One of the challenging issues in affective computing is to give a machine the ability to recognize the affective states with intensity of a person. Few studies are directed toward this goal by categorizing affective behavior of the person into a set of discrete categories. But still two problems exist: gesture is not yet a concern as a channel of affective communication in interactive technology, and existing systems only model discrete categories but not affective dimensions, e.g., intensity. Modeling the intensity of emotion has been well addressed in synthetic autonomous agent and virtual environment literature, but there is an evident lack of attention in other important research areas such as affective computing, machine vision, and robotic. In this work, we propose an affective gesture recognition system that can recognize the emotion of a child and the intensity of the emotion states in a scenario of game playing. We used levels of cognitive and non-cognitive appraisal factors to estimate intensity of emotion. System has an intelligent agent (called Mix) that takes these factors into consideration and adapt the game state to create a more positive interactive environment for the child.

An effective human-robot interaction is essential for wide penetration of service robots into the market. Such robot needs a vision system to recognize objects. It is, however, difficult to realize vision systems that can work in various conditions. More robust techniques of object recognition and image segmentation are essential. Thus, we have proposed to use the human user's assistance for objects recognition through speech. This paper presents a system that recognizes objects in occlusion and/or multicolor cases using geometric and photometric analysis of images. Based on the analysis results, the system makes a hypothesis of the scene. Then, it asks the user for confirmation by describing the hypothesis. If the hypothesis is not correct, the system generates another hypothesis until it correctly understands the scene. Through experiments on a real mobile robot, we have confirmed the usefulness of the system.

Extracting visual motion is very important for understanding dynamic actions and for extracting dynamic events from video sequences. Recently, it was shown that some invariants on motions can be extracted from sequential images and applied for recognizing motions from images viewed from arbitrary viewpoints. Unfortunately, these space-time invariants were limited for planar motions viewed from affine cameras. In this paper, we propose a method for computing space-time invariants on non-planar motions viewed from two perspective cameras. The extracted invariants are applied for distinguishing 3D motions from video sequences viewed from arbitrary viewpoints.

The authors propose a single-layer hollow-waveguide 8-way Butler matrix. All components of the Butler matrix are in a single layer which contributes to low-cost fabrication. To reduce the length of the couplers, a step structure is installed in the coupled region. 50% length reduction is obtained in comparison with the conventional design using reflection-suppressing posts in the coupled region. The total size of the matrix is 17.1λg6.0λg. The full structure of the matrix is fabricated by hollow waveguides at 22 GHz band and the total measured loss is only 0.25 dB.

We developed a system that detects the vehicle driving immediately ahead of one's own car in the same lane and measures the distance to and relative speed of that vehicle to prevent accidents such as rear-end collisions. The system is the first in the industry to use non-scanning millimeter-wave radar combined with a sturdy stereo image sensor, which keeps cost low. It can operate stably in adverse weather conditions such as rain, which could not easily be done with previous sensors. The system's vehicle detection performance was tested, and the system can correctly detect vehicles driving 3 to 50 m ahead in the same lane with higher than 99% accuracy in clear weather. Detection performance in rainy weather, where water drops and splashes notably degraded visibility, was higher than 90%.

Expressions are presented for the probability of target detection and the measurement accuracy of the detection, taking into account the effects of antenna beam-pointing error. Evaluation of these expressions requires numerical integration, which is computationally expensive. Approximate but analytic and efficient expressions are also presented. Numerical examples are given to present the relative accuracy of our analytic approximations.

The effects of rapid thermal annealing (RTA) on bias-stress-induced base leakage were investigated in InGaP/GaAs collector-up heterojunction bipolar transistors (C-up HBTs) fabricated with boron ion implantation. C-up HBTs annealed at 700 for 1 s had negligible leakage, while non-annealed C-up HBTs had leakage (with an activation energy, Ea, of 0.17 eV) that exponentially increased with bias time. Because this Ea is almost the same as that of the hole traps (0.25 eV) observed in the InGaP emitters of non-annealed C-up HBTs, we attribute the leakage to hole tunneling from bases to emitters. By reducing the initial trap density using RTA, we stabilized current gain even after 1,030 h of testing at a junction temperature of 210 and a collector current density of 40 kA/cm2.

