This paper introduces the hardware platform of the structured light processing based on depth imaging to perform a 3D modeling of cluttered workspace for home service robots. We have discovered that the degradation of precision and robustness comes mainly from the overlapping of multiple codes in the signal received at a camera pixel. Considering the criticality of separating the overlapped codes to precision and robustness, we proposed a novel signal separation code, referred to here as "Hierarchically Orthogonal Code (HOC)," for depth imaging. The proposed HOC algorithm was implemented by using hardware platform which applies the Xilinx XC2V6000 FPGA to perform a real time 3D modeling and the invisible IR (Infrared) pattern lights to eliminate any inconveniences for the home environment. The experimental results have shown that the proposed HOC algorithm significantly enhances the robustness and precision in depth imaging, compared to the best known conventional approaches. Furthermore, after we processed the HOC algorithm implemented on our hardware platform, the results showed that it required 34 ms of time to generate one 3D image. This processing time is about 24 times faster than the same implementation of HOC algorithm using software, and the real-time processing is realized.

Automatic monitoring of food intake in free living conditions is still an open problem to solve. This paper presents a novel necklace-type wearable system embedded with a piezoelectric sensor to monitor ingestive behavior by detecting skin motion from the lower trachea. Detected events are incorporated for food classification. Unlike the previous state-of-the-art piezoelectric sensor based system that employs spectrogram features, we have tried to fully exploit time-domain based signals for optimal features. Through numerous evaluations on the length of a frame, we have found the best performance with a frame length of 70 samples (3.5 seconds). This demonstrates that the chewing sequence carries important information for food classification. Experimental results show the validity of the proposed algorithm for food intake detection and food classification in real-life scenarios. Our system yields an accuracy of 89.2% for food intake detection and 80.3% for food classification over 17 food categories. Additionally, our system is based on a smartphone app, which helps users live healthy by providing them with real-time feedback about their ingested food episodes and types.

The researches on predicting and removing of lithographic hot-spots have been prevalent in recent semiconductor industries, and known to be one of the most difficult challenges to achieve high quality detection coverage. To provide physical design implementation with designer's favors on fixing hot-spots, in this paper, we present a noble and accurate hot-spot detection method, so-called "leveling and scoring" algorithm based on weighted combination of image quality parameters (i.e., normalized image log-slope (NILS), mask error enhancement factor (MEEF), and depth of focus (DOF)) from lithography simulation. In our algorithm, firstly, hot-spot scoring function considering severity level is calibrated with process window qualification, and then least-square regression method is used to calibrate weighting coefficients for each image quality parameter. In this way, after we obtain the scoring function with wafer results, our method can be applied to future designs of using the same process. Using this calibrated scoring function, we can successfully generate fixing guidance and rule to detect hot-spot area by locating edge bias value which leads to a hot-spot-free score level. Finally, we integrate the hot-spot fixing guidance information into layout editor to facilitate the user-favorable design environment. Applying our method to memory devices of 60 nm node and below, we could successfully attain sufficient process window margin to yield high mass production.