This paper presents the use of 3 axis accelerometer for getting the gait information including the number of gaits, stride and walking distance. Travel distance is usually calculated from the double integration of the accelerometer output with respect to time; however, the accumulated errors due to the drift are inevitable. The orientation change of the accelerometer also causes error because the gravity is added to the measured acceleration. Unless three axis orientations are completely identified, the accelerometer alone does not provide correct acceleration for estimating the travel distance. We proposed a way of minimizing the error due to the change of the orientation. Pedestrian localization is implemented with the heading angle and the travel distance. Heading angle is estimated from the rate gyro and the magnetic compass measurements. The performance of the localization is presented with experimental data.

A recent report demonstrated that in human aging brain after menopause/andropause luteinizing hormone (LH) is localized in the cytoplasm of pyramidal neurons of hippocampus and a significant increase of LH is also detected in the cytoplasm of pyramidal neurons and neurofibrillary tangles of Alzheimer's disease brain compared to age-matched control brain. It was suggested that the decreased steroid hormone production and the resulting LH expression in the neurons vulnerable to Alzheimer's disease pathology may have some relevance to the development of Alzheimer's disease. It is, however, unclear whether the presence of LH in neurons of human aging and Alzheimer's disease brain is due to intracellular LH expression or to LH uptake from extracellular sources, since gonadotropins are known to cross the blood brain barrier. Moreover, there is no report by using the brain of experimental animal that LH is expressed in such neurons as found in the human brain. In the present study, we found that LH immunoreactivity is localized in the pyramidal neurons of cerebral cortex and hippocampus of 12 and 18 months old rats but can not detect any immunoreactivity for LH in the young adult (3-5 months old) rats. To confirm that these LH immunoreactivity results from de novo synthesis in the brain but not the uptake from extracellular space, we performed RT-PCR and found that mRNA for LH is detected in several regions of brain including cerebral cortex and hippocampus. These findings suggest us that LH expression in old rat brain may play an important role in aging process of rat brain.

In this paper, we present a global localization and position error compensation method in a known indoor environment using magnet hall sensors. In previous our researches, it was possible to compensate the pose errors of xe, ye, θe correctly on the surface of indoor environment with magnets sets by regularly arrange the magnets sets of identical pattern. To improve the proposed method, new strategy that can realize the global localization by changing arrangement of magnet pole is presented in this paper. Total six patterns of the magnets set form the unique landmarks. Therefore, the virtual map can be built by using the six landmarks randomly. The robots search a pattern of magnets set by rotating, and obtain the current global pose information by comparing the measured neighboring patterns with the map information that is saved in advance. We provide experimental results to show the effectiveness of the proposed method for a differential drive wheeled mobile robot.

This paper proposes a low-complexity indoor localization method of mobile robot under the dynamic environment by fusing the landmark image information from an ordinary camera and the distance information from sensor nodes in an indoor environment, which is based on sensor network. Basically, the sensor network provides an effective method for the mobile robot to adapt to environmental changes and guides it across a geographical network area. To enhance the performance of localization, we used an ordinary CCD camera and the artificial landmarks, which are devised for self-localization. Experimental results show that the real-time localization of mobile robot can be achieved with robustness and accurateness using the proposed localization method.

In this paper, a localization error recovery method based on bias estimation is provided for outdoor localization of mobile robot using different-type sensors. In the previous data integration method with DGPS, it is difficult to localize mobile robot due to multi-path phenomena of DGPS. In this paper, fault data due to multi-path phenomena can be recovered by bias estimation. The proposed data integration method uses a Kalman filter based estimator taking into account a bias estimator and a free-bias estimator. A performance evaluation is shown through an outdoor experiment using mobile robot.