In this paper, we study how a multidimensional local binary pattern (LBP) texture feature data can be visually explored and analyzed. The goal is to determine how true paper properties can be characterized with local texture features from visible light images. We utilize isometric feature mapping (Isomap) for the LBP texture feature data and perform non-linear dimensionality reduction for the data. These 2D projections are then visualized with original images to study data properties. Visualization is utilized in the manner of selecting texture models for unlabeled data and analyzing feature performance when building a training set for a classifier. The approach is experimented on with simulated image data illustrating different paper properties and on-line transilluminated paper images taken from a running paper web in the paper mill. The simulated image set is used to acquire quantitative figures on the performance while the analysis of real-world data is an example of semi-supervised learning.

For hybrid Multimedia-on-Demand (MoD) systems which support broadcast, batch and interactive services, the charging scheme employed plays an important role in the delivery of good service quality to users, while also determining the revenue generated for the service provider. In this letter a new charging scheme is proposed. This scheme provides the same quality of service to the users as previous charging schemes while providing higher revenue. Numerical results are presented to evaluate the performance of the new charging scheme in comparison with previous schemes.

This paper proposes a novel image processing method based on a percolation model. The percolation model is used to represent the natural phenomenon of the permeation of liquid. The percolation takes into account the connectivity among the neighborhoods. In the proposed method, a cluster formation by the percolation process is performed first. Then, feature extraction from the cluster is carried out. Therefore, this method is a type of scalable window processing for realizing a robust and flexible feature extraction. The effectiveness of proposed method was verified by experiments on crack detection, noise reduction, and edge detection.

In the present paper, we propose a method for reconstructing the surfaces of objects from stereo data. Both the fitness of stereo data to surfaces and interrelation between the surfaces are defined in the framework of a three-dimensional (3-D) Markov Random Field (MRF) model. The surface reconstruction is accomplished by searching for the most likely state of the MRF model. Three experimental results are shown for synthetic and real stereo data.

This paper proposes a gonio-spectral imaging system for measuring light reflection on an object surface by using two robot arms, a multi-band lighting system, and a monochrome digital camera. It allows four degrees of freedom in incident and viewing angles necessary for full parametrization of a reflection model function. Spectral images captured for various incident and viewing angles are warped as if they were all captured from the same viewing direction. The intensity of reflected light is thus recorded in a normalized image form for any incident and viewing directions. The normalized images are used to estimate reflection model parameters at each surface point. To ensure point-wise reflection modeling, a calibration method is also proposed based on a geometrical model of the robot arms and camera. The proposed system can deal with objects with surface texture. Experiments are done on system calibration, reflection model, and spectral estimation. The results using colored objects show the feasibility of the proposed imaging system.

Active Shape Model (ASM) has been shown to be a powerful tool to aid the interpretation of images, especially in face alignment. ASM local appearance model parameter estimation is based on the assumption that residuals between model fit and data have a Gaussian distribution. Moreover, to generate an allowable face shape, ASM truncates coefficients of shape principal components into the bounds determined by eigenvalues. In this paper, an algorithm of modeling local appearances, called AdaBoosted ASM, and a shape parameter optimization method are proposed. In the algorithm of modeling the local appearances, we describe our novel modeling method by using AdaBoosted histogram classifiers, in which the assumption of the Gaussian distribution is not necessary. In the shape parameter optimization, we describe that there is an inadequacy on controlling shape parameters in ASM, and our novel method on how to solve it. Experimental results demonstrate that the AdaBoosted histogram classifiers improve robustness of landmark displacement greatly, and the shape parameter optimization solves the inadequacy problem of ASM on shape constraint effectively.

Road traffic incidents analysis has shown that a third of them occurs without any conflict which indicates problems with road following. In this paper a driving safety assistance system is introduced, whose aim is to prevent the driver drifting off or running off the road. The road following system is based on a frontal on-board monocular camera. In order to get a high degree of reliability and robustness, an original combination of three different algorithms is performed. Low level results from the first two algorithms are used to compute a reliability indicator and to update a high level model through the third algorithm using Kalman filtering. Searching areas of the road sides for the next image are also updated. Experimental results show the reliability and the robustness of this original association of three different algorithms. Various road situations are addressed, including roads with high curvature. A multi-lanes extension is also presented.

