In the process of recording terahertz digital hologram, the hologram is easy to be contaminated by speckle noise, which leads to lower resolution in imaging system and affects the reconstruction results seriously. Thu...
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ISBN:
(纸本)9781510607729;9781510607736
In the process of recording terahertz digital hologram, the hologram is easy to be contaminated by speckle noise, which leads to lower resolution in imaging system and affects the reconstruction results seriously. Thus, the study of filtering algorithms applicable for de-speckling terahertz digital holography image has important practical values. In this paper, non-local means filtering and guided bilateral filtering were brought to process the real image reconstructed from continuous-wave terahertz coaxial digital hologram. For comparison, median filtering, bilateral filtering and robust bilateral filtering were introduced as conventional methods to denoise the real image. Then all the denoising results were evaluated. The comparison indicates that the guided bilateral filter manifests the optimal denoising effect for the terahertz digital holography image, both significantly suppressing speckle noise, and effectively preserving the useful information on the reconstructed image.
HJ-1A/B NDVI (HJ NDVI) time-series data possess relatively high spatio-temporal resolution which is significant for the research on urban areas. However, its application is hindered by noise resulting from the restric...
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HJ-1A/B NDVI (HJ NDVI) time-series data possess relatively high spatio-temporal resolution which is significant for the research on urban areas. However, its application is hindered by noise resulting from the restrictions of imaging quality and limits of the satellite platform. The NDVI noise reduction is necessary. Some noise-reduction techniques including the asymmetric Gaussian filter (AG), the double logistic filter (DL), the Savitzky-Golay (S-G) filter and the harmonic analysis (Hants) of NDVI time-series have been used to carry out the NDVI time series reconstruction, and based on the comparison results of different filter, S-G filter is the optimal in the application on urban areas. Finally, urban vegetation mapping is carried out based on the new HJ NDVI.
The use of Device-to-Device (D2D) communication in various networks is expected to grow in the coming years. The D2D distance-based device sociality service is affected by distance awareness between the D2D devices. T...
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ISBN:
(纸本)9788996865063
The use of Device-to-Device (D2D) communication in various networks is expected to grow in the coming years. The D2D distance-based device sociality service is affected by distance awareness between the D2D devices. Thus, the wireless distance awareness between smart devices should be accomplished easily, accurately, and immediately. When smart devices are used in the distance-based distance sociality service, the smart device needs to know the distance to other smart devices in order to aware the space sociality. Several methods, such as receive signal strength indication (RSSI), time of arrival (ToA), and time difference of arrival (TDoA), can be used to aware the distance between D2D devices. Among these methods, the RSSI system can aware the D2D distance easily and inexpensively because most smart devices can estimate the received signal strength. However, estimating the distance using a RSSI is difficult due to inaccuracies. We conducted a preliminary test to discover the relationship between the actual distance and a Bluetooth RSSI under near field environment. Through the results of this test, we realize that the near field distance is hard to be classified due to the inaccuracy of Bluetooth RSSI. Therefore, in this paper, the near field distance awareness algorithm is proposed to reduce measurement errors by alleviating fluctuations in a Bluetooth signal. To evaluate the effectiveness of the proposed algorithm, the distance awareness is compared using different filtering algorithms, such as, a low-pass filer (LPF), a Kalman filter, and a particle filter under a meeting room environment. The proposed algorithm showed the best results in terms of the coefficient of determination, standard deviation, and measurement range.
Wheelset is one of the vital components of the ***,the wheels are regularly detected by using ultrasonic technology to check cracks,especially in wheel *** order to eliminate the failure risks of wheels,daily dynamic ...
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ISBN:
(纸本)9781467392396
Wheelset is one of the vital components of the ***,the wheels are regularly detected by using ultrasonic technology to check cracks,especially in wheel *** order to eliminate the failure risks of wheels,daily dynamic wheelset inspecting system is needed during the light maintenance period.A way-side arrayed ultrasonic technology is described in this paper to detect wheel *** using a specially designed track structure,the arrayed ultrasonic probes are arranged between the double-track for wheel rim *** the testing results,Φ3mm side drill hole in the wheel rim can be well detected at the running speed of 30～40km/*** noise is effectively suppressed by filtering algorithm,thus to improve the signal to noise ratio and the positive alarming *** present,the technology has been successfully used in Chinese high-speed train maintenance centers,rolling stocks and locomotive maintenance depots.
The AtMostNValue global constraint, which restricts the maximum number of distinct values taken by a set of variables, is a well known NP-Hard global constraint. The weighted version of the constraint, AtMostWValue, w...
