• Journal of Aerospace System Engineering
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• v.10 no.1
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• pp.8-14
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• 2016

The optimal estimation of a bearing only target tracking problem be achieved through the solution of the Fokker-Planck equation and the Bayesian update. Recently, a nonlinear filtering algorithm using a direct quadrature method of moments in which the associated Fokker-Planck equation can be propagated efficiently and accurately was proposed. Although this approach has demonstrated its promising in the field of nonlinear filtering in several examples, the "degeneracy" phenomenon, similar to that which exists in a typical particle filter, occasionally appears because only the weights are updated in the modified Bayesian rule in this algorithm. Therefore, in this paper to enhance the performance, a more stable measurement update process based upon the update equation in the Extended Kalman filters and a more accurate initialization and re-sampling strategy for weight and abscissas are proposed. Simulations are used to show the effectiveness of the proposed filter and the obtained results are promising.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.1-5
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• 2020

By adapting black body leadflame and thin film type Green A filter, we successfully demonstrated night vision imaging system (NVIS) Green A compatible LED. Fabricated NVIS compatible LEDs show small form factor compared to that of commercialized NVIS compatible LED. Especially, NVIS radiance and chromaticity of MIL-STD-3009 specification can be satisfied simultaneously and easily by controlling the color temperature of the white LED as well as the concentration of the Green A dye and the thickness of the Green A filter. The optimal dye concentration of the NVIS Green A filter is expected to be about 1 wt%. The results of this study are expected to contribute to miniaturization, weight reduction and localization of avionic display and lighting devices.

• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.54-60
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• 2021

Various digital devices were supplied throughout the Fourth Industrial Revolution. Accordingly, the importance of data processing has increased. Data processing significantly affects equipment reliability. Thus, the importance of data processing has increased, and various studies have been conducted on this topic. This study proposes a modified Gaussian filter algorithm based on a fuzzy membership function. The proposed algorithm calculates the Gaussian filter weight considering the standard deviation of the filtering mask and computes an estimate according to the fuzzy membership function. The final output is calculated by adding or subtracting the Gaussian filter output and estimate. To evaluate the proposed algorithm, simulations were conducted using existing additive white Gaussian noise removal algorithms. The proposed algorithm was then analyzed by comparing the peak signal-to-noise ratio and differential image. The simulation results show that the proposed algorithm has superior noise reduction performance and improved performance compared to the existing method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3145-3162
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• 2023

As an important technology of the internetwork, wireless sensor network technique plays an important role in indoor localization. Non-line-of-sight (NLOS) problem has a large effect on indoor location accuracy. A location algorithm based on improved particle filter and directional probabilistic data association (IPF-DPDA) for WSN is proposed to solve NLOS issue in this paper. Firstly, the improved particle filter is proposed to reduce error of measuring distance. Then the hypothesis test is used to detect whether measurements are in LOS situations or NLOS situations for N different groups. When there are measurements in the validation gate, the corresponding association probabilities are applied to weight retained position estimate to gain final location estimation. We have improved the traditional data association and added directional information on the original basis. If the validation gate has no measured value, we make use of the Kalman prediction value to renew. Finally, simulation and experimental results show that compared with existing methods, the IPF-DPDA performance better.

• Journal of Applied Biological Chemistry
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• v.42 no.1
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• pp.34-39
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• 1999

In search of a method to achieve sustainable agricultural practices, a trickling filter was employed for the piggery wastewater treatment, where rice straw was the support medium in place of more commonly used materials. The filtrate from the trickling filter were applied to a soil, on which orchard grass (Dactylic glomerata L.) was grown followed by amendment of the treated straw medium. Orchard grass was cut twice, and growth parameters and yields were measured. Soil chemical properties before and after harvesting orchard grass were analyzed. Development of the forage crop was greatly enhanced by the application of the filtrate in terms of fresh weight, dry weight, and the absorption of nutrients. Better growth and higher nutrient uptake were found in the second cutting of the orchard grass. Organic matter content, ionic intensity, exchangeable cations, and phosphate of the soil increased with the application of the filtrate. The relative high concentrations of salts in the piggery trickling filtrate, expressed in electrical conductivity and content of sodium, exerted no detrimental effect on the crop and soil.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.2
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• pp.188-194
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• 2014

