Hideaki Orii

Ultrasonically enhanced extraction of luteolin and apigenin from the leaves of Perilla frutescens (L.) Britt. using liquid carbon dioxide and ethanol

The present study reports on the ultrasonic enhancement of the liquid carbon dioxide (CO2) extraction of luteolin and apigenin from the leaves of Perilla frutescens (L.) Britt., to which ethanol is added as a cosolvent. The purpose of this research is also to investigate the effects of the particle size, temperature, pressure, irradiation power, irradiation time, and ethanol content in the liquid CO2 solution on the extraction yield using single-factor experiments. We qualitatively and quantitatively analyzed the yields in the extract using HPLC (high-performance liquid chromatography). The liquid CO2 mixed with ethanol was used at temperatures of 5, 20 and 25 °C with extraction pressures from 8 to 14 MPa. The yields of luteolin and apigenin in the extraction were clearly enhanced by the ultrasound irradiation, but the selectivity of the extract was not changed. The yields of luteolin and apigenin in the extract were also significantly improved by adjusting the operating temperature, the irradiation time, and the ethanol content in the liquid CO2 solution, but no change in the selectivity of the extract was observed.

Gas-saturated solution process to obtain microcomposite particles of alpha lipoic acid/hydrogenated colza oil in supercritical carbon dioxide.

Abstract Alpha lipoic acid (ALA), an active substance in anti-aging products and dietary supplements, need to be masked with an edible polymer to obscure its unpleasant taste. However, the high viscosity of the ALA molecules prevents them from forming microcomposites with masking materials even in supercritical carbon dioxide (scCO2). Therefore, the purpose of this study was to investigate and develop a novel production method for microcomposite particles for ALA in hydrogenated colza oil (HCO). Microcomposite particles of ALA/HCO were prepared by using a novel gas-saturated solution (PGSS) process in which the solid-dispersion method is used along with stepwise temperature control (PGSS-STC). Its high viscosity prevents the formation of microcomposites in the conventional PGSS process even under strong agitation. Here, we disperse the solid particles of ALA and HCO in scCO2 at low temperatures and change the temperature stepwise in order to mix the melted ALA and HCO in scCO2. As a result, a homogeneous dispersion of the droplets of ALA in melted HCO saturated with CO2 is obtained at high temperatures. After the rapid expansion of the saturated solution through a nozzle, microcomposite particles of ALA/HCO several micrometers in diameter are obtained.

Tactile texture recognition using convolutional neural networks for time-series data of pressure and 6-axis acceleration sensor

Tactile texture is an important factor to determine to impression of an object. To measure a feeling of an object, a numerical evaluation method for tactile texture is required. In this paper, we propose a novel recognition method for tactile texture using deep convolutional neural networks. In proposed framework, the tactile texture information is obtained by analyzing a time-series data of a pressure sensor and 6-axis acceleration sensor. Thus, the system configuration is simple, and it is possible to construct the system inexpensively.

Pseudo-normal Image Synthesis from Chest Radiograph Database for Lung Nodule Detection

The purpose of this study is to develop a new computer aided diagnosis (CAD) system for a plain chest radiograph. It is difficult to distinguish lung nodules from a chest radiograph. Therefore, CAD systems enhancing the lung nodules have been actively studied. The most notable achievements are temporal subtraction (TS) based systems. The TS method can suppress false alarms comparatively because it uses the chest radiograph of the same person. However, the TS method cannot be applied to initial visitors because it requires the past chest radiograph of themselves. In this study, to overcome the absence of past image for a patient himself, a pseudo-normal image is synthesized from a database containing other patient’s chest radiographs that have already been diagnosed as normal by medical specialists. And then, the lung nodules are emphasized by subtracting the synthesized normal image from the target image.

Skeletonization of decorative characters by graph spectral decomposition

Decorative characters are widely used in various scenes. Practical optical character reader is required to deal with not only common fonts but also complex designed fonts. However, since the appearances of decorated characters are complicated, most general character recognition systems cannot give good performances on decorated characters. In this paper, an algorithm that can extract characters essential structure from a decorative character is proposed. This algorithm is applied in preprocessing of character recognition. Experimental results show character skeletons are clearly extracted from very complex decorative characters.