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The AtMostNValue global constraint, which restricts the maximum number of distinct values taken by a set of variables, is a well known NP-Hard global constraint. The weighted version of the constraint, AtMostWValue, where each value is associated with a weight or cost, is a useful and natural extension. Both constraints occur in many industrial applications where the number and the cost of some resources have to be minimized. This paper introduces a new filtering algorithm based on a Lagrangian relaxation for both constraints. This contribution is illustrated on problems related to facility location, which is a fundamental class of problems in operations research and management sciences. Preliminary evaluations show that the filtering power of the Lagrangian relaxation can provide significant improvements over the state-of-the-art algorithm for these constraints. We believe it can help to bridge the gap between constraint programming and linear programming approaches for a large class of problems related to facility location.
LMS algorithm and RLS algorithms are used in adaptive FIR filters in this paper. The experimental conditions and parameter were set. Simulation results show that the LMS algorithm convergence slower, but guarantees th...
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LMS algorithm and RLS algorithms are used in adaptive FIR filters in this paper. The experimental conditions and parameter were set. Simulation results show that the LMS algorithm convergence slower, but guarantees the stability and low complexity, suitable for speed demand is not high, low cost systems applications, RLS algorithm can achieve rapid convergence, suitable for application in environmental change faster system, but calculating relative complex and poor stability.
The paper is devoted to the optimal filtering problem of the Markov jump state given the multivariate point observations. The investigated observation system is set in terms of the martingale representation. The disti...
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We focus on crowd-powered filtering, i.e., filtering a large set of items using humans. filtering is one of the most commonly used building blocks in crowdsourcing applications and systems. While solutions for crowd-p...
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We focus on crowd-powered filtering, i.e., filtering a large set of items using humans. filtering is one of the most commonly used building blocks in crowdsourcing applications and systems. While solutions for crowd-powered filtering exist, they make a range of implicit assumptions and restrictions, ultimately rendering them not powerful enough for real-world applications. We describe two approaches to discard these implicit assumptions and restrictions: one, that carefully generalizes prior work, leading to an optimal, but often-times intractable solution, and another, that provides a novel way of reasoning about filtering strategies, leading to a sometimes sub-optimal, but efficiently computable solution (that is provably close to optimal). We demonstrate that our techniques lead to significant reductions in error of up to 30-40% for fixed cost over prior work in a novel crowdsourcing application: peer evaluation in online courses.
The reliability of delivering packets through multi-hop intermediate nodes is a significant issue in the mobile ad hoc networks (MANETs). The distributed mobile nodes establish connections to form the MANET, which may...
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The reliability of delivering packets through multi-hop intermediate nodes is a significant issue in the mobile ad hoc networks (MANETs). The distributed mobile nodes establish connections to form the MANET, which may include selfish and misbehaving nodes. Recommendation based trust management has been proposed in the literature as a mechanism to filter out the misbehaving nodes while searching for a packet delivery route. However, building a trust model that adopts recommendations by other nodes in the network is a challenging problem due to the risk of dishonest recommendations like bad-mouthing, ballot-stuffing, and collusion. This paper investigates the problems related to attacks posed by misbehaving nodes while propagating recommendations in the existing trust models. We propose a recommendation based trust model with a defence scheme, which utilises clustering technique to dynamically filter out attacks related to dishonest recommendations between certain time based on number of interactions, compatibility of information and closeness between the nodes. The model is empirically tested under several mobile and disconnected topologies in which nodes experience changes in their neighbourhood leading to frequent route changes. The empirical analysis demonstrates robustness and accuracy of the trust model in a dynamic MANET environment.
This study proposes an efficient anomalous behaviour detection framework using trajectory analysis. Such framework includes the trajectory pattern learning module and the online abnormal detection module. In the patte...
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This study proposes an efficient anomalous behaviour detection framework using trajectory analysis. Such framework includes the trajectory pattern learning module and the online abnormal detection module. In the pattern learning module, a coarse-to-fine clustering strategy is utilised. Vehicle trajectories are coarsely grouped into coherent clusters according to the main flow direction (MFD) vectors followed by a three-stage filtering algorithm. Then a robust K-means clustering algorithm is used in each coarse cluster to get fine classification by which the outliers are distinguished. Finally, the hidden Markov model (HMM) is used to establish the path pattern within each cluster. In the online detection module, the new vehicle trajectory is compared against all the MFD distributions and the HMMs so that the coherence with common motion patterns can be evaluated. Besides that, a real-time abnormal detection method is proposed. The abnormal behaviour can be detected when happening. Experimental results illustrate that the detection rate of the proposed algorithm is close to the state-of-the-art abnormal event detection systems. In addition, the proposed system provides the lowest false detection rate among selected methods. It is suitable for intelligent surveillance applications.
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