One of the main problems of topological localization in a real indoor environment is variations in the environment caused by dynamic objects and changes in illumination. Another problem arises from the sense of topological localization itself. Thus, a robot must be able to recognize observations at slightly different positions and angles within a certain topological location as identical in terms of topological localization. In this paper, a possible solution to these problems is addressed in the domain of global topological localization for mobile robots, in which environments are represented by their visual appearance. Our approach is formulated on the basis of a probabilistic model called the Bayes filter. Here, marginalization of dynamics in the environment, marginalization of viewpoint changes in a topological location, and fusion of multiple visual features are employed to measure observations reliably, and action-based view transition model and action-associated topological map are used to predict the next state. We performed experiments to demonstrate the validity of our proposed approach among several standard approaches in the field of topological localization. The results clearly demonstrated the value of our approach.

Nowadays, research on human-robot interaction has been getting increasing attention. In the research field of human-robot interaction, speech signal processing in particular is the source of much interest. In this paper, we report a speaker localization system with six microphones for a humanoid robot called MAHRU from KIST and propose a time delay of arrival (TDOA)-based feature matrix with its algorithm based on the minimum sum of absolute errors (MSAE) for sound source localization. The TDOA-based feature matrix is defined as a simple database matrix calculated from pairs of microphones installed on a humanoid robot. The proposed method, using the TDOA-based feature matrix and its algorithm based on MSAE, effortlessly localizes a sound source without any requirement for calculating approximate nonlinear equations. To verify the solid performance of our speaker localization system for a humanoid robot, we present various experimental results for the speech sources at all directions within 5 m distance and the height divided into three parts.

This paper presents a multi-robot localization based on multidimensional scaling (MDS) in spite of the existence of incomplete and noisy data. While the traditional algorithms for MDS work on the full-rank distance matrix, there might be many missing data in the real world due to occlusions. Moreover, it has no considerations to dealing with the uncertainty due to noisy observations. We propose a robust MDS to handle both the incomplete and noisy data, which is applied to solve the multi-robot localization problem. To deal with the incomplete data, we use the Nyström approximation which approximates the full distance matrix. To deal with the uncertainty, we formulate a Bayesian framework for MDS which finds the posterior of coordinates of objects by means of statistical inference. We not only verify the performance of MDS-based multi-robot localization by computer simulations, but also implement a real world localization of multi-robot team. Using extensive empirical results, we show that the accuracy of the proposed method is almost similar to that of Monte Carlo Localization(MCL).

To achieve autonomous mobile robot navigation, accurate localization technique is the fundamental issue that should be addressed. In augmented reality, the position of a user is required for location-based services. This paper presents indoor localization using infrared reflective artificial landmarks. In order to minimize the disturbance to the user and to provide the ease of installation, the passive landmarks are used. The landmarks are made of coated film which reflects the infrared light efficiently. Infrared light is not visible, but the camera can capture the reflected infrared light. Once the artificial landmark is identified, the camera’s relative position/orientation is estimated with respect to the landmark. In order to reduce the number of the required artificial landmarks for a given environment, the pan/tilt mechanism is developed together with the distortion correction algorithm.

In this paper, we present a Sound Source Localization (SSL) based GCC (Generalized Cross Correlation)–PHAT (Phase Transform) and new measurement method of angle with robot auditory system for a network-based intelligent service robot. The main goal of this paper is to analysis performance of TDOA and GCC-PHAT sound source localization method and new angle measurement method is compared. We use GCC-PHAT for measuring time delays between several microphones. And sound source location is calculated by using time delays and new measurement method of angle. The robot platform used in this work is wever-R2, which is a network-based intelligent service robot developed at Intelligent Robot Research Division in ETRI.

We propose a optimal fusion method for localization of multiple robots utilizing correlation between GPS on each robot in common workspace. Each mobile robot in group collects position data from each odometer and GPS receiver and shares the position data with other robots. Then each robot utilizes position data of other robot for obtaining more precise estimation of own position. Because GPS data errors in common workspace have a close correlation, they contribute to improve localization accuracy of all robots in group. In this paper, we simulate proposed optimal fusion method of odometer and GPS through virtual robots and position data.

