Assessing worker skill is essential for optimizing high-complexity, low-output production tasks that are consequently difficult to automate for increased efficiency over human work. Identifying specific, accessible metrics to identify workers as beginners versus experts may also help design more efficient training regimens, as well as help uncover the determinants of skill. In this study, atemporal (timestamp removed) eye tracking data from 16 subjects performing a soldering task were analyzed using a variety of machine learning models, including k-nearest neighbors (KNNs) and decision trees, to examine if worker task skill could be assessed from nonsequential eye movement and pupil size data alone. We further investigated whether feature extraction via principal component analysis (PCA) could be used to improve the performance, efficiency, and robustness of the prediction models. PCA was selected due to its algorithmic efficiency and previously demonstrated ability to improve the performance of tree algorithms due to the alignment of data on independent axes. We find that fine-tree models trained on 95% PCA data had the most consistent performance classifying expert and beginner sessions across unseen sessions from training subjects and sessions from unseen testing subjects (68.88% mean, 80.97% median session accuracy from training subjects; 72.64% mean, 75.13% median session accuracy from testing). This model also showed strong robustness to outlier trends, suggesting it was able to extract generalizable eye tracking trends that do not rely on sequential context but are still indicative of worker skill.

According to D. Norman's seven-stage model (1988), individuals use strategies, comprising goals and intentions within their overarching goal, to determine their next action; such strategies, moreover, are themselves adjusted by individuals based on the outcomes of prior actions. In human-robot collaboration scenarios, robots should thus follow the changing goals of humans and help them to explore their optimal collaboration strategies. However, previous research has focused on approaches that align with human intentions, neglecting the variability of the underlying goals. As a result, robots cannot adapt to human goals as these goals change implicitly. In this study, we propose a hierarchical structure to infer the actions humans can take toward a certain goal, enabling robots to support humans even when they implicitly change their goal. The proposed system estimates the most likely human goal based on the individual's current actions. When the inferred human goal changes, the robot confirms the goal with the human through conversation and then makes a proposal that aligns with the human's goals. In a human-robot table tennis scenario, this approach enhanced responsiveness to changing goals, fostering more perceptually collaborative and efficient interactions.

Identifying causes of unknown failures during the manufacturing process is quite difficult because of the uncertainty nature of the failures. As the manufacturing process becomes more complicated, failure-cause identification becomes more time consuming due to the increased number of causal candidates to be checked when addressing anomalies, and the complicatedly intertwined factors are difficult to discern. To solve these problems, various methods of anomaly detection and ranking have been developed using sensor data gathered during the manufacturing process. However, once an anomaly occurs at any point in the process, it propagates to other steps. Conventional anomaly detection methods are not sufficient against the complicated and unpredictable anomalies. In this study, we developed a novel anomaly ranking method based on a causal model. It employs a fusion model of human knowledge and sensor data to acquire a suitable causal model for accurate ranking. Experiments were conducted on a real packaging machine, the results of which showed a roughly 30% reduction of the ranking average compared with that of the conventional methods.

In order to enhance and improve labor productivity, we have developed an automated system for evaluating the worker’s skills by using labeled latent Dirichlet allocation (L-LDA). Since the L-LDA learns the characteristic motions automatically, we do not need to find and define any features of the motion. The elemental processes are analyzed by the L-LDA. The worker’s skills are evaluated based on the analyzed time series elemental process data. The evaluated worker’s skills are correctness, stability, speed, and rhythm of the work. The results confirmed that our proposed evaluation system is capable of automatically providing a new analysis over the conventional evaluation method with only working time. For example, an evaluation experiment was done to one subject. This result showed the speed category to be lower than the other categories. Then, we know that the subject lacks parallelism work skill. These results give new knowledge that never obtained by the conventional evaluation method.

This paper describes a fuzzy proficiency evaluation system by using myoelectric potential. This system evaluates proficiency level in soldering operation as an example of manufacturing process an industry. We classified them as beginners and experts, and confirmed the difference by proficiency level by analyzing the surface myoelectric potential measured during work. As the result, the difference found at the integrated myoelectric potential during the operation. This difference decreased with the number of operations, and this was confirmed by the degree of proficiency. Therefore depending on proficiency level as a numerical value in which the difference appeared, the displacement of the integrated myoelectric potential of the working time and the previous work was converted into fuzzy degree, and the degree of proficiency was defined and evaluated using their affiliation degree. As the results, we succeeded in designing an evaluation system in which differences appeared in proficiency level.

