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Journal articles on the topic 'Threshold-based'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Poornima, B., Y. Ramadevi, and T. Sridevi. "Threshold Based Edge Detection Algorithm." International Journal of Engineering and Technology 3, no. 4 (2011): 400–403. http://dx.doi.org/10.7763/ijet.2011.v3.260.

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2

TOSUN, A. "Threshold-based declustering." Information Sciences 177, no. 5 (March 1, 2007): 1309–31. http://dx.doi.org/10.1016/j.ins.2006.08.011.

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3

Mittal, Mamta, Rajendra Kumar Sharma, Varinder Pal Singh, and Raghvendra Kumar. "Adaptive Threshold Based Clustering." International Journal of Information System Modeling and Design 10, no. 1 (January 2019): 42–59. http://dx.doi.org/10.4018/ijismd.2019010103.

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Partitioning-based clustering methods have various challenges especially user-defined parameters and sensitivity to initial seed selections. K-means is most popular partitioning based method while it is sensitive to outlier, generate non-overlap cluster and non-deterministic in nature due to its sensitivity to initial seed selection. These limitations are regarded as promising research directions. In this study, a deterministic approach which do not requires user defined parameters during clustering; can generate overlapped and non-overlapped clusters and detect outliers has been proposed. Here, a minimum support value has been adopted from association rule mining to improve the clustering results. Further, the improved approach has been analysed on artificial and real datasets. The results demonstrated that datasets are well clustered with this approach too and it achieved success to generate almost same number of clusters as present in real datasets.

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4

Gan, Xiao Ying, and Bin Liu. "Shamir Threshold Based Encryption." Applied Mechanics and Materials 52-54 (March 2011): 709–12. http://dx.doi.org/10.4028/www.scientific.net/amm.52-54.709.

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This paper Shamir threshold scheme based on the protection of private keys, by constructing a Lagrange interpolating polynomial to achieve in the real environment using the key shared information systems, computation and communication in the case of less , the program can prevent the system key is lost, damaged, and from the enemy's attack, reduce the responsibility of the key holder, but also can reduce the success rate of an adversary to decipher the key. An example is the feasibility of the program.

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5

Boneva, Lena, Richard Harrison, and Matt Waldron. "Threshold-based forward guidance." Journal of Economic Dynamics and Control 90 (May 2018): 138–55. http://dx.doi.org/10.1016/j.jedc.2018.02.003.

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6

Wang, Hai-Kun, Hong-Zhong Huang, Yan-Feng Li, and Yuan-Jian Yang. "Condition-Based Maintenance With Scheduling Threshold and Maintenance Threshold." IEEE Transactions on Reliability 65, no. 2 (June 2016): 513–24. http://dx.doi.org/10.1109/tr.2015.2487578.

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7

Guo, Gencheng, and Mrinal Mandal. "On optimal threshold and structure in threshold system based detector." Signal Processing 92, no. 1 (January 2012): 170–78. http://dx.doi.org/10.1016/j.sigpro.2011.06.016.

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8

Gomide, F. A., and A. F. Rocha. "Neurofuzzy Components based on Threshold." IFAC Proceedings Volumes 25, no. 6 (May 1992): 131–36. http://dx.doi.org/10.1016/s1474-6670(17)50893-9.

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9

Jenila, A., and J. Joslin Iyda. "Threshold Based Human Face Detection." Procedia Engineering 38 (2012): 2748–53. http://dx.doi.org/10.1016/j.proeng.2012.06.321.

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10

Nam, Sung Sik, Mohamed-Slim Alouini, Hong-Chuan Yang, and Khalid A. Qaraqe. "Threshold-based parallel multiuser scheduling." IEEE Transactions on Wireless Communications 8, no. 4 (April 2009): 2150–59. http://dx.doi.org/10.1109/twc.2009.080784.

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11

Ruddell, Benjamin. "Threshold Based Footprints (for Water)." Water 10, no. 8 (August 3, 2018): 1029. http://dx.doi.org/10.3390/w10081029.

