Yutaka Hata

In April 2013, the University of Hyogo was transformed into the municipal university corporation. Therefore, the business-use systems for financial, traveling-expenses, personal affairs and salary, which was using the prefectual system by exclusive-use PCs, s introduced newly. Furthermore, the cost of such a change would be considerable, we have discussed the burden sharing with the Hyogo prefecture. In this paper, we explain the design and introduction of new systems.

This paper describes an object classification method using infrared TOF camera. The method detects moving objects from a distance distribution data. The objects are extracted by k-means clustering method based on fuzzy inference. To classify them, fuzzy if-then rules and fuzzy membership functions are derived from the knowledge of human characteristics. The system classifies them to toddler, child, adult and the other object such as baggage and animal. In the experiment, we employed seven volunteers, two dogs and a box. The system successfully classified them. © 2013, TSI® Press.

Hypoxic-ischemic encephalopathy is a typical cerebral disorder of newborn babies. For diagnosis of the neonatal cerebral disorders, the measurement of cerebral volume and surface area is effective for quantitatively evaluating the morphological change. The measurement needs brain segmentation process in magnetic resonance (MR) images. However, there are few studies for newborn brain segmentation. This study proposes an automated brain segmentation method for the newborn brain in MR images. The automated method can improve the diagnosis of the newborn cerebral disorders with efficiency and high accuracy. The proposed method first constructs fuzzy models using learning dataset. The fuzzy models express brain features by fuzzy membership functions. Next, the proposed method applies the deformable surface model based on the fuzzy models to subject's head MR images, and estimates the subject's brain region. To validate the proposed method, it has been applied to 10 newborn subjects (revised ages are - 1 month and 1 month), and compare the segmentation result with those of the conventional methods. Leave-one-out-cross validation (LOOCV) test was conducted. The mean accuracy was 93.6 ± 3.7%, the mean sensitivity was 98.9 ± 0.5, and the mean G-metrics was 4.4 ± 0.8. © 2013, TSI® Press.

This paper describes noninvasive diagnosis of blood flow for Transcranial Doppler (TCD) ultrasonography. This trans-skull ultrasonic system measures the blood flow velocity in the human brain using an ultrasonic array probe with the center frequency of 1.0 MHz. The system determines the blood flow and locates the blood vessel using Doppler effects, which are defined by the center of gravity in the frequency domain. We employ three different thickness silicon tubes, which imitate blood vessels. This system detected the flow direction by Doppler Effect under skull and visualized the skull and flow direction. Under the conditions with 0.16-0.27 mm thickness bone and our equipment specification, we confirmed to enable to diagnose the blood flow under the bone. This result is superior to measure the blood flow from any part of the adult human skull. Moreover, we extracted the region of skull and blood vessel automatically. © 2013 Copyright TSI® Press.

Acceleration of three dimensional measurement technique play an important role for medical and material science, so we improve that of measurement by using moire pattern with fuzzy inference and parallel processing.

This paper proposes a people locating method with room layout estimation by a thermal sensor. In the system, the sensor is attached to the ceiling and it acquires 16 × 16 elements spatial temperatures–thermal distribution. The distributions are analyzed to estimate people positions. Firstly, room temperature is removed from thermal distribution. Secondly, a distinction map including people positions is estimated with four fuzzy rules. In this procedure, an O-F (Object-Floor) map is calculated to verify people positions by temperature. The O-F map shows brief room layout and is employed to prevent miss detection of people positions. In the experiment, we measured a room to evaluate detection ability of our system. As the experimental result, the system successfully located people.

This paper describes practical applications on computational medical and health care technology. First, we briefly describe medical image processing for diagnosing. Next, we demonstrate three ultrasonic surgery support systems for orthopedic surgeon, rectum cancer surgeon and urologist. In them, image and signal processing plays a primary role to solve each problem. Second, we describe home health care system. This goal is not clinical use but home use to pay consciousness to health. In it, we introduce a mat senor system, which detects heart rate and respiration, and a thermopile senor system, which detects human moving trajectory. Finally, body weight prediction methods are shown by using autoregressive model. As the results, the models successfully predict the body weights. © 2013 IEEE.

