No abstract

Abstract. This paper presents a stand-alone ubiquitous evolvable hardware (u-EHW) system that is effective for automated heart disease diagnosis applications. The proposed u-EHW system consists of a novel reconfigurable evolvable hardware (rEHW) chip, an evolvable embedded processor, and a hand-held terminal. Through adaptable reconfiguration of the filter components, the proposed u-EHW system can effectively remove various types of noise from ECG signals. Filtered signals are sent to a PDA for automated heart disease diagnosis, and diagnosis results with filtered signals are sent to the medical doctor's computer for final decision. The rEHW chip features FIR filter evolution capability, which is realized using a genetic algorithm. A parallel genetic algorithm evolves FIR filters to find the optimal filter combination configuration, associated parameters, and the structure of the feature space adaptively to noisy environments for adaptive signal processing. The embedded processor implements feature extraction and a classifier for each group of signal types.

Recently, anti-glare (AG) surface treatment technology has been considered as a standard process to enhance the visibility of electronic display devices. For AG, the hydrofluoric acid (HF)-based chemical etch method is the most common approach for the current display glass industry. However, in order to overcome the environmental and durability degradation problems of the HF-based chemical etch method, this paper proposes an eco-friendly physical surface treatment technology using the sandblasting method. Based on the preliminary analysis results using the central composite design (CCD) method-based response surface modeling methodology (RSM), additional experiments and analyses were performed for process modeling and optimal process recipe generation. To characterize the sandblasting process, the mean value of haze was considered as the process output, and the pressure of the nozzle, the distance of the nozzle from the surface of glass, the glass feed rate, and the grit size of the abrasives were considered as process inputs. Based on the process model using the statistical response surface regression method and machine learning-based approaches, the proposed method can generate optimized process recipes for various haze targets of 10%, 20%, and 30%, with an average haze difference of 0.84%, 0.02%, and 0.86%, and maximum deviations of 1.26%, 1.14%, and 1.4%, respectively. Through the successful completion of this work, it is expected that the proposed surface treatment method can be applied to various products including mobile phones, tablet PCs, and windshields of vehicles.

Abstract. This paper presents a stand-alone ubiquitous evolvable hardware (u-EHW) system that is effective for automated heart disease diagnosis applications. The proposed u-EHW system consists of a novel reconfigurable evolvable hardware (rEHW) chip, an evolvable embedded processor, and a hand-held terminal. Through adaptable reconfiguration of the filter components, the proposed u-EHW system can effectively remove various types of noise from ECG signals. Filtered signals are sent to a PDA for automated heart disease diagnosis, and diagnosis results with filtered signals are sent to the medical doctor's computer for final decision. The rEHW chip features FIR filter evolution capability, which is realized using a genetic algorithm. A parallel genetic algorithm evolves FIR filters to find the optimal filter combination configuration, associated parameters, and the structure of the feature space adaptively to noisy environments for adaptive signal processing. The embedded processor implements feature extraction and a classifier for each group of signal types.

No abstract