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Thresholds are an emergent property of complex systems and Coupled Natural Human Systems (CNH) because they indicate “tipping points” where a complicated array of social, environmental, and/or economic processes combine to substantially change a system’s state. Because of the elegance of the concept, thresholds have emerged as one of the primary tools by which socio-political systems simplify, define, and especially regulate complex environmental impacts and resource scarcity considerations. This paper derives a general framework for the use of thresholds to calculate scarcity footprints, and presents a volumetric Threshold-based Water Footprint (TWF), comparing it with the Blue Water Footprint (BWF) and the Relevant for Environmental Deficiency (RED) midpoint impact indicator. Specific findings include (a) one requires all users’ BWF to calculate an individual user’s TWF, whereas one can calculate an individual user’s BWF without other users’ data; (b) local maxima appear in the Free from Environmental Deficiency (FED) efficiency of the RED metric due to its nonlinear form; and (c) it is possible to estimate the “effective” threshold that is approximately implied by the RED water use impact metric.

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12

Kara, Ferdi, and Hakan Kaya. "Threshold-Based Selective Cooperative-NOMA." IEEE Communications Letters 23, no. 7 (July 2019): 1263–66. http://dx.doi.org/10.1109/lcomm.2019.2914918.

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13

Swartz, Conrad M., and Nikolaus Michael. "Age-Based Seizure Threshold Determination." Journal of ECT 29, no. 1 (March 2013): 18–20. http://dx.doi.org/10.1097/yct.0b013e3182656e5d.

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14

Verma, Chanchal. "Dynamic Threshold MOSFET Based Comparators." International Journal for Research in Applied Science and Engineering Technology 9, no. 4 (April 30, 2021): 292–99. http://dx.doi.org/10.22214/ijraset.2021.33594.

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15

Shen, Zhong-hua, and Xiu-yuan Yu. "Threshold signature scheme with threshold verification based on multivariate linear polynomial." Journal of Shanghai Jiaotong University (Science) 16, no. 5 (October 2011): 551–56. http://dx.doi.org/10.1007/s12204-011-1186-4.

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16

Lee, Sang Il, and Seong Joon Yoo. "Threshold-based portfolio: the role of the threshold and its applications." Journal of Supercomputing 76, no. 10 (September 14, 2018): 8040–57. http://dx.doi.org/10.1007/s11227-018-2577-1.

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17

Tarhini, Ghinwa M., and Reda Shbib. "Detection of Brain Tumor in MRI Images Using Watershed and Threshold-Based Segmentation." International Journal of Signal Processing Systems 8, no. 1 (March 2020): 19–25. http://dx.doi.org/10.18178/ijsps.8.1.19-25.

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18

Kwon, Soon-H. "Entropy and AMBE-based Threshold Selection." Journal of Korean Institute of Intelligent Systems 21, no. 3 (June 25, 2011): 347–52. http://dx.doi.org/10.5391/jkiis.2011.21.3.347.

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19

Peeters, Roel, Dave Singelée, and Bart Preneel. "Threshold-Based Location-Aware Access Control." International Journal of Handheld Computing Research 2, no. 3 (July 2011): 22–37. http://dx.doi.org/10.4018/jhcr.2011070102.

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Designing a secure, resilient and user-friendly access control system is a challenging task. In this article, a threshold-based location-aware access control mechanism is proposed. This design uniquely combines the concepts of secret sharing and distance bounding protocols to tackle various security vulnerabilities. The proposed solution makes use of the fact that the user carries around various personal devices. This solution offers protection against any set of or fewer compromised user’s devices, with being an adjustable threshold number. It removes the single point of failure in the system, as access is granted when one carries any set of user’s devices. Additionally it supports user-centered management, since users can alter the set of personal devices and can adjust the security parameters of the access control scheme towards their required level of security and reliability.

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20

Zimmer, Mary Ann. "Threshold Concepts in Problem-based Learning." Wabash Center Journal on Teaching 1, no. 2 (April 15, 2020): 113. http://dx.doi.org/10.31046/wabashcenter.v1i2.1732.

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21

Huixian, Li, Gao Jin, Wang Lingyun, and Pang Liaojun2. "MPKC-based Threshold Proxy Signcryption Scheme." International Arab Journal of Information Technology 17, no. 2 (February 28, 2019): 196–206. http://dx.doi.org/10.34028/iajit/17/2/7.