Productivity improvements in manufacturing industries are strongly sought after while considering the reduced labor power in developed countries. In response to these needs, a quantified evaluation method of worker's proficiency is key to realizing sophisticated skill developments. In previous studies, worker's proficiency in manufacturing operations was evaluated by the number of products produced per unit time or takt time. Therefore, it is difficult to understand how workers accomplish the manufacturing operations and/or tasks with specific motions and objects derived from the hand, body, and eye movements. To overcome this limitation, we investigated the quantified degree of worker's skills in four elemental processes using methods to evaluate the sophistication level between sensory and motor connectivity in the human brain's information processing. To realize these methods, we conducted a three-month experiment and measured eye and body movements of 20 participants working on a manufacturing line. Our method defines the four elemental processes to show the differences in the degree of worker's proficiency between experts and novices. The quantified evaluation results indicated that expert workers had higher proficiency levels for every elemental process compared with novice workers. These results show the possibility that novices are not as proficient as experts when memorizing correct procedures as they are more likely to discriminate a specific point to accomplish tasks due to immature memory functions within the brain.

In this study, we analyzed a body information feature in the soldering process in order to develop an evaluative method of the worker skills. First, we found the three types body information features. The found features were the work position, the way to hold a soldering iron, and the effect of the dominant eye and the age. Using the features of the work position and the way to hold a soldering iron, we classified the work rank into four classes. As a result of evaluating operation time and the product quality for these classes, it was confirmed that operation time and the product quality were improved as the work rank improves.

This paper describes a framework of blood pressure variation factor analysis. We design a system in order to support improving our lifestyle habit. The system classifies subjects into several variation groups in order to evaluate a personal characteristic of blood pressure change. Furthermore, we describe a blood pressure seasonal variation evaluation method by using normalized cross-correlation coefficient between blood pressure and outdoor temperature. As the result, we showed the dependencies among blood pressure, body weight and the temperature.

This paper proposes a method for predicting human posture by thermal array sensors. In our previous work, we studied a system for estimating human posture by the sensors. The system successfully estimated human posture for elderly people living in a nursing home. As a further step to our previous work, this paper presents a human posture prediction method using Bayesian inference with the sensors. Our method predicts that posture changes at the next sample with Bayesian inference which employs characteristics of a past posture. In the experiment, we applied the results of our previous work and synthesized data of assumed daily movements. In conclusion, we obtained the basis of an effective posture prediction method using Bayesian inference and a thermal array sensor network.

Blood Pressure readings are widely accepted as a measure to determine the risk of non-communicable diseases such as hypertension and stroke. Affordable healthcare devices and sensors allow individuals to monitor blood pressure at home or at a local service point on a daily basis. ICT based healthcare systems interpret the readings and give feedback to individuals or may trigger a telemedicine call to a remote doctor. This paper introduces case studies for ICT healthcare studies undertaken in Japan and Bangladesh. Blood Pressure data collected by the Omron WellnessLink (500,000 readings) and the Kyushu University/Grameen Portable Heath Clinic (21,252 readings) are examined for similarities and differences. The results show similarities in gender and temporal influences. Males have higher blood pressure and readings appear to be rhythmic according to day and month. The differences indicate that the mean Systolic Blood Pressure (SBP) for Japanese males is higher than Bangladesh males and SBP for Bangladesh females is higher than Japanese females. The impact of climate is stronger on Japanese SBP than Bangladesh SBP. The Bangladesh data shows progressive increase in SBP in each ten year age category until 80 years; this is also reflected by BMI categories. The study reveals that affordable devices connected to basic ICT based healthcare systems reveal underlying factors in the Blood Pressure variability.

This paper describes a human posture estimation system by using two thermal array sensors. In the system, the sensors are attached to ceiling and wall of a room, and acquire temperature distributions by 16 × 16 elements. The temperature distributions represent a state of temperature in the room, and they are analyzed to estimate human posture. Human posture is estimated by time-series posture transition diagram and the sum of temperature. In our experiment, we measured the temperature distributions in a room modeled as private room in a nursing home. As the results, the system successfully estimated human posture.

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 x 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 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.

