Detection Of Changes In Distribution Research Articles

Detection and Diagnosis of Distribution Changes of Degree Ratio in Complex Networks

The detection and diagnosis problems of distribution changes of degree ratio in complex networks are studied in this paper. We not only give the asymptotic expressions of the in-control and out-of-control average run lengths in detecting the distribution change by the cumulative sum chart, but also provide an effective and practicable method to transform the detection problem of the high dimensional Markov chain into a one-dimensional problem. Moreover, three multi-charts each based on the reference transition probabilities, the principal components, and the entropy statistics are presented to deal with the diagnosis problem. Finally, a real financial network which describes the dynamics and random correlations among 90 assets is investigated to demonstrate the detection and diagnosis performance of both the reference-free Cuscore chart and the multi-chart based on the entropy statistics.

Remote Sensing Approaches to Observe The Significance of Landuse Change on Water Storage at Oil Palm Plantation in Kuala Langat South Forest Reserve, Malaysia

Currently, land-use change from non-oil palm to oil palm plantation is increasing, especially on peat soil areas in Malaysia. If changes in soil moisture slows the rate of heat transfer, and the boundary layer is influenced by the contact with the Earth's surface, important research to understand the impact of land use change to oil palm plantation on the atmospheric boundary layer is in identifying the soil water content changes within time. Therefore, remote sensing was used as the approach to observe the significance of land-use change on water storage at oil palm plantation in Kuala Langat South Forest Reserve, in Malaysia. For the research, two approaches were used, i.e. land-use change analysis (classifying the land-use within the plantation using Google Earth-based imageries) and normalized difference vegetation index (NDV) analysis (using both Landsat MSS and Landsat ETM+ satellite imageries). As results, for the land-use change analysis from 2009 to 2012, assuming that oil palms occur within a certain land may indicate the presence of sufficient water within them, changes of 3.79% of oil palm coverage showed that decreased changes of water storage were predominantly (showed as open lands) in the north-west part of the ten hectares area. On the other hand, there are certain numbers of drained areas on the boundaries of each block of the oil palm plantations (besides the roads) indicating that roads were expanding within the three year period. Though most of the areas were unchanged, an increased number of oil palms were present, occurring at most of the areas of the ten hectares plantation region. For the NDVI results, there was only a very small amount of unchanged land-use; rather most of the areas had changed massively during the past 40 years (from 1974 to 2014). Moreover, the NDVI change increase were most significantly occurring at agricultural areas, including the oil palm plantation of Kuala Langat South Forest Reserve. It could be observed that substantial values (NDVI range of 0.3 to 0.4) occurred within the central area of each oil palm block, showing that soil water contents were different at such regions. Although a number of areas had shown that soil water content may have increased, reductions of change happen to occur significant in land-uses where constructions had taken place, such as the Kuala Lumpur International Airport (including runways and other open areas), buildings, and many others. Maps for both approaches were carried out as well, i.e. land-use classification and oil palm change detection maps (for the landuse change approach), and NDVI distribution and NDVI change detection maps (for the NDVI approach).

Combined SAGE II–GOMOS ozone profile data set for 1984–2011 and trend analysis of the vertical distribution of ozone

Abstract. We have studied data from two satellite occultation instruments in order to generate a high vertical resolution homogeneous ozone time series of 26 yr. The Stratospheric Aerosol and Gas Experiment (SAGE) II solar occultation instrument and the Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument measured ozone profiles in the stratosphere and mesosphere from 1984–2005 and 2002–2012, respectively. Global coverage, good vertical resolution, and the self-calibrating measurement method make data from these instruments valuable for the detection of changes in vertical distribution of ozone over time. As both instruments share a common measurement period from 2002–2005, it is possible to inter-calibrate the data sets. We investigate how well these measurements agree with each other and combine all the data to produce a new stratospheric ozone profile data set. Above 55 km, SAGE II measurements show much less ozone than the GOMOS nighttime measurements as a consequence of the well-known diurnal variation of ozone in the mesosphere. Between 35–55 km, SAGE II sunrise and sunset measurements differ from GOMOS' measurements to different extents. Sunrise measurements show 2% less ozone than GOMOS, whereas sunset measurements show 4% more ozone than GOMOS. Differences can be explained qualitatively by the diurnal variation of ozone in the stratosphere recently observed by SMILES and modeled by chemical transport models. Between 25–35 km, SAGE II sunrise and sunset measurements and GOMOS measurements agree within 1%. The observed ozone bias between collocated measurements of SAGE II sunrise/sunset and GOMOS night measurements is used to align the two data sets. The combined data set covers the time period 1984–2011, latitudes 60° S–60° N, and the altitude range of 20–60 km. Profile data are given on a 1 km vertical grid, and with a resolution of 1 month in time and 10° in latitude. The combined ozone data set is analyzed by fitting a time series model to the data. We assume a linear trend with an inflection point (so-called "hockey stick" form). The best estimate for the point of inflection was found to be the year 1997 for ozone between altitudes 35 and 45 km. At all latitudes and altitudes from 35 to 50 km we find a clear change in ozone trend before and after the inflection time. From 38 to 45 km, a negative trend of 4% per decade (statistically significant at 95% level) at the equator has changed to a small positive trend of 0–2% per decade. At mid-latitudes, the negative trend of 4–8% per decade has changed to to a small positive trend of 0–2% per decade. At mid-latitudes near 20 km, the ozone loss has still increased whereas in the tropics a recovery is ongoing.

