Dynamic Threshold Method Research Articles

A comprehensive analysis of phenological changes in forest vegetation of the Funiu Mountains, China

This paper reports the phenological response of forest vegetation to climate change (changes in temperature and precipitation) based on Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time-series images from 2000 to 2015. The phenological parameters of forest vegetation in the Funiu Mountains during this period were determined from the temperature and precipitation data using the Savitzky–Golay filter method, dynamic threshold method, Mann-Kendall trend test, the Theil-Sen estimator, ANUSPLIN interpolation and correlation analyses. The results are summarized as follows: (1) The start of the growing season (SOS) of the forest vegetation mainly concentrated in day of year (DOY) 105–120, the end of the growing season (EOS) concentrated in DOY 285–315, and the growing season length (GSL) ranged between 165 and 195 days. There is an evident correlation between forest phenology and altitude. With increasing altitude, the SOS, EOS and GSL presented a significant delayed, advanced and shortening trend, respectively. (2) Both SOS and EOS of the forest vegetation displayed the delayed trend, the delayed pixels accounted for 76.57% and 83.81% of the total, respectively. The GSL of the forest vegetation was lengthened, and the lengthened pixels accounted for 61.21% of the total. The change in GSL was mainly caused by the decrease in spring temperature in the region. (3) The SOS of the forest vegetation was significantly partially correlated with the monthly average temperature in March, with most correlations being negative; that is, the delay in SOS was mainly attributed to the temperature decrease in March. The EOS was significantly partially correlated with precipitation in September, with most correlations being positive; that is, the EOS was clearly delayed with increasing precipitation in September. The GSL of the forest vegetation was influenced by both temperature and precipitation throughout the growing season. For most regions, GSL was most closely related to the monthly average temperature and precipitation in August.

Financial Development and Innovation-Led Growth: Is Too Much Finance Better?

We show that the expansion of financial sector may hurt innovative activities and hence the innovation-led growth, using data on 50 countries over the 1990-2016 period. Countries with higher level of financial development are found to have a smaller positive or insignificant effect on innovation. The marginal effect of innovation on growth is a decreasing function of financial development. Using a dynamic panel threshold method we re-examine the possible non-linearity between finance, innovation, and growth. We find that innovation exhibits an insignificant effect on output growth when credit to the private sector exceeds the level of 60% as a share of GDP. These results are not driven by banking crises, the long run effect of 2007-2008 financial crisis, or the ongoing European sovereign debt crisis.

Adaptive Threshold Generation for Fault Detection with High Dependability for Cyber-Physical Systems

Cyber-physical systems (CPS) applied to safety-critical or mission-critical domains require high dependability including safety, security, and reliability. However, the safety of CPS can be significantly threatened by increased security vulnerabilities and the lack of flexibility in accepting various normal environments or conditions. To enhance safety and security in CPS, a common and cost-effective strategy is to employ the model-based detection technique; however, detecting faults in practice is challenging due to model and environment uncertainties. In this paper, we present a novel generation method of the adaptive threshold required for providing dependability for the model-based fault detection system. In particular, we focus on statistical and information theoretic analysis to consider the model and environment uncertainties, and non-linear programming to determine an adaptive threshold as an equilibrium point in terms of adaptability and sensitivity. To do this, we assess the normality of the data obtained from real sensors, define performance measures representing the system requirements, and formulate the optimal threshold problem. In addition, in order to efficiently exploit the adaptive thresholds, we design the storage so that it is added to the basic structure of the model-based detection system. By executing the performance evaluation with various fault scenarios by varying intensities, duration and types of faults injected, we prove that the proposed method is well designed to cope with uncertainties. In particular, against noise faults, the proposed method shows nearly 100% accuracy, recall, and precision at each of the operation, regardless of the intensity and duration of faults. Under the constant faults, it achieves the accuracy from 85.4% to 100%, the recall of 100% from the lowest 54.2%, and the precision of 100%. It also gives the accuracy of 100% from the lowest 83.2%, the recall of 100% from the lowest 43.8%, and the precision of 100% against random faults. These results indicate that the proposed method achieves a significantly better performance than existing dynamic threshold methods. Consequently, an extensive performance evaluation demonstrates that the proposed method is able to accurately and reliably detect the faults and achieve high levels of adaptability and sensitivity, compared with other dynamic thresholds.

