aliasing signal can greatly reduce the hardware. The pseudo aliasing generator consists of the Hadamard transform and a fixed FIR filter. The requirement for the word length of the FIR filter can be reduced about 50% without Look-up Table (LUT) compared with a conventional compensation filter.

We have studied the generation of regrasping motion for a four-fingered robot using evolutionary programming (EP). EP has the advantage of finding the set of optimal numerical values but it requires many iterations to find it. The regrasping strategy obtained cannot be applied to other object shapes than those used in the search process. We consider that the regrasping strategy generated by EP should be reused for other sizes of object. We propose an expansion of the method for other sizes of grasped object. We show the effectiveness of the method with experimental results.

The control of multi-fingered robot hands has been the subject of interest. To manipulate some object with regrasping motion, there are many parameters to be determined; the grasping points, grasping forces, regrasping phases, finger allocation and so on. It is difficult to optimize such manipulation parameters for achieving effective manipulation. We propose a generation method of regrasping motion for a four-fingered robot hand using evolutionary programming (EP). In this case, EP determines not only finger motions, but also rotation of the target object in order to increase its manipulability. In manipulating an object like a rectangular solid, it is very important to change the object directions and to grasp another face according to the task progress because the grasping faces tend to be limited to only two. The evolutionary optimization method is generally able to find optimal solutions without a supervisor after much iteration, which makes it almost impractical to apply a real robot directly. Therefore we control the real robot hand with an optimal controller that has been generated in computer simulations. We show the effectiveness of the proposed acquisition method for the regrasping motion with experimental results.