In this letter, a new channel-adaptive beamforming method is proposed for OFDMA systems with smart antennas. In the method, the size of a cluster for resource unit is determined adaptively according to a region-splitting criterion. It is shown by simulations that the proposed method shows good performance in both frequency-flat and frequency-selective channels.

Congestion control in the Internet consists of two main components: the TCP Additive-Increase Multiplicative-Decrease (AIMD) mechanism on sending windows implemented by end-users, and the Active Queue Management (AQM) scheme implemented in the routers which improves the effectiveness of congestion control. TCP connection is regarded as a feedback control system. Comparably, AQM is classified as a flow controller. There are several kinds of time delays in the network, such as propagation delay, queuing delay in the buffer of the router, etc. The time delays cause degradation of performance and instability of the network. A Smith Predictor is commonly used in feedback control of plants with significant time delays to implement effective compensation. In this paper, a Smith Predictor-based PI-controller for AQM (SPPA) is proposed, which uses a TCP reference model and an average Round-Trip Time (RTT) to reduce unfavorable effects of time delays in TCP networks. The drop probability is calculated by a Proportional-Integral (PI) controller based on the prediction error. When a mismatch exists in between the actual model of the TCP process and the reference model employed by the SPPA, we demonstrate conditions under which the network is stable. The performance, robustness and effectiveness of the proposed SPPA are all evaluated using simulations. The performance of the SPPA is compared with some typical AQMs, such as the Adaptive RED, the PI-controller, and the Proportional-Differential (PD) controller.

We present a practical method of dealing with the influences of floating dummy metal fills, which are inserted to assist planarization by chemical-mechanical polishing (CMP) process, in extracting interconnect capacitances for system-on-chip (SoC) designs. The method is based on reducing the thicknesses of dummy metal layers according to electrical field theory. We also clarify the influences of dummy metal fills on the parasitic capacitance, signal delay, and crosstalk noise. Moreover, we address that interlayer dummy metal fills have more significant influences than intralayer ones in terms of the impact on coupling capacitances. When dummy metal fills are ignored, the error of capacitance extraction can be more than 30%, whereas the error of the proposed method is less than about 10% for many practical geometries. We also demonstrate, by comparison with capacitance results measured for a 90-nm test chip, that the error of the proposed method is less than 8%.

Error performance of DS-CDMA is discussed over independent Rayleigh faded multipath channel employing selective Rake (SRake) receiver. Simple-to-evaluate and accurate error probabilities are given following Holtzman's simplified improved Gaussian approximation (SIGA). Comparing with SIGA, the validity of standard Gaussian approximation (SGA) is then verified. It is shown that SGA is accurate for SRake until some number of combined paths beyond which it becomes optimistic. It is also shown that as compared to single user performance, the SRake performance is relatively less degraded by multiple access interference (MAI) while the number of combined paths is small.

This paper proposes a novel speech synthesis method to generate human-like natural speech. The conventional unit-selection-based synthesis method selects speech units from a large database, and concatenates them with or without modifying the prosody to generate synthetic speech. This method features highly human-like voice quality. The method, however, has a problem that a suitable speech unit is not necessarily selected. Since the unsuitable speech unit selection causes discontinuity between the consecutive speech units, the synthesized speech quality deteriorates. It might be considered that the conventional method can attain higher speech quality if the database size increases. However, preparation of a larger database requires a longer recording time. The narrator's voice quality does not remain constant throughout the recording period. This fact deteriorates the database quality, and still leaves the problem of unsuitable selection. We propose the plural unit selection and fusion method which avoids this problem. This method integrates the unit fusion used in the unit-training-based method with the conventional unit-selection-based method. The proposed method selects plural speech units for each segment, fuses the selected speech units for each segment, modifies the prosody of the fused speech units, and concatenates them to generate synthetic speech. This unit fusion creates speech units which are connected to one another with much less voice discontinuity, and realizes high quality speech. A subjective evaluation test showed that the proposed method greatly improves the speech quality compared with the conventional method. Also, it showed that the speech quality of the proposed method is kept high regardless of the database size, from small (10 minutes) to large (40 minutes). The proposed method is a new framework in the sense that it is a hybrid method between the unit-selection-based method and the unit-training-based method. In the framework, the algorithms of the unit selection and the unit fusion are exchangeable for more efficient techniques. Thus, the framework is expected to lead to new synthesis methods.

CR (Computed Radiography) is characterized by high sensitivity and wide dynamic range. Moreover, it has the advantage of being able to transfer exposed images directly to a computer-aided detection (CAD) system which is not possible using conventional film digitizer systems. This paper proposes a high-performance clustered microcalcification detection system for CR mammography. Before detecting and classifying candidate regions, the system preprocesses images with a normalization step to take into account various imaging conditions and to enhance microcalcifications with weak contrast. Large-scale experiments using images taken under various imaging conditions at seven hospitals were performed. According to analysis of the experimental results, the proposed system displays high performance. In particular, at a true positive detection rate of 97.1%, the false positive clusters average is only 0.4 per image. The introduction of geometrical features of each microcalcification for identifying true microcalcifications contributed to the performance improvement. One of the aims of this study was to develop a system for practical use. The results indicate that the proposed system is promising.

