Britt, Charles L.; Bracalente, Emedio M.
1992-01-01
The algorithms used in the NASA experimental wind shear radar system for detection, characterization, and determination of windshear hazard are discussed. The performance of the algorithms in the detection of wet microbursts near Orlando is presented. Various suggested algorithms that are currently being evaluated using the flight test results from Denver and Orlando are reviewed.
Passive Infrared Detection of Microburst Induced Low Level Wind Shear
1990-05-17
airborne microburst detector is needed. A low cost, low maintenance system that will provide real-time, accurate detection of this LLWS hazard. Having the...R.L. Kurkowski, and F. Caracena, 1983: Airborne operation of an infrared low-level windshear predicition system, J. Aircraft,20, 170-173. , and R.L...and App. Meteor., Vol. 23, 898-915. Appendix A PREDICITION OF ATMOSPHERIC TRANSMISSION AND RADIANCE Over the past 50 years various computer codes
Microburst nowcasting applications of GOES
Pryor, Kenneth L
2011-01-01
Recent testing and validation have found that the Geostationary Operational Environmental Satellite (GOES) microburst products are effective in the assessment and short-term forecasting of downburst potential and associated wind gust magnitude. Two products, the GOES sounder Microburst Windspeed Potential Index (MWPI) and a new bi-spectral GOES imager brightness temperature difference (BTD) product have demonstrated capability in downburst potential assessment. In addition, a comparison study between the GOES-R Convective Overshooting Top (OT) Detection and MWPI algorithms has been completed for cases that occurred during the 2007 to 2009 convective seasons over the southern Great Plains. Favorable results of the comparison study include a statistically significant negative correlation between the OT minimum temperature and MWPI values and associated measured downburst wind gust magnitude. The negative functional relationship between the OT parameters and wind gust speed highlights the importance of updraft s...
Microburst windspeed potential assessment: progress and developments
Pryor, Kenneth L
2008-01-01
A suite of products has been developed and evaluated to assess hazards presented by convective downbursts to aircraft in flight derived from the current generation of GOES. The existing suite of GOES microburst products employs the sounder to calculate risk based on conceptual models of favorable environmental profiles for convective downburst generation. Accordingly, a diagnostic nowcasting product, the Microburst Windspeed Potential Index, is designed to infer attributes of a favorable microburst environment. In addition, a GOES-West imager microburst algorithm that employs brightness temperature differences between band 3 (upper level water vapor), band 4 (longwave infrared window), and split window band 5 has been developed to supplement the sounder-derived products. This paper provides an updated assessment of the sounder MWPI and imager microburst algorithms, presents case studies demonstrating effective operational use of the microburst products, and presents validation results for the 2008 convective ...
Microbursts as an aviation wind shear hazard
Fujita, T. T.
1981-01-01
The downburst-related accidents or near-misses of jet aircraft have been occurring at the rate of once or twice a year since 1975. A microburst with its field comparable to the length of runways can induce a wind shear which endangers landing or liftoff aircraft; the latest near miss landing of a 727 aircraft at Atlanta, Ga. in 1979 indicated that some microbursts are too small to trigger the warning device of the anemometer network at major U.S. airports. The nature of microbursts and their possible detection by Doppler radar are discussed, along with proposed studies of small-scale microbursts.
Downbursts and microbursts - An aviation hazard. [downdrafts beneath thunderstorms
Fujita, T. T.
1980-01-01
Downburst and microburst phenomena occurring since 1975 are studied, based on meteorological analyses of aircraft accidents, aerial surveys of wind effects left behind downbursts, and studies of sub-mesoscale wind systems. It is concluded that microbursts beneath small, air mass thunderstorms are unpredictable in terms of weather forecast. Most aircraft incidents, however, were found to have occurred in the summer months, June through August. An intense microburst could produce 150 mph horizontal winds as well as 60 fps downflows at the tree-top level. The largest contributing factor to aircraft difficulties seemed to be a combination of the headwind decrease and the downflow. Anemometers and/or pressure sensors placed near runways were found effective for detecting gust fronts, but not for detecting downbursts. It is recommended that new detection systems placed on the ground or airborne, be developed, and that pilots be trained for simulated landing and go-around through microbursts.
MAXIS Balloon Observations of Electron Microburst Precipitation
Millan, R. M.; Hunter, A. E.; McCarthy, M. P.; Lin, R. P.; Smith, D. M.
2003-12-01
Quantifying and understanding losses is an integral part of understanding relativistic electron variability in the radiation belts. SAMPEX observations indicate that electron microburst precipitation is a major loss mechanism during active periods; the loss of relativistic electrons during a six hour period due to microburst precipitation was recently estimated to be comparable to the total number of trapped electrons in the outer zone (Lorentzen et al., 2001). Microburst precipitation was first observed from a balloon (Anderson and Milton, 1964), but these early measurements were only sensitive to MAXIS 2000 long duration balloon campaign. MAXIS was launched from McMurdo Station in Antarctica carrying a germanium spectrometer, a BGO scintillator and two X-ray imagers designed to measure the bremsstrahlung produced by precipitating electrons. The balloon circumnavigated the south pole in 18 days covering magnetic latitudes ranging from 58o-90o South. During the week following a moderate geomagnetic storm (with Dst reaching -91 nT), MAXIS detected a total of over 16 hours of microburst precipitation. We present high resolution spectra obtained with the MAXIS germanium spectrometer which allow us to determine the precipitating electron energy distribution. The precipitating distribution will then be compared to the trapped distribution measured by the GPS and LANL satellites. We also examine the spatial distribution of the precipitation.
Simplest solar microbursts flux and circular polarization at 22 GHz
Kaufmann, P.; Correia, E.; Costa, J.E.R.; Sawant, H.S.; Zodi Vaz, A.M.
1985-01-01
The simplest solar microwave microbursts detected with high sensitivity may be the response to the simpler energetic burst injections. Seventeen events from this category were identified in a series of more than 150 bursts recorded in 21-26 November, 1982. This first systematic study suggests that microbursts e-folding rise times concentrate into two classes of time scales, t greater than 0.05 s and much less than 1 s and t greater than about 0.05 s and less than about 2 s. Microbursts circular polarization presents a dominant steady or slowly varying component that sets in before maximum emission. In some cases a faster component of polarization was found superimposed, which is not always well correlated in time with flux. 23 references.
License plate detection algorithm
Broitman, Michael; Klopovsky, Yuri; Silinskis, Normunds
2013-12-01
A novel algorithm for vehicle license plates localization is proposed. The algorithm is based on pixel intensity transition gradient analysis. Near to 2500 natural-scene gray-level vehicle images of different backgrounds and ambient illumination was tested. The best set of algorithm's parameters produces detection rate up to 0.94. Taking into account abnormal camera location during our tests and therefore geometrical distortion and troubles from trees this result could be considered as passable. Correlation between source data, such as license Plate dimensions and texture, cameras location and others, and parameters of algorithm were also defined.
Detection of algorithmic trading
Bogoev, Dimitar; Karam, Arzé
2017-10-01
We develop a new approach to reflect the behavior of algorithmic traders. Specifically, we provide an analytical and tractable way to infer patterns of quote volatility and price momentum consistent with different types of strategies employed by algorithmic traders, and we propose two ratios to quantify these patterns. Quote volatility ratio is based on the rate of oscillation of the best ask and best bid quotes over an extremely short period of time; whereas price momentum ratio is based on identifying patterns of rapid upward or downward movement in prices. The two ratios are evaluated across several asset classes. We further run a two-stage Artificial Neural Network experiment on the quote volatility ratio; the first stage is used to detect the quote volatility patterns resulting from algorithmic activity, while the second is used to validate the quality of signal detection provided by our measure.
Three-dimensional numerical simulation of the 20 June 1991, Orlando microburst
Proctor, Fred H.
1992-01-01
On 20 June 1991, NASA's Boeing 737, equipped with in-situ and look-ahead wind-shear detection systems, made direct low-level penetrations (300-350 m AGL) through a microburst during several stages of its evolution. This microburst was located roughly 20 km northeast of Orlando International Airport and was monitored by a Terminal Doppler Weather Radar (TDWR) located about 10 km south of the airport. The first NASA encounter with this microburst (Event 142), at approximately 2041 UTC, was during its intensification phase. At flight level, in-situ measurements indicated a peak 1-km (averaged) F-factor of approximately 0.1. The second NASA encounter (Event 143) occurred at approximately 2046 UTC, about the time of microburst peak intensity. It was during this penetration that a peak 1-km F-factor of approximately 17 was encountered, which was the largest in-situ measurement of the 1991 summer deployment. By the third encounter (Event 144), at approximately 2051 UTC, the microburst had expanded into a macroburst. During this phase of evolution, an in-situ 1-km F-factor of 0.08 was measured. The focus of this paper is to examine this microburst via numerical simulation from an unsteady, three-dimensional meteorological cloud model. The simulated high-resolution data fields of wind, temperature, radar reflectivity factor, and precipitation are closely examined so as to derive information not readily available from 'observations' and to enhance our understanding of the actual event. Characteristics of the simulated microburst evolution are compared with TDWR and in-situ measurements.
THE APPROACHING TRAIN DETECTION ALGORITHM
S. V. Bibikov
2015-09-01
Full Text Available The paper deals with detection algorithm for rail vibroacoustic waves caused by approaching train on the background of increased noise. The urgency of algorithm development for train detection in view of increased rail noise, when railway lines are close to roads or road intersections is justified. The algorithm is based on the method of weak signals detection in a noisy environment. The information statistics ultimate expression is adjusted. We present the results of algorithm research and testing of the train approach alarm device that implements the proposed algorithm. The algorithm is prepared for upgrading the train approach alarm device “Signalizator-P".