Lifestyle diseases are strongly associated with lifestyle disease and the serious case of cardiovascular events which are main causes of long term nursing care. They would have strong impacts on the coming super aging society. In response to the problems to be solved, the notion technology of Systems Health Care and its technology have been proposed and developed by the authors to support health care in home and medical especially for lifestyle modification. In the framework, the important tools of Health Management Technology are investigated to be effective and efficient roles in the process of health management. The tools are Index, Criterion, and Causality which plays the roles in the health management functions of measurement, recognition, and estimation respectively. The applications are studied from the views of the tools; i.e., visceral fat and blood pressure are employed as the indices of vital signs and daily activities, sleep condition, and weight standing for diet as lifestyle habits. © 2013 IEEE.

This paper proposes a people locating method with room layout estimation by a thermal sensor. In the system, the sensor is attached to the ceiling and it acquires 16 × 16 elements spatial temperatures - Thermal distribution. The distributions are analyzed to estimate people positions. Firstly, room temperature is removed from thermal distribution. Secondly, a distinction map including people positions is estimated with four fuzzy rules. In this procedure, an O-F (Object-Floor) map is calculated to verify people positions by temperature. The O-F map shows brief room layout and is employed to prevent miss detection of people positions. In the experiment, we measured a room to evaluate detection ability of our system. As the experimental result, the system successfully located people. © 2013 IEEE.

Purpose Magnetic resonance imaging (MRI) is often used to detect and treat neonatal cerebral disorders. However, neonatal MR image interpretation is limited by intra- and inter-observer variability. To reduce such variability, a template-based computer-aided diagnosis system is being developed, and several methods for creating templates were evaluated. Method Spatial normalization for each individual's MR images is used to accommodate the individual variation in brain shape. Because the conventional normalization uses as adult brain template, it can be difficult to analyze the neonatal brain, as there are large difference between the adult brain and the neonatal brain. This article investigates three approaches for defining a neonatal template for 1-week-old newborns for diagnosing neonatal cerebral disorders. The first approach uses an individual neonatal head as the template. The second approach applies skull stripping to the first approach, and the third approach produces a template by averaging brain MR images of 7 neonates. To validate the approaches, the normalization accuracy was evaluated using mutual information and anatomical landmarks. Results The experimental results of 7 neonates (revised age 5.6 ± 17.6 days) showed that normalization accuracy was significantly higher with the third approach than with the conventional adult template and the other two approaches (P < 0.01). Conclusion Three approaches to neonatal brain template matching for spinal normalization of MRI scans were applied, demonstrating that a population average gave the best results. © 2011 CARS.

Neonatal cerebral disorders such as hypoxic-ischemic encephalopathy might deform brain shape, and reduce cerebral function. For cerebral disorders diagnosis, it is effective to measure cerebral volume and surface area using head magnetic resonance (MR) image. The measurement requires a brain segmentation process. However, an automated segmentation algorithm has not been established. This study proposes a new brain segmentation method in newborn head MR images. The proposed method uses a fuzzy object shape model, which is produced from some learning datasets. It segments the brain region by maximizing a fuzzy degree of a fuzzy deformable contour model based on the fuzzy object shape model and MR signal. The fuzzy degree is estimated by using expert knowledge of the brain MR images. In order to validate segmentation accuracy of the proposed method, we applied the proposed method to 12 newborn subjects. Subject&#039;s revised ages were between -1 month and 1 month. In 12 subjects, 9 subjects were used for creation of the fuzzy object shape model. And, the remained subjects were used for evaluation. The segmentation accuracy has been evaluated by using sensitivity and false-positive ratio, which were calculated by comparing with delineation result (ground truth).

After 2011 Tohoku Earthquake, all enterprises in Japan must save energy continually. Especially, to save energy while keeping the production quality and the production volume is required in the manufacturing. This paper describes a visualization technique to analyze causalities among the productivity indices and energy for improving energy efficiency of factory equipment. As the result of the visualization in our factory, our proposed technique could successfully improve the energy efficiency on a molding machine, a press machine and compressors without a negative effect to the productivity that means production volume and supply pressure.

