• Journal of Sensor Science and Technology
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• v.30 no.6
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• pp.376-380
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• 2021

The accurate detection of hydrogen gas molecules is considered to be important for industrial safety. However, the selective detection of the gas using semiconductive metal oxides (SMOs)-based sensors is challenging. Here, we describe the fabrication of H₂ sensors in which a nanocellulose/graphene oxide (GO) hybrid membrane is attached to SnO₂ nanosheets (NSs). One-dimensional (1D) nanocellulose fibrils are attached to the surface of GO NSs (GONC membrane) by mixing GO and nanocellulose in a solution. The as-prepared GONC membrane is employed as a sacrificial template for SnO₂ NSs as well as a molecular sieving membrane for selective H₂ filtration. The combination of GONC membrane and SnO₂ NSs showed substantial selectivity to hydrogen gas (R_air / R_gas > 10 @ 0.8 % H₂, 100 ℃) with noise level responses to interfering gases (H₂S, CO, CH₃COCH₃, C₂H₅OH, and NO₂). These remarkable sensing results are attributed mainly to the molecular sieving effect of the GONC membrane. These results can facilitate the development of a highly selective H₂ detector using SMO sensors.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.195-201
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• 2024

With the Covid-19(Corona Virus) spread all around the world, people are using this propaganda and the desperate need of the citizens to know the news about this mysterious virus by spreading fake news. Some Countries arrested people who spread fake news about this, and others made them pay a fine. And since Social Media has become a significant source of news, .there is a profound need to detect these fake news. The main aim of this research is to develop a web-based model using a combination of machine learning algorithms to detect fake news. The proposed model includes an advanced framework to identify tweets with fake news using Context Analysis; We assumed that Natural Language Processing(NLP) wouldn't be enough alone to make context analysis as Tweets are usually short and do not follow even the most straightforward syntactic rules, so we used Tweets Features as several retweets, several likes and tweet-length we also added statistical credibility analysis for Twitter users. The proposed algorithms are tested on four different benchmark datasets. And Finally, to get the best accuracy, we combined two of the best algorithms used SVM ( which is widely accepted as baseline classifier, especially with binary classification problems ) and Naive Base.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1057-1068
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• 2022

This paper compares the combination performance of feature point-based matching algorithms as a study to confirm the matching possibility between image taken by a user and a virtual texture image with the goal of developing mobile-based real-time image positioning technology. The feature based matching algorithm includes process of extracting features, calculating descriptors, matching features from both images, and finally eliminating mismatched features. At this time, for matching algorithm combination, we combined the process of extracting features and the process of calculating descriptors in the same or different matching algorithm respectively. V-World 3D desktop was used for the virtual indoor texture image. Currently, V-World 3D desktop is reinforced with details such as vertical and horizontal protrusions and dents. In addition, levels with real image textures. Using this, we constructed dataset with virtual indoor texture data as a reference image, and real image shooting at the same location as a target image. After constructing dataset, matching success rate and matching processing time were measured, and based on this, matching algorithm combination was determined for matching real image with virtual image. In this study, based on the characteristics of each matching technique, the matching algorithm was combined and applied to the constructed dataset to confirm the applicability, and performance comparison was also performed when the rotation was additionally considered. As a result of study, it was confirmed that the combination of Scale Invariant Feature Transform (SIFT)'s feature and descriptor detection had the highest matching success rate, but matching processing time was longest. And in the case of Features from Accelerated Segment Test (FAST)'s feature detector and Oriented FAST and Rotated BRIEF (ORB)'s descriptor calculation, the matching success rate was similar to that of SIFT-SIFT combination, while matching processing time was short. Furthermore, in case of FAST-ORB, it was confirmed that the matching performance was superior even when 10° rotation was applied to the dataset. Therefore, it was confirmed that the matching algorithm of FAST-ORB combination could be suitable for matching between virtual texture image and real image.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.2 no.1 s.2
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• pp.25-40
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• 2003

The purpose of this thesis is development of detection algorithm for stop-line detector. Detail detection area is set in basing detection area($1.8{\times}4.0m$) and traffic information(volume, occupancy, nonoccupancy) is collected by passive infrared detector at designing detection area. The basis detection area($1.8{\times}4.0m$) is named existing PIR and detection area applied on development algorithm is named proposal PIR. The proposal PIR is collected data such volume, occupancy, nonoccupancy, speed and lane change, but this thesis is limited to evaluate for volume, occupancy and nonoccupancy The procedure and each step of being developed algorithm is described in the next (1) The detection area of proposal PIR is made up of 2 of $1.8{\times}0.6m$ size(the detection area is named 1 and 3) and 1 of $1.8{\times}1.78m$ size(the detection area is named 2) (2) The image detection area is set on monitor to analyze outdoor photographing data then video frame analysis has been done by analyzer. (3) The occupancy, nonoccupancy and speed data of vehicle have been collected with the detection area 1 and 3 and lane change has been collected with combination of detection area 1, 2 and 3 The MAD and MAPE have been utilized to being compared with volume, occupancy and nonoccupancy for the field application and evaluation of a algorithm As the result, the proposal PIR data have been identified superior to the existing PIR data and the effect has been improved its information(volume, occupancy and nonoccupancy)