Recent developments in microburst nowcasting using GOES
Pryor, Kenneth L
2010-01-01
Recent testing and validation have found that the Geostationary Operational Environmental Satellite (GOES) microburst products are effective in the assessment and short-term forecasting of downburst potential and associated wind gust magnitude. Two products, the GOES sounder Microburst Windspeed Potential Index (MWPI) and a new two-channel GOES imager brightness temperature difference (BTD) product have demonstrated capability in downburst potential assessment. The MWPI, a diagnostic nowcasting product, is designed to infer attributes of a favorable microburst environment: large CAPE and a convective mixed layer with a steep temperature lapse rate and low relative humidity in the surface layer. These conditions foster intense convective downdrafts due to evaporational cooling as precipitation descends in the sub-cloud layer. More recently, a new application of the brightness temperature difference (BTD) between GOES imager channels 3 and 4 has been developed. It has been found that the BTD between GOES infrar...
The GOES Microburst Windspeed Potential Index
Pryor, K L
2007-01-01
A suite of products has been developed and evaluated to assess hazards presented by convective downbursts to aircraft in flight derived from the current generation of Geostationary Operational Environmental Satellite (GOES) (I-M). The existing suite of GOES microburst products employs the GOES sounder to calculate risk based on conceptual models of favorable environmental profiles for convective downburst generation. A GOES sounder-derived wet microburst severity index (WMSI) product to assess the potential magnitude of convective downbursts, incorporating convective available potential energy (CAPE) as well as the vertical theta-e difference (TeD) between the surface and mid-troposphere has been developed and implemented. Intended to supplement the use of the GOES WMSI product over the United States Great Plains region, a GOES Hybrid Microburst Index (HMI) product has also evolved. The HMI product infers the presence of a convective boundary layer by incorporating the sub-cloud temperature lapse rate as well...
Application of detecting algorithm based on network
张凤斌; 杨永田; 江子扬; 孙冰心
2004-01-01
Because currently intrusion detection systems cannot detect undefined intrusion behavior effectively,according to the robustness and adaptability of the genetic algorithms, this paper integrates the genetic algorithms into an intrusion detection system, and a detection algorithm based on network traffic is proposed. This algorithm is a real-time and self-study algorithm and can detect undefined intrusion behaviors effectively.
A fast meteor detection algorithm
Gural, P.
2016-01-01
A low latency meteor detection algorithm for use with fast steering mirrors had been previously developed to track and telescopically follow meteors in real-time (Gural, 2007). It has been rewritten as a generic clustering and tracking software module for meteor detection that meets both the demanding throughput requirements of a Raspberry Pi while also maintaining a high probability of detection. The software interface is generalized to work with various forms of front-end video pre-processing approaches and provides a rich product set of parameterized line detection metrics. Discussion will include the Maximum Temporal Pixel (MTP) compression technique as a fast thresholding option for feeding the detection module, the detection algorithm trade for maximum processing throughput, details on the clustering and tracking methodology, processing products, performance metrics, and a general interface description.
JAWS data collection, analysis highlights, and microburst statistics
Mccarthy, J.; Roberts, R.; Schreiber, W.
1983-01-01
Organization, equipment, and the current status of the Joint Airport Weather Studies project initiated in relation to the microburst phenomenon are summarized. Some data collection techniques and preliminary statistics on microburst events recorded by Doppler radar are discussed as well. Radar studies show that microbursts occur much more often than expected, with majority of the events being potentially dangerous to landing or departing aircraft. Seventy events were registered, with the differential velocities ranging from 10 to 48 m/s; headwind/tailwind velocity differentials over 20 m/s are considered seriously hazardous. It is noted that a correlation is yet to be established between the velocity differential and incoherent radar reflectivity.
Computationally efficient algorithm for fast transients detection
Soudlenkov, Gene
2011-01-01
Computationally inexpensive algorithm for detecting of dispersed transients has been developed using Cumulative Sums (CUSUM) scheme for detecting abrupt changes in statistical characteristics of the signal. The efficiency of the algorithm is demonstrated on pulsar PSR J0835-4510.
A GOES imager-derived microburst product
Pryor, Kenneth L
2008-01-01
A new multispectral Geostationary Operational Environmental Satellite (GOES) imager product has been developed to assess downburst potential over the western United States employing brightness temperature differences (BTD) between band 3 (upper level water vapor), band 4 (longwave infrared window), and split window band 5. Band 3 is intended to indicate mid to upper-level moisture content and advection while band 5 indicates low-level moisture content. Large BTDs between bands 3 and 5 imply a large relative humidity gradient between the mid-troposphere and the surface, a condition favorable for strong convective downdraft generation due to evaporational cooling of precipitation in the deep sub-cloud layer. In addition, small BTDs between bands 4 and 5 indicate a relatively dry surface layer with solar heating in progress. This paper will outline the development of the GOES-West imager microburst product and present case studies that feature example images, outline potential operational use and assess performa...
Innovative algorithm for cast detection
Gasparini, Francesca; Schettini, Raimondo; Gallina, Paolo
2001-12-01
The paper describes a method for detecting a color cast (i.e. a superimposed dominant color) in a digital image without any a priori knowledge of its semantic content. The color gamut of the image is first mapped in the CIELab color space. The color distribution of the whole image and of the so-called Near Neutral Objects (NNO) is then investigated using statistical tools then, to determine the presence of a cast. The boundaries of the near neutral objects in the color space are set adaptively by the algorithm on the basis of a preliminary analysis of the image color gamut. The method we propose has been tuned and successfully tested on a large data set of images, downloaded from personal web-pages or acquired using various digital and traditional cameras.
Performance Analysis of Cone Detection Algorithms
Mariotti, Letizia
2015-01-01
Many algorithms have been proposed to help clinicians evaluate cone density and spacing, as these may be related to the onset of retinal diseases. However, there has been no rigorous comparison of the performance of these algorithms. In addition, the performance of such algorithms is typically determined by comparison with human observers. Here we propose a technique to simulate realistic images of the cone mosaic. We use the simulated images to test the performance of two popular cone detection algorithms and we introduce an algorithm which is used by astronomers to detect stars in astronomical images. We use Free Response Operating Characteristic (FROC) curves to evaluate and compare the performance of the three algorithms. This allows us to optimize the performance of each algorithm. We observe that performance is significantly enhanced by up-sampling the images. We investigate the effect of noise and image quality on cone mosaic parameters estimated using the different algorithms, finding that the estimat...
Lane Detection Based on Machine Learning Algorithm
Chao Fan; Jingbo Xu; Shuai Di
2013-01-01
In order to improve accuracy and robustness of the lane detection in complex conditions, such as the shadows and illumination changing, a novel detection algorithm was proposed based on machine learning...
Formal verification of a deadlock detection algorithm
Freek Verbeek
2011-10-01
Full Text Available Deadlock detection is a challenging issue in the analysis and design of on-chip networks. We have designed an algorithm to detect deadlocks automatically in on-chip networks with wormhole switching. The algorithm has been specified and proven correct in ACL2. To enable a top-down proof methodology, some parts of the algorithm have been left unimplemented. For these parts, the ACL2 specification contains constrained functions introduced with defun-sk. We used single-threaded objects to represent the data structures used by the algorithm. In this paper, we present details on the proof of correctness of the algorithm. The process of formal verification was crucial to get the algorithm flawless. Our ultimate objective is to have an efficient executable, and formally proven correct implementation of the algorithm running in ACL2.
Detecting Danger: The Dendritic Cell Algorithm
Greensmith, Julie; Cayzer, Steve
2010-01-01
The Dendritic Cell Algorithm (DCA) is inspired by the function of the dendritic cells of the human immune system. In nature, dendritic cells are the intrusion detection agents of the human body, policing the tissue and organs for potential invaders in the form of pathogens. In this research, and abstract model of DC behaviour is developed and subsequently used to form an algorithm, the DCA. The abstraction process was facilitated through close collaboration with laboratory- based immunologists, who performed bespoke experiments, the results of which are used as an integral part of this algorithm. The DCA is a population based algorithm, with each agent in the system represented as an 'artificial DC'. Each DC has the ability to combine multiple data streams and can add context to data suspected as anomalous. In this chapter the abstraction process and details of the resultant algorithm are given. The algorithm is applied to numerous intrusion detection problems in computer security including the detection of p...
An edge detection algorithm for imaging ladar
Qi Wang(王骐); Ziqin Li(李自勤); Qi Li(李琦); Jianfeng Sun(孙剑峰); Juncheng Fu(傅俊诚)
2003-01-01
In this paper, the morphological filter based on parametric edge detection is presented and applied toimaging ladar image with speckle noise. This algorithm and Laplacian of Gaussian (LOG) operator arecompared on edge detection. The experimental results indicate the superior performance of this kind ofthe edge detection.
Detection of Illegitimate Emails using Boosting Algorithm
Nizamani, Sarwat; Memon, Nasrullah; Wiil, Uffe Kock
2011-01-01
In this paper, we report on experiments to detect illegitimate emails using boosting algorithm. We call an email illegitimate if it is not useful for the receiver or for the society. We have divided the problem into two major areas of illegitimate email detection: suspicious email detection...... and spam email detection. For our desired task, we have applied a boosting technique. With the use of boosting we can achieve high accuracy of traditional classification algorithms. When using boosting one has to choose a suitable weak learner as well as the number of boosting iterations. In this paper, we...... propose suitable weak learners and parameter settings for the boosting algorithm for the desired task. We have initially analyzed the problem using base learners. Then we have applied boosting algorithm with suitable weak learners and parameter settings such as the number of boosting iterations. We...