We have developed Interaction Builder (IB), a rapid prototyping tool for constructing web-based Multi-Modal Interaction (MMI) applications. The goal of IB is making it easy to develop MMI applications with speech recognition, life-like agents, speech synthesis, web browsing, etc. For this purpose, IB supports the following interface and functions: (1) GUI for implementing MMI systems without the details of MMI and MMI description language, (2) functionalities of handling synchronized multimodal inputs/outputs, (3) a test run mode for run-time testing. The results of evaluation tests showed that the application development cycle using IB was significantly shortened in comparison with the time using a text editor both for MMI description language experts and for beginners.

An embodied interactive agent has a virtual body that is generally drawn by CG animation. We intuitively assume that the agent's body primarily expresses non-verbal messages, or symbolizes its social characteristics through its appearance. However, we have not objectively elucidated the expressive competence of an agent's body beyond the conclusions of our empirical and subjective intuition. Therefore, it is necessary to explore scientifically how users regard the functional competence of an agent's embodiment. Do users attribute the intelligence of an agent to its virtual body? We investigated how users physically interact with an agent which is merely a virtual entity drawn on the display by CG, through "showing" something to the eyes of the agent, "listening" to something from the mouth of the agent, and "speaking" something into the ears of the agent. However, such interaction does not necessarily attribute the intellectual processing function to the agent, and this issue is explored through two psychological experiments.

In this paper, a three-way divider is proposed for a partially-corporate feed in an alternating phase-fed single-layer slotted waveguide array. The divider is placed at the middle of the feed waveguide and reduces the long line effects; the frequency bandwidth is doubled. It is a kind of cross junction with one input port and three output ports; most of the power is equally divided into the right and left halves of the feed waveguide while the rest of power goes straight into the center radiating waveguide. Based upon the moment method design of the three-way divider, an inductive post is introduced for wide band power dividing control to the radiating waveguide. Reflection is below -20 dB over a wide bandwidth of 24.3-26.3 GHz, and the range of power dividing ratio ranges from 1/43 to 1/4. The amplitude and the phase from the two output ports to the feed waveguide are well balanced, and the differences are less than 0.1 dB and 5.0 degrees, respectively. The MoM analysis and the wide band design are verified experimentally in the 4 GHz band.

In order to simultaneously combat both of the inter-carrier interferences (ICIs) and multiple access interferences (MAIs) to achieve reliable performance in multi-carrier code division multiple access (MC-CDMA) systems, this letter proposes a maximum likelihood based scheme for joint frequency offset estimation and multiuser symbol detection. To reduce the computational complexity called for by the joint decision statistic without extra mechanisms, the genetic algorithm (GA) is employed to solve the nonlinear optimization involved. Due to the robustness of the GA, the joint decision statistic can be efficiently solved, and, as shown by furnished simulation results, the proposed approach can offer satisfactory performance in various scenarios.

This paper proposes two space-time joint channel parameter estimation and signal detection algorithms for downlink DS-CDMA systems with multiple-input-multiple-output (MIMO) wireless multipath fading channels. The proposed algorithms initially use the space-time MUSIC to estimate the DOA-delays of the multipath channel. Based on these estimated DOA-delays, a space-time channel decoupler is developed to decompose the multipath downlink channel into a set of independent parallel subchannels. The fading amplitudes of the multipath can then be estimated from the eigen space of the output of the space-time channel decoupler. With these estimated channel parameters, signal detection is carried out by a maximal ratio combiner on a pathwise basis. Computer simulations show that the proposed algorithms outperform the conventional space-time RAKE receiver while having the similar performance compared with the space-time minimum mean square error receiver.

This paper proposes a backlight module which drives multiple cold-cathode fluorescent lamps (CCFLs) with a current mirror technique to equalize the driving current for each lamp. We first adopt a half-bridge parallel-resonant inverter as the main circuit and use a single-input, multiple-output transformer to drive the multi-CCFLs. Next, we introduce current-mirror circuits to create a new current-sharing circuit, in which its current reference node and the parallel-connected multi-load nodes are used to accurately equalize all CCFLs' driving current. This will balance each lamp's brightness and, consequently, improve the picture display quality of the related liquid crystal display (LCD). This paper details the design concept for each component value with the assistance of an actual design example. The results of the example are examined with its actual measurements, which consequently verify the correctness of the proposed control strategy.