This paper proposes a classification system for human activity using a multi-sensor system with a built-in electrocardiograph and 3D accelerometers. The multi-sensor system unconstraintly measures biological information, and provides these data to personal computer by wireless communication. We classify human activity by the biological information. The sensor detects the electrocardiogram and triaxial acceleration data of subject. The subject has several activities such as “Walking”, “Walking Stairs”, “Rest” and “Strength training”. The proposed system classifies these activities by a decision tree. Branch conditions of the decision tree are defined by fuzzy membership functions. These fuzzy membership functions are constructed by exercise intensity, distinction frequency and postures. We compared our proposed method with a method using only acceleration data to show the effectiveness of the multi-sensor system. As the results, the proposed method obtained high classification accuracy.

This paper proposes a human movement trajectory recording method by a thermopile array sensor. In the system, the sensor is attached to the ceiling and it acquires place-dependent temperatures, which is called thermal distribution. The system obtains 4 × 4 pixels thermal distributions from the sensor. The distributions are analyzed to extract human movement trajectory. First, human candidate pixels are detected by background removal with three fuzzy rules on temperature. Second, those pixels are separated by Connected Component Labeling. Then, each human area is identified. Third, human centroid is calculated. Human movement trajectory is defined as a path of those centroids and recorded at all times. In the experiment, subjects performed 15 motion patterns such as standing and walking for an adult male. The system measured each motion during 1~3 minutes. As the experimental result, the system successfully recorded accurate human movement trajectories in the all.

The importance of measuring vital signs and life style activities in ordinary life besides in medical field has been realized more and more. This is because the affect of life style disease in super aging society has been strongly associated with long term nursing care. Additionally, sensing and information technology has been developed for realizing ease of use, cost reduction, and low intrusion. In this article, systems approach to health care is developed by centering home and medical care. The essence of the notion is using sensory data of vital signs and life style activities in both medical field and home by bridging between them to make medical treatment and home self-care efficient and effective. Systems Health Care mainly composes of Health Management and Knowledge Harvesting technologies. First one is designed for continuous health care and improvement by applying index, criterion, and causality. The second is for causal knowledge extraction process from sensory database, which is used in Health Management. The notion and the technologies are studied and discussed in the article. Application studies follow them by centering health care supporting system and employing both vital signal and life style activities monitoring. Blood pressure analysis program used in medical field as vital signal monitoring is employed. Regarding life style activities, active mass monitoring, non-contact sleep monitoring, and weight-loss programs are introduced.

Both burden of human and performance of sensing technology should be carefully considered in bioinstrumentation. The article proposes the ideas of invasion, intrusion, and consciousness in biomedical sensing to improve its comfort and performance. Consideration of the three concepts is very important to realize burden-reduction of human. They also save sensing accuracy when sensing targets and sensor heads are influenced by invasion, intrusion, or consciousness during sensing. Realizing noninvasive, nonintrusive, and unconscious sensing requires solutions against uncertainty in sensory signals and estimation models. Sensory signals from these sensing methods might superimpose various types of signals besides target one. Target signals would be appropriately extracted by the causal analysis-based model. Case studies were investigated by considering the method and experimental results were reported in the article.

This paper proposes a body weight prediction method using auto regressive (AR) model and Fuzzy-AR model. First, we employ 6 persons 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's Information Criterion. In the experiment, we predicted body weight change of next day for those subjects. The AR model obtained 0.798 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 0.558 in correlation coefficient on 2 subjects.

Japanese government has limited CO2emissions and energy consumption for enterprises for these years. Especially, Japanese manufacturing industry have to fight to reduce energy consumption because they consumes a large amount of energy. Meanwhile, productivity must be kept when reducing energy consumption in manufacturing. The paper proposes the method of health monitoring with considering transparent productivity for QCDE optimization in manufacturing process. QCDE stand for Quality, Cost, Delivery, and Energy. he proposed method aims to reduce energy consumption without any QCD reduction by monitoring precise energy consumption of manufacturing equipment. Our proposed method quantifies operational condition of equipments by the energy consumption of equipments. The experimental results yielded that wasted energy consumption by the target equipment was extracted via monitoring its condition and energy consumption.