Mapping High Mountain Lakes Using Space-Borne Near-Nadir SAR Observations

Near-nadir interferometric imaging SAR (Synthetic Aperture Radar) techniques are promising in measuring global water extent and surface height at fine spatial and temporal resolutions. The concept of near-nadir interferometric measurements was implemented in the experimental Interferometric Imaging Radar Altimeters (InIRA) mounted on Chinese Tian Gong 2 (TG-2) space laboratory. This study is focused on mapping the extent of high mountain lakes in the remote Qinghai–Tibet Plateau (QTP) areas using the InIRA observations. Theoretical simulations were first conducted to understand the scattering mechanisms under near-nadir observation geometry. It was found that water and surrounding land pixels are generally distinguishable depending on the degree of their difference in dielectric properties and surface roughness. The observed radar backscatter is also greatly influenced by incidence angles. A dynamic threshold method was then developed to detect water pixels based on the theoretical analysis and ancillary data. As assessed by the LandSat results, the overall classification accuracy is higher than 90%, though the classifications are affected by low backscatter possibly from very smooth water surface. The algorithms developed from this study can be extended to all InIRA land measurements and provide support for the similar space missions in the future.

Remote sensing monitoring of green-up dates in the Xilingol grasslands of northern China and their correlations with meteorological factors

ABSTRACTThe grassland vegetation green-up date helps to initiate the start of grassland grazing and is an important scientific issue in grassland phenology research. This study examined green-up in the Xilingol grasslands of northern China. A double logistic function was used to reconstruct a time series of Satellite Pour l’Observation de la Terre Vegetation (SPOT Vegetation) Normalized Difference Vegetation Index (NDVI) data from 1999 to 2012. The dynamic threshold method was used to monitor the green-up date via remote sensing. The study found that green-up dates occurred between early April and mid-May in most areas. Sample plots indicated that 68% of ground monitoring results were consistent with the remotely sensed green-up date. The root mean square error of the green-up date measured via remote sensing was 8.7 days. The results are summarized as follows. (1) The green-up date of temperate desert grassland usually occurred around the first half of April but could be quite variable (standard deviation = 28.65 days). The green-up date of temperate grassland usually occurred in mid- to late April, and was somewhat variable (standard deviation = 18.45 days). The green-up date of temperate meadow grassland usually occurred in late April or early May with little variability (standard deviation = 12.97 days). (2) Linear trend analyses showed that only 13.9% of the pixels exhibited significant changes over the 14-year time series. However, the linear trend increased from the southeast to the northwest, with 57% of pixels demonstrating earlier green-up. Across the region, the linear trend exhibited an average rate of change of – 1.5 days 10 year–1. In addition, we discuss correlations between green-up dates and meteorological factors at eight meteorological stations and analyse partial correlation coefficients between green-up dates and meteorological factors. The correlations measured at seven meteorological stations were statistically significant (p < 0.05). The average correlative value of deterministic factors was 0.54. The partial correlation coefficients between green-up date and temperature prior to seedling establishment were negative at five stations, but this relationship was significant at only three stations. Partial correlation coefficients between green-up date and precipitation prior to seedling establishment were negative at all eight stations, of which five relationships were significant.

SORD: a new strategy of online replica deduplication in Cloud-P2P

In online Cloud-P2P system, more replicas can lead to lower access delay but more maintenance overhead and vice versa. The traditional strategies of online replica deduplication usually utilize the method of dynamic threshold to delete the redundant replicas. Since the replicas access amount has varied over time, and every replica can bear a certain amount of requests, the replica of being deleted may impact on other nodes, lead to these nodes overload, deteriorating the system performance. But this impact is not paid enough attention in the traditional strategy. To deal with the problem, this paper proposes a new strategy of online replica deduplication (SORD), achieving to reduce the impact on other nodes when deleting a redundant replica. In order to reduce the impact, SORD adopts the method of prediction evaluation to delete the redundant replica. Before deleting a replica, it applies the method of fuzzy clustering analysis to get the optimal deletion replica from the file’s replica set. Based on the historical visiting information of the optimal deletion replica and the capacity of nodes, SORD evaluates the impact on other nodes to decide whether a replica can be deleted. Extensive experiments demonstrate that SORD obtains superior performances in access latency around 5–15% on average and better load balance than other similar methods. Meanwhile, it can remove about 65% redundant replicas.

