Search

The digital waveguide mesh (DWM) has proven to an efficient and accurate method for simulating multi-dimensional wave propagation in various applications such as physical modeling of musical instruments and room acoustics. However, problems appear when fitting a DWM to an arbitrary boundary because of the geometric constraints of a given mesh element. A finer mesh grid is often used in an attempt to resolve this situation, which entails an associated computational cost increase. This paper presents a conformal method for the rectilinear DWM as an efficient alternative. The proposed conformal method aims at better approximating rigid boundaries that are normally not well suited for a rectilinear DWM structure. It is inspired by the conformal method developed for the Finite Domain Time Difference (FDTD) scheme where a cell associated with the boundary is split with respect to a particular criterion and the material constant of the cell is adjusted accordingly [1]. By means of the interleaved waveguide network (IWN) [2], the conformal method is successfully achieved in the DWM.

In this paper, we present a robust RBPF-SLAM algorithm for mobile robots in non-static environments. We propose an approach for sampling particles from multiple ancestor sets, not from just one prior set. This sampling method increases the robustness of SLAM algorithm, because some particles can be updated by only observations consistent with the map, even if observation at certain time step is corrupted by environmental changes. Corrupted observations are filtered out from recursive Bayesian update process by the proposed sampling method. We also present an intermediate path estimation method to use abandoned sensor information reflected from relocated objects for map update. The map can represent the changed configuration of non-static environment by the stored sensor information and the estimated path. Results of simulations and experiments in non-static environments show the robustness of proposed RBPF-SLAM algorithm using sonar sensors.

In this paper, we address the problem of building a grid map using cheap sonar sensors, that is, the problem of using erroneous sensors when seeking to model an environment as accurately as possible. We rely on the inconsistency of information among sonar measurements and the sound pressure of the waves from the sonar sensors to develop a new method of detecting incorrect sonar readings, called the conflict evaluation with sound pressure (CEsp). To fuse the correct measurements into a map, we start with the maximum likelihood approach due to its ability to manage the angular uncertainty of sonar sensors. However, since this approach suffers from heavy computational complexity, we convert it to a light logic problem called the maximum approximated likelihood (MAL) approach. Integrating the MAL approach with the CEsp method results in the conflict-evaluated maximum approximated likelihood (CEMAL) approach. The CEMAL approach generates a very accurate map that is close to the map that would be built by accurate laser sensors, and does not require adjustment of parameters for various environments.

We present robust localization algorithm using particle filter for mobile robots. The blueprint of building that cannot completely represent the real environment or a simple map of environment can be used in this work. With this incomplete information, it will not be easy to estimate the pose of robots because un-modeled obstacles will make the sensor observation inconsistent. Proposed algorithm provides robustness in such a dynamic environment because the effect of corrupted sensor observations can be suppressed by a selective update or a sampling from non-corrupted window. Results of experiments and simulations show the robustness of proposed algorithm.

A WDM-PON with 50-GHz channel spacing employing RSOA-based spectrum sliced sources is presented. Colorless operations over 32 DWDM channels are demonstrated from -10 to 80 °C in 155-Mb/s transmissions over 10 km.

Edges in topological maps have rich information such as shape, numbers of obstacles, locations of obstacles which can be used for a data association. However, no systematic approach on using the edge data for the data association has been reported. This paper proposes a systematic way of utilizing the edge data for data association. First, we explain a Local Generalized Voronoi Angle(LGA) to represent the edge data in 1-dimension. Second, we suggest a key factor extraction procedure from the LGA to reduce the number by 27-28 times for computational efficiency using the wavelet transformation. Finally we propose a way of data association using the key factors of the LGA. Simulations and experiments show that the proposed data association algorithm yields higher probability for similar edges in computationally efficient manner.

In order to achieve the maximum gain along the desired signal, This work propose a beamforming algorithm that utilizes the generalized on-off algorithm. Also, we present a novel demodulation method enhancing the performance of the smart antenna by using the pilot channel of the CDMA2000 1X channel to obtain the exact weight vector.

This paper presents a fast and effective visual tracking and location prediction algorithm for cooperative multiple soccer robots playing a soccer game. The picture image is captured by the system and the motion parameters of each object are then computed from the captured image. Then, an image is selected as a template and 2D color image pattern is extracted from the incoming image and a sequence of images are generated by using a simple linear predictor that speeds up the processing time. Based on the computed tracking parameters, a heuristic path is generated. By using a vision-based command to line-of-sight (CLOS) guidance method that predicts and generates next robot motions. Monitoring the selected object using visual images, the vision system tracks the object and adjusts the velocities and directions of the robots to cope with sudden changes of direction such as bounded ball from an obstacle or expected position errors of the selected object. The simulation results as well as experimental results show that the presented algorithm works well for the soccer game.