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The threshold proxy signcryption can implement signature and encryption simultaneously in one logical step, and can be used to realize the decentralized protection of the group signature key, so it is an efficient technology for network security. Currently, most of the existing threshold proxy signcryption schemes are designed based on the traditional public key cryptosystems, and their security mainly depends on the difficulty of the large integer decomposition and the discrete logarithm. However, the traditional public key cryptosystems cannot resist the quantum computer attack, which makes the existing threshold proxy signcryption schemes based on traditional public key cryptosystems insecure against quantum attacks. Motivated by these concerns, we proposed a threshold proxy signcryption scheme based on Multivariate Public Key Cryptosystem (MPKC) which is one of the quantum attack-resistent public key algorithms. Under the premise of satisfying the threshold signcryption requirements of the threshold proxy, our scheme can not only realize the flexible participation of the proxy signcrypters but also resist the quantum computing attack. Finally, based on the assumption of Multivariate Quadratic (MQ) problem and Isomorphism Polynomial (IP) problem, the proof of the confidentiality and the unforgeability of the proposed scheme under the random oracle model is given.

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22

Rathore, Neeraj. "Dynamic Threshold Based Load Balancing Algorithms." Wireless Personal Communications 91, no. 1 (July 4, 2016): 151–85. http://dx.doi.org/10.1007/s11277-016-3452-0.

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23

Kwon, Soon H. "Threshold selection based on cluster analysis." Pattern Recognition Letters 25, no. 9 (July 2004): 1045–50. http://dx.doi.org/10.1016/j.patrec.2004.03.001.

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24

Zhou, Yan. "Block-based threshold modulation error diffusion." Journal of Electronic Imaging 20, no. 1 (January 1, 2011): 013018. http://dx.doi.org/10.1117/1.3555132.

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25

RAVI, V., MA BIN, and P. RAVI KUMAR. "THRESHOLD ACCEPTING BASED FUZZY CLUSTERING ALGORITHMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14, no. 05 (October 2006): 617–32. http://dx.doi.org/10.1142/s0218488506004229.

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In this paper, two new fuzzy clustering algorithms are proposed based on the global optimization metaheuristic, Threshold Accepting. Their effectiveness is demonstrated in the case of five well-known medium sized data sets viz. Iris, Wine, Glass, E.Coli and Olive oil and a large data set Thyroid. In terms of the least objective functions value, these algorithms named TAFC-1 (Threshold Accepting based Fuzzy Clustering) and TAFC-2 outperformed the well-known Fuzzy C-Means (FCM) algorithm in the case of 4 data sets and in the remaining two data sets, FCM marginally outperformed the TAFC. Xie-Beni cluster validity index is used in arriving at the 'optimal' number of clusters for all the algorithms. Here a novel strategy is proposed whereby the FCM is invoked to find alternative decision vectors whenever the neighbourhood search fails in its pursuit. This hybrid scheme has worked well. In conclusion, these new algorithms can be used as viable and efficient alternatives to the FCM algorithm.

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26

Dai, Y., D. T. Comer, D. J. Comer, and C. S. Petrie. "Threshold voltage based CMOS voltage reference." IEE Proceedings - Circuits, Devices and Systems 151, no. 1 (2004): 58. http://dx.doi.org/10.1049/ip-cds:20040217.

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27

Ghenzi, N., M. Barella, D. Rubi, and C. Acha. "Adaptive threshold in TiO2-based synapses." Journal of Physics D: Applied Physics 52, no. 12 (January 25, 2019): 125401. http://dx.doi.org/10.1088/1361-6463/aafdf3.

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28

Chandra De, Utpal, Madhabananda Das, Debashis Mishra, and Debashis Mishra. "Threshold based brain tumor image segmentation." International Journal of Engineering & Technology 7, no. 3 (August 22, 2018): 1801. http://dx.doi.org/10.14419/ijet.v7i3.12425.

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Image processing is most vital area of research and application in field of medical-imaging. Especially it is a major component in medical science. Starting from radiology to ultrasound (sonography), MRI, etc. in lots of area image is the only source of diagnosis process. Now-a-days, different types of devices are being introduced to capture the internal body parts in medical science to carry the diagnosis process correctly. However, due to various reasons, the captured images need to be tuned digitally to gain the more information. These processes involve noise reduction, segmentations, thresholding etc. . Image segmentation is a process to segment the target area of image to identify the area more prominently. There are different process are evolved to perform the segmentation process, one of which is Image thresholding. Moreover there are different tools are also introduce to perform this step of image thresholding. The recent introduced tool PSO is being used here to segment the MRI scans to identify the brain lesions using image thresholding technique.

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29

Cornwell, Donald. "Space-Based Laser Communications Break Threshold." Optics and Photonics News 27, no. 5 (May 1, 2016): 24. http://dx.doi.org/10.1364/opn.27.5.000024.