A rapid local backlight dimming method for interlaced scanning video

AbstractThis paper presents a rapid local backlight dimming method for interlaced video to improve the efficiency of displaying interlaced video. In the proposed method, we combine the line average deinterlacing algorithm with the local backlight dimming algorithm. In order to reduce the computational complexity and the backlight power consumption, three novel methods are proposed. The first is to add the sparse method to process the deinterlaced image. Second, the improved dynamic threshold method is used to calculate the dimming factor. And third, the odd field and even field of the same frame use the same backlight brightness to do backlight dimming. The simulation results show that the proposed method can reduce the computational complexity and power consumption, and the experiment results verify that the method can provide high display effect.

RESPONSE OF ALPINE GRASSLAND VEGETATION PHENOLOGY TO SNOW ACCUMULATION AND MELT IN NAMCO BASIN

Abstract. Snow/ice accumulation and melt, as a vital part of hydrological processes, is close related with vegetation activities. Taking Namco basin for example, based on multisource remote sensing data and the ground observation data of temperature and precipitation, phenological information was extracted by S-G filtering and dynamic threshold method. Daily snow cover fraction was calculated with daily cloud-free snow cover maps. Evolution characteristics of grassland vegetation greening, growth length and daily snow cover fraction and their relationship were analyzed from 2001 to 2013. The results showed that most of grassland vegetation had advanced greening and prolong growth length trend in Namco basin. There were negative correlations between snow cover fraction and vegetation greening or growth length. The response of vegetation phenology to snow cover fraction is more sensitive than that to temperature in spring. Meanwhile, vegetation growth condition turned worse with advanced greening and prolong growth length. To a certain extent, our research reveals the relationship between grassland vegetation growth cycle and snow in alpine ecosystem. It has provided reference to research the response mechanism of alpine grassland ecosystem to climate changes.

SPATIO-TEMPORAL CHANGES OF NET PRIMARY PRODUCTIVITY AND ITS RESPONSE TO PHENOLOGY IN NORTHEAST CHINA DURING 2000–2015

Abstract. The relationship between net primary productivity (NPP) and phenological changes is of great significance to the study of regional ecosystem processes. In this study, firstly, NPP was estimated with the remote sensing model based on the SPOT-VGT NDVI dataset (2000–2015), meteorological data and the vegetation map in Northeast China. Then, using NDVI time series data which was reconstructed by polynomial fitting, phenology was extracted with the dynamic threshold method. Finally, the relationship between NPP and phenology was analyzed. The results showed that NPP mainly increased in the cropland, grassland, forestland and shrubland; however, vegetation NPP decreased in the ecotone among cropland, grassland and forestland. Correlation analysis suggested that the relationships between NPP and phenological metrics (i.e., the start of the growing season (SOS), the end of the growing season (EOS), the length of the growing season (LOS)) were different due to geographical location. On the whole, there was a positive correlation between NPP and the LOS in the forestland, and negative in the cropland and grassland, indicating that extended LOS can promote the accumulation of forestland NPP. By analyzing the monthly NDVI data during the vigorous growth period, the increase of NPP in the grassland and cropland was mainly due to the better growth from June to August, and shortened LOS did not lead to reduce the NPP. Generally, the response of NPP to phenology in Northeast China were more complex, showing obvious difference of vegetation types and spatial variability, we need to consider topography, community structure and other factors in the further studies.

