The thesis illustrates the development of DSP-based ¡§Relativity Gene Algorithm For Multiple Faces Recognition System＂. The recognition system is divided into three systems: Ellipsoid location system of multiple human faces, Feature points and feature vectors extraction system, Recognition system algorithm of multiple human faces. Ellipsoid location system of multiple human faces is using CCD camera or digital camera to capture image data which will be recognized in any background, and transmitting the image data to SRAM on DSP through the PPI interface on DSP. Then, using relatively genetic algorithm with the face color of skin and ellipsoid information locate face ellipses which are any location and size in complex background. Feature points and feature vectors extraction system finds facial feature points in located human face by many image process skills. Recognition system algorithm of multiple human faces is using decision by majority. Using characteristic vectors compares every vector in the database. Then, we draw out the highest ID. The recognizable result is over. The experimental result of the developed recognition system demonstrates satisfied and efficiency.

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Midline shift (MLS), the amount of displacement of the brain’s midline from its normal symmetric position due to illness or injury, is an important index for clinicians to assess the severity of traumatic brain injury (TBI). In this dissertation, an automated computer-aided midline shift estimation system is proposed. First, a CT slice selection algorithm (SSA) is designed to automatically select a subset of appropriate CT slices from a large number of raw images for MLS detection. Next, ideal midline detection is implemented based on skull bone anatomical features and global rotation assumptions. For the actual midline detection algorithm, a window selection algorithm (WSA) is applied first to confine the region of interest, then the variational level set method is used to segment the image and extract the ventricle contours. With a ventricle identification algorithm (VIA), the position of actual midline is detected based on the identified right and left lateral ventricle contours. Finally, the brain midline shift is calculated using the positions of detected ideal midline and actual midline. One of the important applications of midline shift in clinical medical decision making is to estimate the intracranial pressure (ICP). ICP monitoring is a standard procedure in the care of severe traumatic brain injury (TBI) patients. An automated ICP level prediction model based on machine learning method is proposed in this work. Multiple features, including midline shift, intracranial air cavities, ventricle size, texture patterns, and blood amount, are used in the ICP level prediction. Finally, the results are evaluated to assess the effectiveness of the proposed method in ICP level prediction.

The growing awareness of global warming potential has internationally aroused interest and demand in reducing greenhouse gas emissions produced by human activity. Each year, the UK consumes a significant amount of energy for residential and industrial space heating and domestic hot water production. At present, gas boilers are mostly installed in the domestic water heating which contributes significantly to excessive CO2 emissions and consumption of primary energy resources. However, air-source heat pump system has higher performance efficiency comparing to the traditional gas boiler, which can reduce the carbon dioxide emission and the usage of primary energy resources. The coefficient of efficiency of the heat pump can be range from 2 to 4.5 in various situations. The market shares of heat pump have been predicted to increase in the coming years to meet the requirement of the European Union Commission. There were about 22,000 heat pumps set up in the UK with 18 percent growth comparing to 2016 as reported by BSRIA. A range from 0.6 to 5.7 million heat pumps are estimated by the National Grid to be set up by 2030 to increase the energy efficiency of the UK. Although the energy efficiency of the heat pump is extremely high, there is still a space for improvement in air-source heat pump water heating system. The performance of the heat pump water heating system can be further enhanced if the dissolved gases in its hot water circuit can be efficiently discharged. The undissolved bubbles can stack in a specific position of the radiator, which would cause the cold spot. This could immensely reduce the efficiency of the heat pump water heating system. To avoid this happening, the bubble behaviors in the heat pump water heating system need to be extensively investigated. The better understanding of the bubble behaviors in an air-source heat pump water heating system can contribute to the design of an air evacuation valve and heat pump piping systems. In this thesis, the effects of various heat pump hot water side parameters on gas microbubble diameters and bubble productions were measured and analyzed by varying different experimental conditions. Correspondingly, a summarized conclusion has been presented to predict the gas microbubble's diameter distributions and volumetric void fraction distributions at different operating conditions. These parameters include various system pressures, water flow rates, and saturation ratios. In this thesis, the main results showed that larger average bubble diameter is at the higher water flow rates at heat pump exit. At 2.2 bar condition, when system water flow rate increased from 800 l/h to 1150 l/h, the average bubble diameter increased from 0.086 mm to 0.108 mm. Moreover, the average bubble diameters increase along with the decrease in system pressures. At 1000 l/h condition, when system pressure increased from 2.2 bar to 2.7 bar, the average bubble diameter decreased from 0.100 mm to 0.087 mm. At 850 l/h condition, when system pressure increased from 1.7 bar to 2.5 bar, the average bubble diameter decreased from 0.101 mm to 0.081 mm. In addition, the average bubble diameters slightly increase along with the increase in saturation ratio. Besides, a prediction equation for the bubble diameter distribution in the water pipe was proposed. At SR 1.15 and 2.5 bar condition, when water flow rate increased from 900 l/h to 1100 l/h, volumetric void fraction decreased from 2.25 E-05 to 4.83 E-06. However, at 1000 l/h and SR 1.15 condition, when system pressure increased from 2.2 bar to 2.7 bar, volumetric void fraction decreased from 2.16 E-05 to 3.78 E-06. It is found that the highest city main saturation ratio was achieved at 1.07 at the specific environmental condition.

