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.

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.

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.

We present a method for classifying image pixels of real images into multiple photometric factors: specular reflection, diffuse reflection, attached shadows and cast shadows. Conventional photometric linearization methods cannot correctly classify pixels under near point light sources, since they assume parallel light. To satisfy this assumption, our method utilizes a photometric linearization method that divides images into small regions. It also propagates linearization coefficients from neighboring regions. Our experimental results show that the proposed method can correctly classify image pixels into photometric factors, even if images are obtained under near point light sources.

Time-frequency-selective, i.e., time-variant multipath, fading in orthogonal frequency division multiplexing (OFDM) systems destroys subcarrier orthogonality, resulting in intercarrier interference (ICI). In general, the previously proposed estimation schemes to resolve this problem are only applicable to slowly time-variant channels or suffer from high complexity due to large-sized matrix inversion. In this letter, we propose and develop efficient symbol estimation schemes, called the iterative sequential neighbor search (ISNS) algorithm and the simplified iterative sequential neighbor search (S-ISNS) algorithm. These algorithms achieve enhanced performances with low complexities, compared to the existing estimation methods.

Decode-and-forward cooperative communications protocol (DFP) allows single-antenna users in wireless medium to obtain the powerful benefits of multi-antenna systems without physical antenna arrays. So far, only signal-to-noise ratio (SNR) or square amplitude of path gain has been used to evaluate the reliability of received signals for relays to decide whether to forward the decoded data so as to prevent unsuccessful detection at the relays. In this paper, we propose using log-likelihood ratio (LLR) as an alternative to SNR in the conventional DFP. Closed-form BER expressions for different versions of DFP are also derived and verified by Monte-Carlo simulations. A variety of numerical results reveal the significant superiority of LLR-based DFP to SNR-based DFP regardless of threshold level and relay position under flat Rayleigh fading channel plus additive white Gaussian noise (AWGN).

We describe attempts to have robots behave as embodied knowledge media that will permit knowledge to be communicated through embodied interactions in the real world. The key issue here is to give robots the ability to associate interactions with information content while interacting with a communication partner. Toward this end, we present two contributions in this paper. The first concerns the formation and maintenance of joint intention, which is needed to sustain the communication of knowledge between humans and robots. We describe an architecture consisting of multiple layers that enables interaction with people at different speeds. We propose the use of an affordance-based method for fast interactions. For medium-speed interactions, we propose basing control on an entrainment mechanism. For slow interactions, we propose employing defeasible interaction patterns based on probabilistic reasoning. The second contribution is concerned with the design and implementation of a robot that can listen to a human instructor to elicit knowledge, and present the content of this knowledge to a person who needs it in an appropriate situation. In addition, we discuss future research agenda toward achieving robots serving as embodied knowledge media, and fit the robots-as-embodied-knowledge-media view in a larger perspective of Conversational Informatics.

The rapid hybrid acquisition of PN sequences is proposed for DS/CDMA systems. The system introduces the excision CFAR method into the background power estimation. A mathematical analysis is done for the single path and multipath environments. The detection performance of the proposed scheme is compared with that of other acquisition schemes. Results show that the proposed method has better detection performance if the excision coefficient is properly selected.

We propose a sub-optimal but computationally efficient Modified Fano Detection algorithm (MFD) for V-BLAST systems. This algorithm utilizes the QR decomposition of the channel matrix and the sequential detection scheme based on tree searching to find the optimal symbol sequence. For more reliable signal detection, the decoder is designed to move backward for the specified value at the end of the tree. This results in significant reduction of the complexity while the performance of MFD is comparable to that of ML detector.

A new simply implemented collusion-attack free identity-based non-interactive key sharing scheme (ID-NIKS) has been proposed. A common-key can be shared by executing only once a modular exponentiation which is equivalent to RSA deciphering, and the security depends on the difficulty of factoring and the discrete logarithm problem. Each user's secret information can be generated by solving two simple discrete logarithm problems and synthsizing their solutions by linear combination. The detail comparison with the Maurer-Yacobi's scheme including its modified versions shows that the computational complexity to generate each user's secret information is much smaller and the freedom to select system parameters is much greater than that of the Maurer-Yacobi's scheme. Then our proposed scheme can be implemented very easily and hence it is suitable for practical use.

