We report Ultraviolet (UV)-induced visible light luminescence in artificial-lattice thin films of ion-doped silica glass (silica superstructure thin films). The film was composed of periodic nanometer layers of germanium-doped silica (Ge:SiO2), titanium-doped silica (Ti:SiO2), and tin-doped silica (Sn:SiO2). The thickness of each layer was between 10 and 30 nm. Despite the small thickness of the film (few microns), a relatively bright luminescence of white light was observed, along with cathode-ray luminescence in the superstructure film. In addition, irradiation of the superstructure film with UV light led to light amplification by stimulated emission at 405 nm. The experimental results suggest the potential application of silica superstructure thin films as optical amplifiers.

Spectrum sensing is an important function for dynamic spectrum access (DSA) type cognitive radio systems to detect opportunities for sharing the spectrum with a primary system. The key requirements for spectrum sensing are stability in controlling the probability of false alarm as well as detection performance of the primary signals. However, false alarms can be triggered by noise uncertainty at the secondary devices or unknown interference signals from other secondary systems in realistic radio environments. This paper proposes a robust spectrum sensing method against such uncertainties; it is a kind of cyclostationary feature detection (CFD) approaches. Our proposed method, referred to as maximum cyclic autocorrelation selection (MCAS), compares the peak and non-peak values of the cyclic autocorrelation function (CAF) to detect primary signals, where the non-peak value is the CAF value calculated at cyclic frequencies between the peaks. In MCAS, the desired probability of false alarm can be obtained by setting the number of the non-peak values. In addition, the multiple peak values are combined in MCAS to obtain noise reduction effect and coherent combining gain. Through computer simulations, we show that MCAS can control the probability of false alarm under the condition of noise uncertainty and interference. Furthermore, our method achieves better performance with much less computational complexity in comparison to conventional CFD methods.

This paper proposes a micro-code based Programmable Memory BIST (PMBIST) architecture that can support various kinds of test algorithms. The proposed Non-linear PMBIST (NPMBIST) guarantees high flexibility and high fault coverage using not only March algorithms but also non-linear algorithms such as Walking and Galloping. This NPMBIST has an optimized hardware overhead, since algorithms can be implemented with the minimum bits by the optimized instructions. Finally, various and complex algorithms can be run thanks to its support of multi-loop.

An improved nonlinear circuit model for a GaAs Gunn diode in an oscillator is proposed based on the physical mechanism of the diode. This model interprets the nonlinear harmonic character on the Gunn diode. Its equivalent nonlinear circuit of which can assist in the design of the Gunn oscillator and help in the analysis of the fundamental and harmonic characteristics of the GaAs Gunn diode. The simulation prediction and the experiment of the Gunn oscillator show the feasibility of the nonlinear circuit model for the GaAs Gunn oscillator.

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.

Conventional kernel prefetching schemes have focused on taking advantage of sequential access patterns that are easy to detect. However, it is observed that, on random and even sequential references, they may cause performance degradation due to inaccurate pattern prediction and overshooting. To address these problems, we propose a novel approach to work with existing kernel prefetching schemes, called Reference Pattern based kernel Prefetching (RPP). The RPP can reduce negative effects of existing schemes by identifying one more reference pattern, i.e., looping, in addition to random and sequential patterns and delaying starting prefetching until patterns are confirmed to be sequential or looping.

The extraction of verb synonyms is a key technology to build a verb dictionary as a language resource. This paper presents a co-clustering-based verb synonym extraction approach that increases the number of extracted meanings of polysemous verbs from a large text corpus. For verb synonym extraction with a clustering approach dealing with polysemous verbs can be one problem issue because each polysemous verb should be categorized into different clusters depending on each meaning; thus there is a high possibility of failing to extract some of the meanings of polysemous verbs. Our proposed approach can extract the different meanings of polysemous verbs by recursively eliminating the extracted clusters from the initial data set. The experimental results of verb synonym extraction show that the proposed approach increases the correct verb clusters by about 50% with a 0.9% increase in precision and a 1.5% increase in recall over the previous approach.