Recently, indoor localization systems based on wireless sensor networks have received a great deal of attention because they help achieve high accuracy in position determination by using various algorithms. In order to minimize the error in the estimated azimuth that can occur owing to sensor drift and recursive calculation in these algorithms, we propose a novel relative azimuth estimation algorithm. The advantages of the proposed technique in an indoor environment are that an improved weight average filter is used to effectively reduce impulse noise from the raw data acquired from nodes with inherent errors and a rotational displacement algorithm is applied to obtain a precise relative azimuth without using additional sensors, which can be affected by electromagnetic noise. Results from simulations show that the proposed filter reduces the impulse noise, and the acquired estimation error does not accumulate with time by using proposed algorithm.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.3
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• pp.145-150
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• 2014

This paper presents an effective vision based method for LED traffic light detection at the daytime. First, the proposed method calculates horizontal coordinates to set region of interest (ROI) on input sequence images. Second, the proposed uses color segmentation method to extract region of green and red traffic light. Next, to classify traffic light and another noise, shape filter and haar-like feature value are used. Finally, temporal delay filter with weight is applied to remove blinking effect of LED traffic light, and state and weight of traffic light detection are used to classify types of traffic light. For simulations, the proposed method is implemented through Intel Core CPU with 2.80 GHz and 4 GB RAM, and tested on the urban and rural road video. Average detection rate of traffic light is 94.50 % and average recognition rate of traffic type is 90.24 %. Average computing time of the proposed method is 11 ms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.207-209
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• 2021

In recent years, with the development of artificial intelligence and IoT technology, automation and unmanned technology are in progress in various fields, and the importance of image processing such as object tracking, medical images and object recognition, which are the basis of this, is increasing. In particular, in systems requiring detailed data processing, noise reduction is used as a pre-processing step, but the existing algorithm has a disadvantage that blurring occurs in the filtering process. Therefore, in this paper, we propose a filter algorithm using modified spatial weights to minimize information loss in the filtering process. The proposed algorithm uses mask matching to remove AWGN, and obtains the output of the filter by adding or subtracting the output of the modified spatial weight. The proposed algorithm has superior noise reduction characteristics compared to the existing method and reconstructs the image while minimizing the blurring phenomenon.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.738-754
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• 2024

Side-channel analysis (SCA) is a cryptanalytic technique that exploits physical leakages, such as power consumption or electromagnetic emanations, from cryptographic devices to extract secret keys used in cryptographic algorithms. Recent studies have shown that training SCA models with semi-supervised learning can effectively overcome the problem of few labeled power traces. However, the process of training SCA models using semi-supervised learning generates many pseudo-labels. The performance of the SCA model can be reduced by some of these pseudo-labels. To solve this issue, we propose the HWFilter method to improve semi-supervised SCA. This method uses a Hamming Weight Pseudo-label Filter (HWPF) to filter the pseudo-labels generated by the semi-supervised SCA model, which enhances the model's performance. Furthermore, we introduce a normal distribution method for constructing the HWPF. In the normal distribution method, the Hamming weights (HWs) of power traces can be obtained from the normal distribution of power points. These HWs are filtered and combined into a HWPF. The HWFilter was tested using the ASCADv1 database and the AES_HD dataset. The experimental results demonstrate that the HWFilter method can significantly enhance the performance of semi-supervised SCA models. In the ASCADv1 database, the model with HWFilter requires only 33 power traces to recover the key. In the AES_HD dataset, the model with HWFilter outperforms the current best semi-supervised SCA model by 12%.

• Journal of Biomedical Engineering Research
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• v.25 no.2
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• pp.137-144
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• 2004

ECG signals are often contaminated with high-frequency noise such as muscle artifact, power line interference, and others. In the ECG signal processing, especially during a pre-processing stage, numerous noise removal techniques have been used to reduce these high-frequency noise without much distorting the original signal. This paper proposes a new type of digital filter with a continuously variable cutoff frequency to improve the signal quality This filter consists of a cutoff frequency controller (CFC) and variable cutoff frequency lowpass filter (VCF-LPF). From the noisy input ECG signal, CFC produces a cutoff frequency control signal using the signal slew rate. We implemented VCF-LPF based on two new filter design methods called convex combination filter (CCF) and weight interpolation fille. (WIF). These two methods allow us to change the cutoff frequency of a lowpass filter In an arbitrary fine step. VCF-LPF shows an excellent noise reduction capability for the entire time segment of ECG excluding the rising and falling edge of a very sharp QRS complex. We found VCF-LPF very useful and practical for better signal visualization and probably for better ECG interpretation. We expect this new digital filter will find its applications especially in a home health management system where the measured ECG signals are easily contaminated with high-frequency noises .