• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.5
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• pp.682-686
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• 2016

A fully differential RC calibrator for accurate cut-off frequency of a programmable channel selection filter is proposed. The proposed RC calibrator consists of an RC timer, clock generator, synchronous counter, digital comparator, and control block. To verify the proposed RC calibrator, a six-order Chebyshev programmable low-pass filter with adjustable 3 dB cut-off frequency, which is controlled by the proposed RC calibrator, was implemented in a $0.18-{\mu}m$ CMOS technology. The channel selection filter with the proposed RC calibrator draws 1.8 mA from a 1.8 V supply voltage and the measured 3 dB cut-off frequencies of the channel selection LPF is controlled accurately by the RC calibrator.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.6
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• pp.1101-1112
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• 2011

The Multiuser Eigenmode Transmission (MET) has generated significant interests in literature due to its optimal performance in linear precoding systems. The MET can simultaneously transmit several spatial multiplexing eigenmodes to multiple users which significantly enhance the system performance. The maximum number of users that can be served simultaneously is limited due to the constraints on the number antennas, and thus an appropriate user selection is critical to the MET system. Various algorithms have been developed in previous works such as the enumerative search algorithm. However, the high complexities of these algorithms impede their applications in practice. In this paper, motivated by the necessity of an efficient and effective user selection algorithm, a low complexity recursive user selection algorithm is proposed for the MET system. In addition, the fairness of the MET system is improved by using the combination of the proposed user selection algorithm and the adaptive Proportional Fair Scheduling (PFS) algorithm. Extensive simulations are implemented to verify the efficiency and effectiveness of the proposed algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.12
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• pp.1081-1088
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• 2013

Hyperspectral cameras acquire reflectance values at many different wavelength bands. Dimensions tend to increase because spectral information is stored in each pixel. Several attempts have been made to reduce dimensional problems such as the feature selection using Adaboost and dimension reduction using the Simulated Annealing technique. We propose a novel material detection method that consists of four steps: feature band selection, feature extraction, SVM (Support Vector Machine) learning, and target and specific region detection. It is a combination of the band ratio method and Simulated Annealing algorithm based on detection rate. The experimental results validate the effectiveness of the proposed feature selection and band ratio method.

• Journal of Information Technology Applications and Management
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• v.16 no.1
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• pp.169-183
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• 2009

The purpose of this research, based on the Media Selection Theory, the Technology Acceptance Model, and the Social Influence Theory, is to investigate the influential factors that affect media selection in banking transactions. Analyses showed that for location sensitive bank windows and ATMs(automatic teller machines), defined as offline-based transaction channels, convenience was the variable affecting media selection. However, in the case of online media not related to location, (phone banking, internet banking, and mobile banking) reliability was the significant variable influencing use. The findings show that banking organizations may benefit from identifying traits of media affecting use, and should differentiate customer services for competitive advantage.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.320-326
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• 2016

In existing literature on multiple-input multiple-output (MIMO) relaying communication systems, antenna selection is often implemented by maximizing the channel capacity or the output single-to-noise ratio (SNR). In this paper, we propose an energy-efficient low-complexity antenna selection scheme for MIMO relaying communication systems. The proposed algorithm is based on beamforming and maximizing the Frobenius norm to jointly optimize the transmit power, number of active antennas, and antenna subsets at the source, relaying and destination. We maximize the energy efficiency between the link of source to relay and the link of relay to destination to obtain the maximum energy efficiency of the system, subject to the SNR constraint. Compared to existing antenna selection methods forMIMO relaying communication systems, simulation results demonstrate that the proposed method can save more power in term of energy efficiency, while having lower computational complexity.