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.4
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• pp.2627-2630
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• 2013

Background: Ovarian cancer continues to pose a major challenge to physicians and radiologists. It is the third most common gynecologic malignancy and estimated to be fifth leading cancer cause of death in women, constituting 23% of all gynecological malignancies. Multi-detector computed tomography (MDCT) appears to offer an excellent modality in diagnosing ovarian cancer based on combination of its availability, meticulous technique, efficacy and familiarity of radiologists and physicians. The aim of this study was to compute sensitivity, specificity, positive and negative predictive values and diagnostic accuracy of 64-slice MDCT in classifying ovarian masses; 95% confidence intervals were reported. Materials and Methods: We prospectively designed a cross-sectional analytical study to collect data from July 2010 to August 2011 from a tertiary care hospital in Karachi, Pakistan. A sample of 105 women aged between 15-80 years referred for 64-MDCT of abdomen and pelvis with clinical suspicion of malignant ovarian cancer, irrespective of stage of disease, were enrolled by non-probability purposive sampling. All patients who were already known cases of histologically proven ovarian carcinoma and having some contraindication to radiation or iodinated contrast media were excluded. Results: Our prospective study reports sensitivity, specificity; positive and negative predictive values with 95%CI and accuracy were computed. Kappa was calculated to report agreement among the two radiologists. For reader A, MDCT was found to have 92% (0.83, 0.97) sensitivity and 86.7% (0.68, 0.96) specificity, while PPV and NPV were 94.5% (0.86, 0.98) and 86.7% (0.63, 0.92), respectively. Accuracy reported by reader A was 90.5%. For reader B, sensitivity, specificity, PPV and NPV were 94.6% (0.86, 0.98) 90% (0.72, 0.97) 96% (0.88, 0.99) and 87.1% (0.69, 0.95) respectively. Accuracy computed by reader B was 93.3%. Excellent agreement was found between the two radiologists with a significant kappa value of 0.887. Conclusion: Based on our study results, we conclude MDCT is a reliable imaging modality in diagnosis of ovarian masses accurately with insignificant interobserver variability.

• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.357-367
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• 2012

This paper presents a two-stage methodology for anomaly detection from hyperspectral imagery that consists of transform-based feature extraction and selection, and computation of a local spatial auto-correlation statistic. First, principal component transform and 3D wavelet transform are applied to reduce redundant spectral information from hyperspectral imagery. Then feature selection based on global skewness and the portion of highly skewed sub-areas is followed to find optimal features for anomaly detection. Finally, a local indicator of spatial association (LISA) statistic is computed to account for both spectral and spatial information unlike traditional anomaly detection methodology based only on spectral information. An experiment using airborne CASI imagery is carried out to illustrate the applicability of the proposed anomaly detection methodology. From the experiments, anomaly detection based on the LISA statistic linked with the selection of optimal features outperformed both the traditional RX detector which uses only spectral information, and the case using major principal components with large eigen-values. The combination of low- and high-frequency components by 3D wavelet transform showed the best detection capability, compared with the case using optimal features selected from principal components.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.11
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• pp.71-76
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• 2008

H.264(or MPEG-4/AVC pt.10) is a high performance video coding standard, and is widely used. Variable length code (VLC) of the H.264 standard compresses data using the statistical distribution of values. A decoder parses the compressed bit stream and searches decoded values in lookup tables, and the decoding process is not easy to implement by hardware. We propose an architecture of variable length decoder(VLD) for the H.264 baseline profile(BP) L4. The CAVLD decodes syntax elements using the combination of arithmetic units and lookup tables for the optimized hardware architecture. A barral shifter and a first 1's detector parse NAL bit stream, and are shared by Exp-Golomb decoder and CAVLD. A FIFO memory between CAVLD and the reorder unit and a buffer at the output of the reorder unit eliminate the bottleneck of data stream. The proposed VLD is designed using Verilog-HDL and is implemented using an FPGA. The synthesis result using a 0.18um standard CMOS technology shows that the gate count is 22,604 and the decoder can process HD($1920{\times}1080$) video at 120MHz.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.329-335
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• 2010

