Alumina supported cobalt-ferrum catalysts were prepared using wet impregnation method by applying 3 different conditions, namely hotplate (A), sonication (B) and soaking (C). The alumina supported cobalt-ferrum catalysts were applied in the synthesis of multi-walled carbon nanotubes (MWNTs) using catalytic chemical vapour deposition (CCVD) technique. The morphology and particle size of the cobalt-ferrum catalysts and the MWNTs yield were examined by field emission-scanning electron microscopy (FE-SEM) while the surface elemental composition of the samples was obtained by energy dispersive X-ray analysis (EDX). The morphology of catalysts A, B and C were found to be different, the particle sizes were ranged from 20-40 nm. The diameters of the MWNTs yield from samples A, B and C were found to be related to the catalyst particle size, thus the smaller the catalyst particle, the thinner the MWNTs obtained. The MWNTs with smaller diameter were obtained with higher purity and quality becuase the nanotube surface are free from amorphous carbon. Therefore, different catalyst preparation methods resulted in different sizes of the catalyst particle in order to synthesize MWNTs with desired diameter.

At work, at home, and in some public places, a desktop PC is usually available nowadays. Therefore, it is important for users to be able to play various videos on different PCs smoothly, but the diversity of codec types complicates the situation. Although some mainstream media players can try to download the needed codec automatically, this may fail for average users because installing the codec usually requires administrator privileges to complete, while the user may not be the owner of the PC. We believe an ideal solution should work without users' intervention, and need no special privileges. This paper proposes such a user-friendly, program-transparent solution for Windows-based media players. It runs the media player in a user-mode virtualization environment, and then downloads the needed codec on-the-fly. Because of API (Application Programming Interface) interception, some resource-accessing API calls from the player will be redirected to the downloaded codec resources. Then from the viewpoint of the player, the necessary codec exists locally and it can handle the video smoothly, although neither system registry nor system folders was modified during this process. Besides convenience, the principle of least privilege is maintained and the host system is left clean. This paper completely analyzes the technical issues and presents such a prototype which can work with DirectShow-compatible players. Performance tests show that the overhead is negligible. Moreover, our solution conforms to the Software-As-A-Service (SaaS) mode, which is very promising in the Internet era.

In this paper an algebraic trellis vector quantization (ATVQ) that introduces algebraic codebooks into trellis coded vector quantization (TCVQ) structure is presented. Low encoding complexity and minimum memory storage requirements are achieved using the proposed approach. It exploits advantages of both the TCVQ and the algebraic codebooks to know the delayed decision, the codebook widening, the low computational complexity and the no storage of codebook. This novel vector quantization scheme is used to encode the wideband speech line spectral frequencies (LSF) parameters. Experimental results on wideband speech have shown that ATVQ yields the same performance as the traditional split vector quantization (SVQ) and the TCVQ in terms of spectral distortion (SD). It can achieve a transparent quality at 47 bits/frame with a considerable reduction of memory storage and computation complexity when compared to SVQ and TCVQ.

High Dynamic Range Imaging (HDRI) refers to a set of techniques that can represent a dynamic range of real world luminance. Hence, the HDR image can be used to measure the reflectance property of materials. In order to reproduce the original color of materials using this HDR image, characterization of HDR imaging is needed. In this study, we propose a new HDRI characterization method under a known illumination condition at the HDR level. The proposed method normalizes the HDR image by using the HDR image of a light and balances the tone using the reference of the color chart. We demonstrate that our method outperforms the previous method at the LDR level by the average color difference and BRDF rendering result. The proposed method gives a much better reproduction of the original color of a given material.

The optimization of polarimetric contrast enhancement (OPCE) is a widely used method for maximizing the received power ratio of a desired target versus an undesired target (clutter). In this letter, a new model of the OPCE is proposed based on the Fisher criterion. By introducing the well known two-class problem of linear discriminant analysis (LDA), the proposed model is to enlarge the normalized distance of mean value between the target and the clutter. In addition, a cross-iterative numerical method is proposed for solving the optimization with a quadratic constraint. Experimental results with the polarimetric SAR (POLSAR) data demonstrate the effectiveness of the proposed method.

This paper reviews applications of fuzzy logic to telecommunications and proposes a novel fuzzy combining scheme for cooperative spectrum sensing in cognitive radio systems. A summary of previous applications of fuzzy logic to telecommunications is given outlining also potential applications of fuzzy logic in future cognitive radio systems. In complex and dynamic operational environments, future cognitive radio systems will need sophisticated decision making and environment awareness techniques that are capable of handling multidimensional, conflicting and usually non-predictable decision making problems where optimal solutions can not be necessarily found. The results indicate that fuzzy logic can be used in cooperative spectrum sensing to provide additional flexibility to existing combining methods.

