In recent years, digital maps have been used in a variety of scenarios, including car navigation systems and map information services over the Internet. These digital maps are formed by multiple layers of maps of different scales; the map data most suitable for the specific situation are used. Currently, the production of map data of different scales is done by hand due to constraints related to processing time and accuracy. We conducted research concerning technologies for automatic generation of simplified map data from detailed map data. In the present paper, the authors propose the following: (1) a method to transform data related to streets, rivers, etc. containing widths into line data, (2) a method to eliminate the component points of the data, and (3) a method to eliminate data that lie below a certain threshold. In addition, in order to evaluate the proposed method, a user survey was conducted; in this survey we compared maps generated using the proposed method with the commercially available maps. From the viewpoint of the amount of data reduction and processing time, and on the basis of the results of the survey, we confirmed the effectiveness of the automatic generation of simplified maps using the proposed methods.

This paper introduces a coordinate calculation method for a real-time locating system. A ToA algorithm is used to obtain the target node coordinates, but a conventional DC method, which incurs heavy calculation time, is not suitable for embedded systems. This paper proposes the use of a P-control in the PID control algorithm to resolve real-time locating system issues. Performance measures of the accumulated operator number and position error are evaluated. It is shown that the PID method has less calculation and more robust performance than the DC method.

A continuous belief propagation (BP) decoding algorithm for a hybrid automatic repeat request (ARQ) system is proposed in this paper. The proposed continuous BP decoding algorithm utilizes the extrinsic information generated in the last iteration of the previous transmission for a continuous progression of the decoding through retransmissions. This allows the continuous BP decoding algorithm to accelerate the decoding convergence for codeword determination, especially when the number of retransmissions is large or a currently combined packet has punctured nodes. Simulation results verify the effectiveness of the proposed continuous BP decoding algorithm.

This paper presents an agent-based system for building and operating context-aware services in public spaces, including museums. The system provides users with agents and detects the locations of users and deploys location-aware user-assistant agents at computers near the their current locations by using active RFID-tags. When a visitor moves between exhibits in a museum, this dynamically deploys his/her agent at the computers close to the exhibits by using mobile agent technology. It annotates the exhibits in his/her personalized form and navigate him/her user to the next exhibits along his/her routes. It also introduces user movement as a natural approach to interacting between users and agents. To demonstrate the utility and effectiveness of the system, we constructed location/user-aware visitor-guide services and experimented them for two weeks in a public museum.

The aim of this work is to develop an efficient medical image segmentation technique by fitting a nonlinear shape model with pre-segmented images. In this technique, the kernel principle component analysis (KPCA) is used to capture the shape variations and to build the nonlinear shape model. The pre-segmentation is carried out by classifying the image pixels according to the high level texture features extracted using the over-complete wavelet packet decomposition. Additionally, the model fitting is completed using the particle swarm optimization technique (PSO) to adapt the model parameters. The proposed technique is fully automated, is talented to deal with complex shape variations, can efficiently optimize the model to fit the new cases, and is robust to noise and occlusion. In this paper, we demonstrate the proposed technique by implementing it to the liver segmentation from computed tomography (CT) scans and the obtained results are very hopeful.

Major challenges of the conventional spread-transform dither modulation (STDM) watermarking approach are two-fold: (i) it exploits a fixed watermarking strength (more particularly, the quantization index step size) to the whole cover image; and (ii) it is fairly vulnerable to the amplitude changes. To tackle the above challenges, an adaptive spread-transform dither modulation (ASTDM) approach is proposed in this paper for conducting robust color image watermarking by incorporating a new perceptual model into the conventional STDM framework. The proposed approach exploits a new perceptual model to adjust the quantization index step sizes according to the local perceptual characteristics of a cover image. Furthermore, in contrast to the conventional Watson's model is vulnerable to the amplitude changes, our proposed new perceptual model makes the luminance masking thresholds be consistent with any amplitude change, while keeping the consistence to the properties of the human visual system. In addition, certain color artifacts could be incurred during the watermark embedding procedure, since some intensity values are perceptibly changed to label the watermark. For that, a color artifact suppression algorithm is proposed by mathematically deriving an upper bound for the intensity values according to the inherent relationship between the saturation and the intensity components. Extensive experiments are conducted using 500 images selected from Corel database to demonstrate the superior performance of the proposed ASTDM approach.

We propose a novel topology control scheme that reduces the transmission power of nodes and increases the network connectivity, based on the fact that Cooperative Communication (CC) technology can bridge disconnected networks. Simulation results demonstrate that our scheme greatly increases the connectivity for a given transmission power, compared to other topology control schemes.

