Jiaze Zhang, Xueyong Liu · 2025
The acquisition algorithm affects the performance of Global Positioning System (GPS) receivers. This paper proposes a GPS L1 C/A signal acquisition algorithm based on a dynamic threshold. First, the dynamic threshold for the acquisition algorithm is determined based on noise statistics (i.e., the mean plus K times the standard deviation of samples in the non-peak regions of the signal correlation matrix). Then, a parallel code phase acquisition method is used to search for navigation satellite signals in the received signal. If the peak value in the results exceeds the dynamic threshold value, the satellite is deemed to have been acquired. Simulation results show that the use of a dynamic threshold improves the acquisition probability under different SNR conditions.
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Chen Hao and Wang Yitong · 2012
Cited by 18
DU Jian-zhong, Su‐Juan Qin, Qiaoyan Wen et al. · Physics Letters A · 2006
Cited by 3Open access
Ariane Chapelle, Ariane Szafarz · IMA Journal of Management Mathematics · 2006
Besides multiple voting rights, board representation and shareholders’ monitoring, control tunnelling over firms can be reached through pyramids, cross-ownership and other complex features. This phenomenon is frequent in Europe and Asia. However, the theoretical literature has not yet converged towards a well-defined and robust measurement of integrated control that takes into account the threshold for control as applied in practice. Based on graph theory, this paper aims at filling this gap and proposes a new algorithm for evaluating the control tunnelling exerted by the firms’ ultimate shareholders. Then, the paper discusses the various forms of control existing next to voting shares, like multiple voting rights, board representation and active monitoring, before suggesting ways to include them into the modelling of control.
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Jingjing Wang (140791), Tao Sun (91038), Ni Gao (633715) et al. · Figshare · 2015
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Huawei Zhang, Nan Li · Applied Mechanics and Materials · 2012
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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N. N. Necula · IEEE Transactions on Computers · 1968
Abstract—An algorithm for multithreshold threshold element synthesis, generalizing Haring's "run measure minimization" algorithm, is presented. The algorithm is based on the possibility of assigning equal weights to variables in a symmetry group of a given Boolean function (for totally or partially symmetric Boolean functions). Thus, although a supplementary operation is required for determining the symmetry group of the Boolean functions, both an important reduction in the number of tests required to minimize the number of thresholds and a more economic solution are assured, when synthesizing totally or partially symmetric Boolean functions.
Cited by 2
Hong Jiang, Zhang Ren · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
A novel adaptive multi threshold image segmentation algorithm is proposed in this paper. This proposed segmentation algorithm has two unique characteristics: it fits the 1-D graylevel histogram of the image by potential base function and thereby adaptively determines the classification number by potential function clustering; based on the graylevel co-occurrence matrix, it acquires the multi segmentation thresholds which makes the shape connectivity maximum according to the shape connectivity criterion. Both theoretical analysis and simulation results indicate that the performance of this new adaptive multi threshold segmentation algorithm is superior to those of the conventional threshold segmentation algorithms. And it has not only a low computing cost, but also shows quite good segmentation effect. Besides, it is insensitive to noises and interferences.
Cited by 5
Christian A. Lang, Yuan‐Chi Chang, J. R. Smith · IEEE Transactions on Knowledge and Data Engineering · 2004
Assume a database storing N objects with d numerical attributes or feature values. All objects in the database can be assigned an overall score that is derived from their single feature values (and the feature values of a user-defined query). The problem considered here is then to efficiently retrieve the k objects with minimum (or maximum) overall score. The well-known threshold algorithm (TA) was proposed as a solution to this problem. TA views the database as a set of d sorted lists storing the feature values. Even though TA is optimal with regard to the number of accesses, its overall access cost can be high since, in practice, some list accesses may be more expensive than others. We therefore propose to make TA access cost aware by choosing the next list to access such that the overall cost is minimized. Our experimental results show that this overall cost is close to the optimal cost and significantly lower than the cost of prior approaches.
Cited by 4
L. Yang, Zhian Deng · 2023
With the increasing complexity of the electromagnetic environment of electronic countermeasures, the detection of radar pulse signals under low signal-to-noise ratio is becoming more and more demanding. Based on this detection performance requirement, an adaptive dynamic double-threshold signal detection algorithm is proposed based on the digital channelization and autocorrelation algorithms. The high threshold is used to detect signals with higher signal-to-noise ratios, ensuring better real-time signal detection. The low threshold is used to detect possible low signal-to-noise signals, ensuring better detection of weak signals. Simulation experiments are carried out on the algorithm, and the results show that the algorithm can effectively improve the real-time and weak signal detection ability of signal detection.