This letter proposes a new adaptive filtering method that uses the last L desired signal samples as an extra input vector, besides the existing input data, to reduce mean square error. We have improved the convergence rate by adopting the squared norm of the past error samples, in addition to the modified cost function. The modified variable error-data normalized step-size least mean square algorithm provides fast convergence, ensuring a small final misadjustment. Simulation results indicate its superior mean square error performance, while its convergence rate equals that of existing methods. In addition, the proposed algorithm shows superior tracking capability when the system is subjected to an abrupt disturbance.

A simple distributed Medium Access Control (MAC) protocol for cognitive wireless networks is proposed. It is assumed that the network is slotted, the spectrum is divided into a number of channels, and the primary network statistical aggregate traffic model on each channel is given by independent Bernoulli random variables. The objective of the cognitive MAC is to maximize the exploitation of the channels idle time slots. The cognitive users can achieve this aim by appropriate hopping between the channels at each decision stage. The proposed protocol is based on the rule of least failures that is deployed by each user independently. Using this rule, at each decision stage, a channel with the least number of recorded collisions with the primary and other cognitive users is selected for exploitation. The performance of the proposed protocol for multiple cognitive users is investigated analytically and verified by simulation. It is shown that as the number of users increases the user decision under this protocol comes close to the optimum decision to maximize its own utilization. In addition, to improve opportunity utilization in the case of a large number of cognitive users, an extension to the proposed MAC protocol is presented and evaluated by simulation.

This paper studies reflection and transmission of a TE plane wave from a two-dimensional random slab with statistically anisotropic fluctuation by means of the stochastic functional approach. By starting with a representation of the random wavefield presented in the previous paper [IEICE Trans. Electron., vol.E92-C, no.1, pp.77-84, Jan. 2009], a solution algorithm of the multiple renormalized mass operator is newly shown even for anisotropic fluctuation. The multiple renormalized mass operator, the first-order incoherent scattering cross section and the optical theorem are numerically calculated and illustrated in figures. The relation between statistical properties and anisotropic fluctuation is discussed.

In deep-submicron technology, several state-of-the-art architectural synthesis flows have already adopted the distributed register architecture to cope with the increasing wire delay by allowing multicycle communication. In this article, we regard communication synthesis targeting a refined regular distributed register architecture, named RDR-GRS, as a problem of simultaneous data transfer routing and scheduling for global interconnect resource minimization. We also present an innovative algorithm with regard of both spatial and temporal perspectives. It features both a concentration-oriented path router gathering wire-sharable data transfers and a channel-based time scheduler resolving contentions for wires in a channel, which are in spatial and temporal domain, respectively. The experimental results show that the proposed algorithm can significantly outperform existing related works.

This paper presents a novel method using multi-objective optimization algorithm to automatically find the best solution from a topology library of analog circuits. Firstly this method abstracts the Pareto-front of each topology in the library by SPICE simulation. Then, the Pareto-front of the topology library is abstracted from the individual Pareto-fronts of topologies in the library followed by the theorem we proved. The best solution which is defined as the nearest point to specification on the Pareto-front of the topology library is then calculated by the equations derived from collinearity theorem. After the local searching using Nelder-Mead method maps the calculated best solution backs to design variable space, the non-dominated best solution is obtained. Comparing to the traditional optimization methods using single-objective optimization algorithms, this work can efficiently find the best non-dominated solution from multiple topologies for different specifications without additional time-consuming optimizing iterations. The experiments demonstrate that this method is feasible and practical in actual analog designs especially for uncertain or variant multi-dimensional specifications.

As device feature size decreases, interconnection delay becomes the dominating factor of circuit total delay. Distributed-register architectures can reduce the influence of interconnection delay. They may, however, increase circuit area because they require many local registers. Moreover original distributed-register architectures do not consider control signal delay, which may be the bottleneck in a circuit. In this paper, we propose a high-level synthesis method targeting generalized distributed-register architecture in which we introduce shared/local registers and global/local controllers. Our method is based on iterative improvement of scheduling/binding and floorplanning. First, we prepare shared-register groups with global controllers, each of which corresponds to a single functional unit. As iterations proceed, we use local registers and local controllers for functional units on a critical path. Shared-register groups physically located close to each other are merged into a single group. Accordingly, global controllers are merged. Finally, our method obtains a generalized distributed-register architecture where its scheduling/binding as well as floorplanning are simultaneously optimized. Experimental results show that the area is decreased by 4.7% while maintaining the performance of the circuit equal with that using original distributed-register architectures.