• Industrial Engineering and Management Systems
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• v.14 no.2
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• pp.210-219
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• 2015

A classification task requires an exponentially growing amount of computation time and number of observations as the variable dimensionality increases. Thus, reducing the dimensionality of the data is essential when the number of observations is limited. Often, dimensionality reduction or feature selection leads to better classification performance than using the whole number of features. In this paper, we study the possibility of utilizing the Markov blanket discovery algorithm as a new feature selection method. The Markov blanket of a target variable is the minimal variable set for explaining the target variable on the basis of conditional independence of all the variables to be connected in a Bayesian network. We apply several Markov blanket discovery algorithms to some high-dimensional categorical and continuous data sets, and compare their classification performance with other feature selection methods using well-known classifiers.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.113-122
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• 2015

Feature subset selection is as a pre-processing step in learning algorithms. In this paper, we propose an efficient algorithm, ModifiedFAST, for feature subset selection. This algorithm is suitable for text datasets, and uses the concept of information gain to remove irrelevant and redundant features. A new optimal value of the threshold for symmetric uncertainty, used to identify relevant features, is found. The thresholds used by previous feature selection algorithms such as FAST, Relief, and CFS were not optimal. It has been proven that the threshold value greatly affects the percentage of selected features and the classification accuracy. A new performance unified metric that combines accuracy and the number of features selected has been proposed and applied in the proposed algorithm. It was experimentally shown that the percentage of selected features obtained by the proposed algorithm was lower than that obtained using existing algorithms in most of the datasets. The effectiveness of our algorithm on the optimal threshold was statistically validated with other algorithms.

• Journal of Communications and Networks
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• v.10 no.2
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• pp.175-185
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• 2008

In this paper, we investigate the effect of cooperative and selection relaying schemes on multiuser diversity in downlink cellular systems with fixed relay stations (RSs). Each mobile station (MS) is either directly connected to a base station (BS) and/or connected to a relay station. We first derive closed-form solutions or upper-bound of the ergodic and outage capacities of four different downlink data relaying schemes: A direct scheme, a relay scheme, a selection scheme, and a cooperative scheme. The selection scheme selects the best access link between the BS and an MS. For all schemes, the capacity of the BS-RS link is assumed to be always larger than that of RS-MS link. Half-duplex channel use and repetition based relaying schemes are assumed for relaying operations. We also analyze the system capacity in a multiuser diversity environment in which a maximum signal-to-noise ratio (SNR) scheduler is used at a base station. The result shows that the selection scheme outperforms the other three schemes in terms of link ergodic capacity, link outage capacity, and system ergodic capacity. Furthermore, our results show that cooperative and selection diversity techniques limit the performance gain that could have been achieved by the multiuser diversity technique.

• The Journal of Information Systems
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• v.14 no.1
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• pp.171-195
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• 2005

The current study investigates major factors which influence the consumer's selection of internet shopping malls. Based on the technology acceptance model(TAM)(Davis, 1989) and trust theory(McKnight & Chervany, 2001), consumer selection factors from marketing research(Burke, 1997;Dodds et al, 2001), perceived usefulness, perceived ease of use, trust, service quality, and product price were hypothesized as to affect the consumer's decision to choose one's specific internet shopping malls. The study developed a research model to explain the shopping mall selection and collected the survey responses from 312 internet shopping mall users. The results of the current research indicate that all the research variables employed in the study, perceived usefulness, perceived ease of use, trust, service quality, and product price, are found to significantly influence the consumer's shopping mall selection decision. Among the influencing factors, price, service quality, and trust showed a greater effect on the shopping mall selection than usefulness and ease of use. This result implies that purchase-related variables such as price and service quality may be more critical to attracting customers and thereby raising the sales volume of the shopping mall, than the web site's usefulness and ease of use.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3725-3748
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• 2018

Text data distribution is often imbalanced. Imbalanced data is one of the challenges in text classification, as it leads to the loss of performance of classifiers. Many studies have been conducted so far in this regard. The proposed solutions are divided into several general categories, include sampling-based and algorithm-based methods. In recent studies, feature selection has also been considered as one of the solutions for the imbalance problem. In this paper, a novel one-sided feature selection known as probabilistic feature selection (PFS) was presented for imbalanced text classification. The PFS is a probabilistic method that is calculated using feature distribution. Compared to the similar methods, the PFS has more parameters. In order to evaluate the performance of the proposed method, the feature selection methods including Gini, MI, FAST and DFS were implemented. To assess the proposed method, the decision tree classifications such as C4.5 and Naive Bayes were used. The results of tests on Reuters-21875 and WebKB figures per F-measure suggested that the proposed feature selection has significantly improved the performance of the classifiers.