Nonlinear distortions in power amplifiers (PAs) generate spectral regrowth at the output, which causes interference to adjacent channels and errors in digitally modulated signals. This paper presents a novel method to evaluate adjacent channel leakage power ratio (ACPR) and error vector magnitude (EVM) from the amplitude-to-amplitude (AM/AM) and amplitude-to-phase (AM/PM) characteristics. The transmitted signal is considered to be complex Gaussian distributed in orthogonal frequency-division multiplexing (OFDM) systems. We use the Mehler formula to derive closed-form expressions of the PAs output power spectral density (PSD), ACPR and EVM for memoryless PA and memory PA respectively. We inspect the derived relationships using an OFDM signal in the IEEE 802.11a WLAN standard. Simulation results show that the proposed method is appropriate to predict the ACPR and EVM values of the nonlinear PA output in OFDM systems, when the AM/AM and AM/PM characteristics are known.

A multiple-peak negative differential resistance (NDR) circuit made of standard Si-based metal-oxide-semiconductor field-effect-transistor (MOS) and SiGe-based heterojunction bipolar transistor (HBT) is demonstrated. We can obtain a three-peak I-V curve by connecting three cascoded MOS-HBT-NDR circuits by suitably designing the MOS parameters. This novel three-peak NDR circuit possesses the adjustable current-voltage characteristics and high peak-to-valley current ratio (PVCR). We can adjust the PVCR values to be as high as 11.5, 6.5, and 10.3 for three peaks, respectively. Because the NDR circuit is a very strong nonlinear element, we discuss the extrinsic hysteresis phenomena in this multiple-peak NDR circuit. The effect of series resistance on hysteresis phenomena is also investigated. Our design and fabrication of the NDR circuit is based on the standard 0.35 µm SiGe BiCMOS process.

We propose requirement validation criteria and a method based on the interaction between actors in an information system. We focus on the cyclical transitions of one actor's situation against another and clarify observable stimuli and responses based on these transitions. Both actors' situations can be listed in a state transition table, which describes the observable stimuli or responses they send or receive. Examination of the interaction between both actors in the state transition tables enables us to detect missing or defective observable stimuli or responses. Typically, this method can be applied to the examination of the interaction between a resource managed by the information system and its user. As a case study, we analyzed 332 requirement defect reports of an actual system development project in Japan. We found that there were a certain amount of defects regarding missing or defective stimuli and responses, which can be detected using our proposed method if this method is used in the requirement definition phase. This means that we can reach a more complete requirement definition with our proposed method.

This paper presents hybrid type-II automatic repeat request (H-ARQ) for wireless wearable body area networks (BANs) based on ultra wideband (UWB) technology. The proposed model is based on three schemes, namely, high rate optimized rate compatible punctured convolutional codes (HRO-RCPC), Reed Solomon (RS) invertible codes and their concatenation. Forward error correction (FEC) coding is combined with simple cyclic redundancy check (CRC) error detection. The performance is investigated for two channels: CM3 (on-body to on-body) and CM4 (on-body to a gateway) scenarios of the IEEE802.15.6 BAN channel models for BANs. It is shown that the improvement in performance in terms of throughput and error protection robustness is very significant. Thus, the proposed H-ARQ schemes can be employed and optimized to suit medical and non-medical applications. In particular we propose the use of FEC coding for non-medical applications as those require less stringent quality of service (QoS), while the incremental redundancy and ARQ configuration is utilized only for medical applications. Thus, higher QoS is guaranteed for medical application of BANs while allowing coexistence with non-medical applications.

In this letter, we propose an improved speech/ nonspeech classification method to effectively classify a multimedia source. To improve performance, we introduce a feature based on spectral duration analysis, and combine recently proposed features such as high zero crossing rate ratio (HZCRR), low short time energy ratio (LSTER), and pitch ratio (PR). According to the results of our experiments on speech, music, and environmental sounds, the proposed method obtained high classification results when compared with conventional approaches.

With the progress of ubiquitous technology, ubiquitous learning presents new opportunities to learners. Situations of a learner can be grasped through analyzing the learner's actions collected by sensors, RF-IDs, or cameras in order to provide support at proper time, proper place, and proper situation. Training for acquiring skills and enhancing physical abilities through exercise and experience in the real world is an important domain in u-learning. A training program may last for several days and has one or more training units (exercises) for a day. A learner's performance in a unit is considered as short term state. The performance in a series of units may change with patterns: progress, plateau, and decline. Long term state in a series of units is accumulatively computed based on short term states. In a learning/training program, it is necessary to apply different support strategies to adapt to different states of the learner. Adaptation in learning support is significant, because a learner loses his/her interests easily without adaptation. Systems with the adaptive support usually provide stimulators to a learner, and a learner can have a great motivation in learning at beginning. However, when the stimulators reach some levels, the learner may lose his/her motivation, because the long term state of the learner changes dynamically, which means a progress state may change to a plateau state or a decline state. In different long term learning states, different types of stimulators are needed. However, the stimulators and advice provided by the existing systems are monotonic without changeable support strategies. We propose a mutual adaptive support. The mutual adaptation means each of the system and the learner has their own states. On one hand, the system tries to change its state to adapt to the learner's state for providing adaptive support. On the other hand, the learner can change its performance following the advice given based on the state of the system. We create a ubiquitous pet (u-pet) as a metaphor of our system. A u-pet is always with the learner and encourage the leaner to start training at proper time and to do training smoothly. The u-pet can perform actions with the learner in training, change its own attributes based on the learner's attributes, and adjust its own learning rate by a learning function. The u-pet grasps the state of the learner and adopts different training support strategies to the learner's training based on the learner's short and long term states.

