• Journal of Sensor Science and Technology
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• v.30 no.6
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• pp.357-363
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• 2021

We developed a chemiresistive anion sensor using highly conductive carbon nanotube fibers (CNTFs) functionalized with anion receptors. Mechanically robust CNTFs were prepared via wet-spinning utilizing the nematic liquid crystal properties of CNTs in chlorosulfonic acid (CSA). For anion detection, polymeric receptors composed of dual-hydrogen bond donors, including thiourea 1, squaramide 2, and croconamide 3, were prepared and bonded non-covalently on the surface of the CNTFs. The binding affinities of the anion receptors were studied using UV-vis titrations. The results revealed that squaramide 2 exhibited the highest binding affinity toward AcO^-, followed by thiourea 1 and croconamide 3. This trend was consistent with the chemiresistive sensing responses toward AcO^- using functional CNTFs. Selective anion sensing properties were observed that CNTFs functionalized with squaramide 2 exhibited a response of 1.08% toward 33.33 mM AcO^-, while negligible responses (<0.1%) were observed for other anions such as Cl^-, Br^-, and NO₃^-. The improved response was attributed to the internal charge transfer of dual-hydrogen bond donors owing to the deprotonation of the receptor upon the addition of AcO^-.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.6
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• pp.309-317
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• 2022

In this paper, we propose a machine learning-based copy-move forgery detection network with dual branches. Because the rotation or scaling operation is frequently involved in copy-move forger, the conventional convolutional neural network is not effectively applied in detecting copy-move tampering. Therefore, we divide the input into rotation-invariant and scaling-invariant features based on the wavelet coefficients. Each of the features is input to different branches having the same structure, and is fused in the combination module. Each branch comprises feature extraction, correlation, and mask decoder modules. In the proposed network, VGG16 is used for the feature extraction module. To check similarity of features generated by the feature extraction module, the conventional correlation module used. Finally, the mask decoder model is applied to develop a pixel-level localization map. We perform experiments on test dataset and compare the proposed method with state-of-the-art tampering localization methods. The results demonstrate that the proposed scheme outperforms the existing approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.5095-5111
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• 2016

The Probability Hypothesis Density (PHD) filter is a suboptimal approximation and tractable alternative to the multi-target Bayesian filter based on random finite sets. However, the PHD filter fails to track newborn targets when the target birth intensity is unknown prior to tracking. In this paper, a dual detection-guided newborn target intensity PHD algorithm is developed to solve the problem, where two schemes, namely, a newborn target intensity estimation scheme and improved measurement-driven scheme, are proposed. First, the newborn target intensity estimation scheme, consisting of the Dirichlet distribution with the negative exponent parameter and target velocity feature, is used to recursively estimate the target birth intensity. Then, an improved measurement-driven scheme is introduced to reduce the errors of the estimated number of targets and computational load. Simulation results demonstrate that the proposed algorithm can achieve good performance in terms of target states, target number and computational load when the newborn target intensity is not predefined in multi-target tracking systems.

• ETRI Journal
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• v.41 no.5
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• pp.658-669
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• 2019

For dual-channel time-frequency (TF) overlapped signals with low sparsity in underdetermined blind source separation (UBSS), this paper proposes an effective method based on interval probability to estimate and expand the types of mixing matrices. First, the detection of TF single-source points (TF-SSP) is used to improve the TF sparsity of each source. For more distinguishability, as the ratios of the coefficients from different columns of the mixing matrix are close, a local peak-detection mechanism based on interval probability (LPIP) is proposed. LPIP utilizes uniform subintervals to optimize and classify the TF coefficient ratios of the detected TF-SSP effectively in the case of a high level of TF overlap among sources and reduces the TF interference points and redundant signal features greatly to enhance the estimation accuracy. The simulation results show that under both noiseless and noisy cases, the proposed method performs better than the selected mainstream traditional methods, has good robustness, and has low algorithm complexity.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.2
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• pp.196-200
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• 1987

The following methods are proposed for implementing digital dual tone multi-frequency (DTMF) receiver: using infinite impulse response(IIR) digital filters, period-counting algorithm, discrete Fourier transform(DFT), and fast Fourier transform(FFT)[2]. The PARCOR(Partical Correlation) analysis method which has been widly used in the speech signal processing area is applied to the dual tone multi-frequency(DTMF) signal detection. This method is easy to implement digitally and stronger to digit simulation of speech than any other methods proposed up to date. Since sampling rate of 4KHz is used in the DTMF receiver for the detection of input DTMF signal originally sampled at 8KHz, it effects two times higher multiplexing efficiency.