Detection of Illegitimate Emails using Boosting Algorithm
2011-01-01
In this paper, we report on experiments to detect illegitimate emails using boosting algorithm. We call an email illegitimate if it is not useful for the receiver or for the society. We have divided the problem into two major areas of illegitimate email detection: suspicious email detection...... and spam email detection. For our desired task, we have applied a boosting technique. With the use of boosting we can achieve high accuracy of traditional classification algorithms. When using boosting one has to choose a suitable weak learner as well as the number of boosting iterations. In this paper, we...
Adaptive Genetic Algorithm Model for Intrusion Detection
K. S. Anil Kumar
2012-09-01
Full Text Available Intrusion detection systems are intelligent systems designed to identify and prevent the misuse of computer networks and systems. Various approaches to Intrusion Detection are currently being used, but they are relatively ineffective. Thus the emerging network security systems need be part of the life system and this ispossible only by embedding knowledge into the network. The Adaptive Genetic Algorithm Model - IDS comprising of K-Means clustering Algorithm, Genetic Algorithm and Neural Network techniques. Thetechnique is tested using multitude of background knowledge sets in DARPA network traffic datasets.
Seizure detection algorithms based on EMG signals
Conradsen, Isa
Background: the currently used non-invasive seizure detection methods are not reliable. Muscle fibers are directly connected to the nerves, whereby electric signals are generated during activity. Therefore, an alarm system on electromyography (EMG) signals is a theoretical possibility. Objective......: to show whether medical signal processing of EMG data is feasible for detection of epileptic seizures. Methods: EMG signals during generalised seizures were recorded from 3 patients (with 20 seizures in total). Two possible medical signal processing algorithms were tested. The first algorithm was based...... on the amplitude of the signal. The other algorithm was based on information of the signal in the frequency domain, and it focused on synchronisation of the electrical activity in a single muscle during the seizure. Results: The amplitude-based algorithm reliably detected seizures in 2 of the patients, while...
SAMPEX relativistic micorbursts: PET spectra and comparison to DREP and balloon microbursts
Liang, X.; Comess, M.; Smith, D. M.; Selesnick, R. S.; Sample, J. G.; Millan, R. M.
2011-12-01
Relativistic(> 1 MeV) electron microbursts may account for significant relativistic electron losses from the outer belt. We will present the spectral characteristics of relativistic microbursts observed with the Proton/Electron telescope (PET) on board the Solar Anomalous Magnetospheric Particle Explorer (SAMPEX) satellite from 1992 to 2004. We find that these events, concentrated in the morning sector, are well fitted by an exponential spectrum with e-folding energies of 100-375 keV in the 0.5-4 MeV range. We have compared the time-averaged precipitation rate from relativistic microbursts with the time-avearged rate from duskside Relativistic Electron Precipitation (DREP), and find that microbursts appear more important 100 keV microburst e-folding energies contrast with 16 hours of microburst data from the MeV Auroral X-ray Imaging and Spectroscopy (MAXIS) balloon campaign, which show exponential microburst spectra with folding energies ranging from 50-105 keV. We used the Monte Carlo simulation package GEANT3 calculate the count-rate spectra that would have been expected from MAXIS from the SAMPEX microburst spectra. We use these simulations to address the apparent contradictions between the satellite and balloon pictures of microbursts in anticipation of the upcoming flights of the Balloon Array for RBSP Relativistic Electron Losses (BARREL).
Nearest Neighbour Corner Points Matching Detection Algorithm
Zhang Changlong
2015-01-01
Full Text Available Accurate detection towards the corners plays an important part in camera calibration. To deal with the instability and inaccuracies of present corner detection algorithm, the nearest neighbour corners match-ing detection algorithms was brought forward. First, it dilates the binary image of the photographed pictures, searches and reserves quadrilateral outline of the image. Second, the blocks which accord with chess-board-corners are classified into a class. If too many blocks in class, it will be deleted; if not, it will be added, and then let the midpoint of the two vertex coordinates be the rough position of corner. At last, it precisely locates the position of the corners. The Experimental results have shown that the algorithm has obvious advantages on accuracy and validity in corner detection, and it can give security for camera calibration in traffic accident measurement.
An Adaptive Clustering Algorithm for Intrusion Detection
QIU Juli
2007-01-01
In this paper,we introduce an adaptive clustering algorithm for intrusion detection based on wavecluster which was introduced by Gholamhosein in 1999 and used with success in image processing.Because of the non-stationary characteristic of network traffic,we extend and develop an adaptive wavecluster algorithm for intrusion detection.Using the multiresolution property of wavelet transforms,we can effectively identify arbitrarily shaped clusters at different scales and degrees of detail,moreover,applying wavelet transform removes the noise from the original feature space and make more accurate cluster found.Experimental results on KDD-99 intrusion detection dataset show the efficiency and accuracy of this algorithm.A detection rate above 96% and a false alarm rate below 3% are achieved.
Modified guidance laws to escape microbursts with turbulence
Atilla Dogan
2002-01-01
Full Text Available This paper introduces Modified Altitude- and Dive-Guidance laws for escaping a microburst with turbulence. The goal is to develop a procedure to estimate the highest altitude at which an aircraft can fly through a microburst without running into stall. First, a new metric is constructed that quantifies the aircraft upward force capability in a microburst encounter. In the absence of turbulence, the metric is shown to be a decreasing function of altitude. This suggests that descending to a low altitude may improve safety in the sense that the aircraft will have more upward force capability to maintain its altitude. In the presence of stochastic turbulence, the metric is treated as a random variable and its probability distribution function is analytically approximated as a function of altitude. This approximation allows us to determine the highest safe altitude at which the aircraft may descend, hence avoiding to descend too low. This highest safe altitude is used as the commanded altitude in Modified Altitude- and Dive-Guidance. Monte Carlo simulations show that these Modified Altitude- and Dive-Guidance strategies can decrease the probability of minimum altitude being lower than a given value without significantly increasing the probability of crash.
Lightning detection and exposure algorithms for smartphones
Wang, Haixin; Shao, Xiaopeng; Wang, Lin; Su, Laili; Huang, Yining
2015-05-01
This study focuses on the key theory of lightning detection, exposure and the experiments. Firstly, the algorithm based on differential operation between two adjacent frames is selected to remove the lightning background information and extract lighting signal, and the threshold detection algorithm is applied to achieve the purpose of precise detection of lightning. Secondly, an algorithm is proposed to obtain scene exposure value, which can automatically detect external illumination status. Subsequently, a look-up table could be built on the basis of the relationships between the exposure value and average image brightness to achieve rapid automatic exposure. Finally, based on a USB 3.0 industrial camera including a CMOS imaging sensor, a set of hardware test platform is established and experiments are carried out on this platform to verify the performances of the proposed algorithms. The algorithms can effectively and fast capture clear lightning pictures such as special nighttime scenes, which will provide beneficial supporting to the smartphone industry, since the current exposure methods in smartphones often lost capture or induce overexposed or underexposed pictures.
Lane Detection Based on Machine Learning Algorithm
Chao Fan
2013-09-01
Full Text Available In order to improve accuracy and robustness of the lane detection in complex conditions, such as the shadows and illumination changing, a novel detection algorithm was proposed based on machine learning. After pretreatment, a set of haar-like filters were used to calculate the eigenvalue in the gray image f(x,y and edge e(x,y. Then these features were trained by using improved boosting algorithm and the final class function g(x was obtained, which was used to judge whether the point x belonging to the lane or not. To avoid the over fitting in traditional boosting, Fisher discriminant analysis was used to initialize the weights of samples. After testing by many road in all conditions, it showed that this algorithm had good robustness and real-time to recognize the lane in all challenging conditions.
Adaptive sampling algorithm for detection of superpoints
CHENG Guang; GONG Jian; DING Wei; WU Hua; QIANG ShiQiang
2008-01-01
The superpoints are the sources (or the destinations) that connect with a great deal of destinations (or sources) during a measurement time interval, so detecting the superpoints in real time is very important to network security and management. Previous algorithms are not able to control the usage of the memory and to deliver the desired accuracy, so it is hard to detect the superpoints on a high speed link in real time. In this paper, we propose an adaptive sampling algorithm to detect the superpoints in real time, which uses a flow sample and hold module to reduce the detection of the non-superpoints and to improve the measurement accuracy of the superpoints. We also design a data stream structure to maintain the flow records, which compensates for the flow Hash collisions statistically. An adaptive process based on different sampling probabilities is used to maintain the recorded IP ad dresses in the limited memory. This algorithm is compared with the other algo rithms by analyzing the real network trace data. Experiment results and mathematic analysis show that this algorithm has the advantages of both the limited memory requirement and high measurement accuracy.
Network algorithms for detection of radiation sources
Rao, Nageswara S.V.; Sen, Satyabrata; Prins, Nicholas J. [Computer Science and Mathematics Div, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Cooper, Daniel A.; Ledoux, Robert J.; Costales, James B.; Kamieniecki, Krzysztof; Korbly, Steven E.; Thompson, Jeffrey K.; Batcheler, James [Passport Systems Inc., N. Billerica, MA 01862 (United States); Brooks, Richard R. [Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634 (United States); Wu, Chase Q. [Department of Computer Science, University of Memphis, Memphis, TN 38152 (United States)
2015-06-01
Networks of radiation counters have been recently developed for detecting low-level, hazardous radiation sources, and they have been utilized in indoor and outdoor characterization tests. Subsequently, the test measurements have been “replayed” using multiple sub-networks, which enabled the analysis of various scenarios beyond the tests. We present a particle filter algorithm that combines measurements from gamma counters across the network to detect radiation sources. Using replays from an outdoor test, we construct a border monitoring scenario that consists of twelve 2 in.×2 in. NaI detectors or counters deployed on the periphery to monitor a 42×42 m{sup 2} region. A {sup 137}Cs source is moved across this region, starting several meters outside and finally moving away from it. The measurements from individual, pairs and boundary detectors are replayed using the particle filter algorithm. The algorithm outputs demonstrate, both quantitatively and qualitatively, the benefits of networking all boundary counters: the source is detected meters before it enters the region, while being inside, and until moving several meters away. On the other hand, when counters are used individually or in pairs, the source is detected for much shorter durations, and sometimes not detected at all while inside the region.