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30

Zhang, L., P. Bao, and Q. Pan. "Threshold analysis in wavelet-based denoising." Electronics Letters 37, no. 24 (2001): 1485. http://dx.doi.org/10.1049/el:20010971.

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31

Golubchik, Leana, and John C. S. Lui. "Open problems for threshold-based systems." ACM SIGMETRICS Performance Evaluation Review 28, no. 4 (March 2001): 6–8. http://dx.doi.org/10.1145/544397.544400.

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32

Li, Qingjian, Yan Liang, Zhenzhou Lu, and Guangyi Wang. "Threshold‐type memristor‐based memory circuit." International Journal of Circuit Theory and Applications 49, no. 5 (March 15, 2021): 1515–31. http://dx.doi.org/10.1002/cta.2976.

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33

Wang, Yan Qing, Lu Lu Zhuang, and Chao Xia Shi. "Construction Research on Multi-Threshold Segmentation Based on Improved Otsu Threshold Method." Advanced Materials Research 1046 (October 2014): 425–28. http://dx.doi.org/10.4028/www.scientific.net/amr.1046.425.

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The aim of unstructured road detection is the recognition of road region and the tracking of road boundary. An improved Otsu threshold method available in double-threshold segmentation was proposed in this paper to resolve the multi-peaks problem in both complicated background and target. This method remedies the mis-segmentation of Otsu threshold method caused by huge diversity of between-class variances and refines the segmentation precision by matching the proposed Otsu edges with the weighted Canny edges. According to the irregular characteristics of the unstructured road and the requirement of the real-time motion planning for intelligent vehicle, improved Otsu threshold method was proposed. The experiment results in different road environments demonstrated the validity and real-time of the proposed method.

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34

Ni, Wei Chuan, Bai Hui Zhu, and Zhi Ping Wan. "A De-Nosing Algorithm of Wavelet Threshold Based on Efficient Threshold Function." Advanced Materials Research 709 (June 2013): 624–27. http://dx.doi.org/10.4028/www.scientific.net/amr.709.624.

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Image digitization and transmission process often subject to outside interference that was easy to let the image turn into de-noising image; General de-noising was easy made the image details blurred. Against the phenomenon, this paper using "mathematical microscope" said with wavelet transform, and according to the inherent characteristics of the human eye's visual. Put a new optimize the scan mode of the wavelet coefficients, and proposes a new threshold de-noising algorithm. At last, decrease the overhead of unnecessary coding algorithm; simplified scanning path to reduce，decrease encoding time and improve de-noising ability to effect, making the algorithm de-noising while protecting image details; Tests showed that the research to achieve the purpose of the above study and stability de-noising advantage.

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35

Soni, Ankit Kumar, and Prof Madhuvan Dixit. "Study of Energy Efficient Images with Just Noticeable Difference Threshold Based on Feature Transform." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 924–27. http://dx.doi.org/10.31142/ijtsrd23215.

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36

Li, Jiajia, Botao Wang, Guoren Wang, and Yifei Zhang. "Probabilistic threshold query optimization based on threshold classification using ELM for uncertain data." Neurocomputing 174 (January 2016): 211–19. http://dx.doi.org/10.1016/j.neucom.2015.05.122.

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37

Pettenghi, Hector, María J. Avedillo, and José M. Quintana. "Using multi-threshold threshold gates in RTD-based logic design: A case study." Microelectronics Journal 39, no. 2 (February 2008): 241–47. http://dx.doi.org/10.1016/j.mejo.2007.05.015.

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38

Talukder, Ashis, Md Golam Rabiul Alam, Nguyen H. Tran, Dusit Niyato, Gwan Hoon Park, and Choong Seon Hong. "Threshold Estimation Models for Linear Threshold-Based Influential User Mining in Social Networks." IEEE Access 7 (2019): 105441–61. http://dx.doi.org/10.1109/access.2019.2931925.

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39

Han, Joon‐Kyu, Mun‐Woo Lee, Ji‐Man Yu, and Yang‐Kyu Choi. "A Single Transistor‐Based Threshold Switch for a Bio‐Inspired Reconfigurable Threshold Logic." Advanced Electronic Materials 7, no. 5 (March 30, 2021): 2100117. http://dx.doi.org/10.1002/aelm.202100117.