Color conversion algorithm for color blindness using self-organizing map

The symptoms of “color blindness” are due to an innate lack or deficit of cone cells that recognize colors. People with color blindness have difficulty discriminating combinations of specific colors. In this paper, we propose a novel color conversion algorithm for color blindness. In the proposed method, the difficult colors for discrimination by color blindness are detected and converted for the legibility. This color conversion is consist of a color clustering using self-organizing map (SOM) and a analysis of the map structure. To validate the effectiveness of proposed method, it is applied to a image have various color combinations.

Image Enlargement with Lost High-Frequency Components Estimation Using Clustered Eigenspace-BPLP

In this paper, in order to realize the image enlargement with high performance, we propose a new enlargement method, which restores high-frequency components lost in the interpolation-based methods, by using high-frequency estimation. The estimation is based on the eigenspace method proposed by Amano et al.. The proposed enlargement method can generate lost high-frequency components by using pairs of low- and high-frequency components of the original image, and is referred as back projection for lost high-frequency components. Experimental results show the proposed method can achieve promising enlargement.

A SIFT Feature-Based Template Matching Method for Detecting and Counting Objects in Life Space

Autonomous robots are at advanced stage in various fields, and they are expected to autonomously work at the scenes of nursing care or medical care in the near future. In this paper, we focus on object counting task by images. Since the number of objects is not a mere physical quantity, it is difficult for conventional phisical sensors to measure such quantity and an intelligent sensing with higher-order recognition is required to accomplish such counting task. It is often that we count the number of objects in various situations. In the case of several objects, we can recognize the number at a glance. On the other hand, in the case of a dozen of objects, the task to count the number might become troublesome. Thus, simple and easy way to enumerate the objects automatically has been expected. In this study, we propose a method to recognize the number of objects by image. In general, the target object to count varies according to users request. In order to accept the users various requests, the region belonging to the desired object in the image is selected as a template. Main process of the proposed method is to search and count regions which resembles the template. To achieve robustness against spatial transformation, such as translation, rotation, and scaling, scale-invariant feature transform (SIFT) is employed as a feature. To show the effectiveness, the proposed method is applied to few images containing everyday objects, e.g., binders, cans etc.

Image completion with generation of rotated patterns and efficient matching

Image completion is a method for completing missing parts caused by the removal of foreground or background elements from an image in a plausible way. In the conventional method, the missing parts of an image are completed by optimizing the objective function, which is defined based on pattern similarity between the missing region and the rest of the image (data region). However, the resultant image could be deteriorated according to variations of patterns in the data region. Although augmenting the pattern variations in the data region leads to good completion result, the processing time to search the plausible pattern in the augmented data increases explosively. In this paper, we propose an extended method considering rotated pattern variations of data region as well as suppressing the computational time by utilizing local orientations of rotated patterns. The effectiveness of the proposed method is validated by comparing with the conventional method.

Quantitative visualization of gas temperature distribution in atmospheric DC glow corona by spectral image processing

This study presents an experimental method of determining a two-dimensional image of the gas temperature in atmospheric DC glow corona by spectral imaging. A steady-state glow corona discharge as a partial discharge was generated by applying positive DC voltage to rod-to-plane electrode in synthetic air (N2:79%, O2:21%) at atmospheric pressure. Spectral imaging displays an image, and measures its specific spectra at each pixel in the image. The spectral images were taken by an Intensified CCD camera (ICCD) with ultra-narrow band-pass filters, corresponding to head and tail of nitrogen second positive system band (0-2) (2P(0-2)). The gas temperature was estimated from the emission intensity ratio between the head and tail of 2P(0-2). In this result, the change in the distribution of gas temperature in a positive DC glow discharge due to the amplitude of applied voltage was clearly visualized by spectral image processing.