Spatial indexing of high-dimensional data based on relative approximation

We propose a novel index structure, the A-tree (approximation tree), for similarity searches in high-dimensional data. The basic idea of the A-tree is the introduction of virtual bounding rectangles (VBRs) which contain and approximate MBRs or data objects. VBRs can be represented quite compactly and thus affect the tree configuration both quantitatively and qualitatively. First, since tree nodes can contain a large number of VBR entries, fanout becomes large, which increases search speed. More importantly, we have a free hand in arranging MBRs and VBRs in the tree nodes. Each A-tree node contains an MBR and its children VBRs. Therefore, by fetching an A-tree node, we can obtain information on the exact position of a parent MBR and the approximate position of its children. We have performed experiments using both synthetic and real data sets. For the real data sets, the A-tree outperforms the SR-tree and the VA-file in all dimensionalities up to 64 dimensions, which is the highest dimension in our experiments. Additionally, we propose a cost model for the A-tree. We verify the validity of the cost model for synthetic and real data sets. Edited by T. Sellis. Received: December 8, 2000 / Accepted: March 20, 2002 Published online: September 25, 2002     

Real-Time Omnidirectional Image Sensors

Conventional T.V. cameras are limited in their field of view. A real-time omnidirectional camera which can acquire an omnidirectional (360 degrees) field of view at video rate and which could be applied in a variety of fields, such as autonomous navigation, telepresence, virtual reality and remote monitoring, is presented. We have developed three different types of omnidirectional image sensors, and two different types of multiple-image sensing systems which consist of an omnidirectional image sensor and binocular vision. In this paper, we describe the outlines and fundamental optics of our developed sensors and show examples of applications for robot navigation.     

Numerical prediction of sound generated from flows with a low Mach number

Numerical computations of sound generated from flows with a low Mach number are presented based on Lighthill’s acoustic analogy with an assumption that sound does not alter the flow field from which it is generated. The source fluctuations of the flow field are computed by a large-eddy simulation (LES) with Dynamic Smagorinsky Model (DSM) and they are fed to the following acoustical computation as input data. An explicit/implicit finite element method with second order accuracy both in time and space is used for flow field discretization. The method is applied to the prediction of sound in three different classes of problems: far-field sound generated from flow around a bluff body, sound resulting from blade-stator interaction of turbomachinery and sound due to a turbulent boundary layer on an aerofoil. The computed frequency spectra of the sound show a fairly good agreement with the measured spectra for all the cases.     

Gait-based human age estimation using age group-dependent manifold learning and regression

Multimedia Tools and Applications - Human age estimation from gait is expected to be an important technology for a variety of applications such as automatic customer counting for marketing research...     

Mixing speed-controlled gold nanoparticle synthesis with pulsed mixing microfluidic system

Gold nanoparticles with diameters of a few tens of nanometer and a narrow size distribution were synthesized using a pulsed mixing method with a microfluidic system which consists of a Y-shaped mixing microchannel and two piezoelectric valveless micropumps. This mixing method enables control of the mixing speed of gold salts and reducing agent by changing the switching frequency of the micropumps, which was our focus to improve the particle size distribution, which is an essential parameter in gold nanoparticle synthesis. In the proposed method, the mixing time was inversely proportional to the switching frequency and the minimum mixing time was 95 ms at a switching frequency of 200 Hz. During synthesis experiments, the mean diameter of the synthesized gold nanoparticles was found to increase, and the coefficient of variation of particle size was found to decrease with decreasing mixing time. We successfully improved the coefficient of variation to less than 10% for a mean diameter of around 40 nm.     