In our study, prediction of the number of asthmatic attacks in Himeji city next month from 1 to 4 years old and 65 years old or older. Fuzzy-AR model learns from 2001 to 2005 and predicts the number of asthmatic attacks using data from 2006 to 2010. We evaluated our prediction results by using correlation coefficient between predicted value and ground truth value and compared Fuzzy-AR model and AR model. As a result, 65 years old or older was not able to be predicted by each model. However in age of 1 to 4, Fuzzy-AR model was able to predict higher correlation coefficient than AR model.

AR model is a model that is applied to time-series data in statistics, is used to predict the future from past data. In this study, we predicted using the AR model the number of asthmatic attacks of each region in Himeji, investigated the relevance of the region and cause of asthmatic attacks, and aimed to improve the accuracy of prediction system the number of asthmatic attacks by age. The result is estimated the number of asthmatic attacks in 2006-2010 from data 2001-2005, were compared with the true value. In a particular region, good results was obtained in the prediction of the elderly had been considered a low autocorrelation. Therefore we confirmed that asthmatic attacks involved not only generation but also region.

There are no methods for noninvasively detecting epleptogenic focus, which is the part of the brain from which epileptic discharges originate. This study investigates a method using magnetic resonance (MR) images. The method is composed of (1) granulation, (2) feature extraction, and (3) pattern classification. Granulation is to construct a set of voxels, and feature extraction and classification are done for each granule. Feature extraction is to calculate a set of numbers that express features of granule, and pattern classification is to estimate the degree of epleptogenic focus by evaluating the features.

Neonatal cerebral disorders such as hypoxic-ischemic encephalopathy might deform the brain shape, and reduce patient&#039;s cerebral functions. Early detecting and rapid cure of the cerebral disorders prevents the cerebral disorders developing worse. For quantitative diagnosis of the cerebral disorders, measurement of cerebral volume and surface area is effective. In this research, we propose an automated brain segmentation method for newborn brain. The proposed method produces fuzzy object models from learning dataset. Fuzzy object models express brain features by fuzzy membership functions. Using the FOMs, deformable surface model estimates subject&#039;s brain region. We applied the proposed method to 12 newborn subjects. From experimental results, the proposed method can segment the brain segmentation with high segmentation accuracy.

This paper proposes a body weight prediction method using Fuzzy-autoregressive (AR) model. New Fuzzy-AR model is formed by including fuzzy membership function which changes AR parameter in autoregressive (AR) model. We employed 452 volunteers, and collected their body weight time-series data during 730 days. We use body weight data from 1st to 365th day as learning data to determine the Fuzzy-AR models. After AR parameters are determined by Yule-Walker equation, we calculate the order, p, of the AR model for each volunteer based on Akaike's Information Criterion (AIC). In our experiment, we predicted body weight change for next p days for those subjects. In the Fuzzy-AR model, we make a fuzzy membership function based on the order of the AR model. As the result, the Fuzzy-AR model obtained higher correlation coefficient between predicted and truth values than the AR model on all volunteers. In addition, the Fuzzy-AR model obtained smaller mean absolute prediction error than the AR model. © 2012 IEEE.

This paper describes a seminiferous tubules evaluation using an ultrasonic probe. In this system, we evaluate a diameter of seminiferous tubules for azoospermia patients. We employ a 5.0MHz ultrasonic single probe. In the experiment, we employ large and small nylon lines as the healthy and unhealthy seminiferous tubules. We made ball shape phantom from small and large lines in total 24. We acquire the waveforms by the ultrasonic probe and calculate amplitude values from the data that band pass filters applied. We then calculate cumulative relative frequency of amplitude values. Fuzzy if-then rules are made for the cumulative relative frequency of large and small lines. We evaluate a rate of large lines among all lines by using the fuzzy MIN-MAX center-of-gravity method. In the result, the mean absolute error was 5.98 %. The correlation coefficient was 0.98. The proposed method thus successfully evaluated the rate of the large lines. © 2012 IEEE.