A vessel segmentation method for serialized cerebralvascular DSA images based on spatial feature point set of rotating coordinate system

Cerebrovascular pathology is one of the main fatal diseases which seriously affect the human's health. Extracting the accurate image of cerebral vascular tissue is the key of clinical diagnosis. However, the motion artifacts in DSA images seriously affected the quality of vascular subtraction image. In this paper, an automatic and accurate segmentation method is presented to extract the vascular region in the live image of brain. Firstly, a coarse registration for the live image and the mask image is implemented. And then, the SIFT algorithm is utilized to detect geometrical feature points in the serialized subtraction images. After that, a spatial model of rotating coordinate system and a calculative strategy of contextual information are designed to eliminate the error feature points. Finally, based on a dynamic threshold method, the blood vessel image can be obtained by region growing. The context information in the adjacent subtraction images is fully used. The experimental result shows that the segmented cerebral vascular image is satisfactory. This method can provide accurate vessel image data for the clinical operation based on DSA interventional therapy.

Spatial and temporal variation of vegetation phenology and its response to climate changes in Qaidam Basin from 2000 to 2015

Based on TIMESAT 3.2 platform, MODIS NDVI data (2000–2015) of Qaidam Basin are fitted, and three main phenological parameters are extracted with the method of dynamic threshold, including the start of growth season (SGS), the end of growth season (EGS) and the length of growth season (LGS). The spatial and temporal variation of vegetation phenology and its response to climate changes are analyzed respectively. The conclusions are as follows: (1) SGS is mainly delayed as a whole. Areas delayed are more than the advanced in EGS, and EGS is a little delayed as a whole. LGS is generally shortened. (2) With the altitude rising, SGS is delayed, EGS is advanced, and LGS is shortened and phenophase appears a big variation below 3000 m and above 5000 m. (3) From 2000 to 2015, the temperature appears a slight increase along with a big fluctuation, and the precipitation increases evidently. (4) Response of phenophase to precipitation is not obvious in the low elevation humid regions, where SGS arrives early and EGS delays; while, in the upper part of the mountain regions, SGS delays and EGS advances with temperature rising, SGS arrives early and EGS delays with precipitation increasing.

3-D image measurement of the sea for disaster prevention

To reduce the loss of human life in disasters, we have developed an early warning system for typhoons and tsunamis. The system is designed to monitor the change of the sea level from a distance of 20 km. And it can alert a coming tsunami 20 min before reaching the coast. Now, GPS Buoys and observation networks are used to forecast tsunami. However, the GPS buoys are not conducive to monitoring wide ranges of the sea. The observation networks have the disadvantages of high installation costs and difficult maintenance. In this paper, a measurement system of long distance sea level height for disaster prevention has been constructed based on a 3-D image measurement technique. First, to monitor a wide range of the sea surface, this system utilizes the pan and tilt stages and angle sensors to rotate the cameras. To improve the accuracy of long distance 3-D image measurement, a long-distance calibration method of camera system has been proposed based on the scale poles. In addition, all the equipment can be put in one place so it is very convenient to maintain. In this experiment, a dynamic threshold method is proposed in blocks to extract the waves automatically, accompanied by a wave feature matrix matching method in two long-distance images. Finally, the average value of wave height is used as the height of sea level. As a practical use result, a comparison has been made between the measurement results and the data of Japan Meteorological Agency (JMA).

Residential carbon dioxide emissions at the urban scale for county-level cities in China: A comparative study of nighttime light data

Abstract Carbon emissions from residential energy consumption in urban areas play a pivotal role in meeting emission targets. It is more beneficial to decomposing the emission reduction target to subnational units. This study aims to map urban residential carbon emissions at a finer spatial resolution to offer reference to disaggregating the carbon-reduction targets down to each sub-unit. With the launch of Suomi National Polar-orbiting Partnership (NPP) satellite, the day-night band of Visible Infrared Imaging Radiometer Suite (VIIRS) onboard represents a major advancement compared with Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) in terms of radiometric accuracy, spatial resolution and geometric quality. Therefore, the useful proxy was utilized to model the spatial distribution of residential carbon emissions at the urban scale for county-level cities in China. Firstly, the dynamic optimal threshold method was used to extract urban built-up areas based on NPP-VIIRS data and the statistical data. Secondly, comparison was conducted between the NPP-VIIRS data and both original and saturation-corrected DMSP-OLS data to explore the performance of NPP-VIIRS data. Finally, the spatial characteristics were analyzed in detail at the regional, provincial and prefectural level, respectively. The results show that the NPP-VIIRS data performed better in both statistic regression and spatial comparison. The spatial patterns indicated that there was an obvious north-south differentiation, especially the carbon emission density (defined as carbon emissions per grid), which was significantly higher in Northeastern China than that of other regions. The climate elements positively contributed to the increase of carbon emissions in residential sector, especially stimulating emissions related to building heating.