Este trabalho apresenta um método computacional que classifica as mamografias no padrão de densidade BI-RADS, visando auxiliar a detecção precoce do câncer de mama, seja essa realizada por análise visual ou por auxílio computadorizado. A classificação das mamografias em bancos padronizados objetiva eliminar conflitos entre laudos mamográficos de diferentes profissionais, bem como quanto à conduta médica a ser seguida. Entretanto, o estabelecimento de bancos feito visualmente e principalmente em períodos diferentes dificulta sua uniformização, proporcionando uma classificação muito subjetiva e relativamente grosseira em conseqüência a grande variação entre e inter observadores. O método desenvolvido permitiu classificar as imagens independentemente da subjetividade própria à observação visual de quem organizou o banco ou da técnica de exposição aos raios X utilizada. Os resultados foram superiores a 92% mesmo para bancos de imagens totalmente diferentes. Esses resultados foram obtidos respeitando-se as possíveis diferenças de interpretações de diversas equipes médicas. Além do estabelecimento de banco de mamografias com limiares entre as composições bem quantificadas, com esta ferramenta, tanto os estagiários poderão ser treinados para classificar as imagens no padrão de densidades do BI-RADS, respeitando as particularidades locais, quanto os resultados dos CAD poderão ser comparados. / This thesis presents a computational method that classifies the mammography into the composition of the breast tissue density patterns described in the BI-RADS protocol, intended to help in the early detection of breast cancer, either if this detection happens to be realized by visual analysis or by computerized support. The classification of the mammography in standardized database intends to eliminate issues between mammography awards of distinct professionals and the correct medical conduct to be followed. However, the determination of database only visually, especially in different periods, difficult it\'s to standardize, causing an extremely subjective classification and relatively superficial in consequence of the large inter-and intraobserver variability. The method allows classifying the images independently of the subjective quality of the visual analysis from who organized the database or from the technique of the exposition to X-ray employed. The results were superior of 92% even to database totally distinct. These results were obtained respecting eventual differences of interpretation from several medical groups. Beside the establishment of mammography database with thresholding between the well quantified categories, this methodology will consent to probationers to be trained for classify the images according to the composition of the breast tissue density patterns described in the BI-RADS, respecting its local particularity. Likewise, with this methodology, the results from CAD would be compared.

Este trabalho apresenta um método computacional que classifica as mamografias no padrão de densidade BI-RADS, visando auxiliar a detecção precoce do câncer de mama, seja essa realizada por análise visual ou por auxílio computadorizado. A classificação das mamografias em bancos padronizados objetiva eliminar conflitos entre laudos mamográficos de diferentes profissionais, bem como quanto à conduta médica a ser seguida. Entretanto, o estabelecimento de bancos feito visualmente e principalmente em períodos diferentes dificulta sua uniformização, proporcionando uma classificação muito subjetiva e relativamente grosseira em conseqüência a grande variação entre e inter observadores. O método desenvolvido permitiu classificar as imagens independentemente da subjetividade própria à observação visual de quem organizou o banco ou da técnica de exposição aos raios X utilizada. Os resultados foram superiores a 92% mesmo para bancos de imagens totalmente diferentes. Esses resultados foram obtidos respeitando-se as possíveis diferenças de interpretações de diversas equipes médicas. Além do estabelecimento de banco de mamografias com limiares entre as composições bem quantificadas, com esta ferramenta, tanto os estagiários poderão ser treinados para classificar as imagens no padrão de densidades do BI-RADS, respeitando as particularidades locais, quanto os resultados dos CAD poderão ser comparados. / This thesis presents a computational method that classifies the mammography into the composition of the breast tissue density patterns described in the BI-RADS protocol, intended to help in the early detection of breast cancer, either if this detection happens to be realized by visual analysis or by computerized support. The classification of the mammography in standardized database intends to eliminate issues between mammography awards of distinct professionals and the correct medical conduct to be followed. However, the determination of database only visually, especially in different periods, difficult it\'s to standardize, causing an extremely subjective classification and relatively superficial in consequence of the large inter-and intraobserver variability. The method allows classifying the images independently of the subjective quality of the visual analysis from who organized the database or from the technique of the exposition to X-ray employed. The results were superior of 92% even to database totally distinct. These results were obtained respecting eventual differences of interpretation from several medical groups. Beside the establishment of mammography database with thresholding between the well quantified categories, this methodology will consent to probationers to be trained for classify the images according to the composition of the breast tissue density patterns described in the BI-RADS, respecting its local particularity. Likewise, with this methodology, the results from CAD would be compared.

科学研究費補助金 研究種目:一般研究(C) 課題番号:60580177 研究代表者:飯田 孝夫 研究期間:1985-1986年度