For diagnosis of the neonatal cerebral disorders, it is useful to measure cerebral volume and surface area using magnetic resonance (MR) image. To measure cerebral volume and surface area, a brain segmentation method is required. However, there are few studies on automated neonatal brain segmentation. This study proposes an automated brain segmentation method for neonatal brain. The proposed method segments the cerebral region by using fuzzy deformable model. Then, the segmented area is evaluated with respect to the shape of whole head, and segmentation parameters of fuzzy deformable model are updated. Segmentation, evaluation and parameter update processes are iterated until satisfying predefined condition. The initial parameters are selected using Bayesian classification. Experimental results in 34 neonatal subjects (revised age between -2weeks and 1day and 2 years 5 months) showed that the brain region was segmented with sensitivity 99.4% and false-positive rate of 29.6%.

脳疾患を超早期に発見・治療を行うために，新生児を対象とした脳疾患診断法が求められている．成人脳 MR (magnetic resonance) 画像を用いた脳疾患の計算機診断支援システムの一つとして，VBM (voxel-based morphometry) が提案されている．VBM では診断対象者の脳形状を標準脳に正規化し，統計解析によって疾患部位領域を検出する．しかし従来の脳形状正規化法では成人脳由来の標準脳が用いられているため，新生児脳への適用が困難と考えられる．本研究では，新生児脳の正規化法を3種提案する．第 1 の提案法では新生児の単一個人脳を標準脳とする．第 2 の提案法では第 1 の手法に頭蓋除去を前処理として適用する．第 3 の提案法では複数の新生児脳から標準脳を作成する．これら 3 種の提案法と従来法を 14 例の新生児脳 (修正齢- 4 週から 4 週) に適用した．解剖学的ランドマークと相互情報量を用いて精度比較を行った結果，提案法は従来法に比べ有意に高精度な結果が得られた．In order to diagnose the cerebral diseases in early stage, a diagnosing method for neonates is required. As a diagnosis method using adult magnetic resonance (MR) images, voxel-based morphometry (VBM) has been proposed. VBM normalizes an evaluating brain into a template, and detects disease area using statistical analysis. However, because the conventional normalization method uses an adult template, it is difficult to analysis to the neonatal brain. This study proposes three new normalization approaches for the neonatal brain. The 1st approach defines an individual brain as a template. The 2nd approach introduces skull stripping into preprocessing of the 1st approach. The 3rd approach produces a neonatal template from some neonatal brains. Three approaches and conventional method were applied to 14 neonatal brains (revised age were between -4 and 4 weeks). The normalization accuracy evaluated by using anatomical landmarks and mutual information showed that the proposed methods were superior to the conventional method.

80% pediatric intractable epilepsy patients are accompanied with cortical dysplasia. Although electrocorticogram is high invasive, it is used to detect cortical dysplasia, today. The detection method using MR images is non-invasive for the human body. However, automated methods for detecting cortical dysplasia with MR images of pediatric brain are not established yet. In addition, image features on the pediatric brain MR images of cortical dysplasia are not clarified. This paper proposes an estimation method for detecting cortical dysplasia in pediatric brain MR images. And, fractal dimension and texture features are calculated. The experimental results in three patients with cortical dysplasia showed that a mean sensitivity of 94.3 %, a mean specificity of 84.5 %, and a mean efficiency of 87.2 %.

In order to diagnose the cerebral diseases in the early stage, diagnosing method for neonatal brain must be studied. Voxel-based morphometry (VBM) is an approach to diagnose the cerebral diseases using MR (magnetic resonance) images. VBM normalizes the evaluating brain MR images into a template, and evaluates the distribution of gray matter density statistically. Because the template is produced from adult brain MR images, it is difficult to apply the conventional normalization to the neonatal brain. In this article, three new normalization approaches for the neonatal brain are proposed. The 1st approach defines an individual neonatal brain as a template. The 2nd approach introduces skull stripping into the 1st approach. The 3rd approach produces a neonatal template brain from some neonatal brains. Normalization accuracy was evaluated by using mutual information, position of anterior and posterior commissures (AC and PC) and angle of AC-PC line. The results in 14 neonates (revised age between -4 and 4 weeks) showed that the normalization accuracy of the 3rd approach was significantly higher than the conventional normalization and the other two approaches.