A hybrid heuristic resource allocation model for computational grid for optimal energy usage

Computational grid helps in faster execution of compute intensive jobs. The resource allocation for the job execution in computational grid demands a lot of characteristic parameters to be optimised but in the process the green aspect is ignored. Reducing the energy consumption in computational grid is a major recent issue among researchers. The conventional systems, which offer energy efficient scheduling strategies, ignore other quality of service parameters while scheduling the jobs. The proposed work tries to optimise the energy for resource allocation and at the same time makes no compromise on other related characteristic parameters. A hybrid model, that uses genetic algorithm and graph theory concept has been proposed for this purpose. In this model, an energy saving mechanism is implemented using a dynamic threshold method followed by genetic algorithm to further consolidate the saving. Eventually, a graph theory concept of Minimum Spanning Tree (MST) is applied. The performance of the proposed model has been studied by its simulation. The result reveals the benefits achieved with the proposed model for optimal energy with resource allocation in the grid.

GPU-Accelerated Dynamic Wavelet Thresholding Algorithm for X-Ray CT Metal Artifact Reduction

The computed tomography (CT) imaging technique has been used to diagnose widespread disease for many years; however, severe streaking artifacts have been observed to appear in the reconstructed images of examinate contains metal objects. In this paper, we propose a dynamic wavelet thresholding metal artifact reduction (MAR) algorithm based on a statistic iterative reconstruction (SIR) model for 2-D fan beam CT. Cubic spline interpolation is utilized to remove blocky black artifacts caused by incomplete projections, and it also makes the solution closer to the optimum. The dynamic wavelet thresholding method contains the benefits of both wavelet soft and hard thresholding methods and promotes the sparsity of the image, which is used to erase residual streaking artifacts and accelerate the convergence of the SIR. The algorithm is accelerated by graphics processing unit programming and costs 14.44s with 40 iterations, meeting the demand of clinical practice. The performance of the proposed algorithm is compared with two classical MAR algorithms: the total variation (TV) constraint SIR algorithm and the reweighted TV constraint SIR algorithm. The experimental datasets include nine artificial datasets. Both qualitative and quantitative evaluation results show the outstanding performance of the proposed algorithm in suppressing the metal artifacts and preserving the image details.

Does environmental Kuznets curve hypothesis exist? Evidence from dynamic panel threshold

ABSTRACTCarbon dioxide (CO2) emissions have been rising globally and have raised public concern regarding their detrimental effects to human life. This article investigates the validity of the Environmental Kuznets Curve (EKC) hypothesis in developing countries in a nonlinear framework. The article applies the dynamic panel threshold method, which is able to estimate the EKC turning point. The main findings reveal the existence of a nonlinear relationship between income (GDP per capita) and carbon dioxide emissions. Precisely, for developing countries (low and middle income) the results reveal that GDP per capita is positive and significantly related to CO2 emissions, below and above the threshold. These results challenge the validity of the EKC hypothesis in developing countries, suggesting that developing countries are still below the desired income turning point, at which better economic development will lead to reduced environmental damage. Based on the study findings, it may be inappropriate for the policymakers in developing countries to adopt the EKC postulate as the theoretical basis for policies favouring economic growth.