Satellite Detection of Spatial Distribution and Temporal Changes of Surface Soil Moisture at Three Gorges Dam Region from 2003 to 2011

Abstract Knowledge of the spatial distribution and temporal changes of the land surface parameters at the Three Gorges Dam (TGD) region is essential to understanding the changes of hydrological processes and climate systems possibly brought by TGD. Based on accumulated observations for years from a spaceborne passive microwave radiometer, this study presents and analyzes the spatial and temporal distribution of soil moisture in the TGD region. Major drought and flood events are identified from the satellite-derived soil moisture products. Moreover, the areas around the largest freshwater lakes of China, the Dongting and Poyang Lakes, are frequently subjected to drought events, which might be partially related to the impoundment of TGD since the year 2006. Data analysis further reveals a statistically significant drying trend in May in the middle and lower reaches of the Yangtze River over the years 2003–11. These analyses indicate that water shortage becomes a realistic challenge for the once water abundant Yangtze River region, and more considerations on the possible consequences brought by climate changes are needed for the operation of TGD.

Level dominance for the detection of changes in level distribution in sound streams

Sound streams were generated by randomly choosing the levels of tone pips from two different distributions, A and B. Of the 18 tone pips, the first nine were drawn from distribution A and the second nine from distribution B, or the opposite. The listeners' task was to indicate order, A-B or B-A. In two conditions the A and B distributions differed in mean (condition 1) or variance (condition 2). In contrast to an ideal observer, listeners' strategies were consistent across the two conditions. Analyses suggest that listeners relied primarily on the more intense tone pips in making their decisions.

Determining changes in the distribution and abundance of a Rhyzopertha dominica phosphine resistance allele in farm grain storages using a DNA marker

The lesser grain borer, Rhyzopertha dominica (F.), is a highly destructive pest of stored grain that is strongly resistant to the fumigant phosphine (PH3 ). Phosphine resistance is due to genetic variants at the rph2 locus that alter the function of the dihydrolipoamide dehydrogenase (DLD) gene. This discovery now enables direct detection of resistance variants at the rph2 locus in field populations. A genotype assay was developed for direct detection of changes in distribution and frequency of a phosphine resistance allele in field populations of R. dominica. Beetles were collected from ten farms in south-east Queensland in 2006 and resampled in 2011. Resistance allele frequency increased in the period from 2006 to 2011 on organic farms with no history of phosphine use, implying that migration of phosphine-resistant R. dominica had occurred from nearby storages. Increasing resistance allele frequencies on organic farms suggest local movement of beetles and dispersal of insects from areas where phosphine has been used. This research also highlighted for the first time the utility of a genetic DNA marker in accurate and rapid determination of the distribution of phosphine-resistant insects in the grain value chain. Extending this research over larger landscapes would help in identifying resistance problems and enable timely pest management decisions.