Total knee arthroplasty (TKA) is an operation that substitutes artificial knee joint of injured knee joint. The 3D kinematics of the postoperative artificial knee joint is focused in the research area of orthopaedics. There are some studies based on 2-D/3-D image registration of 2-D fluoroscopy images and 3-D geometric model. However, these conventional studies are designed for still statics image analysis. This article proposes a method for analyzing 3D knee joint kinematics by using 2-D/3-D image registration with particle filter method. The method can analyze 3D knee joint kinematics with no outlier in inter-frame. By applying the method to computer-synthesized digital radiograph (DR) images, the estimation accuracy was evaluated. In addition, the effectiveness of the method was validated by applying it to DR video of TKA applied knee joint.

This paper proposes the human body weight prediction method using autoregressive (AR) model. We employ 474 volunteers&#039; body weight change data of 730 days. AR model predicts human body weight of a day from these time-series data. We calculate the order of AR model for each volunteer by Akaike&#039;s Information Criterion (AIC) and AR parameter by Yule-Walker equation. We use body weight change data from 1st to 365th day to fix AR model. In the experiment, we predicted body weight change of next day in those from 366th to 730th day. As the result, we obtained high correlation coefficients between predicted and truth values on all volunteers.

This paper describes a foot age estimation system using fuzzy logic. The method estimates the foot age from sole pressure distribution change data. The sole pressure data is acquired by a mat type load distribution sensor. The proposed method extracts step length, step center of sole pressure width, distance of single support period and time of double support period as gait features. The fuzzy degrees for young age, middle age and elderly groups are calculated from these gait features. The foot age of the walking person on the sensor is estimated by fuzzy MIN-MAX center of gravity method. In the experiment, the proposed method estimated foot ages with good correlation coefficient.

This paper describes a testicular tubules evaluation using ultrasonic array probe. In this system, we evaluate a diameter of testicular tubules for azoospermia patients. We employ an ultrasonic array probe. We employ cumulative relative frequency of amplitude values as the evaluation index. In the experiment, we employ large and small nylon lines as the good and bad testicular tubules. Amplitude of large nylon line echo is larger than that of small nylon line echo. For the evaluation, we calculate cumulative relative frequency amplitude of acquisition data. Fuzzy if-then rules are made by the cumulative relative frequency of large and small lines. We evaluate a rete of large lines among all lines by using the fuzzy MIN-MAX center-of-gravity method. In this experiment, the proposed method successfully evaluated the rate of the large lines.

The cerebral disorders often accompany the deformation of the brain shape. Many physicians use magnetic resonance (MR) images to diagnose cerebral disorders. Extraction of cerebral surface from MR images is a basic work to evaluate human brain MR images. Many methods for cerebral surface extraction have been proposed. However, it is very difficult especially for neonatal MR images to extract cerebral surface because of brain size and folded sulci. Therefore, The extraction method for neonatal MR images is proposed a little. Especially, The particle based method can expect to extract the complicated sulci with high accuracy. However, It is difficult to estimate the change probability of particle classes which are cerebrospinal fluid, gray matter, and white matter. This paper proposes a new method to estimate the change probability of particle classes using fuzzy inference technique. The feature values of fuzzy inference are ratio of particle in a voxel, particle density, and gray matter thickness. The proposed method was applied to neonatal MR images. The experimental results were evaluated by using ground truth data given by physicians.

XPS(X-ray Photoelectron Spectroscopy) is a technique to evaluate the composition of surface neighborhood of material often used for material development. In recent years, it is applied to analyses of organism and biomolecule. In the present study, it proposes a new technique that uses a quantum-chemistry calculation and a fuzzy inference as a theoretical tool for details and a highly accurate analysis of this photoelectron spectrum and the effectiveness of the technique was examined that an inner shell electronic spectrum of tens of well known organic molecule is applied.We perform the calculations of inner shell electron binding energy for 15 organic molecules and these calculated values are good agreement with experimental results by fuzzy regression analysis, which are better than the previous work.