Pulse transit time detection based on waveform time domain feature and dynamic difference threshold

Aiming at the defects that the traditional pulse transit time (PTT) detection methods are sensitive to changes in photoplethysmography (PPG) signal and require heavy computation, we proposed a new algorithm to detect PTT based on waveform time domain feature and dynamic difference threshold. We calculated the PTT by using dynamic difference threshold method to detect the R-waves of electrocardiogram (ECG), shortening the main peak detection range in PPG signal according to the characteristics of the waveform time domain, and using R wave to detect the main peak of PPG signal. We used the American MIMIC database and laboratory test data to validate the algorithm. The experimental results showed that the proposed method could accurately extract the feature points and detect PTT, and the PTT detection accuracies of the measurements and the database samples were 99.1% and 97.5%, respectively. So the proposed method could be better than the traditional methods.

Avian Influenza Risk Surveillance in North America with Online Media.

The use of Internet-based sources of information for health surveillance applications has increased in recent years, as a greater share of social and media activity happens through online channels. The potential surveillance value in online sources of information about emergent health events include early warning, situational awareness, risk perception and evaluation of health messaging among others. The challenge in harnessing these sources of data is the vast number of potential sources to monitor and developing the tools to translate dynamic unstructured content into actionable information. In this paper we investigated the use of one social media outlet, Twitter, for surveillance of avian influenza risk in North America. We collected AI-related messages over a five-month period and compared these to official surveillance records of AI outbreaks. A fully automated data extraction and analysis pipeline was developed to acquire, structure, and analyze social media messages in an online context. Two methods of outbreak detection; a static threshold and a cumulative-sum dynamic threshold; based on a time series model of normal activity were evaluated for their ability to discern important time periods of AI-related messaging and media activity. Our findings show that peaks in activity were related to real-world events, with outbreaks in Nigeria, France and the USA receiving the most attention while those in China were less evident in the social media data. Topic models found themes related to specific AI events for the dynamic threshold method, while many for the static method were ambiguous. Further analyses of these data might focus on quantifying the bias in coverage and relation between outbreak characteristics and detectability in social media data. Finally, while the analyses here focused on broad themes and trends, there is likely additional value in developing methods for identifying low-frequency messages, operationalizing this methodology into a comprehensive system for visualizing patterns extracted from the Internet, and integrating these data with other sources of information such as wildlife, environment, and agricultural data.

A QoS-Aware Dynamic Bandwidth Allocation in PON Networks

In the increasing demand of bandwidth hungry applications and services, Passive Optical Network (PON) is consideration of as solitary of the majority optimistic fiber based access system which will offer a cost effective way out to meet the mushrooming development of high bandwidth applications. To endow with first-rate network access to the consumers of a PON system have to utilize a competent bandwidth distribution algorithm, which allot the accessible bandwidth dynamically to the users based on their requirement. This paper proposes a framework and details of a scheduling method based on Dynamic Bandwidth Allocation which meets the Quality of Service (QoS) requirements for differentiated data services in PON networks. The performance of an Optical Line Terminal and Optical Network Units (ONU) can be analysed which explains the QoS of a PON network. For achieving higher bandwidth usage, the proposed method discards leftover unutilized slots through dynamic queue threshold method. This results in accurate bandwidth allocation to each ONU. Simulation results show that the proposed method achieves a better quality aware network for performing an ONU Scheduling.

Non-linearity in Debt and Return Relationship: Evidence from Dynamic Panel Threshold Method

Background and objective: Moderate debt usage increases returns during economic boom, but high debt could decreases returns during economic recession. This study examines if there is a threshold debt level in the debt-returns relationship. Methodology: This study applies dynamic panel-threshold method to determine optimal debt level beyond which further increases in debt decreases returns. This study finds a threshold effect of 20.570% between debt ratio and return on equity. If the debt ratio is lower than 20.570%, a 1% increases in debt ratio increase return on equity by 0.128%. But, when the debt ratio is higher than 20.570%, a 1% increase in debt ratio decreases return on equity by 0.050%. Results: The results suggest that there is an optimal debt ratio of 20.570% at which point further increase in debt decreases return on equity. Conclusion: These results support the tradeoff theory, which suggests that there is an optimum debt level that maximizes returns.