In Malay language, there are no conjugations and declensions and affixes have important grammatical functions. In Malay, the same word may function as a noun, an adjective, an adverb, or, a verb, depending on its position in the sentence. Although extensively simple root words are used in informal conversations, it is essential to use the precise words in formal speech or written texts. In Malay, to make sentences clear, derivative words are used. Derivation is achieved mainly by the use of affixes. There are approximately a hundred possible derivative forms of a root word in written language of the educated Malay. Therefore, the composition of Malay words may be complicated. Although there are several types of stemming algorithms available for text processing in English and some other languages, they cannot be used to overcome the difficulties in Malay word stemming. Stemming is the process of reducing various words to their root forms in order to improve the effectiveness of text processing in information systems. It is essential to avoid both over-stemming and under-stemming errors. We have developed a new Malay stemmer (stemming algorithm) for removing inflectional and derivational affixes. Our stemmer uses a set of affix rules and two types of dictionaries: a root-word dictionary and a derivative-word dictionary. The use of set of rules is aimed at reducing the occurrence of under-stemming errors, while that of the dictionaries is believed to reduce the occurrence of over-stemming errors. We performed an experiment to evaluate the application of our stemmer in text mining software. For the experiment, text data used were actual web pages collected from the World Wide Web to demonstrate the effectiveness of our Malay stemming algorithm. The experimental results showed that our stemmer can effectively increase the precision of the extracted Boolean expressions for text categorization.

In this paper, we present a discriminative word-character hybrid model for joint Chinese word segmentation and POS tagging. Our word-character hybrid model offers high performance since it can handle both known and unknown words. We describe our strategies that yield good balance for learning the characteristics of known and unknown words and propose an error-driven policy that delivers such balance by acquiring examples of unknown words from particular errors in a training corpus. We describe an efficient framework for training our model based on the Margin Infused Relaxed Algorithm (MIRA), evaluate our approach on the Penn Chinese Treebank, and show that it achieves superior performance compared to the state-of-the-art approaches reported in the literature.

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.

Based on provided convenient design equations for slow wave transmission lines and metamaterial line, a very compact rat-race hybrid coupler is proposed using three slow wave lines and one metamaterial line. At the design frequency of 2 GHz, the size of the proposed coupler is 2.3 cm2.7 cm, which is a 74% reduction compared with the conventional one. Despite the considerable size reduction, the theoretical bandwidths based on | S11|, | S31|, ∠ S21-∠ S31, and ∠ S24-∠ S34, have been improved by 9%, 7%, 31%, and 59%, respectively. The measured performances are in reasonable agreement with the theoretical ones.

To realize dynamic spectrum access (DSA), spectrum sensing is performed to detect the presence or absence of primary users (PUs). This paper proposes a sensing architecture. This architecture enables use cases such as DSA with PU detection using a single spectrum sensor and DSA with distributed sensing, such as cooperative sensing, collaborative sensing, and selective sensing. In this paper we focus on distributed sensing. These sensing schemes employ distributed spectrum sensors (DSSs) where each sensor uses energy detection (ED) in Rayleigh fading environment. To theoretically analyze the performance of the three sensing schemes, a closed-form expression for the probability of detection by ED with selective combining (SC) in Rayleigh fading environment is derived. Applying this expression to the PU detection problem, we obtain analytical models of the three sensing schemes. Analysis shows that at 5-dB signal-to-noise ratio (SNR) and with a false alarm rate of 0.004, the probability of detection is increased from 0.02 to 0.3 and 0.4, respectively, by cooperative sensing and collaborative sensing schemes using using three DSSs. Results also show that the selected sensing scheme matches the performance of the collaborative sensing scheme. Moreover, it provides a low false alarm rate.

In this paper, a sidelobe suppression technique for orthogonal frequency division multiplexing (OFDM)-based cognitive radios (CR) is proposed. In the OFDM-based CR systems, after the CR terminal executes spectrum sensing, it transmits a CR packet by activating the subcarriers in the frequency bands where no signals are detected (hereinafter, these subcarriers are called "active subcarrier") and by disabling (nulling) the subcarriers in the frequency bands where the signals are detected. In this situation, a problem arises in that the signals that leak from the active subcarriers to the null subcarriers may interfere with the primary systems. Therefore, this signal leakage has to be minimized. In many OFDM-based wireless communication systems, one packet or frame consists of multiple OFDM symbols and the discontinuity between the consecutive OFDM symbols causes the signal leakage to the null subcarriers. In the proposed method, signal leakage to the null subcarriers is suppressed by regenerating null subcarriers in the frequency-domain signal of the whole packet as follows. One CR packet consisting of multiple OFDM symbols having null subcarriers and guard interval (GI) is buffered and oversampled, and then the oversampled signal is Fourier transformed at once and consequently the frequency-domain signal of the packet is obtained. The null subcarriers in the frequency-domain signal are zeroed again, and then the signal is inverse Fourier transformed and transmitted. The proposed method significantly suppresses the signal leakage. The spectral power density, the peak-to-average power ratio (PAPR) and the packet error rate (PER) performances of the proposed method are evaluated by computer simulations and the effectiveness of the proposed method is shown.