Mkuzangwe, NNP
2015-08-01
Full Text Available This work implements two anomaly detection algorithms for detecting Transmission Control Protocol Synchronized (TCP SYN) flooding attack. The two algorithms are an adaptive threshold algorithm and a cumulative sum (CUSUM) based algorithm...
Algorithms For Detection Of Correlation Spots
Scholl, Marija S.; Udomkesmalee, Suraphol
1993-01-01
Three algorithms provide for improved postprocessing of outputs of optical correlators based on binary phase-only filters. Detect correlation spots. Function in presence of noise and executed rapidly. First algorithm starts processing correlation-image data while data fed out of video camera and digitized for subsequent analysis. Second involves convolution of correlation image with small-window two-dimensional impulse-response function followed by threshold operation in which negative values of convolution integral set to zero. Third affects generation as well as postprocessing of correlation image.
A NOVEL THRESHOLD BASED EDGE DETECTION ALGORITHM
Y. RAMADEVI,
2011-06-01
Full Text Available Image segmentation is the process of partitioning/subdividing a digital image into multiple meaningful regions or sets of pixels regions with respect to a particular application. Edge detection is one of the frequently used techniques in digital image processing. The level to which the subdivision is carried depends on theproblem being viewed. Edges characterize boundaries and are therefore a problem of fundamental importance in image processing. There are many ways to perform edge detection. In this paper different Edge detection methods such as Sobel, Prewitt, Robert, Canny, Laplacian of Gaussian (LOG are used for segmenting the image. Expectation-Maximization (EM algorithm, OSTU and Genetic algorithms are also used. A new edge detection technique is proposed which detects the sharp and accurate edges that are not possible with the existing techniques. The proposed method with different threshold values for given input image is shown that ranges between 0 and 1 and it are observed that when the threshold value is 0.68 the sharp edges are recognised properly.
Detecting Neonatal Seizures With Computer Algorithms.
Temko, Andriy; Lightbody, Gordon
2016-10-01
It is now generally accepted that EEG is the only reliable way to accurately detect newborn seizures and, as such, prolonged EEG monitoring is increasingly being adopted in neonatal intensive care units. Long EEG recordings may last from several hours to a few days. With neurophysiologists not always available to review the EEG during unsociable hours, there is a pressing need to develop a reliable and robust automatic seizure detection method-a computer algorithm that can take the EEG signal, process it, and output information that supports clinical decision making. In this study, we review existing algorithms based on how the relevant seizure information is exploited. We start with commonly used methods to extract signatures from seizure signals that range from those that mimic the clinical neurophysiologist to those that exploit mathematical models of neonatal EEG generation. Commonly used classification methods are reviewed that are based on a set of rules and thresholds that are either heuristically tuned or automatically derived from the data. These are followed by techniques to use information about spatiotemporal seizure context. The usual errors in system design and validation are discussed. Current clinical decision support tools that have met regulatory requirements and are available to detect neonatal seizures are reviewed with progress and the outstanding challenges are outlined. This review discusses the current state of the art regarding automatic detection of neonatal seizures.
The BAST algorithm for transit detection
Renner, S; Erikson, A; Hedelt, P; Kabath, P; Titz, R; Voss, H
2008-01-01
The pioneer space mission for photometric exoplanet searches, CoRoT, steadily monitors about 12000 stars in each of its fields of view. Transit detection algorithms are applied to derive promising planetary candidates, which are then followed-up with ground-based observations. We present BAST (Berlin Automatic Search for Transits), a new algorithm for periodic transit detection, and test it on simulated CoRoT data. BAST searches for box-shaped signals in normalized, filtered, variability-fitted, and unfolded light curves. A low-pass filter is applied to remove high-frequency signals, and linear fits to subsections of data are subtracted to remove the star's variability. A search for periodicity is then performed in transit events identified above a given detection threshold. Some criteria are defined to better separate planet candidates from binary stars. From the analysis of simulated CoRoT light curves, we show that the BAST detection performance is similar to that of the Box-fitting Least-Square (BLS) meth...
Parallelization of Edge Detection Algorithm using MPI on Beowulf Cluster
Haron, Nazleeni; Amir, Ruzaini; Aziz, Izzatdin A.; Jung, Low Tan; Shukri, Siti Rohkmah
In this paper, we present the design of parallel Sobel edge detection algorithm using Foster's methodology. The parallel algorithm is implemented using MPI message passing library and master/slave algorithm. Every processor performs the same sequential algorithm but on different part of the image. Experimental results conducted on Beowulf cluster are presented to demonstrate the performance of the parallel algorithm.
Algorithms for Anomaly Detection - Lecture 2
CERN. Geneva
2017-01-01
The concept of statistical anomalies, or outliers, has fascinated experimentalists since the earliest attempts to interpret data. We want to know why some data points don’t seem to belong with the others: perhaps we want to eliminate spurious or unrepresentative data from our model. Or, the anomalies themselves may be what we are interested in: an outlier could represent the symptom of a disease, an attack on a computer network, a scientific discovery, or even an unfaithful partner. We start with some general considerations, such as the relationship between clustering and anomaly detection, the choice between supervised and unsupervised methods, and the difference between global and local anomalies. Then we will survey the most representative anomaly detection algorithms, highlighting what kind of data each approach is best suited to, and discussing their limitations. We will finish with a discussion of the difficulties of anomaly detection in high-dimensional data and some new directions for anomaly detec...
Algorithms for Anomaly Detection - Lecture 1
CERN. Geneva
2017-01-01
The concept of statistical anomalies, or outliers, has fascinated experimentalists since the earliest attempts to interpret data. We want to know why some data points don’t seem to belong with the others: perhaps we want to eliminate spurious or unrepresentative data from our model. Or, the anomalies themselves may be what we are interested in: an outlier could represent the symptom of a disease, an attack on a computer network, a scientific discovery, or even an unfaithful partner. We start with some general considerations, such as the relationship between clustering and anomaly detection, the choice between supervised and unsupervised methods, and the difference between global and local anomalies. Then we will survey the most representative anomaly detection algorithms, highlighting what kind of data each approach is best suited to, and discussing their limitations. We will finish with a discussion of the difficulties of anomaly detection in high-dimensional data and some new directions for anomaly detec...
Optical Algorithm for Cloud Shadow Detection Over Water
2013-02-01
5] R. Amin, A. Gilerson, J. Zhou, B. Gross, F. Moshary, and S. Ahmed, "Im- pacts of atmospheric corrections on algal bloom detection techniques...optical algorithms to detect and classify harmful algal blooms from space. His current research interests include optical algorithm development...algorithm, remote sensing, shadow detection 16. SECURITY CLASSIFICATION OF: a. REPORT Unclassified b. ABSTRACT Unclassified c. THIS PAGE
A new fuzzy edge detection algorithm
SunWei; XiaLiangzheng
2003-01-01
Based upon the maximum entropy theorem of information theory, a novel fuzzy approach for edge detection is presented. Firsdy, a definition of fuzzy partition entropy is proposed after introducing the concepts of fuzzy probability and fuzzy partition. The relation of the probability partition and the fuzzy c-partition of the image gradient are used in the algorithm. Secondly, based on the conditional probabilities and the fuzzy partition, the optimal thresholding is searched adaptively through the maximum fuzzy entropy principle, and then the edge image is obtained. Lastly, an edge-enhancing procedure is executed on the edge image. The experimental results show that the proposed approach performs well.
Botnet Propagation Via Public Websited Detection Algorithm
Jonas Juknius
2011-08-01
Full Text Available The networks of compromised and remotely controlled computers (bots are widely used in many Internet fraudulent activities, especially in the distributed denial of service attacks. Brute force gives enormous power to bot masters and makes botnet traffic visible; therefore, some countermeasures might be applied at early stages. Our study focuses on detecting botnet propagation via public websites. The provided algorithm might help with preventing from massive infections when popular web sites are compromised without spreading visual changes used for malware in botnets.Article in English
CLADA: cortical longitudinal atrophy detection algorithm.
Nakamura, Kunio; Fox, Robert; Fisher, Elizabeth
2011-01-01
Measurement of changes in brain cortical thickness is useful for the assessment of regional gray matter atrophy in neurodegenerative conditions. A new longitudinal method, called CLADA (cortical longitudinal atrophy detection algorithm), has been developed for the measurement of changes in cortical thickness in magnetic resonance images (MRI) acquired over time. CLADA creates a subject-specific cortical model which is longitudinally deformed to match images from individual time points. The algorithm was designed to work reliably for lower resolution images, such as the MRIs with 1×1×5 mm(3) voxels previously acquired for many clinical trials in multiple sclerosis (MS). CLADA was evaluated to determine reproducibility, accuracy, and sensitivity. Scan-rescan variability was 0.45% for images with 1mm(3) isotropic voxels and 0.77% for images with 1×1×5 mm(3) voxels. The mean absolute accuracy error was 0.43 mm, as determined by comparison of CLADA measurements to cortical thickness measured directly in post-mortem tissue. CLADA's sensitivity for correctly detecting at least 0.1mm change was 86% in a simulation study. A comparison to FreeSurfer showed good agreement (Pearson correlation=0.73 for global mean thickness). CLADA was also applied to MRIs acquired over 18 months in secondary progressive MS patients who were imaged at two different resolutions. Cortical thinning was detected in this group in both the lower and higher resolution images. CLADA detected a higher rate of cortical thinning in MS patients compared to healthy controls over 2 years. These results show that CLADA can be used for reliable measurement of cortical atrophy in longitudinal studies, even in lower resolution images.
