We report the growth of carbon nanofibers (CNFs) from carbon particles by chemical vapor deposition (CVD) with ultrasonic neblizer using ethanol as carbon source. Dense CNFs having diameters of several tens of nanometers have been successfully synthesized by the CVD without using any metal catalysts. The carbon particles formed from decompostion of fullerene were found to be suitable for the synthesis of CNFs. Details of the optimum conditions for producing CNFs and the expected growth mechanism are also described.

Recently, many researchers tackle accurate object recognition algorithms and many algorithms are proposed. However, these algorithms have some problems caused by variety of real environments such as a direction change of the object or its shading change. The new tracking algorithm, Cascade Particle Filter, is proposed to fill such demands in real environments by constructing the object model while tracking the objects. We have been investigating to implement accurate object recognition on embedded systems in real-time. In order to apply the Cascade Particle Filter to embedded applications such as surveillance, automotives, and robotics, a hardware accelerator is indispensable because of limitations in power consumption. In this paper we propose a hardware implementation of the Discrete AdaBoost algorithm that is the most computationally intensive part of the Cascade Particle Filter. To implement the proposed hardware, we use PICO Express, a high level synthesis tool provided by Synfora, for rapid prototyping. Implementation result shows that the synthesized hardware has 1,132,038 transistors and the die area is 2,195 µm 1,985 µm under a 0.180 µm library. The simulation result shows that total processing time is about 8.2 milliseconds at 65 MHz operation frequency.

This paper presents a new particle swarm optimizer characterized by growing tree topology. If a particle is stagnated then a new particle is born and is located away from the trap. Depending on the property of objective problems, particles are born successively and the growing swarm constitutes a tree-topology. Performing numerical experiments for typical benchmarks, the algorithm efficiency is evaluated in several key measures such as success rate, the number of iterations and the number of particles. As compared with other basic PSOs, we can suggest that the proposed algorithm has efficient performance in optimization with low-cost computation.

The Dst index is the most popular measure of a scale of magnetic storms, and it is widely used as a monitor of the conditions of the Earth's magnetosphere. Since the Dst index contains contributions from multiple magnetospheric phenomena, it is important to distinguish each of the contributions in order to obtain meaningful information about the conditions of the magnetosphere. There have been several efforts which modeled temporal evolution of the Dst index empirically, and these empirical models considers some contributions separately. However, they take only short-term varations into accout, and contributions from phenomena which show long-term variations are neglected. In the present study, we have developed a technique for estimating the component of long-term variations of the Dst index using solar wind data and a nonlinear empirical model. The newly-developed technique adopts an algorithm which is similar to the particle filter. This algorithm allows an on-line processing of a long sequence of Dst data, which would enable a real-time estimation of system variables in a nonlinear system model. The estimates of the long-term variations can be used for accurate estimation of other contributions to the Dst index, which would provide credible information about the conditions of the magnetosphere. The framework proposed in the present study could be applied for the purpose of continuous real-time monitoring of the environment of the magnetosphere.

Particle filter has attracted increasing attention from researchers of object tracking due to its promising property of handling nonlinear and non-Gaussian systems. In this paper, we mainly explore the problem of precisely estimating observation likelihoods of particles in the joint feature-spatial space. For this purpose, a mixture Gaussian kernel function based similarity is presented to evaluate the discrepancy between the target region and the particle region. Such a similarity can be interpreted as the expectation of the spatial weighted feature distribution over the target region. To adapt outburst of object motion, we also present a method to appropriately adjust state transition model by utilizing the priors of motion speed and object size. In comparison with the standard particle filter tracker, our tracking algorithm shows the better performance on challenging video sequences.

Particle Swarm Optimization (PSO) is a search method which utilizes a set of agents that move through the search space to find the global minimum of an objective function. The trajectory of each particle is determined by a simple rule incorporating the current particle velocity and exploration histories of the particle and its neighbors. Since its introduction by Kennedy and Eberhart in 1995, PSO has attracted many researchers due to its search efficiency even for a high dimensional objective function with multiple local optima. The dynamics of PSO search has been investigated and numerous variants for improvements have been proposed. This paper reviews the progress of PSO research so far, and the recent achievements for application to large-scale optimization problems.