Clinical Resistance and Decreased Susceptibility inStreptococcus suisIsolates from Clinically Healthy Fattening Pigs

Streptococcus suis (S. suis) has often been reported as an important swine pathogen and is considered as a new emerging zoonotic agent. Consequently, it is important to be informed on its susceptibility to antimicrobial agents. In the current study, the Minimum Inhibitory Concentration (MIC) population distribution of nine antimicrobial agents has been determined for nasal S. suis strains, isolated from healthy pigs at the end of the fattening period from 50 closed or semiclosed pig herds. The aim of the study was to report resistance based on both clinical breakpoints (clinical resistance percentage) and epidemiological cutoff values (non-wild-type percentage). Non-wild-type percentages were high for tetracycline (98%), lincomycin (92%), tilmicosin (72%), erythromycin (70%), tylosin (66%), and low for florfenicol (0%) and enrofloxacin (0.3%). Clinical resistance percentages were high for tetracycline (95%), erythromycin (66%), tylosin (66%), and low for florfenicol (0.3%) and enrofloxacin (0.3%). For tiamulin, for which no clinical breakpoint is available, 57% of the isolates did not belong to the wild-type population. Clinical resistance and non-wild-type percentages differed substantially for penicillin. Only 1% of the tested S. suis strains was considered as clinically resistant, whereas 47% of the strains showed acquired resistance when epidemiological cutoff values were used. In conclusion, MIC values for penicillin are gradually increasing, compared to previous reports, although pigs infected with strains showing higher MICs may still respond to treatment with penicillin. The high rate of acquired resistance against tiamulin has not been reported before. Results from this study clearly demonstrate that the use of different interpretive criteria contributes to the extent of differences in reported antimicrobial resistance results. The early detection of small changes in the MIC population distribution of isolates, while clinical failure may not yet be observed, provides the opportunity to implement appropriate risk management steps.

Distributed Change Detection

Change detection has traditionally been seen as a centralized problem. Many change detection problems are however distributed in nature and the need for distributed change detection algorithms is therefore significant. In this paper a distributed change detection algorithm is proposed. The change detection problem is first formulated as a convex optimization problem and then solved distributively with the alternating direction method of multipliers (ADMM). To further reduce the computational burden on each sensor, a homotopy solution is also derived. The proposed method have interesting connections with Lasso and compressed sensing and the theory developed for these methods are therefore directly applicable.

Consensus based distributed change detection using Generalized Likelihood Ratio methodology

In this paper a novel distributed algorithm derived from the Generalized Likelihood Ratio is proposed for real time change detection using sensor networks. The algorithm is based on a combination of recursively generated local statistics and a global consensus strategy, and does not require any fusion center. The problem of detection of an unknown change in the mean of an observed random process is discussed and the performance of the algorithm is analyzed in the sense of a measure of the error with respect to the corresponding centralized algorithm. The analysis encompasses asymmetric constant and randomly time varying matrices describing communications in the network, as well as constant and time varying forgetting factors in the underlying recursions. An analogous algorithm for detection of an unknown change in the variance is also proposed. Simulation results illustrate characteristic properties of the algorithms including detection performance in terms of detection delay and false alarm rate. They also show that the theoretical analysis connected to the problem of detecting change in the mean can be extended to the problem of detecting change in the variance.

Study with positron emission tomography of the osmotic opening of the dog blood-brain barrier for quinidine and morphine.

A canine model was used to evaluate the possibilities offered by positron emission tomography (PET) for the study of drug distribution in the brain during altered states of the blood-brain barrier (BBB). PET was used to monitor the changes in the distribution of [11C]quinidine and [11C]morphine resulting from BBB-disruption by intracarotid infusion of a hyperosmolar mannitol solution. Injection of Evans blue dye allowing post-mortem evaluation of the degree of BBB-opening was used as a reference method. Brain radioactivity concentrations observed after i.v. injection of either [11C] quinidine or [11C]morphine were markedly increased by intracarotid mannitol infusion, whereas they were not affected by saline infusion. For both drugs a close correlation was found between the radioactivity concentrations and the degree of Evans blue staining within the brain hemispheres and within smaller regions of interest corresponding to quadrants of a hemisphere. This parallelism between the findings for radioactivity concentrations and Evans blue staining suggests that PET allows the detection of in-vivo changes in brain distribution of drugs resulting from alterations of the BBB permeability.