Network Intrusion Detection based on GMKL Algorithm
Li Yuxiang
2013-06-01
Full Text Available According to the 31th statistical reports of China Internet network information center (CNNIC, by the end of December 2012, the number of Chinese netizens has reached 564 million, and the scale of mobile Internet users also reached 420 million. But when the network brings great convenience to people's life, it also brings huge threat in the life of people. So through collecting and analyzing the information in the computer system or network we can detect any possible behaviors that can damage the availability, integrity and confidentiality of the computer resource, and make timely treatment to these behaviors which have important research significance to improve the operation environment of network and network service. At present, the Neural Network, Support Vector machine (SVM and Hidden Markov Model, Fuzzy inference and Genetic Algorithms are introduced into the research of network intrusion detection, trying to build a healthy and secure network operation environment. But most of these algorithms are based on the total sample and it also hypothesizes that the number of the sample is infinity. But in the field of network intrusion the collected data often cannot meet the above requirements. It often shows high latitudes, variability and small sample characteristics. For these data using traditional machine learning methods are hard to get ideal results. In view of this, this paper proposed a Generalized Multi-Kernel Learning method to applied to network intrusion detection. The Generalized Multi-Kernel Learning method can be well applied to large scale sample data, dimension complex, containing a large number of heterogeneous information and so on. The experimental results show that applying GMKL to network attack detection has high classification precision and low abnormal practical precision.
Photon Counting Using Edge-Detection Algorithm
Gin, Jonathan W.; Nguyen, Danh H.; Farr, William H.
2010-01-01
New applications such as high-datarate, photon-starved, free-space optical communications require photon counting at flux rates into gigaphoton-per-second regimes coupled with subnanosecond timing accuracy. Current single-photon detectors that are capable of handling such operating conditions are designed in an array format and produce output pulses that span multiple sample times. In order to discern one pulse from another and not to overcount the number of incoming photons, a detection algorithm must be applied to the sampled detector output pulses. As flux rates increase, the ability to implement such a detection algorithm becomes difficult within a digital processor that may reside within a field-programmable gate array (FPGA). Systems have been developed and implemented to both characterize gigahertz bandwidth single-photon detectors, as well as process photon count signals at rates into gigaphotons per second in order to implement communications links at SCPPM (serial concatenated pulse position modulation) encoded data rates exceeding 100 megabits per second with efficiencies greater than two bits per detected photon. A hardware edge-detection algorithm and corresponding signal combining and deserialization hardware were developed to meet these requirements at sample rates up to 10 GHz. The photon discriminator deserializer hardware board accepts four inputs, which allows for the ability to take inputs from a quadphoton counting detector, to support requirements for optical tracking with a reduced number of hardware components. The four inputs are hardware leading-edge detected independently. After leading-edge detection, the resultant samples are ORed together prior to deserialization. The deserialization is performed to reduce the rate at which data is passed to a digital signal processor, perhaps residing within an FPGA. The hardware implements four separate analog inputs that are connected through RF connectors. Each analog input is fed to a high-speed 1
Anomaly Detection using the "Isolation Forest" algorithm
CERN. Geneva
2015-01-01
Anomaly detection can provide clues about an outlying minority class in your data: hackers in a set of network events, fraudsters in a set of credit card transactions, or exotic particles in a set of high-energy collisions. In this talk, we analyze a real dataset of breast tissue biopsies, with malignant results forming the minority class. The "Isolation Forest" algorithm finds anomalies by deliberately “overfitting” models that memorize each data point. Since outliers have more empty space around them, they take fewer steps to memorize. Intuitively, a house in the country can be identified simply as “that house out by the farm”, while a house in the city needs a longer description like “that house in Brooklyn, near Prospect Park, on Union Street, between the firehouse and the library, not far from the French restaurant”. We first use anomaly detection to find outliers in the biopsy data, then apply traditional predictive modeling to discover rules that separate anomalies from normal data...
Improving Polyp Detection Algorithms for CT Colonography: Pareto Front Approach.
Huang, Adam; Li, Jiang; Summers, Ronald M; Petrick, Nicholas; Hara, Amy K
2010-03-21
We investigated a Pareto front approach to improving polyp detection algorithms for CT colonography (CTC). A dataset of 56 CTC colon surfaces with 87 proven positive detections of 53 polyps sized 4 to 60 mm was used to evaluate the performance of a one-step and a two-step curvature-based region growing algorithm. The algorithmic performance was statistically evaluated and compared based on the Pareto optimal solutions from 20 experiments by evolutionary algorithms. The false positive rate was lower (pPareto optimization process can effectively help in fine-tuning and redesigning polyp detection algorithms.
A real time vehicles detection algorithm for vision based sensors
Płaczek, Bartłomiej
2011-01-01
A vehicle detection plays an important role in the traffic control at signalised intersections. This paper introduces a vision-based algorithm for vehicles presence recognition in detection zones. The algorithm uses linguistic variables to evaluate local attributes of an input image. The image attributes are categorised as vehicle, background or unknown features. Experimental results on complex traffic scenes show that the proposed algorithm is effective for a real-time vehicles detection.
The embeddability of lane detection algorithms on heterogeneous architectures
Saussard, Romain; Bouzid, Boubker; Vasiliu, Marius; Reynaud, Roger
2015-01-01
International audience; Lane detection plays a crucial role for Advanced Driver As-sitance System (ADAS) or autonomous driving applications. Literature shows a lot of lane detection algorithms can work in real time with good results. However, they require much computer processing and cannot be embedded in a vehicle ECU without deep software optimizations. In this paper, we discuss the embeddability of lane detection algorithms by comparing state-of-the-art algorithms in terms of functional pe...
Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm
Greensmith, Julie; Tedesco, Gianni
2010-01-01
Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system, providing the initial detection of pathogenic invaders. Research into this family of cells has revealed that they perform information fusion which directs immune responses. We have derived a Dendritic Cell Algorithm based on the functionality of these cells, by modelling the biological signals and differentiation pathways to build a control mechanism for an artificial immune system. We present algorithmic details in addition to experimental results, when the algorithm was applied to anomaly detection for the detection of port scans. The results show the Dendritic Cell Algorithm is sucessful at detecting port scans.
A new real-time tsunami detection algorithm
Chierici, Francesco; Embriaco, Davide; Pignagnoli, Luca
2017-01-01
Real-time tsunami detection algorithms play a key role in any Tsunami Early Warning System. We have developed a new algorithm for tsunami detection based on the real-time tide removal and real-time band-pass filtering of seabed pressure recordings. The algorithm greatly increases the tsunami detection probability, shortens the detection delay and enhances detection reliability with respect to the most widely used tsunami detection algorithm, while containing the computational cost. The algorithm is designed to be used also in autonomous early warning systems with a set of input parameters and procedures which can be reconfigured in real time. We have also developed a methodology based on Monte Carlo simulations to test the tsunami detection algorithms. The algorithm performance is estimated by defining and evaluating statistical parameters, namely the detection probability, the detection delay, which are functions of the tsunami amplitude and wavelength, and the occurring rate of false alarms. Pressure data sets acquired by Bottom Pressure Recorders in different locations and environmental conditions have been used in order to consider real working scenarios in the test. We also present an application of the algorithm to the tsunami event which occurred at Haida Gwaii on 28 October 2012 using data recorded by the Bullseye underwater node of Ocean Networks Canada. The algorithm successfully ran for test purpose in year-long missions onboard abyssal observatories, deployed in the Gulf of Cadiz and in the Western Ionian Sea.
Hybrid Collision Detection Algorithm based on Image Space
XueLi Shen
2013-07-01
Full Text Available Collision detection is an important application in the field of virtual reality, and efficiently completing collision detection has become the research focus. For the poorly real-time defect of collision detection, this paper has presented an algorithm based on the hybrid collision detection, detecting the potential collision object sets quickly with the mixed bounding volume hierarchy tree, and then using the streaming pattern collision detection algorithm to make an accurate detection. With the above methods, it can achieve the purpose of balancing load of the CPU and GPU and speeding up the detection rate. The experimental results show that compared with the classic Rapid algorithm, this algorithm can effectively improve the efficiency of collision detection.
Effective LiDAR Damage Detection: Comparing Two Detection Algorithms
BIAN Haitao; BAI Libin; WANG Xiaoyu; LIU Wangiu; CHEN Shenen; WANG Shengguo
2011-01-01
The health conditions of highway bridges is critical for sustained transportation operations. US federal government mandates that all bridges built with public funds are to be inspected visually every two years.There is a growing consensus that additional rapid and non-intrusive methods for bridge damage evaluation are needed. This paper explores the potential of applying ground-based laser scanners for bridge damage evaluation. LiDAR has the potential of providing high-density, full-field surface static imaging. Hence, it can generate volumetric quantification of concrete corrosion or steel erosion. By recording object surface topology, LiDAR can detect different damages on the bridge structure and differentiate damage types according to the surface flatness and smoothness. To determine the effectiveness of LiDAR damage detection, two damage detection algorithms are presented and compared using scans on actual bridge damages. The results demonstrate and validate LiDAR damage quantification, which can be a powerful tool for bridge condition evaluation.