Digital imaging detectors can use a variety of detection materials to convert X-ray radiation either to light or directly to electron charge. Many detectors such as amorphous silicon flat panels, CCDs, and CMOS photodiode arrays incorporate a scintillator screen to convert x-ray to light. The digital radiography systems based on semiconductor detectors, commonly referred to as flat panel detectors, are gaining popularity in the clinical & hospital. The X-ray detectors are described between a-Silicon based indirect type and a-Selenium based direct type. The DRS of detectors is used to convert the x-ray to electron hole pairs. Image processing is described by specific image features: Latitude compression, Contrast enhancement, Edge enhancement, Look up table, Noise suppression. The image features are tuned independently. The final enhancement result is a combination of all image features. The parameters are altered by using specific image features in the different several hospitals. The image in a radiological report consists of two image evaluation processes: Clinical image parameters and MTF is a descriptor of the spatial resolution of a digital imaging system. We used the edge test phantom and exposure procedure described in the IEC 61267 to obtain an edge spread function from which the MTF is calculated. We can compare image in the processing parameters to change between original and processed image data. The angle of the edge with respect to the axes of detector was varied in order to determine the MTF as a function of direction. Each MTF is integrated within the spatial resolution interval of 1.35-11.70 cycles/mm at the 50% MTF point. Each image enhancement parameters consists of edge, frequency, contrast, LUT, noise, sensitometry curve, threshold level, windows. The digital device is also shown to have good uniformity of MTF and image parameters across its modality. The measurements reported here represent a comprehensive evaluation of digital radiography system designed for use in the DRS. The results indicate that the parameter enables very good image quality in the digital radiography. Of course, the quality of image from a parameter is determined by other digital devices in addition to the proper clinical image.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1526-1526
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• 2001

To improve the accuracy of sweetness sensor in automated sorting operations, it is necessary to clarify unevenness of the sugar content distribution within fruits. And it is expected that the technique to evaluate the content distribution in fruits contribute to the development of the near-infrared (NIR) imaging spectroscopy. Sugiyama (1999) had succeeded to visualize the distribution of the sugar content on the surface of a half-cut green fresh melon. However, this method cannot be applied to red flesh melons because it depends on information of the absorption band of chlorophyll (676 nm), which is affected by the color of the fresh. The objective of this study was to develop the universal visualization method depends on the absorption band of sugar, which can be applied to various kinds of melons and other fruits. The relationship between the sugar contents and absorption spectra of both green and red fresh melons were investigated by using a NIR spectrometer to determine the absorption band of sugar. The combination of 2$\^$nd/ derivative absorbances at 902 nm and 874 nm was highly correlated with the sugar contents. The wavelength of 902 nm is attributed to the absorption band of sugar. A cooled charge-coupled device (CCD) imaging camera which has 16 bit (65536 steps) A/D resolution was equipped with rotating band-pass filter wheel and used to capture the spectral absorption images of the flesh of a vertically half-cut red fresh melon. The advantage of the high A/D resolution in this research is that each pixel of the CCD is expected to function as a detector of the NIR spectrometer for quantitative analysis. Images at 846 nm, 874 nm, 902 nm and 930 nm were acquired using this CCD camera. Then the 2$\^$nd/ derivative absorbances at 902 nm and 874 nm at each pixel were calculated using these four images. On the other hand, parts of the same melon were extracted for capturing the images and squeezed for the measurement of sugar content. Then the calibration curve between the combination of 2$\^$nd/ derivative absorbances at 902 nm and 874 nm and sugar content was developed. The calibration method based on NIR spectroscopy techniques was applied to each pixel of the images to convert the 2$\^$nd/ derivative absorbances into the Brix sugar content. Mapping the sugar content value of each pixel with linear color scale, the distribution of the sugar content was visualized. As a result of the visualization, it was quantitatively confirmed that the Brix sugar contents are low at the near of the skin and become higher towards the seeds. This result suggests that the visualization technique by the NIR imaging spectroscopy could become a new useful method fer quality evaluation of melons.

• The Journal of the Korea Contents Association
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• v.13 no.3
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• pp.204-210
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• 2013

The patients who had been operated total mastectomy are different from general women in their breasts thickness due to breast surgery. As a result, digital chest image from total mastectomy patients will be different attenuation. The main objective for this study is to show that a proper Ion chamber standard combination measuring MTF which is objective basis for Digital image, when be x-ray for total mastectomy patient. We have designed the unique number that shown Left is 1, Right is 2, Center is 3 and have put the edge phantom on detector ion chamber. Lastly, we have obtained experiment images. The evaluations of all image quality have measured by 50% MTF of spatial resolution and absorption dose using Matlab(R2007a). The result showed that average exposure condition, MTF value, absorption dose for 1+3 and 2+3 combinations were 2.745 mAs, 1.925 lp/mm, 0.688 mGy. Consequently, that showed high MTF, DQE and low dose than other combinations. Therefore, a proper changes of ion chambers are able to improve image quality and to reduce radiation exposure when be X-ray for total mastectomy patients. Also, it will be possible to standard for application chamber combination and utilization on clinical detection.