An analysis of muscles from ultrasound image using morphological information of fascia and thoracic vertebra

In this paper, we propose a new fully computerized image analysis method for measuring the thickness of muscles from ultrasound image obtained by muscle endurance test using morphological information of fascia and thoracic vertebra. Firstly, we divide the image into lumbar region and thoracolumbar region by the difference of density in image for measuring the thickness of muscles. In lumbar region, we notice that the intensity of fascia is relatively higher than other parts. Thus, we measure the thickness of muscles surrounding the fascia area. In the process, we apply median filter to candidate fascia areas for extracting candidate muscle layers between fascias. Then, the thickness of muscles we measure is that of the third layer. In thoracolumbar region, we apply region expansion method for classifying the region into subcutaneous fat part and part including thoracic vertebra. Then, we apply counting method and evolutionary computation search model to find the measuring location that is in between subcutaneous fat area and thoracic vertebra. In experiment, the proposed method is effective in measuring the thickness of muscles and avoids failures of previous studies. The performance of this approach is sufficiently comparable to that of medical experts.     

Item recommendation based on context-aware model for personalized u-healthcare service

A personalized service in the ubiquitous environment is to provide services or items, which reflect personal tastes, attitudes, and contexts. It is impossible to reflect the context information generated in u-healthcare environments due to the existing recommendation system performing the recommendation using the information directly input by users and application usage record only. This study develops a context-aware model using the context information provided by the context information model. The study applies it to the extraction of the missing value in a collaborative filtering process. The context-aware model reflects the information that selects items by users according to the appropriate context using the C-HMM and provides it to users. The solution of the missing value in the preference significantly affects the recommendation accuracy in a preference based item supply method. Thus, this study developed a new collaborative filtering for ubiquitous environments by reflecting the missing preference value and reflecting it to the collaborative filtering using the context-aware model. Also, the validity of this method will be evaluated by applying it to menu services in u-healthcare services.     

Motion optimization using state-dispersion-based phase space partitions

This paper addresses a phase space partitioning problem in motion planning systems. A class of kinematic and dynamic motion planning systems, including rapid semioptimal motion-planning (RASMO), uses partitions for phase spaces in cumulative optimization criteria. In these systems, a partition results in a uniquely planned motion with a quality that is determined by a selected optimization criterion. In this paper, state-dispersion-based phase space partitioning (SDPP) that generates adaptive partitions is proposed. These partitions allow the motion planning systems to plan better motions. Uniform partitions and adaptively fixed partitions of SDPP are compared under several conditions using RASMO and a double inverted pendulum model while setting the optimality criterion of RASMO to time. The results reveal that RASMO with SDPP plans smaller time motions than those obtained with RASMO using uniform partitions.     

Correlation of vapor-liquid equilibria for binary mixtures with free energy-based equation of state mixing rules: Carbon dioxide with alcohols, hydrocarbons, and several other compounds

The correlation of vapor-liquid equilibrium data for high-pressure carbon dioxide systems is of interest in a number of industrial applications, including supercritical extraction. Here, we consider the correlation of data for 12 binary systems of carbon dioxide separately with alcohols, with hydrocarbons, and with acetone, benzene, and water. The Wong-Sandler (W-S) and modified Huron — Vidal first order (MHV1) free energy-based equation of state mixing rules (the W-S and MHV1 models) were used in the calculations. Both combined equation of state+free energy models generally resulted in good correlations of the experimental data over wide ranges of temperature and pressure with temperature — independent parameters. However, for the carbon dioxide+water system, the W-S model produced an 11% average absolute deviation in pressure, while no parameter that resulted in an AAD in pressure of less than 20% could be found for the MHV1 model.     

Case-based context ontology construction using fuzzy set theory for personalized service in a smart home environment

To provide context-based personalized services utilizing smart appliances in a smart home environment, we propose a framework for PersonAlized Service disCovery Using FuZZY-based CBR and Context Ontology (PASCUZZY). Basically, the PASCUZZY framework is implemented on case-based context ontology. To generate and manage the case instances on the case-based context ontology, we adopt the fuzzy set theory to transpose numerical-type context data sensed from the surrounding environment. The context is transposed to linguistic-type context instances on the context ontology. In addition, to formalize and manage the context and services as multi-attributed data, the context ontology was developed reflecting the structure of cases borrowed from case-based reasoning. Furthermore, we propose adaptation methods to adjust the generic fuzzy membership functions depending on the inhabitants’ context. It is performed by modifying the values of the membership number and/or modifying the numbers of the linguistic terms that are based on the inhabitants’ context to affect the membership numbers. The adapted membership functions return the personalized degree of memberships depending on the specialized context of a specific fuzzy variable. Inevitably, the number of cases on the case-based context ontology will be increased from time to time. We apply Ward’s method not only to reduce the search effort via a hierarchical clustering on the case-based context ontology but also to find the most similar service as a solution to the new context. To verify the superiority of the PASCUZZY framework, we perform two kinds of evaluations. First, we evaluate the effectiveness of the adaptation of the fuzzy membership functions. Second, we verify the effectiveness of the application of a clustering method to the case instances of the case-based context ontology to identify the most similar service. Results of the experiment verified the effectiveness and superiority of the PASCUZZY framework.     