Because almost all nations have come into aging society, it has been much more important to provide systems approach on health care than ever. There are many studies on human body from the view points of diseases however health has not been clearly defined. It would be difficult to clarify the notion of health because of various concepts of values and some cultural issues. With regarding this situation, Systems Healthcare would be new study area considering human health and wellness based on sensing and information technology. There have been developed various smart devices and services to prevent diseases and to improve health based on sensory data to realize the condition of our health. Causality between lifestyle and vital signals plays a important role for realizing them. In this article, the notion of Systems Healthcare is proposed and discussed.

This paper describes a method for a heartbeat and respiratory rate monitoring system using air pressure sensors and ultrasonic oscillosensor. By using these sensors, we propose a detection method of the state of human and an extraction method of heartbeat and respiratory rate in bed by fuzzy logic. Our method was examined on four healthy volunteers. We successfully detected the state of human and extracted heartbeat and respiratory signals. In our method, fuzzy logic plays a primary role in the detection of the state and extraction of heartbeat and respiratory signals. An experiment on four healthy volunteers was done. Consequently, our proposed method noninvasively and successfully detects the state of human and extracted heartbeat and respiratory rate in the bed by using the unconstrained sensors.

This paper describes a method for a respiratory rate monitoring system by an air pressure sensor. By using this sensor, we propose a detection method of a respiratory rate for human in bed by fuzzy logic. Our method was examined on four healthy volunteers. We successfully detected the respiratory rate and the time of apnea state. In our method, fuzzy logic plays a primary role in the detection of respiratory points. The experimental results showed that the error ratio of respiratory rate was 1.3% and the error of time of apnea state was 1.1 seconds. Consequently, this system can noninvasively detect the respiratory rate and the time of apnea state by using an unconstrained device.

Recently, the increase of care burden due to the increase of number of the elderly dementia patients is a matter of concern in Japan. However, dementia of the elderly tends to be wrongly recognized as the effect of aging, and there are many cases in which early detection are difficult. In this paper, we focus on the cognitive impairment as one of the core symptoms of dementia, and propose the fuzzy estimation system to detect the level of dementia through monitoring the participants' sleep using air pressure and ultrasonic sensor systems which were developed by our laboratory. As a result of applying this method to twenty-three women in a nursing home, we could confirm the high correlation between the degree of dementia and the truth value, the score of Revised Hasegawa's dementia scale.

This paper proposes functional assessment system of autonomic nervous system by the heart rate variability using an air cushion sensor. The air cushion sensor can unconstraintly detect vital information by sitting down on the sensor. We perform functional assessment of autonomic nervous system by heart rate variability obtained by the system. We built the real time display system for visualizing the autonomic nervous system functions. In this system, we employ fuzzy membership functions with dynamic parameter to detect RR intervals. The experimental results show that we detect RR intervals with the correlation coefficient of 0.846 with comparison to that of electrocardiograph. Then, the errors of the HF (index of parasympathetic system) and the LF/HF (index of sympathetic system) are 18.34% and 16.99%, respectively.

In a factory setting, the zero-breakdown concept of manufacturing processes has been eagerly anticipated. In this article, the notion of machine health management technology is introduced in response to this requirement. The technology employs a cyclical and evolutional problem-solving approach using cause-effect structures. This paper will utilize an automatic acquisition method by using sensing data of machine behaviors. The problem to be solved here is to diagnose an anomaly using tact time delays of manufacturing lines. The cause-effect structure will illustrate the relationship between tact time delay anomalies and machine behaviors that lead to the anomalous conditions. The cause of the tact time delay can be identified by applying the cause-effect structure acquired through adopting a novel statistical processing approach using I/O signals of programmable logic controllers (PLCs). Experiments were conducted using an experimental manufacturing line to validate the effectiveness of the proposed method.

In order to realize heart rate monitoring on a bed, there are mainly two types of approaches similar to other signal processing applications: frequency domain analysis and time-series domain analysis. In frequency domain analysis, FFT is widely used to extract heart rate from obtained signals. Since FFT assumes constant frequency, it cannot be used for extracting microscopic variability of heart rate. In time-series domain analysis, pattern matching based on autocorrelation is commonly used. The method is not only advanced in sensitivity to heart rate variability, but it is also sensitive to unexpected noise. In response to these problems, heart rate monitoring technology is proposed by using air pressure sensor. In this paper, a heart rate estimation algorithm employing fuzzy logic is proposed and effectiveness of fuzzy logic applied to biomedical sensing is discussed. The experiments were conducted to validate the effectiveness of the proposed technology by comparing it with other methods such as pattern matching based on autocorrelation.