The 5S rRNA gene is considered as valuable resource for chromosome landmarks and evolutionary studies. In this study, the tandem repeat unit of 5S rRNA gene containing the coding sequence and non‐transcribed spacer (NTS) region was amplified by PCR, cloned, and sequenced from Allium wakegi (2n=XY=16) and the two ancestors, A. cepa and A. fistulosum. Although there were intraspecific variations among the clones of each species, we could construct each consensus sequences for A. cepa and A. fistulosum and two consensus sequences for A. wakegi. The sequence of the 120 bp coding region was completely homologous among the consensus sequences from the Allium species examined. However, the sequence in the NTS region was significantly variable and informative in genome analysis. The variations were found to be clustered between the positions 230 and 269, and also distributed broadly as single nucleotide. A. wakegi could be divided into two classes by the 5S rRNA sequences (AWAK‐1 and AWAK‐2). AWAK‐1 showed high homology to the genome of A. cepa and AWAK‐2 to that of A. fistulosum. Fluorescence in situ hybridization technique was applied to analyze the distribution of 5S gene loci. In A. wakegi, 5S rRNA sequences were detected in two different chromosomes, each one showing the pattern identical to the chromosome donated from each genome of both ancestors.

Abstract It is essential to estimating positions of multiple robots in order to perform cooperative task in common workspace. Accordingly, we propose a new approach of cooperative localization for multiple robots utilizing correlation among GPS errors in common workspace. Assuming that GPS data of individual robot are correlated strongly as the distance among robots are close, it is confirmed that the proposed method provides improved localization accuracy. In addition, we define two operational parameters to apply proposed method in multiple robot system. With mentioned two parameters, we present a practical solution to accumulated position error in traveling long distance.

Recently, with the development of service robots and with the new concept of ubiquitous world, the position estimation of mobile objects has been raised to an important problem. As pre-liminary research results, some of the localization schemes are introduced, which provide the absolute location of the moving objects subjected to large errors. To implement a precise and convenient localization system, a new absolute position estimation method for a mobile robot in indoor environment is proposed in this paper. Design and implementation of the localization system comes from the usage of active beacon systems (based upon RFID technology). The active beacon system is composed of an RFID receiver and an ultra-sonic transmitter: 1. The RFID receiver gets the synchronization signal from the mobile robot and 2. The ultra-sonic transmitter sends out the traveling signal to be used for measuring the distance. Position of a mobile robot in a three dimensional space can be calculated basically from the distance information from three beacons and the absolute position information of the beacons themselves. Since it is not easy to install the beacons at a specific position precisely, there exists a large localization error and the installation time takes long. To overcome these problems, and provide a precise and convenient localization system, a new auto calibration algorithm is developed in this paper. Also the extended Kalman filter has been adopted for improving the localization accuracy during the mobile robot navigation. The localization accuracy improvement through the proposed auto calibration algorithm and the extended Kalman filter has been demonstrated by the real experiments.

We present an implementation of particle filter algorithm for global localization and kidnap recovery of mobile robot. Firstly, we propose an algorithm for efficient particle initialization using sonar line features. And then, the average likelihood and entropy of normalized weights are used as a quality measure of pose estimation. Finally, we propose an active kidnap recovery by adding new particle set. New and independent particle set can be initialized by monitoring two quality measures. Added particle set can re-estimate the pose of kidnapped robot. Experimental results demonstrate the capability of our global localization and kidnap recovery algorithm.

세포의 대사과정에서 생성되는 활성산소종(reactive oxygen species : ROS)은 세포의 성숙과 발생 과정을 저해하며, 인간의 생식 수관에서 불임의 원인이 된다. 많은 세포생물학적 연구를 통해 ROS에 대한 세포 내의 보호 기작이 밝혀지고 있다. Activating transcription factor 4(ATF4)는 세포 내에서 산화적 스트레스를 비롯한 여러 스트레스 요인으로부터 세포를 보호하는 기작에 관여하는 중요한 인자로서, 스트레스