Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

The problem of which cues, internal or external, permit a person to label and identify his own emotional state has been with us since the days that James (1890) first tendered his doctrine that \"the bodily changes follow directly the perception of the exciting fact, and that our feeling of the same changes as they occur is the emotion\" (p. 449). Since we are aware of a variety of feeling and emotion states, it should follow from James' proposition that the various emotions will be accompanied by a variety of differentiable bodily states. Following James' pronouncement, a formidable number of studies were undertaken in search of the physiological differentiators of the emotions. The results, in these early days, were almost uniformly negative. All of the emotional states experi-

"Cognitive, social, and physiological determinants of emotional state": Erratum

Interpersonal Diagnosis of Personality

A self-contained direction sensing radio frequency identification (RFID) reader is developed employing a dual-directional antenna for automated target acquisition and docking of a mobile robot in indoor environments. The dual-directional antenna estimates the direction of arrival (DOA) of signals from a transponder by using the ratio of the received signal strengths between two adjacent antennas. This enables the robot to continuously monitor the changes in transponder directions and ensures reliable docking guidance to the target transponder. One of the technical challenges associated with this RFID direction finding is to sustain the accuracy of the estimated DOA that varies according to environmental conditions. It is often the case that the robot loses its way to the target in a cluttered environment. To cope with this problem, the direction correction algorithm is proposed to triangulate the location of the transponder with the most recent three DOA estimates. Theoretical simulation results verify the reliability of the proposed algorithm that quantifies the potential error in the DOA estimation. Using the algorithm, we validate mobile robot docking to an RFID transponder in an office environment occupied by obstacles.

Direction Sensing RFID Reader for Mobile Robot Navigation

Handbook of Research on Ambient Intelligence and Smart Environments: Trends and Perspectives covers the cutting-edge aspects of AMI applications, specifically those involving the effective design, realization, and implementation of a comprehensive AmI application. This pertinent publication targets researchers and practitioners in Ambient Intelligence, as well as those in ubiquitous and pervasive computing, artificial intelligence, sensor networks, knowledge representation, automated reasoning and learning, system and software engineering, and man-machine interfaces.

Handbook of research on ambient Intelligence and smart environments: trends and perspectives

Inverse kinematics computation using an artificial neural network that learns the inverse kinematics of a robot arm has been employed by many researchers. However, the inverse kinematics system of typical robot arms with joint limits is a multivalued and discontinuous function. Since it is difficult for a well-known multilayer neural network to approximate such a function, a correct inverse kinematics model cannot be obtained by using a single neural network. In order to overcome the discontinuity of the inverse kinematics function, we proposed a novel modular neural network system that consists of a number of expert neural networks. Each expert approximates the continuous part of the inverse kinematics function. The proposed system uses the forward kinematics model for selection of experts. When the number of the experts increases, the computation time for calculating the inverse kinematics solution also increases without using the parallel computing system. In order to reduce the computation time, we propose a novel expert selection by using the performance prediction networks which directly calculate the performances of the experts.

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Inverse kinematics learning by modular architecture neural networks with performance prediction networks

Various coordinated control schemes are explored in the multi-operator-multi-robot (MOMR) tele-collaborative system through a network with time delay. Multi-robot cooperation has rapidly emerged in many possible applications such as the plant maintenance, construction, and surgery, because it would have a significant advantage over a single robot in such cases. Thus, time-delayed control of a multi-robot system is expected to play an important role in remote operations, too. However, the effect of time-delay would pose a more difficult problem to the MOMR teleoperation systems and seriously affect their performance. In this work, first, we have built an experimental system to investigate the remote cooperation in MOMR teleoperation. Then, different coordinated control methods are proposed to cope with the collision, arising from the time delay over the network. To verify the validity of the proposed schemes, we have carried out various experiments on a planar block arrangement by two slave arms employing graphic simulators and a LAN subject to a significant communication delay.

Remote coordinated controls in multiple telerobot cooperation

This paper presents the design and control of a novel assistive robotic walker that we call “JAIST active robotic walker (JARoW)”. JARoW is developed to provide potential users with sufficient ambulatory capability in an efficient, cost-effective way. Specifically, our focus is placed on how to allow easier maneuverability by creating a natural interface between the user and JARoW. For the purpose, we develop a rotating infrared sensor to detect the user’s lower limb movement. The implementation details of the JARoW control algorithms based on the sensor measurements are explained, and the effectiveness of the proposed algorithms is verified through experiments. Our results confirmed that JARoW can autonomously adjust its motion direction and velocity according to the user’s walking behavior without requiring any additional user effort.

Nak Young Chong

Papers

Direction Sensing RFID Reader for Mobile Robot Navigation

Handbook of research on ambient Intelligence and smart environments: trends and perspectives

Inverse kinematics learning by modular architecture neural networks with performance prediction networks

Remote coordinated controls in multiple telerobot cooperation

Design and control of JAIST active robotic walker