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40

Berlin, K., and A. Padmapriya. "Performance Analysis of Threshold based Image Encryption." International Journal of Computer Applications 99, no. 12 (August 20, 2014): 30–33. http://dx.doi.org/10.5120/17428-8321.

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41

Garg, Rachit, Maitreyee Dutta, and Ramteke Mamta G. "Threshold based Approach for Image Blind Deconvolution." International Journal of Computer Applications 101, no. 6 (September 18, 2014): 37–42. http://dx.doi.org/10.5120/17694-8665.

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42

Zhang, Zhi-Hua, Si-Rong Zhang, Wen-Jin Yu, Jian-Jun Li, Bei Gong, and Wei Jiang. "A Threshold Signature Scheme Based on TPM." International Journal of Communications, Network and System Sciences 04, no. 10 (2011): 622–25. http://dx.doi.org/10.4236/ijcns.2011.410075.

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43

Chien, Hung‐Yu, Jinn‐Ke Jan, and Yuh‐Min Tseng. "Partially Blind Threshold Signature Based on RSA." Informatica 14, no. 2 (January 1, 2003): 155–66. http://dx.doi.org/10.15388/informatica.2003.011.

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44

Zhang, Guo-yin, Xiao-jing Fu, and Chun-guang Ma. "A Dynamic Threshold Attributes-based Signcryption Scheme." Journal of Electronics & Information Technology 34, no. 11 (July 10, 2013): 2680–86. http://dx.doi.org/10.3724/sp.j.1146.2012.00342.

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45

Ernawan, Ferda. "Robust Image Watermarking Based on Psychovisual Threshold." Journal of ICT Research and Applications 10, no. 3 (October 31, 2016): 228–42. http://dx.doi.org/10.5614/itbj.ict.res.appl.2016.10.3.3.

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46

Zhang, Hua Wei, and Nan Li. "A Threshold-Based Improved Algorithm of PTS." Applied Mechanics and Materials 235 (November 2012): 53–57. http://dx.doi.org/10.4028/www.scientific.net/amm.235.53.

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Compared with the single carrier systems, orthogonal frequency division multiplexing (OFDM) has many advantages of higher bandwidth efficiency and stronger anti-multi-path ability. However, OFDM technology also has some disadvantages, including the multi-carrier synchronization and higher peak-to-average power ratio. In order to solve the higher peak-to-average power ratio problem, this article combines the double-level optimal search method and Kwon suboptimal method, proposes a threshold-based double-level optimal search algorithm. The method is an improved PTS algorithm and is a linear process. The simulation proves that, with the same optimization grouping parameters and auxiliary information, the new algorithm with a reasonable threshold, which has a smaller loss of reducing PAPR performance, greatly reduces the system complexity.

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47

Hoffmann, Peter, Christoph Menz, and Arne Spekat. "Bias adjustment for threshold-based climate indicators." Advances in Science and Research 15 (June 8, 2018): 107–16. http://dx.doi.org/10.5194/asr-15-107-2018.

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Abstract. A method is presented which applies bias adjustments to climate indicators that are based on fixed thresholds, e.g., the number of hot days with the maximum temperature exceeding 30 ∘C or the number of days with heavy precipitation in exceedance of 20 mm rainfall. The bias adjustment first identifies the percentile of the required threshold value in reference climate data. Then it computes the value of this percentile for the individual historical climate model simulations – here an ensembles of EURO-CORDEX model runs, including dynamical and statistical models. Finally, the climate indicator is re-calculated for each model. The method is applied to climate projections as well, giving further insight into the projected development of the ensemble for extreme conditions. It is assessed that communication to the public and decision makers is improved by expressing these changes in extremes based on absolute values.

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48

ZHANG, Yu-lei, Xiao-dong YANG, and Cai-fen WANG. "ID-based bidirectional threshold proxy re-signature." Journal of Computer Applications 31, no. 1 (March 21, 2011): 127–28. http://dx.doi.org/10.3724/sp.j.1087.2011.00127.

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49

Eryilmaz, S. "Random threshold models based on multivariate observations." Journal of Statistical Planning and Inference 113, no. 2 (May 2003): 557–68. http://dx.doi.org/10.1016/s0378-3758(01)00319-6.

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50

Chunli, Wang, Zhang Chunlei, and Zhang pengtu. "Denoising algorithm based on wavelet adaptive threshold." Physics Procedia 24 (2012): 678–85. http://dx.doi.org/10.1016/j.phpro.2012.02.100.

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