• Bulletin of the Korean Chemical Society
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• v.35 no.4
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• pp.1159-1164
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• 2014

A simple, highly sensitive and selective method based on the rhodamine B-covered gold nanoparticle with dual-readout (colorimetric and fluorometric) detection for $\small{L}$-cysteine is proposed. A mechanism is that citrate-stabilized AuNPs were modified with RB by electrostatic interaction, which enables the nanometal surface energy transfer (NSET) from the RB to the AuNPs, quenching the fluorescence. In the presence of $\small{L}$-cysteine, it was used as a competitor in the NSET by the strongly Au-S bonding to release RB from the Au surface and recover the fluorescence, and the red-to-purple color change quickly, which was monitored simply by the naked eye. Under the optimum conditions, the detection limit is as low as 10 nM. The method possessed the advantages of simplicity, rapidity and sensitivity at the same time. The method was also successfully applied to the determination of $\small{L}$-cysteine in human urine samples, and the results were satisfying.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1383-1391
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• 2018

A new nonlinear filtering algorithm for effectively denoising images corrupted by the random-valued impulse noise, called dual sliding statistics switching median (DSSSM) filter is presented in this paper. The proposed DSSSM filter is made up of two subunits; i.e. Impulse noise detection and noise filtering. Initially, the impulse noise detection stage of DSSSM algorithm begins by processing the statistics of a localized detection window in sorted order and non-sorted order, simultaneously. Next, the median of absolute difference (MAD) obtained from both sorted statistics and non-sorted statistics will be further processed in order to classify any possible noise pixels. Subsequently, the filtering stage will replace the detected noise pixels with the estimated median value of the surrounding pixels. In addition, fuzzy based local information is used in the filtering stage to help the filter preserves the edges and details. Extensive simulations results conducted on gray scale images indicate that the DSSSM filter performs significantly better than a number of well-known impulse noise filters existing in literature in terms of noise suppression and detail preservation; with as much as 30% impulse noise corruption rate. Finally, this DSSSM filter is algorithmically simple and suitable to be implemented for electronic imaging products.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.139-147
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• 2014

In this paper, we present a novel method for simultaneous detection and tracking of multiple objects using dual-layer particle filtering. The proposed dual-layer particle sampling (DLPS) algorithm consists of parent-particles (PP) in the first layer for detecting multiple objects and child-particles (CP) in the second layer for tracking objects. In the first layer, PPs detect persons using a classifier trained by the intersection kernel support vector machine (IKSVM) at each particle under a randomly selected scale. If a certain PP detects a person, it generates CPs, and makes an object model in the detected object region for tracking the detected object. While PPs that have detected objects generate CPs for tracking, the rest of PPs still move for detecting objects. Experimental results show that the proposed method can automatically detect and track multiple objects, and efficiently reduce the processing time using the sampled particles based on motion distribution in video sequences.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.9
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• pp.1110-1116
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• 2004

Experimental measurements of laser-induced ion mobility(LIIM) were performed for ethene/air premixed flames operated near the soot inception point. Soot was ionized using a pulsed laser operated at 532 nm. The ionization signal was collected with a tungsten electrode located in the post-flame region. ionization signals were collected using both a single electrode and dual electrode configuration. Prior LIIM studies have focused on the use of a single biased electrode to generate the electric field, with the burner head serving as the path to ground. In many practical combustion systems, a path to ground is not readily available. To apply the LIIM diagnostic to these geometries, a dual electrode geometry must be employed. The influence of electrode configuration, flame equivalence ratio, and flame height on ionization signal detection was determined. The efficacy of the LIIM diagnostic to detect soot inception in the post-flame region of a premixed flame using a dual electrode configuration was investigated. For the different dual electrode configurations tested, the dual parallel electrode geometry was observed to be most sensitive to detect the soot inception point in a premixed flame.

• Journal of KIISE:Software and Applications
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• v.30 no.12
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• pp.1228-1238
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• 2003

This paper presents a new and practical method based on computer vision for detecting the monitor position where the user is looking. In general, the user tends to move both his face and eyes in order to gaze at certain monitor position. Previous researches use only one wide view camera, which can capture a whole user's face. In such a case, the image resolution is too low and the fine movements of user's eye cannot be exactly detected. So, we implement the gaze detection system with dual camera systems(a wide and a narrow view camera). In order to locate the user's eye position accurately, the narrow view camera has the functionalities of auto focusing and auto panning/tilting based on the detected 3D facial feature positions from the wide view camera. In addition, we use dual R-LED illuminators in order to detect facial features and especially eye features. As experimental results, we can implement the real-time gaze detection system and the gaze position accuracy between the computed positions and the real ones is about 3.44 cm of RMS error.