This paper proposes a functional assessment system of autonomic nervous system using an air pressure sensor. The air pressure sensor can unconstraintly detect vital information by placing it under the mattress in bed. We perform functional assessment of autonomic nervous system by heart rate variability obtained by the system. In this system, we employ fuzzy membership functions with dynamic parameter to detect RR intervals. The experimental results show that we detect RR intervals with the correlation coefficient of 0.851 with comparison to that of electrocardiograph. Then the errors of the HF (index of parasympathetic system) and the LF/HF (index of sympathetic system) are 11.98% and 22.18%, respectively.

This paper presents an efficient algorithm for iris recognition using phase-based image matching--an image matching technique using phase components in 2D Discrete Fourier Transforms (DFTs) of given images. Experimental evaluation using CASIA iris image databases (versions 1.0 and 2.0) and Iris Challenge Evaluation (ICE) 2005 database clearly demonstrates that the use of phase components of iris images makes possible to achieve highly accurate iris recognition with a simple matching algorithm. This paper also discusses major implementation issues of our algorithm. In order to reduce the size of iris data and to prevent the visibility of iris images, we introduce the idea of 2D Fourier Phase Code (FPC) for representing iris information. The 2D FPC is particularly useful for implementing compact iris recognition devices using state-of-the-art Digital Signal Processing (DSP) technology.

This paper presents a technique for high-accuracy correspondence search between two rectified images using 1D phase-only correlation (POC). The correspondence search between stereo images can be reduced to 1D search through image rectification. However, we usually employ block matching with 2D rectangular image blocks for finding the best matching point in the 1D search. We propose the use of 1D POC (instead of 2D block matching) for stereo correspondence search. The use of 1D POC makes possible significant reduction in computational cost without sacrificing reconstruction accuracy compared with the 2D POC-based approach. Also, the resulting reconstruction accuracy is much higher than those of conventional stereo matching techniques using SAD (sum of absolute differences) and SSD (sum of squared differences) combined with sub-pixel disparity estimation.

This report describes a fracture surgery support system by using an eddy current. In fracture surgery, screw holes on an intramedullary nail are in invisible situation. Although conventional X-ray methods can visualize screw holes in bone, they pose a danger due to X-ray exposure. Therefore, a system to find screw holes without X-ray exposure is required. We solved this problem using a concept of soft computing and searching signals obtained by the eddy current. The screw hole position is lactated by using knowledge on the shape of an intramedullary nail, which means the size of the screw holes and the position of the not flat, after extracting information of the intramedullary nail from the searching signals. By using this knowledge, we can select only needful information. As a result, the screw hole positions could be identified with the smallest error of 0.99 mm when the distance between the probe and the intramedullary nail was 8. 00 mm The screw was successfully inserted to the screw holes in this accuracy.

Manufacturing processes are actually alive because the factors of the processes changes continuously and suddenly. In response to the change problems, sophisticated knowledge of skilled human experts has been applied to solve them flexibly. However, this kind of solution is subjective, inconsistent, and dependent on certain experts. Deskilling is a key to realize stable manufacturing with reasonable quality, cost, and delivery. Many types of automated systems have been deployed at the manufacturing process for realizing the deskilling. However, there still remain problems in flexibility against the changes. In this article, an automated sensory inspection system is employed to discuss the problems and the flexible solution against the changes. Based on the discussion, two types of solutions are proposed. The first one is fusion model and the second, human-machine collaborative decision making. Both solutions are considering the data distribution maturity in the manufacturing line.

A major approach for palmprint recognition today is to extract feature vectors corresponding to individual palmprint images and to perform palmprint matching based on some distance metrics. One of the difficult problems in feature-based recognition is that the matching performance is significantly influenced by many parameters in feature extraction process, which may vary depending on environmental factors of image acquisition. This paper presents a palmprint recognition algorithm using phase-based image matching. The use of phase components in 2D (two-dimensional) discrete Fourier transforms of palmprint images makes possible to achieve highly robust palmprint recognition. Experimental evaluation using a palmprint image database clearly demonstrates an efficient matching performance of the proposed algorithm.