An AuSn reflow process using hydrogen radicals as a way to avert the cleaning of flux residues was investigated for its application to solder bumping. AuSn particles (manufactured by a gas atomizer) smaller than 5 µm, which are difficult to reflow by conventional methods that use rosin mildly activated (RMA) flux, were used for the experiments. In this process, the reduction effect by the hydrogen radicals removes the surface oxides of the AuSn particles. Excellent wetting between 1-µm-diameter AuSn particles and Ni metallization occurred in hydrogen plasma. Using hydrogen radicals, 100 µm-diameter AuSn bumps without voids were successfully formed at a peak temperature of 300. The average bump shear strength was approximately 73 gf/bump. Bump inspection after shear testing showed that a fracture had occurred between the Au/Ni/Cr under bump metallurgy (UBM) and Si substrate, suggesting sufficient wetting between the AuSn bump and the UBM.

Particle Image Velocimetry (PIV) is a widely used tool for the measurement of the different kinematic properties of the fluid flow. In this measurement technique, a pulsed laser light sheet is used to illuminate a flow field seeded with tracer particles and at each instance of illumination, the positions of the particles are recorded on digital CCD cameras. The resulting two camera frames can then be processed by various techniques to obtain the velocity vectors. One such techniques involve the tracking of the individual particles so as to identify the displacement of the every particles present in the flow field. The displacement of individual particles thus determined gives the velocity information if divided by known time interval. The accuracy as well as efficiency of such measurement systems depend upon the reliability of the algorithms to track those particles. In the present work, a cellular neural network based algorithm has been proposed. Performance test has been carried out using the standard flow images. It performs well in comparison to the existing algorithms in terms of reliability, accuracy and processing time.

The insoluble Prussian-blue (PB) pigment becomes possible to disperse in aqueous solution by covering their surfaces with ferrocyanide anions. The thin film fabricated with these water-dispersible PB nanoparticles shows evident electrochromic color changes between +0.8 V to -0.4 V on an ITO substrate. The mass change of the thin film during an electrochemical reaction is measured by means of electrochemical quartz crystal microbalance (EQCM). According to the EQCM analysis, the filling rate of water-dispersible PB nanoparticles in the film is 37.7% as compared with an assumed perfect crystal PB film.

The task of joint channel and data estimation based on the maximum likelihood principle is addressed using a continuous and discrete particle swarm optimization (PSO) algorithm over additive white Gaussian noise channels. The PSO algorithm works at two levels. At the upper level continuous PSO estimates the channel while at the lower level, discrete PSO detects the data. Simulation results indicate that under the same conditions, PSO outperforms the best of the published alternatives.

This letter proposes a novel scheme of joint antenna combination and symbol detection in multi-input multi-output (MIMO) systems, which simultaneously determines the antenna combination coefficients to lower the RF chains and designs the minimum bit error rate (MBER) detector to mitigate the interference. The joint decision statistic, however, is highly nonlinear and the particle swarm optimization (PSO) algorithm is employed to reduce the computational overhead. Simulations show that the new approach yields satisfactory performance with reduced computational overhead compared with pervious works.

We fabricated the floating gate for silicon-on-insulator nonvolatile memory devices with In2O3 nano-particles embedded in polyimide insulator. Self-assembled In2O3 nano-particles were created by chemical reaction between the biphenyl dianhydride-p-phenylenediamine polymer precursor and indium films. The particles size and density of In2O3 nano-particles were 7 nm and 61011 cm-2, respectively. The current-voltage and retention time of fabricated device were characterized by using semiconductor parameter analyzer. A significant threshold voltage shift of fabricated nano-floating gate memory devices obtained, because of the charging effects of In2O3 nano-particles. And a memory window measured about 1 V at initial status.