Intelligent healthcare service based on context inference using smart device

In the field of “U-Healthcare Service”, many studies have been actively conducted to develop “smart device”-based healthcare applications that enable healthcare providers and patients to be better served through interoperations among various kinds of sensors and wireless network interfaces. In particular, contemporary intelligent healthcare services not only recognize users’ context information through smart devices, computers, and so forth, but also acquire information from heterogeneous sensors to achieve context-aware inference services. Among such information, the weather information is tightly related to diseases such as asthma and allergies. Therefore, there is a high demand for research to utilize the weather information for healthcare services. In this paper, we propose a context inference-based intelligent healthcare service that exploits both the weather conditions information and the diverse healthcare ontologies available on the Internet. The proposed service aims at modeling a context ontology in users’ healthcare service environment and defining the inference rules, thereby accomplishing a satisfactory real-time healthcare service.     

Defect detection using feature point matching for non-repetitive patterned images

Defect detection is an important technology for the quality control in the production process of wafer, TFT-LCD and PCB. Inspection is performed using the finished product’s image. The images are classified into two different groups—images with a repetitive pattern on a regular cycle and images without a repetitive pattern. A standard object for comparison is required, because manual defect detection is not possible for areas without repetitive patterns. In such areas, defect detection occurs through contrasting a reference pattern to the pattern being inspected. Methods of inspection using reference image have been researched but have limitations due to their requirement of precise alignment of the images. This paper proposes a method of defect detection to overcome such limitation using feature point matching. Feature points are extracted using a corner detector and detects defect by finding a correspondence between two feature point sets. Performance of the proposed method is evaluated by using Wafer SEM images and compared with conventional methods. Experiment results demonstrate the proposed method achieves higher detection accuracy than conventional methods and is less sensitive to alignment error and noise.     

Liver vasculature refinement with multiple 3D structuring element shapes

Delineating anatomical structures and other regions of interest is an important component of assisting and automating specific diagnostic, radiological, and surgical tasks. In this paper, a segmentation approach for liver region delineation is proposed, which is based on hysteresis thresholding followed by texture analysis with statistical moments. After that, the region growing method is applied to extract a hepatic vessel tree followed by hepatic vasculature refinement with multiple 3D structuring element shapes. The structure and morphology of the vascular network and its relationship with tumors and liver segments are of major interest to surgeons planning liver surgeries. Knowing the refined major vasculatures is important for surgeons to plan resection into liver segments for tumor treatment, and dissection into right and left lobes to assess accurate liver volume in determining donor suitability for liver transplantation. Therefore, an automated hepatic vessel segmentation scheme followed by vasculature refinement is recommended for planning tumor resections and living donor liver transplants. In addition, these vessel extraction and refinement methods combined with liver region segmentation techniques can also be applicable to extract tree-like organ structures such as carotid artery, renal artery, coronary artery, and airway paths from various medical imaging modalities.     

Atmospheric Dependence on Dielectric Loss of 1/6Ba5Nb4O15· 5/6BaNb2O6 Ceramics

Samples of 1/6Ba₅Nb₄O₁₅·5/6BaNb₂O₆ along with the pure end members, Ba₅Nb₄O₁₅ and BaNb₂O₆, were sintered under low oxygen partial pressure. The degradation mechanisms of dielectric loss in this reducing atmosphere have been studied. We found that the degradation occurred primarily due to the formation of oxygen vacancies caused by the reduction of Nb⁵⁺. This was determined by measuring the electrical conductivity, and through X-ray photoelectron spectroscopy. More importantly, the dielectric loss of 1/6Ba₅Nb₄O₁₅·5/6BaNb₂O₆ samples with higher temperature stability was further decreased on sintering in a reducing atmosphere. This observation has been explained by considering the increased porosity and formation of a reduced second phase, Ba_0.65NbO₃.     

Cure kinetics of epoxy formulations of the type used in advanced composites

An analysis of the cure kinetics of three different formulations composed of tetraglycidyl 4,4′-diaminodiphenyl methane (TGDDM) epoxy resin and diaminodiphenyl sulfone (DDS) was performed. A series of isothermal tests was run, and the experimentally obtained results were checked against the proposed kinetic model. An autocatalyzed mechanism with the overall reaction order of 2 was found to adequately describe the cure kinetics. An increase in reaction rate was observed at higher temperature and higher DDS concentration. For a given formulation, the extent of reaction corresponding to the maximum reaction rate was independent of temperature. A secondary exotherm was detected, particularly in formulations with low DDS concentration, at approximately 40% conversion. At that point, the rate of primary amine–epoxide reaction decreases, and other reactions dominate the curing process. Such a mechanism is likely to cause a formation of an inhomogeneous thermoset morphology.