The security system in night is very important, and the system that watches a person&#039;s movements is useful in the security. However, animals may cause the false detection on the system in night. This paper describes a classification system of human, animal-like, and the other object from distance distribution video image measured by an infrared laser camera. This camera radiates infrared waves (850nm) and receives reflected ones. It calculates the distance distribution based on the time of flight principle. To classify the objects, firstly, we do background subtraction and noise rejection in each frame of the distance distribution. Secondly, we extract features from each object such as the height, roughness, aspect ratio. Thirdly, we make fuzzy if-then rules from knowledge of human and quadrupedal being so as to classify the object to human, animal-like and the other object. Here, we made the fuzzy membership function with respect to each feature. Finally, we classify the object to one with the highest fuzzy degree among human, animal-like and the other object. In the experiment, we set up the camera in room and tested some cases. The method successfully classified them in real time processing.

This paper describes a trans-skull ultrasonic system that locates blood flow in the brain. In this system, we use an ultrasonic array probe with the center frequency of 1.0 MHz. The system determines the blood flow vector and location of blood flow by Doppler effect. This Doppler effect is examined by the center of gravity shift in the frequency domain. We use three silicon tubes of different thickness imitated to blood vessel and scapula of cow imitated to skull. We confirmed the change of frequency quantity of Doppler effect according to water current. The experimental result shows that the system detects location of water current and water current vector by Doppler effect under scapula of cow, and do automatic extracting of water current vector.

This paper describes a biometric personal authentication method based on fuzzy logic using dynamics of sole pressure distribution while walking. The method employs a pair of right and left sole pressure data. These data are acquired by a mat type load distribution sensor. The proposed method has two processes. First, we calculate a fuzzy degree of each sole pressure data. In this process, we extract several gait features based on weight shift and shape of footprint. Fuzzy if-then rules for each registered person are introduced. In it, their parameters are statistically optimized in learning process. Second, we combine fuzzy degrees of right and left sole. In this process, we employ five operators. The method authenticates walking person with the combined fuzzy degree. We calculate the fuzzy degree of an interest person for all registered persons, and identify the interest person as the registered person with the highest fuzzy degree. While, we verify the interest person as the target person if the fuzzy degree of the interest person calculated for a target person is higher than a threshold. In an experiment on 50 volunteers, we obtained low false rejection and false acceptance rates.

This paper proposes the body weight prediction method using autoregressive (AR) model and Fuzzy-AR model. First, we employ 842 subject body weight change data of 365 days. AR model predicts body weight of a day from these time-series data. We calculate an order of AR model for each person by Akaike&#039;s Information Criterion (AIC). In the experiment, we predicted body weight change of next day for those subjects. The AR model obtained 0.842 in correlation coefficient between predicted and truth values. Second, we propose a Fuzzy-AR model that predicts body weight of next p days from last p days, where p is the order of AR model. In this method, we propose a Fuzzy-AR model with the fuzzy membership function using last p days data. In the experiment, the Fuzzy-AR model obtained 0.396, while the AR model obtained 0.250 in correlation coefficient on 10 subjects.

This paper describes a testicular tubules evaluation using 1.0MHz ultrasonic array probe. In this system, we evaluate a diameter of testicular tubules. We employ an ultrasonic array probe with the center frequency of 1.0MHz. We employ cumulative relative frequency of amplitude values as the evaluation index. In the experiment, we employ 24 nylon lines as the testicular tubules. Amplitude of large nylon line echo is larger than that of small nylon echo. For the evaluation, we calculate cumulative relative frequency amplitude of acquisition data. Fuzzy if-then rules are made by the cumulative relative frequency of large and small lines. We evaluate a rate of large lines among all lines by using the fuzzy MIN-MAX center-of-gravity method. In this experiment, the proposed method successfully evaluated the rate of the large lines.