The computation involved in multiuser detection (MUD) for multicarrier CDMA (MC-CDMA) based on maximum likelihood (ML) principle grows exponentially with the number of users. Particle swarm optimization (PSO) with soft decisions has been proposed to mitigate this problem. The computational complexity of PSO, is comparable with genetic algorithm (GA), but is much less than the optimal ML detector and yet its performance is much better than GA.

Due to the computational complexity of the optimum maximum likelihood detector (OMD) growing exponentially with the number of users, suboptimum techniques have received significant attention. We have proposed the particle swarm optimization (PSO) for the multiuser detection (MUD) in asynchronous multicarrier code division multiple access (MC-CDMA) system. The performance of PSO based MUD is near optimum, while its computational complexity is far less than OMD. Performance of PSO-MUD has also been shown to be better than that of genetic algorithm based MUD (GA-MUD) at practical SNR.

Visual tracking is required by many vision applications such as human-computer interfaces and human-robot interactions. However, in daily living spaces where such applications are assumed to be used, stable tracking is often difficult because there are many objects which can cause the visual occlusion. While conventional tracking techniques can handle, to some extent, partial and short-term occlusion, they fail when presented with complete occlusion over long periods. They also cannot handle the case that an occluder such as a box and a bag contains and carries the tracking target inside itself, that is, the case that the target invisibly moves while being contained by the occluder. In this paper, to handle this occlusion problem, we propose a method for visual tracking by a particle filter, which switches tracking targets autonomously. In our method, if occlusion occurs during tracking, a model of the occluder is dynamically created and the tracking target is switched to this model. Thus, our method enables the tracker to indirectly track the "invisible target" by switching its target to the occluder effectively. Experimental results show the effectiveness of our method.

The computational complexity of the optimum maximum likelihood detector (OMLD) does not allow its utility for multi-user detection (MUD) in code division multiple access (CDMA) systems. As proposed in this letter, particle swarm optimization (PSO) with soft decision offers a much more efficient option with few parameters to be adjusted, flexibility to implement, that gives a much faster convergence compared to OMLD. It outperforms the conventional detector, the genetic algorithm approach and the standard suboptimal detectors considered in the literature.

Particle swarm optimization (PSO), inspired by social psychology principles and evolutionary computations, has been successfully applied to a wide range of continuous optimization problems. However, research on discrete problems has been done not much even though discrete binary version of PSO (BPSO) was introduced by Kennedy and Eberhart in 1997. In this paper, we propose a modified BPSO algorithm, which escapes from a local optimum by employing a bit change mutation. The proposed algorithm was tested on De jong's suite and its results show that BPSO with the proposed mutation outperforms the original BPSO.

A particle for artificial magnetic materials in microwave frequency is proposed. It has simple structure composed of two parallel metal strips and is suitable to make a thin material extending in the transverse plane. In order to grasp the characteristic the effective permeability is formulated in the form of a transmission line. The characteristics of effective permeability are discussed based on the transmission line model for miniaturization and increase of the permeability. After discussing the reflection from materials with negative permeability or negative permittivity, a high impedance material is constituted. Total reflection with zero phase from the material composed of modified magnetic particles is measured in a waveguide.

In this work particle swarm optimization (PSO) aided with radial basis functions (RBF) has been suggested to carry out multiuser detection (MUD) for synchronous direct sequence code division multiple access (DS-CDMA) systems. The performance of the proposed algorithm is compared to that of other standard suboptimal detectors and genetic algorithm (GA) assisted MUD. It is shown to offer better performance than the others especially if there are many users.

This paper presents a novel feature extraction algorithm based on particle swarms for processing hyperspectral imagery data. Particle swarm optimization, originally developed for global optimization over continuous spaces, is extended to deal with the problem of feature extraction. A formulation utilizing two swarms of particles was developed to optimize simultaneously a desired performance criterion and the number of selected features. Candidate feature sets were evaluated on a regression problem. Artificial neural networks were trained to construct linear and nonlinear models of chemical concentration of glucose in soybean crops. Experimental results utilizing real-world hyperspectral datasets demonstrate the viability of the method. The particle swarms-based approach presented superior performance in comparison with conventional feature extraction methods, on both linear and nonlinear models.