Real-time Simulation of Large Aircraft Flying Through Microburst Wind Field
Gao Zhenxing; Gu Hongbin; Liu Hui
2009-01-01
This article deals with real-time hi-fi simulation of large aircraft flying in turbulent wind in a simulator to study its takeoff and landing behavior in microburst wind shear. A parameterized three-dimensional (3D) microburst model is built up on the basis of vortex ring and Rankine vortex principle. Complicated microburst wind fields are simulated by means of vortex ring declination and multi-vortex superposition. Based on the modeling data of Boeing 747-100, a dynamic model with wind shear effects considered is established and a general method to modify the aerodynamic model is proposed. A controller for longitudinal and lateral escapes is designed and verified in simulated microburst wind field. Results indicate that, with high extensibility, reasonability and effectiveness, the 3D microburst model with wind shear effects considered is fit to simulate real wind fields. Different escape schemes can be adopted to fly through a wind field from different locations. The model can be used for real-time flight simulation in a flight simulator.
Statistical Properties of Microbursts Derived from the FIREBIRD-II CubeSats
Shumko, M.; Sample, J.; Klumpar, D. M.; Spence, H. E.; Crew, A. B.
2016-12-01
The FIREBIRD-II CubeSats have been successfully operating in a high-inclination, low-earth-orbit since January 31st, 2015. The FIREBIRD CubeSats were designed to observe relativistic electron microbursts. A microburst is a sharp increase (often greater than an order of magnitude) in precipitating electron flux lasting 100 ms. Each CubeSat contains two solid state detectors with complementary geometric factors which measure electrons from 200 keV to 1 MeV. Observations are gathered in campaigns, and the two data products are Context and HiRes. Context is an electron count rate from two energy channels at a 6 s cadence, and HiRes is at a cadence as fast as 12.5 ms. Context data is downlinked for the entirety of the campaign, but due to a tight telemetry budget, only a specified subset of HiRes data is downlinked. Microbursts have been simultaneously observed when the CubeSats were separated by as little as 10 km. A technique to automatically identify microbursts in the data using wavelet analysis is evaluated. Various fitting techniques are applied to the bursts and used to determine distributions of microburst parameters such as duration, repetition and dispersion as a function of energy.
A NEW RECURSIVE ALGORITHM FOR MULTIUSER DETECTION
Wang Lei; Zheng Baoyu; Li Lei; Chen Chao
2009-01-01
Based on the synthesis and analysis of recursive receivers,a new algorithm,namely partial grouping maximization likelihood algorithm,is proposed to achieve satisfactory performance with moderate computational complexity.During the analysis,some interesting properties shared by the proposed procedures are described.Finally,the performance assessment shows that the new scheme is superior to the linear detector and ordinary grouping algorithm,and achieves a bit-error rate close to that of the optimum receiver.
A Vehicle Detection Algorithm Based on Deep Belief Network
Hai Wang
2014-01-01
Full Text Available Vision based vehicle detection is a critical technology that plays an important role in not only vehicle active safety but also road video surveillance application. Traditional shallow model based vehicle detection algorithm still cannot meet the requirement of accurate vehicle detection in these applications. In this work, a novel deep learning based vehicle detection algorithm with 2D deep belief network (2D-DBN is proposed. In the algorithm, the proposed 2D-DBN architecture uses second-order planes instead of first-order vector as input and uses bilinear projection for retaining discriminative information so as to determine the size of the deep architecture which enhances the success rate of vehicle detection. On-road experimental results demonstrate that the algorithm performs better than state-of-the-art vehicle detection algorithm in testing data sets.
A New Iterate SR Algorithm for Adaptive Multiuser Detection
孙丽萍; 胡光锐
2004-01-01
The sign algorithm has been extensively investigated for digital echo cancellation application and other adaptive filtering applications. In this paper, we use the blind averaging Signregressor (SR) algorithm for adaptive multiuser detection.It is another least mean square (LMS) algorithm and eliminates the need for multiplication in the adaptive algorithm.The new algorithm not only reduces the calculation complexity but also has good convergence character. Simulations indicate that this algorithm can adapt to the changes of the environment quickly and improve the stability of the SIR.
Local Community Detection Algorithm Based on Minimal Cluster
Yong Zhou
2016-01-01
Full Text Available In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.
Epidemic features affecting the performance of outbreak detection algorithms
Kuang Jie
2012-06-01
Full Text Available Abstract Background Outbreak detection algorithms play an important role in effective automated surveillance. Although many algorithms have been designed to improve the performance of outbreak detection, few published studies have examined how epidemic features of infectious disease impact on the detection performance of algorithms. This study compared the performance of three outbreak detection algorithms stratified by epidemic features of infectious disease and examined the relationship between epidemic features and performance of outbreak detection algorithms. Methods Exponentially weighted moving average (EWMA, cumulative sum (CUSUM and moving percentile method (MPM algorithms were applied. We inserted simulated outbreaks into notifiable infectious disease data in China Infectious Disease Automated-alert and Response System (CIDARS, and compared the performance of the three algorithms with optimized parameters at a fixed false alarm rate of 5% classified by epidemic features of infectious disease. Multiple linear regression was adopted to analyse the relationship of the algorithms’ sensitivity and timeliness with the epidemic features of infectious diseases. Results The MPM had better detection performance than EWMA and CUSUM through all simulated outbreaks, with or without stratification by epidemic features (incubation period, baseline counts and outbreak magnitude. The epidemic features were associated with both sensitivity and timeliness. Compared with long incubation, short incubation had lower probability (β* = −0.13, P Conclusions The results of this study suggest that the MPM is a prior algorithm for outbreak detection and differences of epidemic features in detection performance should be considered in automatic surveillance practice.
Extended Approximate String Matching Algorithms To Detect Name Aliases
Shaikh, Muniba; Memon, Nasrullah; Wiil, Uffe Kock
2011-01-01
. An extension to widely used ASM algorithms is proposed to detect the name aliases that generate as a result of transliteration. This paper aims to improve the accuracy of the basic ASM algorithms in order to detect correct aliases. The experimental evaluation shows that proposed extension increases...
A Fast Shot Transition Detecting Algorithm on MPEG Sequences
ZhengPeng; XueHai-feng; ZhouDong-ru
2003-01-01
In order to process video data efficiently, a video segmenting technique must be required. We propose a fast shot transition detecting algorithm directly on MPEG compressed video sequence. The algorithm can detect not only abrupt transition, but also gradual transition. The computing cost of the algorithm is low, because we directly use the type of rnacroblocks and motion vectors that MPEG compressed video provides. The result of experiment is rather well
A distributed deadlock detection algorithm for mobile computing system
CHENG Xin; LIU Hong-wei; ZUO De-cheng; JIN Feng; YANG Xiao-zong
2005-01-01
The mode of mobile computing originated from distributed computing and it has the un-idempotent operation property, therefore the deadlock detection algorithm designed for mobile computing systems will face challenges with regard to correctness and high efficiency. This paper attempts a fundamental study of deadlock detection for the AND model of mobile computing systems. First, the existing deadlock detection algorithms for distributed systems are classified into the resource node dependent (RD) and the resource node independent (RI) categories, and their corresponding weaknesses are discussed. Afterwards a new RI algorithm based on the AND model of mobile computing system is presented. The novelties of our algorithm are that: 1 ) the blocked nodes inform their predecessors and successors simultaneously; 2 ) the detection messages ( agents )hold the predecessors information of their originator; 3) no agent is stored midway. Additionally, the quit-inform scheme is introduced to treat the excessive victim quitting problem raised by the overlapped cycles. By these methods the proposed algorithm can detect a cycle of size n within n - 2 steps and with ( n2 - n - 2)/2 agents. The performance of our algorithm is compared with the most competitive RD and RI algorithms for distributed systems on a mobile agent simulation platform. Experiment results point out that our algorithm outperforms the two algorithms under the vast majority of resource configurations and concurrent workloads. The correctness of the proposed algorithm is formally proven by the invariant verification technique.
A Comparative Analysis of Community Detection Algorithms on Artificial Networks
Yang, Zhao; Tessone, Claudio Juan
2016-01-01
Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms' computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most sui...
Kurita, Satoshi; Miyoshi, Yoshizumi; Blake, J. Bernard; Reeves, Geoffery D.; Kletzing, Craig A.
2016-04-01
It has been suggested that whistler mode chorus is responsible for both acceleration of MeV electrons and relativistic electron microbursts through resonant wave-particle interactions. Relativistic electron microbursts have been considered as an important loss mechanism of radiation belt electrons. Here we report on the observations of relativistic electron microbursts and flux variations of trapped MeV electrons during the 8-9 October 2012 storm, using the SAMPEX and Van Allen Probes satellites. Observations by the satellites show that relativistic electron microbursts correlate well with the rapid enhancement of trapped MeV electron fluxes by chorus wave-particle interactions, indicating that acceleration by chorus is much more efficient than losses by microbursts during the storm. It is also revealed that the strong chorus wave activity without relativistic electron microbursts does not lead to significant flux variations of relativistic electrons. Thus, effective acceleration of relativistic electrons is caused by chorus that can cause relativistic electron microbursts.
GARD: a genetic algorithm for recombination detection
Kosakovsky Pond, Sergei L; Posada, David; Gravenor, Michael B; Woelk, Christopher H; Frost, Simon D W
2006-01-01
.... We developed a likelihood-based model selection procedure that uses a genetic algorithm to search multiple sequence alignments for evidence of recombination breakpoints and identify putative recombinant sequences...