Monitoring Voltage-Dependent Charge Displacement of Shaker B-IR K+ Ion Channels Using Radio Frequency Interrogation

Here we introduce a new technique that probes voltage-dependent charge displacements of excitable membrane-bound proteins using extracellularly applied radio frequency (RF, 500 kHz) electric fields. Xenopus oocytes were used as a model cell for these experiments, and were injected with cRNA encoding Shaker B-IR (ShB-IR) K+ ion channels to express large densities of this protein in the oocyte membranes. Two-electrode voltage clamp (TEVC) was applied to command whole-cell membrane potential and to measure channel-dependent membrane currents. Simultaneously, RF electric fields were applied to perturb the membrane potential about the TEVC level and to measure voltage-dependent RF displacement currents. ShB-IR expressing oocytes showed significantly larger changes in RF displacement currents upon membrane depolarization than control oocytes. Voltage-dependent changes in RF displacement currents further increased in ShB-IR expressing oocytes after ∼120 µM Cu2+ addition to the external bath. Cu2+ is known to bind to the ShB-IR ion channel and inhibit Shaker K+ conductance, indicating that changes in the RF displacement current reported here were associated with RF vibration of the Cu2+-linked mobile domain of the ShB-IR protein. Results demonstrate the use of extracellular RF electrodes to interrogate voltage-dependent movement of charged mobile protein domains — capabilities that might enable detection of small changes in charge distribution associated with integral membrane protein conformation and/or drug–protein interactions.

Generalized Analysis of a Distributed Energy Efficient Algorithm for Change Detection

We propose an energy efficient distributed cooperative Change Detection scheme called DualCUSUM based on Page's CUSUM algorithm. In the algorithm, each sensor runs a CUSUM and transmits only when the CUSUM is above some threshold. The transmissions from the sensors are fused at the physical layer. The channel is modeled as a Multiple Access Channel (MAC) corrupted with noise. The fusion center performs another CUSUM to detect the change. The algorithm performs better than several existing schemes when energy is at a premium. We generalize the algorithm to also include nonparametric CUSUM and provide a unified analysis. Our results show that while the false alarm probability is smaller for observation distribution with a lighter tail, the detection delay is asymptotically the same for any distribution. Consequently, we provide a new viewpoint on why parametric CUSUM performs better than nonparametric CUSUM. In the process, we also develop new results on a reflected random walk which can be of independent interest.

Distributed Change Detection Based on a Consensus Algorithm

In this paper a novel distributed recursive algorithm is proposed for real time change detection using sensor networks. The algorithm is based on a combination of geometric moving average control charts generating local statistics and a global consensus strategy; it does not require any fusion center, so that the final decision is made by testing the state of any node in the network with respect to a given common threshold. The mean-square error with respect to the centralized solution defined by a weighted sum of the local statistics is analyzed in the case of constant asymmetric consensus matrices with constant and time varying forgetting factors in the underlying recursions, assuming spatially and temporally correlated data. These results are consistently extended to the case of time varying random consensus matrices, encompassing asymmetric gossip schemes, lossy networks and intermittent measurements, proving that the algorithm can be an efficient tool for practice. The given simulation results illustrate the main characteristics of the proposed algorithm, including the consensus matrix design, the mean square error with respect to the centralized solution as a function of the forgetting factor, the obtained detection quality expressed using deflection and estimation of the instant of parameter change.

Consensus-based Page's test in sensor networks

Page's test is a well-known statistical technique to approach quickest detection problems, namely the detection of an abrupt change in the statistical distribution of a certain monitored phenomenon. Running consensus is a recently proposed signal processing procedure aimed at reaching agreement among the nodes of a fully flat network, and its peculiar feature is the simultaneity of two stages: that of acquiring new measurements by the sensors, and that of data fusion involving inter-sensor communications. In this paper we study a quickest detector based on the running consensus scheme, and compare it to a bank of independent Page's tests. Exploiting insights from previous studies, we propose closed-form analytical approximations of the performances of these detection schemes and address a comparison in terms of relative efficiencies. The approximated performance figures are then checked by simulation to validate the analysis and to investigate non-asymptotic scenarios.

Distributed Change Detection Based on a Consensus Algorithm

In this paper a novel consensus based distributed recursive algorithm is proposed for real time change detection using sensor networks. Convergence of the algorithm to the optimal centralized solution defined by a weighted sum of the results of local signal processing is proved in the cases of constant and time varying forgetting factors of the underlying recursions, assuming correlated data and different local values of the parameter changes. Simulation results illustrate characteristic properties of the algorithms.