This paper addresses problems appearing in restoration algorithms based on utilizing both Tikhonov and bilateral total variation (BTV) regularization. The former regularization assumes that prior information has Gaussian distribution which indeed fails at edges, while the later regularization highly depends on the selected bilateral filter's parameters. To overcome these problems, we propose a locally adaptive regularization. In the proposed algorithm, we use general directional regularization functions with adaptive weights. The adaptive weights are estimated from local patches based on the property of the partially restored image. Unlike Tikhonov regularization, it can avoid smoothness across edges by using adaptive weights. In addition, unlike BTV regularization, the proposed regularization function doesn't depend on parameters' selection. The convexity conditions as well as the convergence conditions are derived for the proposed algorithm.

In this paper, we use frequency-domain equalization (FDE) to create coherent optical single-carrier (CO-SC) transmission systems that are very tolerant of chromatic dispersion (CD) and polarization mode dispersion (PMD). The efficient transmission of a 25-Gb/s NRZ-QPSK signal by using the proposed FDE is demonstrated under severe CD and PMD conditions. We also discuss the principle of FDE and some techniques suitable for implementing CO-SC-FDE. The results show that a CO-SC-FDE system is very tolerant of CD and PMD and can achieve high transmission rates over single mode fiber without optical dispersion compensation.

A variety of ZnO belt-like structures were synthesized by the heat treatment of ZnS substrates with Ga droplets in the air, and their morphological and structural properties were investigated. Three types of ZnO belts with flat surfaces of (20), (100) and (23) were obtained. As comparison, the ZnO crystal growth was examined by the thermal oxidation of ZnS only. These results highlight the promise of the heat treatment with Ga in the synthesis of oxide nanostructures.

Since the compressed data, which are frequently used in computer systems and communication systems, are very sensitive to errors, several error recovery methods for data compression have been proposed. Error recovery method for LZ77 coding, one of the most popular universal data compression methods, has been proposed. This cannot be applied to LZSS coding, a variation of LZ77 coding, because its compressed data consist of variable-length codewords. This paper proposes a burst error recovery method for LZSS coding. The error sensitive part of the compressed data are encoded by unary coding and moved to the beginning of the compressed data. After these data, a synchronization sequence is inserted. By searching the synchronization sequence, errors in the error sensitive part are detected. The errors are recovered by using a copy of the part. Computer simulation says that the compression ratio of the proposed method is almost equal to that of LZ77 coding and that it has very high error recovery capability.

This paper presents a new multi-layered artificial immune system architecture using the ideas generated from the biological immune system for solving combinatorial optimization problems. The proposed methodology is composed of five layers. After expressing the problem as a suitable representation in the first layer, the search space and the features of the problem are estimated and extracted in the second and third layers, respectively. Through taking advantage of the minimized search space from estimation and the heuristic information from extraction, the antibodies (or solutions) are evolved in the fourth layer and finally the fittest antibody is exported. In order to demonstrate the efficiency of the proposed system, the graph planarization problem is tested. Simulation results based on several benchmark instances show that the proposed algorithm performs better than traditional algorithms.

This paper presents an efficient algorithm for incremental buffer insertion and module resizing for a full-placed floorplan. Our algorithm offers a method to use the white space in a given floorplan to resize modules and insert buffers, and at the same time keeps the resultant floorplan as close to the original one as possible. Both the buffer insertion and module resizing are modeled as geometric programming problems, and can be solved extremely efficiently using new developed solution methods. The experimental results suggest that the the wire length difference between the initial floorplan and result are quite small (less than 5%), and the global structure of the initial floorplan are preserved very well.

Block truncation coding (BTC) is an efficient image compression algorithm that generates a constant output bit-rate. For color image compression, vector quantization (VQ) is exploited to improve the coding efficiency. In this letter, we propose an improved VQ based BTC (VQ-BTC) algorithm using template matching and Lloyd quantization (LQ). The experimental results show that the proposed method improves the PSNR by 0.9 dB in average compared to the conventional VQ-BTC algorithms.

To prevent blindness from diabetic retinopathy, periodic screening and early diagnosis are neccessary. Due to lack of expert ophthalmologists in rural area, automated early exudate (one of visible sign of diabetic retinopathy) detection could help to reduce the number of blindness in diabetic patients. Traditional automatic exudate detection methods are based on specific parameter configuration, while the machine learning approaches which seems more flexible may be computationally high cost. A comparative analysis of traditional and machine learning of exudates detection, namely, mathematical morphology, fuzzy c-means clustering, naive Bayesian classifier, Support Vector Machine and Nearest Neighbor classifier are presented. Detected exudates are validated with expert ophthalmologists' hand-drawn ground-truths. The sensitivity, specificity, precision, accuracy and time complexity of each method are also compared.