Dynamic programming algorithm for detecting dim infrared moving targets
He, Lisha; Mao, Liangjing; Xie, Lijun
2009-10-01
Infrared (IR) target detection is a key part of airborne infrared weapon system, especially the detection of poor dim moving IR target embedded in complex context. This paper presents an improved Dynamic Programming (DP) algorithm in allusion to low Signal to Noise Ratio (SNR) infrared dim moving targets under cluttered context. The algorithm brings the dim target to prominence by accumulating the energy of pixels in the image sequence, after suppressing the background noise with a mathematical morphology preprocessor. As considering the continuity and stabilization of target's energy and forward direction, this algorithm has well solved the energy scattering problem that exists in the original DP algorithm. An effective energy segmentation threshold is given by a Contrast-Limited Adaptive Histogram Equalization (CLAHE) filter with a regional peak extraction algorithm. Simulation results show that the improved DP tracking algorithm performs well in detecting poor dim targets.
Raghunathan, Shriram; Gupta, Sumeet K; Markandeya, Himanshu S; Roy, Kaushik; Irazoqui, Pedro P
2010-10-30
Implantable neural prostheses that deliver focal electrical stimulation upon demand are rapidly emerging as an alternate therapy for roughly a third of the epileptic patient population that is medically refractory. Seizure detection algorithms enable feedback mechanisms to provide focally and temporally specific intervention. Real-time feasibility and computational complexity often limit most reported detection algorithms to implementations using computers for bedside monitoring or external devices communicating with the implanted electrodes. A comparison of algorithms based on detection efficacy does not present a complete picture of the feasibility of the algorithm with limited computational power, as is the case with most battery-powered applications. We present a two-dimensional design optimization approach that takes into account both detection efficacy and hardware cost in evaluating algorithms for their feasibility in an implantable application. Detection features are first compared for their ability to detect electrographic seizures from micro-electrode data recorded from kainate-treated rats. Circuit models are then used to estimate the dynamic and leakage power consumption of the compared features. A score is assigned based on detection efficacy and the hardware cost for each of the features, then plotted on a two-dimensional design space. An optimal combination of compared features is used to construct an algorithm that provides maximal detection efficacy per unit hardware cost. The methods presented in this paper would facilitate the development of a common platform to benchmark seizure detection algorithms for comparison and feasibility analysis in the next generation of implantable neuroprosthetic devices to treat epilepsy.
ANOMALY DETECTION IN NETWORKING USING HYBRID ARTIFICIAL IMMUNE ALGORITHM
D. Amutha Guka
2012-01-01
Full Text Available Especially in today’s network scenario, when computers are interconnected through internet, security of an information system is very important issue. Because no system can be absolutely secure, the timely and accurate detection of anomalies is necessary. The main aim of this research paper is to improve the anomaly detection by using Hybrid Artificial Immune Algorithm (HAIA which is based on Artificial Immune Systems (AIS and Genetic Algorithm (GA. In this research work, HAIA approach is used to develop Network Anomaly Detection System (NADS. The detector set is generated by using GA and the anomalies are identified using Negative Selection Algorithm (NSA which is based on AIS. The HAIA algorithm is tested with KDD Cup 99 benchmark dataset. The detection rate is used to measure the effectiveness of the NADS. The results and consistency of the HAIA are compared with earlier approaches and the results are presented. The proposed algorithm gives best results when compared to the earlier approaches.
An efficient and fast detection algorithm for multimode FBG sensing
Ganziy, Denis; Jespersen, O.; Rose, B.
2015-01-01
We propose a novel dynamic gate algorithm (DGA) for fast and accurate peak detection. The algorithm uses threshold determined detection window and Center of gravity algorithm with bias compensation. We analyze the wavelength fit resolution of the DGA for different values of signal to noise ratio...... and different typical peak shapes. Our simulations and experiments demonstrate that the DGA method is fast and robust with higher stability and accuracy compared to conventional algorithms. This makes it very attractive for future implementation in sensing systems especially based on multimode fiber Bragg...
Detection and location algorithm against local-worm
YANG XinYu; SHI Yi; ZHU Huidun
2008-01-01
The spread of the worm causes great harm to the computer network. It has recently become the focus of the network security research. This paper presents a local-worm detection algorithm by analyzing the characteristics of traffic generated by the TCP-based worm. Moreover, we adjust the worm location algorithm, aiming at the differences between the high-speed and the low-speed worm scanning methods. This adjustment can make the location algorithm detect and locate the worm based on different scanning rate. Finally, we verified the validity and efficiency of the proposed algorithm by simulating it under NS-2,
QRS Detection Based on an Advanced Multilevel Algorithm
Wissam Jenkal; Rachid Latif; Ahmed Toumanari; Azzedine Dliou; Oussama El B’charri; Fadel Mrabih Rabou Maoulainine
2016-01-01
This paper presents an advanced multilevel algorithm used for the QRS complex detection. This method is based on three levels. The first permits the extraction of higher peaks using an adaptive thresholding technique. The second allows the QRS region detection. The last level permits the detection of Q, R and S waves. The proposed algorithm shows interesting results compared to recently published methods. The perspective of this work is the implementation of this method on an embedded system ...
Theoretical foundations of NRL spectral target detection algorithms.
Schaum, Alan
2015-11-01
The principal spectral detection algorithms developed at the Naval Research Laboratory (NRL) over the past 20 years for use in operational systems are described. These include anomaly detectors, signature-based methods, and techniques for anomalous change detection. Newer derivations are provided that have motivated more recent work. Mathematical methods facilitating the use of forward models for the prediction of spectral signature statistics are described and a detection algorithm is derived for ocean surveillance that is based on principles of clairvoyant fusion.
Computer algorithms to detect bloodstream infections.
Trick, William E; Zagorski, Brandon M; Tokars, Jerome I; Vernon, Michael O; Welbel, Sharon F; Wisniewski, Mary F; Richards, Chesley; Weinstein, Robert A
2004-09-01
We compared manual and computer-assisted bloodstream infection surveillance for adult inpatients at two hospitals. We identified hospital-acquired, primary, central-venous catheter (CVC)-associated bloodstream infections by using five methods: retrospective, manual record review by investigators; prospective, manual review by infection control professionals; positive blood culture plus manual CVC determination; computer algorithms; and computer algorithms and manual CVC determination. We calculated sensitivity, specificity, predictive values, plus the kappa statistic (kappa) between investigator review and other methods, and we correlated infection rates for seven units. The kappa value was 0.37 for infection control review, 0.48 for positive blood culture plus manual CVC determination, 0.49 for computer algorithm, and 0.73 for computer algorithm plus manual CVC determination. Unit-specific infection rates, per 1,000 patient days, were 1.0-12.5 by investigator review and 1.4-10.2 by computer algorithm (correlation r = 0.91, p = 0.004). Automated bloodstream infection surveillance with electronic data is an accurate alternative to surveillance with manually collected data.
An Adaptive Algorithm to Detect Port Scans
单蓉胜; 李小勇; 李建华
2004-01-01
Detection of port scan is an important component in a network intrusion detection and prevention system. Traditional statistical methods can be easily evaded by stealthy scans and are prone to DeS attacks. This paper presents a new mechanism termed PSD(port scan detection), which is based on TCP packet anomaly evaluation. By learning the port distribution and flags of TCP packets arriving at the protected hosts, PSD can compute the anomaly score of each packet and effectively detect port scans including slow scans and stealthy scans. Experiments show that PSD has high detection accuracy and low detection latency.
An entropy-based unsupervised anomaly detection pattern learning algorithm
YANG Ying-jie; MA Fan-yuan
2005-01-01
Currently, most anomaly detection pattern learning algorithms require a set of purely normal data from which they train their model. If the data contain some intrusions buried within the training data, the algorithm may not detect these attacks because it will assume that they are normal. In reality, it is very hard to guarantee that there are no attack items in the collected training data. Focusing on this problem, in this paper,firstly a new anomaly detection measurement is proposed according to the probability characteristics of intrusion instances and normal instances. Secondly, on the basis of anomaly detection measure, we present a clusteringbased unsupervised anomaly detection patterns learning algorithm, which can overcome the shortage above. Finally, some experiments are conducted to verify the proposed algorithm is valid.
Analysis of Community Detection Algorithms for Large Scale Cyber Networks
Mane, Prachita; Shanbhag, Sunanda; Kamath, Tanmayee; Mackey, Patrick S.; Springer, John
2016-09-30
The aim of this project is to use existing community detection algorithms on an IP network dataset to create supernodes within the network. This study compares the performance of different algorithms on the network in terms of running time. The paper begins with an introduction to the concept of clustering and community detection followed by the research question that the team aimed to address. Further the paper describes the graph metrics that were considered in order to shortlist algorithms followed by a brief explanation of each algorithm with respect to the graph metric on which it is based. The next section in the paper describes the methodology used by the team in order to run the algorithms and determine which algorithm is most efficient with respect to running time. Finally, the last section of the paper includes the results obtained by the team and a conclusion based on those results as well as future work.