Production lines are alive because the important factors of production such as man, machine, material, method, and environment (4M+E for short) change suddenly and continuously. The change causes serious obstacles to achieving the requirements of quality, cost, and delivery (QCD for short). In this article, a progressive solution to the production change problem is proposed and discussed. The target is product inspection of production process. The problem to be solved here is to acquire the inspection model and criteria from multivariate data set extracted from sensing data. The change of 4M + E and QCD in production is reflected in the data distribution; i.e., sophistication of production process is strongly connected with data distribution maturity. The proposed method is to fuse one class SVM as a non-parametric method and MTS as a parametric method according to the data distribution maturity. Experiments were conducted to evaluate performance of the proposed method.

In this paper, we propose a software platform for realizing applications using socially-intelligent agents. Socially-intelligent agents are software agents which employ social intelligence. Although there exist many types of social intelligence, we focus on a type of social intelligence for providing emotional support. Applying the type of social intelligence, our socially-intelligent agents behave like people by simulating social behavior of people. We show how we realize the socially-intelligent agents through an explanation on the conceptual architecture of our software platform. We also show the mechanism for generating social behavior which makes our software platform unique. According to the theory of media equation, we expected socially-intelligent agents will cause good effect for users. To justify our expectation, we implemented an application of our socially-intelligent agents for eLearning. We give explanation about the application and how socially-intelligent agents work in it. We also discuss our ideas on social intelligence and show our direction for future works.

A major approach for fingerprint recognition today is to extract minutiae from fingerprint images and to perform fingerprint matching based on the number of corresponding minutiae pairings. One of the most difficult problems in fingerprint recognition has been that the recognition performance is significantly influenced by fingertip surface condition, which may vary depending on environmental or personal causes. Addressing this problem, this paper presents a fingerprint recognition algorithm using phase-based image matching. The use of phase components in 2D (two-dimensional) discrete Fourier transforms of fingerprint images makes possible to achieve highly robust fingerprint recognition for low-quality fingerprints. Experimental evaluation using a set of fingerprint images captured from fingertips with difficult conditions (e.g., dry fingertips, rough fingertips, allergic-skin fingertips) demonstrates an efficient recognition performance of the proposed algorithm compared with a typical minutiae-based algorithm.

A major approach for iris recognition today is to generate feature vectors corresponding to individual iris images and to perform iris matching based on some distance metrics. One of the difficult problems in feature-based iris recognition is that the matching performance is significantly influenced by many parameters in feature extraction process, which may vary depending on environmental factors of image acquisition. This paper presents an efficient algorithm for iris recognition using phase-based image matching. The use of phase components in 2D (two-dimensional) discrete Fourier transforms of iris images makes possible to achieve highly robust iris recognition in a unified fashion with a simple matching algorithm. Experimental evaluation using an iris image database clearly demonstrates an efficient matching performance of the proposed algorithm.

This paper proposes a face recognition system that uses (i) passive stereo vision to capture three-dimensional (3D) facial information and (ii) 3D matching using a simple ICP (iterative closest point) algorithm. So far, the reported 3D face recognition techniques assume the use of active 3D measurement for 3D facial capture. However, active methods employ structured illumination (structure projection, phase shift, gray-code demodulation, etc.) or laser scanning, which is not desirable in many human recognition applications. A major problem of using passive stereo vision for 3D measurement is its low accuracy, and thus no passive methods for 3D face recognition have been reported previously. Addressing this problem, we have newly developed a high-accuracy 3D measurement system based on passive stereo vision, where phase-based image matching is employed for sub-pixel disparity estimation. This paper presents the first attempt to create a practical face recognition system based on fully passive 3D reconstruction.

As robot technology is evolving and creating a social community between humans and robots, it is necessary to research and develop a new type of intelligence, which we refer to as "social intelligence''. Social intelligence enables natural and socially appropriate interactions. Its importance is gaining a growing interest among not just the human-computer interaction researchers but also robot technology researchers and developers. This article discusses the definition, importance, and benefits of social intelligence in human and robot collaborative systems. The virtual social environment is employed to implement an experimental social intelligence system because of its low cost and high flexibility. Software robots (i.e. agents) with the social intelligence model have been implemented by featuring an emotion model and a personality model under the virtual environment. The social intelligence model that handles affective responses is based on the theories of personality, emotion, and human-media interaction such as cognitive appraisal theory and media equation. The experiment was conducted with the virtual learning collaborative system to examine the effect of the social intelligence model in the collaborative system. The data showed that the users had more positive impressions about the usefulness and the application and learning experience when the cooperative agent displayed some social responses with personality and emotions. It should be noted here that the cooperative agent did not provide any explicit assistance for the human user such as giving clues and showing answers, and yet the user's evaluation on the usefulness of the learning system was influenced by the social agent. The data also suggested that the cooperative agent contributed to the effectiveness of the learning system.