Rebuilding an index is an essential step for database recovery. Fast recovery of the index is a necessary condition for fast database recovery. The B+-Tree is the most popular index structure in database systems. In this paper, we present a fast B+-Tree rebuilding algorithm called Max-PL. The main idea of Max-PL is that at first it constructs a B+-Tree index structure using the pre-stored max keys of the leaf nodes, and then inserts the keys and data pointers into the index. The algorithm employs a pipelining mechanism for reading the data records and inserting the keys into the index. It also exploits parallelisms in several phases to boost the overall performance. We analyze the time complexity and space requirement of the algorithm, and perform the experimental study to compare its performance to other B+-Trees rebuilding algorithms; Sequential Insertion and Batch-Construction. The results show that our algorithm runs on average at least 670% faster than Sequential Insertion and 200% faster than Batch-Construction.

This letter proposes a low-complexity single frequency estimator for flat fading channels. The simplified estimator decreases the number of computations in the calculation of the autocorrelation function (AF) when compared to AF-based conventional estimators. The simplified estimator yields a comparable estimation performance to the existing estimators, while retaining the same frequency range.

The EDD connection admission control scheme has been proposed for supporting real-time communication in packet-switched networks. In the scheme, when a connection establishment request occurs, the worst-case link delay in each link along the connection is calculated to determine whether the request can be accepted or not. In order to calculate the worst-case link delay, we must perform a check called the point schedulability check for each of some discrete time instants (checkpoints). Therefore when there are many checkpoints, the worst-case link delay calculation is time-consuming. We have proposed a high-speed calculation method. The method finds some checkpoints for which the point schedulability check need not be performed and removes such unnecessary checkpoints in advance before a connection establishment request occurs, and the check is performed for each of the remaining checkpoints after the request occurs. However, the method is not so effective under the situation that the maximum packet length in networks is large, because the method can find few unnecessary checkpoints under the situation. This paper proposes a new high-speed calculation method. We relax the condition which determines whether or not the point schedulability check need not be performed for each checkpoint in our previous method and derive a new condition for finding unnecessary checkpoints. Using the proposed method based on the new condition, we can increase the number of unnecessary checkpoints compared to our previous method. Numerical examples which are obtained by extensive simulation show that the proposed method can attain as much as about 50 times speedup.

In this paper we propose a chip-level receiver for optical frequency hopping code-division multiple-access (FH-OCDMA) systems. The proposed chip-level receiver for FH-OCDMA consists of an arrayed waveguide grating (AWG), and photo-detector (PD) for each mark chip, and uses the principles of the chip-level receiver. We analyze the error rate performance of the FH-OCDMA system with the proposed chip-level receiver with treating APD noise, thermal noise, and multi-user interference (MUI) using a Gaussian approximation. We compare the performance of the proposed chip-level receiver to that of the conventional correlation receiver. We show that the proposed chip-level receiver has a better bit error probability and can accommodate more users than the conventional correlation receiver.

In this paper, we present a method for recognition of human activity as a series of actions from an image sequence. The difficulty with the problem is that there is a chicken-egg dilemma that each action needs to be extracted in advance for its recognition but the precise extraction is only possible after the action is correctly identified. In order to solve this dilemma, we use as many models as actions of our interest, and test each model against a given sequence to find a matched model for each action occurring in the sequence. For each action, a model is designed so as to represent any activity containing the action. The hierarchical hidden Markov model (HHMM) is employed to represent the models, in which each model is composed of a submodel of the target action and submodels which can represent any action, and they are connected appropriately. Several experimental results are shown.

A lexicon is an important linguistic resource needed for both shallow and deep language processing. Currently, there are few machine-readable Thai dictionaries available, and most of them do not satisfy the computational requirements. This paper presents the design of a Thai lexicon named the TCL's Computational Lexicon (TCLLEX) and proposes a method to construct a large-scale Thai lexicon by re-using two existing dictionaries and a large number of texts on the Internet. In addition to morphological, syntactic, semantic case role and logical information in the existing dictionaries, a sort of semantic constraint called selectional preference is automatically acquired by analyzing Thai texts on the web and then added into the lexicon. In the acquisition process of the selectional preferences, the so-called Bayesian Information Criterion (BIC) is applied as the measure in a tree cut model. The experiments are done to verify the feasibility and effectiveness of obtained selection preferences.