• International Journal of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.39-46
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• 2019

This paper proposes an adaptive threshold watershed algorithm, which is the method used for image segmentation and boundary detection, which extends the region on the basis of regional minimum point. First, apply adaptive thresholds to determine regional minimum points. Second, it extends the region by applying adaptive thresholds based on determined regional minimum points. Traditional watershed algorithms create over-segmentation, resulting in the disadvantages of breaking boundaries between regions. These segmentation results mainly from the boundary of the object, creating an inaccurate region. To solve these problems, this paper applies an improved watershed algorithm applied with adaptive threshold in regional minimum point search and region expansion in order to reduce over-segmentation and breaking the boundary of region. This resulted in over-segmentation suppression and the result of having the boundary of precisely divided regions. The experimental results show that the proposed algorithm can apply adaptive thresholds to reduce the number of segmented regions and see that the segmented boundary parts are correct.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.230-233
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• 2000

In this paper, we proposed a wavelet-based digital watermarking algorithm using human visual system and subband-adaptive threshold. After the original image is transformed using discrete wavelet transform(DWT), the perceptually significant coefficients of the each subband excluding the lowest level subbands are utilized to embed the watermark. To select perceptually significant coefficients, we use subband-adaptive threshold. For the selected coefficients, the watermark is embedded by rising HVS. We tested the performance of the proposed algorithm compared with conventional watermarking algorithm by computer simulation. The experimental results show that the proposed algorithm is superior to the conventional algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3095-3111
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• 2018

Spectrum sensing (SS) is one of the fundamental tasks for cognitive radio. In SS, decisions can be made via comparing the test statistics with a threshold. Conventional adaptive algorithms for SS usually adjust their thresholds according to the radio environment. This paper concentrates on the issue of adaptive SS whose threshold is adjusted based on the Markovian behavior of primary user (PU). Moreover, Bayesian cost is adopted as the performance metric to achieve a trade-off between false alarm and missed detection probabilities. Two novel adaptive algorithms, including Markov Bayesian energy detection (MBED) algorithm and IMBED (improved MBED) algorithm, are proposed. Both algorithms model the behavior of PU as a two-state Markov process, with which their thresholds are adaptively adjusted according to the detection results at previous slots. Compared with the existing Bayesian energy detection (BED) algorithm, MBED algorithm can achieve lower Bayesian cost, especially in high signal-to-noise ratio (SNR) regime. Furthermore, it has the advantage of low computational complexity. IMBED algorithm is proposed to alleviate the side effects of detection errors at previous slots. It can reduce Bayesian cost more significantly and in a wider SNR region. Simulation results are provided to illustrate the effectiveness and efficiencies of both algorithms.

• The Journal of the Acoustical Society of Korea
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• v.39 no.6
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• pp.585-591
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• 2020

The adaptive transmission techniques are efficient method for underwater acoustic communication to improve the system efficiency by varying transmission parameters according to channel conditions. In this paper, we construct four transmission modes with different data rates using the convolutional codes, which is freely set to size of information bits. On the receiver side, one critical component of adaptive system is to find which mode has best performance. In this paper, we proposed threshold detection algorithm to decide appropriate mode and applied turbo equalization method based on BCJR decoder in order to improve performance. We analyzed the performance of four modes based on threshold detection algorithm through the lake experiment.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.10
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• pp.112-119
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• 1997

Zerotreeimage coding is known as a simple and effective image comprssion algorithm. It has the property that the compression is generated in order of improtance. Conventionally, a fixed threshold is applied to the entire wavelet coefficients regardless of frequency and local features of an image. In this paper, we propose a new zerotree coding scheme with adaptive threshold. The adaptive threshold is determined by human visual characteristics. It is shown that the image quality of the proposed method is better than that of the conventional method.

• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.17-26
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• 2011

We have developed a wearable device that can convert sensor data into real-time step counts. Sensor data on gait were acquired using a triaxial accelerometer. A test was performed according to a test protocol for different walking speeds, e.g., slow walking, walking, fast walking, slow running, running, and fast running. Each test was carried out for 36 min on a treadmill with the participant wearing an Actical device, and the device developed in this study. The signal vector magnitude (SVM) was used to process the X, Y, and Z values output by the triaxial accelerometer into one representative value. In addition, for accurate step-count detection, we used three algorithms: an heuristic algorithm (HA), the adaptive threshold algorithm (ATA), and the adaptive locking period algorithm (ALPA). The recognition rate of our algorithm was 97.34% better than that of the Actical device(91.74%) by 5.6%.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.5
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• pp.361-367
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• 2004

This paper describes design of an algorithm for analyzing human activity using body-fixed 3-axis accelerometer in the small of the back. In the first step, we distinguish static and dynamic activity period using AC signal analysis. Then five postures were classified by applying the threshold in DC signal corresponding to the static activity period. Also, after comparison of average power and taking negative peak signal in the dynamic activity period, the four dynamic activities were classified by adaptive threshold method. To evaluate the performance of the proposed algorithm, the measured signals obtained from six subjects were applied to the proposed algorithm and the results were compared with the simultaneously measured video data. As a result, the activity classification rate of 95.7％ on average was obtained. Overall results show that the proposed classification algorithm has a possibility to be used to analyze the static and dynamic physical activity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.6
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• pp.1627-1648
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• 2012

We propose an adaptive method for detecting plagiarized pairs from a large set of source code. This method is adaptive in that it uses an adaptive algorithm and it provides an adaptive threshold for determining plagiarism. Conventional algorithms are based on greedy string tiling or on local alignments of two code strings. However, most of them are not adaptive; they do not consider the characteristics of the program set, thereby causing a problem for a program set in which all the programs are inherently similar. We propose adaptive local alignment-a variant of local alignment that uses an adaptive similarity matrix. Each entry of this matrix is the logarithm of the probabilities of the keywords based on their frequency in a given program set. We also propose an adaptive threshold based on the local outlier factor (LOF), which represents the likelihood of an entity being an outlier. Experimental results indicate that our method is more sensitive than JPlag, which uses greedy string tiling for detecting plagiarism-suspected code pairs. Further, the adaptive threshold based on the LOF is shown to be effective, and the detection performance shows high sensitivity with negligible loss of specificity, compared with that using a fixed threshold.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.179-185
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• 2019

This paper proposes a block-type classification based image binarization for the implementation of the low-power feature extraction algorithm. The proposed method can be implemented with threshold value re-use technique approach when the image divided into $64{\times}64$ macro blocks size and calculating the threshold value for each block type only once. The algorithm is validated based on quantitative results that only a threshold value change rate of up to 9% occurs within the same image/block type. Existing algorithms should compute the threshold value for 64 blocks when the macro block is divided by $64{\times}64$ on the basis of $512{\times}512$ images, but all suggestions can be made only once for best cases where the same block type is printed, and for the remaining 63 blocks, the adaptive threshold calculation can be reduced by only performing a block type classification process. The threshold calculation operation is performed five times when all block types occur, and only the block type separation process can be performed for the remaining 59 blocks, so 93% adaptive threshold calculation operation can be reduced.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11C
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• pp.1161-1168
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• 2003

In this paper, we propose an improved statistical model-based voice activity detection algorithm and threshold update method. We first improve signal-to-noise ratio by using speech enhancement preprocessing algorithm combined power subtraction method and matched filter, then apply it to LLR test optimum decision rule for improving the performance even in low SNR conditions. And we propose an adaptive threshold update method that was not concerned in any papers. We also perform extensive computer simulations to demonstrate the performance improvement of the proposed VAD algorithm employing the proposed speech enhancement preprocessing algorithm and adaptive threshold update method under various background noise environments. Finally we verify our results by comparing ITU-T G.729 Annex B.