Interface state density was estimated from diode factor n of SiC MIS Schottky diode. The interface state density was the order of 1012 cm-2eV-1, and was same order to the value for the sample carefully prepared by oxidation and post oxidation annealing. The interface state density determined from n was consistent to the value calculated from the capacitance voltage curve of SiO2/nitride/SiC MIS diode by Terman method. High temperature nitridation was effective to reduce the interface state density.

A 0.4-5.8 GHz SiGe-MMIC quadrature modulator (Q-MOD) employing a self current controlled mixer for cognitive radio is described. The self current controlled mixer consists of a Gilbert cell mixer and a self current control circuit which is composed of both a current feedback circuit and an output buffer amplifier. The self current control circuit automatically controls the mixer current according to the output power level, and improves the linearity over wide radio frequency (RF) range. Simulation results show that the proposed Q-MOD realizes 1 dB compression point (P1 dB) improvement of more than 3.0 dB compared to the conventional Q-MOD at the frequencies of 0.4, 0.8, 1.95, 5.2 and 5.8 GHz. The fabricated Q-MOD achieves P1 dB improvement of more than 2.8 dB under the same condition. It also improves the output power with error vector magnitude (EVM) of 3.0% (Pout@EVM=3.0%), and achieves the Pout improvement of more than 2.7 dB under the modulation conditions of UHF wireless system (OFDM/16QAM, 0.4 GHz), W-CDMA (HPSK/QPSK, 0.8 GHz/1.95 GHz) and wireless-LAN (OFDM/64QAM, 5.2 GHz/5.8 GHz).

Channel estimation is a key baseband processing task in wireless systems. Filtering or smoothing algorithms can improve the accuracy of channel estimates and the Discrete Cosine Transform (DCT) can be used for this purpose. By using the DCT, performance will be improved compared to the straight-forward approach of per subcarrier estimation (PSE). However, the complexity of the DCT is not negligible. This paper proposes a low-complexity channel estimation scheme using the DCT. Simulation results show that the performance is improved by more than 1dB compared with PSE in MIMO-OFDM system.

The public safety spectrum is generally under-utilized due to the unique traffic characteristics of bursty and mission critical. This letter considers the application of dynamic spectrum access (DSA) to the combined spectrum of public safety (PS) and commercial (CMR) users in a common shared network that can provide both PS and CMR services. Our scenario includes the 700 MHz Public/Private Partnership which was recently issued by the Federal Communications Commission. We first propose an efficient DSA mechanism to coordinate the combined spectrum, and then establish a call admission control that reflects the proposed DSA in a wideband code division multiple access based network. The essentials of our proposed DSA are opportunistic access to the public safety spectrum and priority access to the commercial spectrum. Simulation results show that these schemes are well harmonized in various network environments.

An OFDMA based channel access scheme is proposed for dynamic spectrum access to utilize frequency spectrum efficiently. Though the OFDMA based scheme is flexible enough to change the bandwidth and channel of the transmitted signals, the OFDMA signal has large PAPR (Peak to Average Power Ratio). In addition, if the OFDMA receiver does not use a filter to extract sub-carriers before FFT (Fast Fourier Transform) processing, the designated sub-carriers suffer large interference from the adjacent channel signals in the FFT processing on the receiving side. To solve the problems such as PAPR and adjacent channel interference encountered in the OFDMA based scheme, this paper proposes a novel dynamic channel access scheme using overlap FFT filter-bank based on single carrier modulation. It also shows performance evaluation results of the proposed scheme by computer simulation.

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.

The topic of Human Computer Interaction (HCI) has been gathering more and more scientific attention of late. A very important, but often undervalued area in this field is human engagement. That is, a person's commitment to take part in and continue the interaction. In this paper we describe work on a humor-equipped casual conversational system (chatterbot) and investigate the effect of humor on a user's engagement in the conversation. A group of users was made to converse with two systems: one with and one without humor. The chat logs were then analyzed using an emotive analysis system to check user reactions and attitudes towards each system. Results were projected on Russell 's two-dimensional emotiveness space to evaluate the positivity/negativity and activation/deactivation of these emotions. This analysis indicated emotions elicited by the humor-equipped system were more positively active and less negatively active than by the system without humor. The implications of results and relation between them and user engagement in the conversation are discussed. We also propose a distinction between positive and negative engagement.