Adaptive detection of distribution changes in medical information systems

AbstractThe number of messages received by a medical information system was modelled as the signal in a stochastic process. Algorithms were developed for detecting changes of mean and variance and reporting the changes of status from normal to abnormal (upward edge), and vice verse (downward edge) under the assumption that the signal was distributed independently and normally in a series of time intervals. System tests have shown that the performance of the fault detecting and reporting in non‐holiday days is generally satisfactory for all laboratory data feeds. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Distributed Bayesian fault diagnosis of jump Markov systems in wireless sensor networks

A Bayesian distributed online change detection algorithm is proposed for monitoring a dynamical system by a wireless sensor network. The proposed solution relies on modelling the system dynamics by a jump Markov system with a finite set of states, including the abrupt change behaviour. For each discrete state, an observed system is assumed to evolve according to a state-space model. The collaborative strategy ensures the efficiency and the robustness of the data processing, while limiting the required communications bandwith. An efficient Rao-Blackwellised Collaborative Particle Filter (RB-CPF) is proposed to estimate the a posteriori probability of the discrete states of the observed systems. The Rao-Blackwellisation procedure combines a Sequential Monte-Carlo (SMC) filter with a bank of distributed Kalman filters. In order to prolong the sensor network lifetime, only few active (leader) nodes are selected according to a spatio-temporal selection protocol. This protocol is based on a trade-off between error propagation, communications constraints and information content complementarity of distributed data. Only sufficient statistics are communicated between leader nodes and their collaborators.

Security and Trust for Wireless Autonomic Networks Systems and Control Methods

We analyze and solve various problems of security, information assurance and trust in dynamic wireless networks. These include detection and defense against attacks, detection of propagating viruses, evaluation of intrusion systems, attacks at the physical, MAC and routing protocols, trust establishment-dynamicsmanagement. We demonstrate persistently that systems and control models and methodologies provide new and powerful techniques to analyze these problems. We describe the use of distributed change detection methods and algorithms for intrusion detection and the use of non-cooperative games for the detection and defense against attacks at all layers. We demonstrate how Bayesian decision theory can be used to evaluate intrusion detection systems and we resolve some key problems in this area. We use game theoretic methods again to develop robust protocols against attacks, including Byzantine ones. We provide an in-depth investigation of trust establishment and computation in such networks. We describe various methods for distributed trust evaluation and the associated trust (and mistrust) ‘spreading’ dynamics. We investigate rules and policies that establish ‘trust-connected’ networks using only local interactions, and find the parameters (e.g. topology type) that speed up or slow down this transition. We describe and explain the phase transition phenomena that we have found in these evolutions. We model the interactions among agents as cooperative games and show that trust can encourage agents to collaborate. This leads us to a fundamental analytical technique, constrained coalitional games, that can be used to evaluate tradeoffs in collaborative networks in various areas: communications, sensors, economics, sociology, biology. We also describe a model for trust evaluation that uses pairwise iterated graph games between the agents to create a ‘trust reputation’ with evolution coupled to the game dynamics. Finally we present a new modeling framework for trust metric evaluation as linear iterations over ordered semirings. This allows us to formulate problems of resilience of trust metrics and trust evaluation to attacks.

Surveillance of individual level disease maps

Methods for the production of individual (address) level disease maps are often retrospective; they estimate a map of the average relative risk of disease over a study period. However, recently, epidemiologists have started to look at weekly or monthly reports of disease and assess them for any change in the distribution of relative risk. For example, in the United States of America, the Centre for Disease Control and Prevention now routinely collects information on over 50 notifiable diseases every week. In this paper we present a method for the detection of a sudden change in the geographical distribution of the disease in a prospective study. The method is based on an estimate of the directional derivative of the conditional probability of a case, given either a case or control has occurred. It is based on standard kernel approaches to nonparametric regression and it is readily applied in any standard statistical software package. Two simulated examples of sudden clustering around a fixed point are provided.

HIGH-CONTRAST NEUTRON IMAGING TO OBSERVE WATER DISTRIBUTION IN INTACT PLANTS

Low energy neutron radiography has good contrast with respect to water concentration when tested with a several plant materials. Therefore, this method is suitable for detection of small changes in water distribution in intact plants. In this study, low energy neutron beams of cold neutron (CN) and very cold neutron (VCN) ports at Kyoto University Reactor (KUR), and the VCN port at Institute of Laue-Langevin (ILL) reactor were used respectively. Low energy neutron radiography images were obtained with a cooled CCD camera system and an imaging plate. Using low energy beams at the ILL reactor, relatively small water changes in thin leaves during rehydration were observed. The transmission thickness was limited because of the low energy. From the computed tomographic (CT) images of cut carnation flowers obtained using beams from the VCN port at KUR it was observed that the calyx diameter was about 10mm. The total time to construct a CT image composed of 180 2D images was 3.5 h.