Because of the large scale of wireless sensor networks, the configuration needs to be done autonomously. In this paper, we present Scalable Data Collection (SDC) protocol, a tree-based protocol for collecting data over multi-hop, wireless sensor networks. The design of the protocol aims to satisfy the requirements of sensor networks that every sensor transmits sensed data to a sink node periodically or spontaneously. The sink nodes construct the tree by broadcasting a solicit packet to discover the child nodes. The sensor receiving this packet decides on an appropriate parent to which it will attach, it then broadcasts the same packet to discover its child nodes. Through this process, the tree is created autonomously without any flooding of the routing packets. SDC avoids periodic updating of routing information but the tree need to be reconstructed upon node failures or adding of new nodes. The states required on each sensor are constant and independent of network size, therefore SDC scales better than the existing protocols. Moreover, each sensor can make forwarding decisions regardless of the knowledge on geographical information. We evaluated the performance of SDC by using the ns-2 simulator and comparing with Directed Diffusion, DSR, AODV, and OLSR. The simulation results demonstrate that SDC achieves much higher delivery ratio, shorter delay, as well as high scalability in various scenarios.

The gamma correction for the CMOS image sensors are implemented by the method of piecewise linear approximation through a look-up table. In this paper, we propose a quantitative criterion to select the piece linear segment with the same output interval for the reduction of the error between the value of piece linear approximation and gamma correction. After the gamma correction is implemented, the average error occurred by implementing color interpolation in each segment is a basis for the optimum selecting of the piece linear segment of the gamma correction for the CMOS image sensors.

In a conventional method based on quadrature 2D Gabor wavelets to extract iris features, the iris recognition is performed by a 256-byte iris code, which is computed by applying the Gabor wavelets to a given area of the iris. However, there is a code redundancy because the iris code is generated by basis functions without considering the characteristics of the iris texture. Therefore, the size of the iris code is increased unnecessarily. In this paper we propose a new feature extraction algorithm based on independent component analysis (ICA) for a compact iris code. We implemented the ICA to generate optimal basis functions which could represent iris signals efficiently. In practice the coefficients of the ICA expansions are used as feature vectors. Then iris feature vectors are encoded into the iris code for storing and comparing individual's iris patterns. Additionally, we introduce a method to refine the ICA basis functions for improving the recognition performance. Experimental results show that our proposed method has a similar equal error rate as a conventional method based on the Gabor wavelets, and the iris code size of our proposed methods is five times smaller than that of the Gabor wavelets.

A recursive quadratic programming (RQP) approach is proposed for multiuser detection in multicarrier code-division multiple-access (MC-CDMA) systems. In this approach, the combinatorial problem associated with the optimal maximum likelihood (ML) detection is relaxed to a quadratic programming (QP) problem first and then a recursive approach is developed to improve the detection performance. Computer simulations are presented which demonstrate that the detector developed based on the proposed approach offers close-to-optimal symbol-error rate (SER) performance which outperforms several existing suboptimal detectors.

We are developing a helper robot that carries out tasks ordered by the user through speech. The robot needs a vision system to recognize the objects appearing in the orders. It is, however, difficult to realize vision systems that can work in various conditions. Thus, we have proposed to use the human user's assistance through speech. When the vision system cannot achieve a task, the robot makes a speech to the user so that the natural response by the user can give helpful information for its vision system. Our previous system assumes that it can segment images without failure. However, if there are occluded objects and/or objects composed of multicolor parts, segmentation failures cannot be avoided. This paper presents an extended system that tries to recover from segmentation failures using photometric invariance. If the system is not sure about segmentation results, the system asks the user by appropriate expressions depending on the invariant values. Experimental results show the usefulness of the system.

Access selection in future multiple radio access environments is considered in this paper from a new perspective, that of the consumer. A model is proposed for the automatic acquisition of user preferences to assist in access selection decision making. The proposed approach uses a two-level Bayesian C-Metanetwork that models individual user preferences in terms of affordable cost, acceptable level of quality of service and reputation of the access networks. User preferences under different contexts, such as leisure and business, are also considered. The model also adapts to the change of user preferences over time. A simulator has been developed to evaluate the proposed model and the simulation results are promising in terms of the proportion of correct preference predictions after a small number of training samples.

In this paper, we propose a new multimodal interaction description language, MIML (Multimodal Interaction Markup Language), which defines dialogue patterns between human and various types of interactive agents. The feature of this language is three-layered description of agent-based interactive systems. The high-level description is a task definition that can easily construct typical agent-based interactive task control information. The middle-level description is an interaction description that defines agent's behavior and user's input at the granularity of dialogue segment. The low-level description is a platform dependent description that can override the pre-defined function in the interaction description. The connection between task-level and interaction-level is realized by generation of interaction description templates from the task level description. The connection between interaction-level and platform-level is realized by a binding mechanism of XML. As a result of the comparison with other languages, MIML has advantages in high-level interaction description, modality extensibility and compatibility with standardized technologies.

The interference imposed on conventional narrow-band systems by impulse radio UWB (IR-UWB) signals is examined by simulations. The Dirac delta function is employed to model the IR-UWB signal to reduce simulation costs. The simulation results show that the statistical characteristics of this interference deviate from Gaussian noise when the frequency band of the narrow-band system includes a half multiple of the data symbol rate of the IR-UWB system. In the case of pulse-position-modulation UWB signals and biorthogonal-coded bipolar-modulation UWB signals, the performance degradation of the narrow-band system depends on the number of pulse positions and the number of orthogonal codes, respectively.