• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.8 no.1
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• pp.18-23
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• 2009

The conventional channel selection method for a ZigBee communication network basically detects the energy values in all channels. In the ISM band, no license is required to use channels in this band, so there may exist various interference factors in this band. It is well known that WLAN is the major interference factor degrading the performance of ZigBee in the ISM band. In this paper, we propose an efficient channel selection algorithm which uses the pattern of two types of WLAN channel uses as the a priori information of IEEE 802.11 band IEEE 802.11g. By using the proposed algorithm, we may save the time required to select channels for the ZigBee communications.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.32 no.6
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• pp.556-562
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• 2013

This paper is aimed to improve performance of Band-Selection speech/audio Coder reconstucted band spectrum that is not sent by the comfort noise. To improve the performance, we use the Spectral Band Replication(SBR) technique instead of substitution of Comfort noise. To synthesize SBR signal, the SBR algorithm is referenced in selected signals and the spectrum synthesized by SBR is injected to non-selected band. Each sub-band spectrum has been energy-weighted by real audio signal. We propose the enhanced the Band-Selection Coder that utilizes synthesized SBR signal from selected signal instead of comfort noise.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.149-154
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• 2021

Unlike or common images, Hyperspectral images were taken by continuous electromagnetic spectral into numerous bands according to wavelengths and are high-capacity high-resolution images. It has more information than ordinary images, so it is used to explore objects and materials. To reduce the amount of information in hyper-spectral images to be processed, band selection is utilized. Existing band selection techniques are heuristic techniques based on statistics, which take a long time and often lack generality and universality. To compensate for this, this paper utilizes quantization concept to draw representative bands through Discrete Range, we use them for band selection algorithm. Experimental results showed that the proposed technique performed much faster than conventional band selection methods, and that the performance accuracy was similar to that of the original even though the number of bands was reduced by one-seventh to one-tenth.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3295-3311
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• 2020

With the wide application of hyperspectral images, it becomes more and more important to compress hyperspectral images. Conventional recursive least squares (CRLS) algorithm has great potentiality in lossless compression for hyperspectral images. The prediction accuracy of CRLS is closely related to the correlations between the reference bands and the current band, and the similarity between pixels in prediction context. According to this characteristic, we present an improved CRLS with adaptive band selection and adaptive predictor selection (CRLS-ABS-APS). Firstly, a spectral vector correlation coefficient-based k-means clustering algorithm is employed to generate clustering map. Afterwards, an adaptive band selection strategy based on inter-spectral correlation coefficient is adopted to select the reference bands for each band. Then, an adaptive predictor selection strategy based on clustering map is adopted to select the optimal CRLS predictor for each pixel. In addition, a double snake scan mode is used to further improve the similarity of prediction context, and a recursive average estimation method is used to accelerate the local average calculation. Finally, the prediction residuals are entropy encoded by arithmetic encoder. Experiments on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 2006 data set show that the CRLS-ABS-APS achieves average bit rates of 3.28 bpp, 5.55 bpp and 2.39 bpp on the three subsets, respectively. The results indicate that the CRLS-ABS-APS effectively improves the compression effect with lower computation complexity, and outperforms to the current state-of-the-art methods.

• Journal of Biosystems Engineering
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• v.40 no.4
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• pp.417-427
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• 2015

Purpose: This study investigated different band selection methods to classify spectrally similar data - obtained from aerial images of healthy citrus canopies and citrus greening disease (Huanglongbing or HLB) infected canopies - using small differences without unmixing endmember components and therefore without the need for an endmember library. However, large number of hyperspectral bands has high redundancy which had to be reduced through band selection. The objective, therefore, was to first select the best set of bands and then detect citrus Huanglongbing infected canopies using these bands in aerial hyperspectral images. Methods: The forward feature selection algorithm (FFSA) was chosen for band selection. The selected bands were used for identifying HLB infected pixels using various classifiers such as K nearest neighbor (KNN), support vector machine (SVM), naïve Bayesian classifier (NBC), and generalized local discriminant bases (LDB). All bands were also utilized to compare results. Results: It was determined that a few well-chosen bands yielded much better results than when all bands were chosen, and brought the classification results on par with standard hyperspectral classification techniques such as spectral angle mapper (SAM) and mixture tuned matched filtering (MTMF). Median detection accuracies ranged from 66-80%, which showed great potential toward rapid detection of the disease. Conclusions: Among the methods investigated, a support vector machine classifier combined with the forward feature selection algorithm yielded the best results.

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.455-467
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• 2017

When performing target detection using hyperspectral imagery, a feature extraction process is necessary to solve the problem of redundancy of adjacent spectral bands and the problem of a large amount of calculation due to high dimensional data. This study proposes a new band selection method using the $L_{2,1}$-norm regression model to apply the feature selection technique in the machine learning field to the hyperspectral band selection. In order to analyze the performance of the proposed band selection technique, we collected the hyperspectral imagery and these were used to analyze the performance of target detection with band selection. The Adaptive Cosine Estimator (ACE) detection performance is maintained or improved when the number of bands is reduced from 164 to about 30 to 40 bands in the 350 nm to 2500 nm wavelength band. Experimental results show that the proposed band selection technique extracts bands that are effective for detection in hyperspectral images and can reduce the size of the data without reducing the performance, which can help improve the processing speed of real-time target detection system in the future.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.127-130
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• 2007

The conventional feature recombination technique is very effective in the band-limited noise condition, but in broad-band noise condition, the conventional feature recombination technique does not produce notable performance improvement compared with the full-band system. To cope with this drawback, we introduce a new technique of sub-band likelihood computation in the feature recombination, and propose a new feature recombination method by using this sub-band likelihood computation. Furthermore, the reliable sub-band selection based on the signal-to-noise ratio is used to improve the performance of this proposed feature recombination. Experimental results shows that the average error reduction rate in various noise condition is more than 27% compared with the conventional full-band speaker identification system.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.116-120
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• 2008

The conventional channel selection method for a ZigBee communication network basically detects the energy values in all channels. In the ISM band, no license is required to use channels in this band, so there may exist various interference factors in this band. It is well known that WLAN is the major interference factor degrading the performance of ZigBee in the ISM band. In this paper, we propose an efficient channel selection algorithm which uses the pattern of WLAN channel uses as the a priori information. By using the proposed algorithm, we may save the time required to select channels for the ZigBee communications.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.12
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• pp.718-728
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• 2014

The multi-band wireless local area network (WLAN) using 60 GHz band and the lower band (typically 2.4 GHz/5 GHz band) can support the very high data rate in short-distance communication using 60 GHz band and the long-distance communication using the lower band. For heightening the efficiency of multi-band WLAN, an band selection scheme is a necessity. In this paper, we propose an effective frequency band selection scheme for multi-band WLANs. By using computer simulation with NS-3, we show the performance of the proposed schemes when the stations suffer from the human blockage and the log-normal shadowing.