As machine evolution continues, human-machine collaboration systems are increasing their importance. In this paper, a social and intelligent agent is discussed as a strategy for improving the effectiveness and efficiency of human-machine collaboration. The proposed approach uses an embedded mind model, which enables agents to interact with the human user consistently, context-dependently, and socially. The relationship between the mind model, personality, and social behavior of the agents is studied through implementation of a prototype system as an e-learning application.

In a fuzzy relational database where a relation is a fuzzy set of tuples and ill-known data are represented by possibility distributions, nested fuzzy queries can be expressed in the Fuzzy SQL language. Although it provides a very convenient way for users to express complex queries, a nested fuzzy query may be very inefficient to process with the naive evaluation method based on its semantics. In conventional databases, nested queries are unnested to improve the efficiency of their evaluation. In this paper, we extend the unnesting techniques to process several types of nested fuzzy queries. An extended merge-join is used to evaluate the unnested fuzzy queries. As shown by both theoretical analysis and experimental results, the unnesting techniques with the extended merge-join significantly improve the performance of evaluating nested fuzzy queries.

Under the downsizing boom and cost decrease in computer-related industries, both business and individual computer users have been greatly influenced. For instance, the users will come up against the chance to handle imprecise data in real life with their computers more than ever. Therefore, solutions for easily developing fuzzy systems are strongly needed. In response to this need, many studies have been done in the fuzzy database area because fuzzy retrieval is one of the suitable ways to deal with those kind of data. In this article, the development of a fuzzy retrieval system which can be used on a personal computer is described, especially from an industrial point of view. Additionally, the effects of fuzzy retrieval are proposed and its thinkable applications are introduced. © 1996 John Wiley & Sons, Inc.

Fuzzy databases have been introduced to deal with uncertain or incomplete information in many applications. The efficiency of processing fuzzy queries in fuzzy databases is a major concern. We provide techniques to unnest nested fuzzy queries of two blocks in fuzzy databases. We show both theoretically and experimentally that unnesting improves the performance of nested queries significantly. The results obtained in the paper form the basis for unnesting fuzzy queries of arbitrary blocks in fuzzy databases.>

Approaches are proposed to allow fuzzy terms to be interpreted according to the context within which they are used. Such an interpretation is natural and useful. A query-dependent interpretation is proposed to allow a fuzzy term to be interpreted relative to a partial answer of a query. A scaling process is used to transform a pre-defined meaning of a fuzzy term into on appropriate meaning in the given context. Sufficient conditions are given for a nested fuzzy query with RELATIVE quantifiers to be unnested for an efficient evaluation. An attribute-dependent interpretation is proposed to model the applications in which the meaning of a fuzzy term in an attribute must be interpreted with respect to values in other related attributes. Two necessary and sufficient conditions for a tuple to have a unique attribute-dependent interpretation are provided. We describe an interpretation system that allows queries to be processed based on the attribute-dependent interpretation of the data. Two techniques, grouping and shifting, are proposed to improve the implementation.>

A new 17-GHz radio interferometer dedicated for solar observations was constructed in two years at Nobeyama, Nagano. It consists of eighty-four 80-cm-diameter antennas arranged in a tee-shaped array extending 490 m in east-west and 220 m in north-south directions. Since late June of 1992, radio full-disk images of the Sun have been observed for 8 h every day. The spatial resolution is 10" and the temporal resolution is 1 s and also 50 ms for selected events. Every 10 s correlator data are synthesized into images in real time and displayed on a monitor screen. The array configuration is optimized to observe the whole Sun with high spatial and temporal resolution and a high dynamic range of images. Image quality of better than 20 dB is realized by incorporation of technical advances in hardware and software, such as (1) low-loss phase-stable optical-fiber cables for local reference signal and IF signals, (2) newly developed phase-stable local oscillators, (3) custom CMOS gate-array LSTs of 1-b quadraphase correlators for 4/spl times/4 combinations, and (4) new image processing techniques to suppress large sidelobe effects due to the solar disk and extended sources.>