A fuel-supply system using movable fuel-supply vehicles (FSVs) is proposed and its performance is evaluated. Both FSVs and vehicles requesting fuel are equipped with networked sensors. The sensors in FSVs are location sensors, and those in vehicles requesting fuel are for determining the remaining amount of fuel and the location of the vehicle. The time from when a fuel request is generated until the request is satisfied is the most basic performance metric for this system. Explicit formulas for the probabilistic distribution of this request time are also derived. Numerical examples show that the number of movable FSVs can be smaller than that of fixed fuel stations by about 80% or more than two orders of magnitude. This result suggests that movable FSVs may reduce the cost of the fuel supply infrastructure for alternative-fuel vehicles such as fuel-cell vehicles.

The establishment of inter-domain traffic engineered paths is a requisite to accomplishing an end-to-end bandwidth guarantee and end-to-end resource optimization. Though the inter-domain paths must be reliable, it is difficult to compute suitable backup inter-domain paths in advance when the traffic engineering information is not disclosed outside of each domain. This means that the inter-domain path computation must satisfy the severe requirement of path establishment delay, since all inter-domain paths traversing the links in failure need to be computed after the failure occurs. Though several inter-domain path computation schemes have been proposed, their relative characteristics remain unknown. First, this paper classifies the conventional inter-domain path computation schemes into two types, i.e. end-to-end and per-domain schemes, and compares their performances under various traffic loads. Based on results of the comparisons, this paper proposes an adaptive inter-domain path computation scheme that can satisfy the severe requirement of the path establishment delay. In this scheme, the domain sequence from the source node to the destination node is divided into multiple sub-domain sequences according to the traffic load in each domain. The end-to-end path computation scheme is applied to the sub-domain sequences under heavy traffic loads, while the per-domain path computation scheme is applied to those under normal traffic loads. The simulation results show that the proposed scheme can adaptively satisfy the requirement for the path establishment delay while it maintains the optimality of path computation, even if the traffic load applied to each domain changes.

A novel signal transition detector design using as few as 8 transistors is presented. The proposed design cleverly exploits the property of a specific internal state transition to mitigate the voltage degradation problem by employing only one extra transistor. It is thus capable of supporting level intact output signals and eliminating DC power consumption in the trailing buffer. The proposed design, featuring low circuit complexity and low power consumption, is considered useful for applications in self-timed circuits. Simulation results show that, when compared with other pass transistor logic based counterpart designs, as much as 46% savings in power and 28% in area can be achieved by the proposed design.

We present analytical expression for inductance of a superconducting stripline, a strip sandwiched by two superconducting ground planes. In our method, we utilize the analytical formula for a perfect-conducting stripline derived by Chang in 1976. To utilize Chang's formula, we first transform the structure of a superconducting stripline into that of a perfect-conducting stripline by reducing the thicknesses of the superconducting layers. The thickness reduction is "λ coth (t/λ)" for each (upper or lower) side, where λ and t are the field penetration depth and the layer thickness, respectively. Then, we apply Chang's formula to the transformed stripline model. The calculated results are in good agreement with the numerical and experimental results.

OFDMA femtocells in the macrocellular network of which frequency reuse factor is 1 cause serious interference to macrocell users, while the femtocells improve the performance of indoor users. In this letter, a novel downlink resource allocation algorithm for OFDMA femtocell networks is proposed to reduce interference between the macrocells and the femtocells. This algorithm allocates femtocell subchannels to avoid interference to macrocell users in the femtocell coverage, and minimizes the total transmission power of the femtocell to reduce the negative effect on the performance of the macrocell. Simulation results are provided to present the performance of the proposed algorithm.

In this paper, we propose a novel target acoustic signal detection approach which is based on non-negative matrix factorization (NMF). Target basis vectors are trained from the target signal database through NMF, and input vectors are projected onto the subspace spanned by these target basis vectors. By analyzing the distribution of time-varying normalized projection error, the optimal threshold can be calculated to detect the target signal intervals during the entire input signal. Experimental results show that the proposed algorithm can detect the target signal successfully under various signal environments.

In this letter, a statistical uncorrelated near class discriminant (SUNCD) approach is proposed for face recognition. The optimal discriminant vector obtained by this approach can differentiate one class and its near classes, i.e., its nearest neighbor classes, by constructing the specific between-class and within-class scatter matrices and using the Fisher criterion. In this manner, SUNCD acquires all discriminant vectors class by class. Furthermore, SUNCD makes every discriminant vector satisfy locally statistical uncorrelated constraints by using the corresponding class and part of its most neighboring classes. Experiments on the public AR face database demonstrate that the proposed approach outperforms several representative discriminant methods.