Cited by 1
郭梅梅 · Computer Science and Application · 2019
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Yong Liu · 2008
In order to obtain optimization threshold for the centroid algorithm,several kinds of influencing factors,such as signal-to-noise ratio,discrete gathering of star point and so on,are studied.Based on theoretical computation,a function is established to depict the relationship between the luminosity of star spot and the threshold with optimization centroid accuracy.Then,by combining the function with the simulation results of discrete gathering of star spot,a method of obtaining the optimization threshold based on luminosity of star spot is proposed.Based on the relationship between optimization threshold and luminosity of the spot,a curve model is established,and the optimization threshold is educed by interpolation.The threshold estimation accuracy of the method is shown,and the centroid accuracy is also compared with those of various thresholding algorithms.The experimental results indicate that the luminosity estimation error of the optimization threshold is less than 3 under different SNR conditions of the camera system,and the centroid locating accuracy by proposed method has raised by 1/100~1/10 pixel level as compared with those of other methods,which can satisfy the requirement of dynamic threshold optimization of centroid algorithm.
Cited by 5
John M. Wild, Ian E. Pacey, Shirley A. Hancock et al. · PubMed · 1999
SITA produced marginally higher mean mean sensitivity compared with that of existing algorithms and markedly reduced examination duration. The reduced between-subject variability of SITA should result in narrower confidence limits for definition of normality.
Cited by 91
William H. Sauer, Joshua M. Cooper, REBECCA W. LAI et al. · Pacing and Clinical Electrophysiology · 2006
In this case report, we describe markedly different pacing thresholds determined by a manual threshold test and the automatic Ventricular Capture Management algorithm. The discrepancy in pacing threshold values reported was due to the difference in the AV intervals used with the different testing methods. We propose that the differences in right ventricular dimensions with altered diastolic filling periods affected the threshold in this patient with a new passive fixation lead in the right ventricular apex.
Cited by 7
Xuxing Ding, Bingbing Wang, Zaijian Wang · ETRI Journal · 2018
The weighted K-nearest neighbor (WKNN) algorithm is used to reduce positioning accuracy, as it uses a fixed number of neighbors to estimate the position. In this paper, we propose a dynamic threshold location algorithm (DH-KNN) to improve positioning accuracy. The proposed algorithm is designed based on a dynamic threshold to determine the number of neighbors and filter out singular reference points (RPs). We compare its performance with the WKNN and Enhanced K-Nearest Neighbor (EKNN) algorithms in test spaces of networks with dimensions of 20 m × 20 m, 30 m × 30 m, 40 m × 40 m and 50 m × 50 m. Simulation results show that the maximum position accuracy of DH-KNN improves by 31.1%, and its maximum position error decreases by 23.5%. The results demonstrate that our proposed method achieves better performance than other well-known algorithms.
Cited by 4Open access
Yan Xia Liu, Li Yan, Bei Dong et al. · Applied Mechanics and Materials · 2013
According to the characteristics of image wavelet decomposition and statistical characteristics of high frequency wavelet coefficients after wavelet decomposition, this article improves the traditional threshold function de-noising algorithm. Improved threshold function not only overcomes wavelet coefficient discontinuity caused by hard threshold function de-noising and certain deviation witch always exists between wavelet coefficients caused by soft threshold function de-noising and its real value. The experimental results show that, the improved threshold function de-noising results are better than traditional soft and hard threshold method either in the visual effects, or the PSNR gain.
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Shilpa Sonawane, P. Malathi, B. B. Musmade · 2022
Lip reading technology is best possible solution of speech recognition in noisy environments. Lip reading is a methodology to interpret by lip movement without the involvement of audio. The accuracy of lip-reading technology is based on accurate mouth region of interest (ROI). Viola Jones algorithm is used for mouth region extraction. The accuracy by viola jones is affected by merge threshold parameter of cascade object detector. Due to incorrect threshold multiple bounding boxes appears for mouth ROI. The correct selection of merge threshold leads to single bounding box on mouth region. The technique to find appropriate threshold to extract mouth ROI is presented in this paper. The algorithm is applied on GRID and LRW dataset. Experiment is tested on both frontal and profile face video frames. The accuracy obtained on frontal face frames from GRID dataset is 100 % while 86.20% accuracy achieved with profile video frames from LRW dataset.
Cited by 1
Shin''Ichi Koike · Zenodo (CERN European Organization for Nuclear Research) · 2015
Publication in the conference proceedings of EUSIPCO, Nice, France, 2015
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Chih‐Hao Chang, Takeshi Emura, Shih‐Feng Huang · Computational Statistics & Data Analysis · 2025
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J. Malar Vizhi, Dr. T. Bhuvaneswari · 2014
Our basic idea is to employ association rule for the purpose of data quality measurement. Strong rule generation is an important area of data mining. We purpose a Genetic Algorithm to generate high quality Association Rules with four metrics they are confidence, completeness, interestingness and comprehensibility. These metrics are combined as an objective fitness function. Fitness function evaluates the quality of each rule. The advantage of using genetic algorithm is to discover high level prediction rules is that they perform a global search and cope better with attribute interaction than the greedy rule induction algorithm often used in data mining. Association Rule Mining is one of the most applicable techniques in data mining. Association rules that satisfy threshold specified by the user are referred to as strong association rules and a considered interesting.
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