This paper proposed a novel intelligent intrusion detection, decision, response system with fuzzy theory. This system utilized the two essential informations: times and time, of the failed login to decide automatically whether this login is a misuse user as alike as experienced system/security administrators. The database of this system isn't preestablished before working but is built and updated automatically during working. And this system is not only notification system but gives the exact and rapid decision and response to a misuse.

In this paper, the performance of Tree-LDPC code [1] is presented based on the min-sum algorithm with scaling and the asymptotic performance in the water fall region is shown by density evolution. We presents that the Tree-LDPC code show a significant performance gain by scaling with the optimal scaling factor [3] which is obtained by density evolution methods. We also show that the performance of min-sum with scaling is as good as the performance of sum-product while the decoding complexity of min-sum algorithm is much lower than that of sum-product algorithm.

Constraint-based software specifications enable run-time monitoring to detect probable risk events and ensure the desired system behavior. SpecTRM-RL is a well-developed constraint-based specification method for computer-controlled systems. However, it is desirable to express constraints in familiar visual models. To provide better visualization and popularity, we developed methods to represent all the SpecTRM-RL constraint types in UML. We have also extended SpecTRM's constraints by adding relational and global constraints, and then expressed them in OCL. Safety verification of these specifications is also proposed. We developed a systematic way to construct fault trees for safety analysis based on UML diagrams. Due to the generality of UML as well as the defensive manner of constraints and fault tree analysis, our approach can be adapted for both general applications and safety-critical applications.

In this paper, we propose a new method to enhance the fault-tolerance of the Content Addressable Network (CAN), which is known as a typical pure P2P system based on the notion of Distributed Hash Table (DHT). The basic idea of the proposed method is to introduce redundancy to the management of index information distributed over the nodes in the given P2P network, by allowing each index to be assigned to several nodes, which was restricted to be one in the original CAN system. To keep the consistency among several copies of indices, we propose an efficient synchronization scheme based on the notion of labels assigned to each copy in a distinct manner. The performance of the proposed scheme is evaluated by simulation. The result of simulations indicates that the proposed scheme significantly enhances the fault-tolerance of the CAN system.

We consider a hybrid direct-sequence frequency-hopped (DS/FH) code division multiple access (CDMA) communication system, where the transmission power, data rate (i.e. spreading gain), and hopping frequency are adapted relative to the channel variations. Instead of random frequency hopping, hopping pattern is adaptively adjusted to obtain the maximum channel gain among available frequency slots. Transmission power and/or data rate are also adapted such that a target transmission quality is maintained. It is shown that the proposed scheme provides a higher average data rate than pure DS/CDMA with power and rate adaptations, subject to the identical bandwidth and average transmission power constraints.

The fast handover protocol adopted in a IPv6 hierarchical structure provides a seamless handover in wireless IP networks by minimizing the handover latency. To reduce the handover latency, the fast handover uses anticipation based on layer 2 trigger. Nonetheless, a mobile node can still lose its connection with the old link during the fast handover procedures. Accordingly, this paper analyzes the handover latency and packet delivery costs associated with fast handover failure cases based on a timing diagram.

Two schemes for fast identification of JPEG coded images are proposed in this paper. The aim is to identify the JPEG images that are generated from the same original image and have equivalent or different compression ratios. Fast identification can be achieved since the schemes work on the quantized Discrete Cosine Transform (DCT) domain. It is not required to inverse the quantization and the DCT. Moreover, only a few coefficients are commonly required for identification. The first approach can avoid identification leakage or false negative (FN), and probably result in a few false positives (FP). The second approach can avoid both FN and FP, with a slightly higher processing time. By combining the two schemes, a faster and a more perfect identification can be achieved, in which FN and FP can be avoided.

In wireless local area networks (WLANs), the necessity of quality-of-service (QoS) control for uplink flows is increasing because interactive applications are becoming more popular. Fairness between flows transmitted by stations with different physical transmission rates must be ensured in QoS control for link-adaptive WLANs, which are widely used nowadays. We propose a novel distributed access scheme called QC-DCA to satisfy these requirements. QC-DCA adaptively controls the parameters of carrier sense multiple access with collision avoidance (CSMA/CA). QC-DCA has two QoS control functions: guarantee and classification. QC-DCA guarantees target throughputs and packet delays by quickly adjusting CSMA/CA parameters. In QoS classification, the difference of throughputs and packet delays between different QoS classes is maintained. These two functions allow QC-DCA to suppress the unfairness caused by differences of transmission rates in the physical layer. We evaluated the throughput and delay performances of our scheme using computer simulations. The results show the viability of our scheme.