Time-series primitive static states for detailing work state and flow of human-operated work machine

This paper proposes a quantification method for a comprehensive work flow in construction work for describing work states in more detail on the basis of analyzing state transitions of primitive static states (PSS), which consist of 16 symbolic work states defined by using on-off state of the lever operations and joint loads for the manipulator and end-effector. On the basis of the state transition rules derived from a transition-condition analysis, practical state transitions (PST), which are common and frequent transitions in arbitrary construction work, are defined. PST can be classified into essential state transition (EST) or nonessential state transitions (NST). EST extracts common phases of work progress and estimates positional relations between a manipulator and an object. NST reveals wasted movements that degrade the efficiency and quality of work. To evaluate comprehensive work flows modeled by combining EST and NST, work-analysis experiments using our instrumented setup were conducted. Results indicate that all the PSS definitely changes on the basis of PST under various work conditions, and work analysis using EST and NST easily reveals work characteristics and untrained tasks related to wasted movements.     

Test bed for characterization and predistortion of power amplifiers

A platform of high accuracy and reliability is crucial for power amplifier (PA) designers to assess the characteristics of the PA prototype, and for the digital predistorter engineers to validate their models and algorithms. Digital processing techniques for building a test bed of high performance for PA characterization and predistortion (PD) are discussed in this article. We also proposed a novel technique to characterize the frequency responses of the vector signal generator and vector signal analyzer independently without using any other instrument. The techniques described in this article are quite useful, especially for narrowband vector transmitters and receivers, to improve their performance. These techniques were experimentally evaluated, both for characterization and PD of a class‐F PA. Test results show that using such techniques can successfully build a test bed of high accuracy and wide bandwidth. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

Protective layer for high‐efficiency PDPs driven at low voltage

Abstract— The sustain pulse voltage of a panel for 66‐kPa Ne + Xe (5–30%) with an (SrCa)O protective layer is 20–40% lower than that with an MgO protective layer. The luminous efficiency of the panel with a Ne + Xe (30%) (SrCa)O protective layer is 1.5 times that of the conventional panel with a Ne + Xe (10%) MgO protective layer; the sustain pulse voltages of these panels are almost the same. The power loss caused by panel capacitance is proportional to the second power of the sustain pulse voltage. Using the (SrCa)O protective layer for Xe (5–30%), the power loss is reduced by 35–60% compared with the MgO protective layer. It follows that, using the (SrCa)O protective layer, we can increase the Xe content with little power loss and thus achieve high‐efficiency PDPs. As for MgO and CaO with Xe ions, electrons are probably ejected from only the defect states. On the other hand, as for the SrO with Xe ions, it is likely that electrons can be ejected from not only defect states but also the valance band. This seems to be the reason why the driving voltage is lower with the (SrCa)O protective layer than with the MgO protective layer.     

Imitating others by composition of primitive actions: A neuro-dynamic model

This paper introduces a novel neuro-dynamical model that accounts for possible mechanisms of action imitation and learning. It is considered that imitation learning requires at least two classes of generalization. One is generalization over sensory–motor trajectory variances, and the other class is on cognitive level which concerns on more qualitative understanding of compositional actions by own and others which do not necessarily depend on exact trajectories. This paper describes a possible model dealing with these classes of generalization by focusing on the problem of action compositionality. The model was evaluated in the experiments using a small humanoid robot. The robot was trained with a set of different actions concerning object manipulations which can be decomposed into sequences of action primitives. Then the robot was asked to imitate a novel compositional action demonstrated by a human subject which are composed from prior-learned action primitives. The results showed that the novel action can be successfully imitated by decomposing and composing it with the primitives by means of organizing unified intentional representation hosted by mirror neurons even though the trajectory-level appearance is different between the ones of observed and those of self-generated.     

A Social Robot that Stands in Line

Recent research in mobile robot navigation make it feasible to utilize autonomous robots in service fields. But, such applications require more than just navigation. To operate in a peopled environment, robots should recognize and act according to human social behavior. In this paper, we present the design and implementation of one such social behavior: a robot that stands in line much as people do. The system employs stereo vision to recognize lines of people, and uses the concept of personal space for modeling the social behavior. Personal space is used both to detect the end of a line and to determine how much space to leave between the robot and the person in front of it. Our model of personal space is based on measurements from people forming lines. We demonstrate our ideas with a mobile robot navigation system that can purchase a cup of coffee, even if people are waiting in line for service.