In the human brain diagnostic system, the imaging of the brain is essential. This paper proposes a YURAGI-Synthesis for brain imaging under the skull. In it, we employ 1.0MHz and 0.5MHz ultrasonic waves. We consider the weighted sum of these waves and attempt to extract the skull depth and image the sulcus under it. We add 1.0MHz and 0.5MHz, and we add the waves of 1.0MHz and Gaussian noise as the YURAGI- Synthesis. As the results, we successfully calculated skull thickness and extracted the sulcus width within the error of 5.86mm and depth within the error of 1.94mm. As for imaging the sulcus under the skull, the highest effectiveness of the synthesized wave is 96.30% when the weight of 0.5MHz waves is 0.60, and the one of YURAGI-Analysis wave is 97.15% when the weight is 0.003. Thus, YURAGI-Synthesis is useful to this study.

Population aging rate is extremely developing in all over the world. Especially, Japan is the forefront nation. Aging is strongly connected with life style disease and the disease is associated with long term nurse care. Life style could be illustrated by life trinity; i.e., diet and meal, daily activity and exercise, and rest and sleep. It is necessary to realize our own life style to change it. By life style change, improvement of health is important. This article proposes systems approach in health care with health management technology framework. It consists of the functionality of measurement, recognition, estimation, and evolution. The important aspects systematically and cyclically improvement in health care activities.

This paper proposes a biosignal detection algorithm aided by fuzzy logic for heart rate and respiration. We employ two kinds of sensors to evaluate the algorithm. One is a air mat sensor consisting of an air tube and a pressure detection device, and the other is an ultrasonic sensor consisting of an ultrasonic probe with 2MHz and water in a small tank. Both sensors detect biosignals of human in bed. This algorithm has dynamically updated parameters to adopt the variety in the biosignal amplitudes and periods of the individuals in fuzzy membership functions. In our experiments, we applied this to healthy male volunteers and evaluated the accuracies of detecting heart beat and respiration. As the results, the algorithm successfully detected the heart beat in the both sensor data and respiration number in the air mat sensor data. We compared them with truth values obtained by ECG or Respiration belt. In addition, we evaluated the accuracies of the dynamically updated parameters in the heart rate detection. Consequently, this algorithm detected both heart and respiration signals with high accuracies.

This paper describes an analysis method of most suitable rasp for the patients by using the ultrasonic system. In it, we employ the single ultrasonic probe. At first, we make a knock to the upper point of the rasp inserted with a hammer which has the trigger signals. By using the knocking signals, we measured the acoustic signals. An ultrasonic probe for measuring is fixed in the upper side of the rasp with a built-in magnet. The acoustic data is changed to the digital data by the AD converter and is sent to the personal computer. In the clinical treatment, one of the indexes to judge how degree the rasp is fixed for the patient is the knocked sound by a hammer when inserting the rasp. In the surgery, the surgeon tries to adapt for the patient from the small size rasp to the larger size rasp in turn. Therefore, we suggest our measurement system which selects the best fixed rasp by using acoustic signal for surgeons and patients of total hip arthroplasty. © 2011 IEEE.

This paper describes a home care system for elderly by four detached multi-sensor network. The sensor network detects several bio-signals in bed. It consists of the following four sensors. An ultrasonic sensor detects human motion. An optical fiber sensor detects respiration. A mat-type sensor detects heart rate and respiration, and heart rate accesses autonomous nerve system. A microphone detects cough with phlegm to suction. The ultrasonic sensor attaches to bed frame, the mat sensor set under the bottom of the mattress in bed, the optical fiber is in mattress, and the microphone set in bed side. The sensor network can detect essential bio-signals such as heart rate, respiration, cough with phlegm to suction and body movement with high accuracy. © 2011 IEEE.

It is important to improve the quality of marine radar images because marine radar systems play a principle role of sea surveillance. However, it is difficult to recover signal strength decay with increasing distance, signals of behind objects and of shaded area. This paper proposes a new approach to improve radar image quality with system of systems (SoS) technology. The SoS is based on a ship-to-ship communication in which ships send and receive radar images, and the SoS constructs the high quality radar images using EM algorithm. Performance was validated using a computer simulation and using radar images acquired from a single radar system at multiple locations. The experimental results showed that the proposed method constructed high quality radar images, and recovered signal decay and signals of behind objects and of shaded area. © 2011 IEEE.