Analysing Threshold Value in Fire Detection Algorithm Using MODIS Data
Bowo E. Cahyono
2012-08-01
Full Text Available MODIS instruments have been designed to include special channels for fire monitoring by adding more spectral thermal band detector on them. The basic understanding of remote sensing fire detection should be kept in mind to be able to improve the algorithm for regional scale detection purposes. It still gives many chances for more exploration. This paper describe the principle of fire investigation applied on MODIS data. The main used algorithm in this research is contextual algorithm which has been developed by NASA scientist team. By varying applied threshold of T4 value in the range of 320-360K it shows that detected fire is significantly changed. While significant difference of detected FHS by changing ∆T threshold value is occurred in the range of 15-35K. Improve and adjustment of fire detection algorithm is needed to get the best accuracy result proper to local or regional conditions. MOD14 algorithm is applied threshold values of 325K for T4 and 20K for ∆T. Validation has been done from the algorithm result of MODIS dataset over Indonesia and South Africa. The accuracy of MODIS fire detection by MOD14 algorithm is 73.2% and 91.7% on MODIS data over Sumatra-Borneo and South Africa respectively
Improvement and implementation for Canny edge detection algorithm
Yang, Tao; Qiu, Yue-hong
2015-07-01
Edge detection is necessary for image segmentation and pattern recognition. In this paper, an improved Canny edge detection approach is proposed due to the defect of traditional algorithm. A modified bilateral filter with a compensation function based on pixel intensity similarity judgment was used to smooth image instead of Gaussian filter, which could preserve edge feature and remove noise effectively. In order to solve the problems of sensitivity to the noise in gradient calculating, the algorithm used 4 directions gradient templates. Finally, Otsu algorithm adaptively obtain the dual-threshold. All of the algorithm simulated with OpenCV 2.4.0 library in the environments of vs2010, and through the experimental analysis, the improved algorithm has been proved to detect edge details more effectively and with more adaptability.
AN IMPROVED SUBSPACE TRACKING ALGORITHM FOR BLIND ADAPTIVE MULTIUSER DETECTION
Xu Changqing; Wang Hongyang; Song Wentao
2004-01-01
As the Projection Approximation Subspace Tracking with deflation(PASTd) algorithm is sensitive to impulsive noise, an improved subspace tracking algorithm is proposed and applied to blind adaptive multi-user detection. Simulation results show that the improved PASTd algorithm not only remains the properties of the conventional PASTdalgorithm, but also has good Bit Error Rate(BER) performance in impulsive noise environment, thus it can effectively improve the system performance.
Statistical Algorithm for the Adaptation of Detection Thresholds
Stotsky, Alexander A.
2008-01-01
Many event detection mechanisms in spark ignition automotive engines are based on the comparison of the engine signals to the detection threshold values. Different signal qualities for new and aged engines necessitate the development of an adaptation algorithm for the detection thresholds...
Practical Algorithms for Subgroup Detection in Covert Networks
Memon, Nasrullah; Wiil, Uffe Kock; Qureshi, Pir Abdul Rasool
2010-01-01
In this paper, we present algorithms for subgroup detection and demonstrated them with a real-time case study of USS Cole bombing terrorist network. The algorithms are demonstrated in an application by a prototype system. The system finds associations between terrorist and terrorist organisations...
A simulation study comparing aberration detection algorithms for syndromic surveillance
Painter Ian
2007-03-01
Full Text Available Abstract Background The usefulness of syndromic surveillance for early outbreak detection depends in part on effective statistical aberration detection. However, few published studies have compared different detection algorithms on identical data. In the largest simulation study conducted to date, we compared the performance of six aberration detection algorithms on simulated outbreaks superimposed on authentic syndromic surveillance data. Methods We compared three control-chart-based statistics, two exponential weighted moving averages, and a generalized linear model. We simulated 310 unique outbreak signals, and added these to actual daily counts of four syndromes monitored by Public Health – Seattle and King County's syndromic surveillance system. We compared the sensitivity of the six algorithms at detecting these simulated outbreaks at a fixed alert rate of 0.01. Results Stratified by baseline or by outbreak distribution, duration, or size, the generalized linear model was more sensitive than the other algorithms and detected 54% (95% CI = 52%–56% of the simulated epidemics when run at an alert rate of 0.01. However, all of the algorithms had poor sensitivity, particularly for outbreaks that did not begin with a surge of cases. Conclusion When tested on county-level data aggregated across age groups, these algorithms often did not perform well in detecting signals other than large, rapid increases in case counts relative to baseline levels.
A Quantum Algorithm Detecting Concentrated Maps.
Beichl, Isabel; Bullock, Stephen S; Song, Daegene
2007-01-01
We consider an arbitrary mapping f: {0, …, N - 1} → {0, …, N - 1} for N = 2 (n) , n some number of quantum bits. Using N calls to a classical oracle evaluating f(x) and an N-bit memory, it is possible to determine whether f(x) is one-to-one. For some radian angle 0 ≤ θ ≤ π/2, we say f(x) is θ - concentrated if and only if [Formula: see text] for some given ψ 0 and any 0 ≤ x ≤ N - 1. We present a quantum algorithm that distinguishes a θ-concentrated f(x) from a one-to-one f(x) in O(1) calls to a quantum oracle function Uf with high probability. For 0 quantum algorithm outperforms random (classical) evaluation of the function testing for dispersed values (on average). Maximal outperformance occurs at [Formula: see text] rad.
Anomaly Detection and Diagnosis Algorithms for Discrete Symbols
National Aeronautics and Space Administration — We present a set of novel algorithms which we call sequenceMiner that detect and characterize anomalies in large sets of high-dimensional symbol sequences that arise...
Acoustic change detection algorithm using an FM radio
Goldman, Geoffrey H.; Wolfe, Owen
2012-06-01
The U.S. Army is interested in developing low-cost, low-power, non-line-of-sight sensors for monitoring human activity. One modality that is often overlooked is active acoustics using sources of opportunity such as speech or music. Active acoustics can be used to detect human activity by generating acoustic images of an area at different times, then testing for changes among the imagery. A change detection algorithm was developed to detect physical changes in a building, such as a door changing positions or a large box being moved using acoustics sources of opportunity. The algorithm is based on cross correlating the acoustic signal measured from two microphones. The performance of the algorithm was shown using data generated with a hand-held FM radio as a sound source and two microphones. The algorithm could detect a door being opened in a hallway.
A Color Image Edge Detection Algorithm Based on Color Difference
Zhuo, Li; Hu, Xiaochen; Jiang, Liying; Zhang, Jing
2016-12-01
Although image edge detection algorithms have been widely applied in image processing, the existing algorithms still face two important problems. On one hand, to restrain the interference of noise, smoothing filters are generally exploited in the existing algorithms, resulting in loss of significant edges. On the other hand, since the existing algorithms are sensitive to noise, many noisy edges are usually detected, which will disturb the subsequent processing. Therefore, a color image edge detection algorithm based on color difference is proposed in this paper. Firstly, a new operation called color separation is defined in this paper, which can reflect the information of color difference. Then, for the neighborhood of each pixel, color separations are calculated in four different directions to detect the edges. Experimental results on natural and synthetic images show that the proposed algorithm can remove a large number of noisy edges and be robust to the smoothing filters. Furthermore, the proposed edge detection algorithm is applied in road foreground segmentation and shadow removal, which achieves good performances.
Osmane, Adnane; Wilson, Lynn B., III; Blum, Lauren; Pulkkinen, Tuija I.
2016-01-01
Using a dynamical-system approach, we have investigated the efficiency of large-amplitude whistler waves for causing microburst precipitation in planetary radiation belts by modeling the microburst energy and particle fluxes produced as a result of nonlinear wave-particle interactions. We show that wave parameters, consistent with large amplitude oblique whistlers, can commonly generate microbursts of electrons with hundreds of keV-energies as a result of Landau trapping. Relativistic microbursts (greater than 1 MeV) can also be generated by a similar mechanism, but require waves with large propagation angles Theta (sub k)B greater than 50 degrees and phase-speeds v(sub phi) greater than or equal to c/9. Using our result for precipitating density and energy fluxes, we argue that holes in the distribution function of electrons near the magnetic mirror point can result in the generation of double layers and electron solitary holes consistent in scales (of the order of Debye lengths) to nonlinear structures observed in the radiation belts by the Van Allen Probes. Our results indicate a relationship between nonlinear electrostatic and electromagnetic structures in the dynamics of planetary radiation belts and their role in the cyclical production of energetic electrons (E greater than or equal to 100 keV) on kinetic timescales, which is much faster than previously inferred.
Hinton, David A.
1988-01-01
An effort is underway by NASA, FAA, and industry to reduce the threat of convective microburst wind shear phenomena to aircraft. The goal is to develop and test a candidate set of strategies for recovery from inadvertent microburst encounters during takeoff. Candidate strategies were developed and evaluated using a fast-time simulation consisting of a simple point-mass performance model of a transport-category airplane and an analytical microburst model. The results indicate that the recovery strategy characteristics that best utilize available airplane energy include an initial reduction in pitch attitude to reduce the climb rate, followed by an increase in pitch up to the stick shaker angle of attack. The stick shaker angle of attack should be reached just as the airplane is exiting the microburst. The shallowest angle of climb necessary for obstacle clearance should be used. If the altitude is higher than necessary, an intentional descent to reduce the airspeed deceleration should be used. Of the strategies tested, two flight-path-angle based strategies had the highest recovery altitudes and the least sensitivity to variations in the encounter scenarios.
Multi-object Detection and Discrimination Algorithms
2015-03-26
official Department of the Army position, policy or decision , unless so designated by other documentation. 9. SPONSORING/MONITORING AGENCY NAME(S) AND...learning, image processing , multi-sensor fusion, classifier development, ground- penetrating radar (GPR), ground boundary detection REPORT DOCUMENTATION...from Multiple Detectors Using Dynamic Programming • Improvements on Multiple Instance Learning Hidden Markov Model for landmine detection in ground
A baseline algorithm for face detection and tracking in video
Manohar, Vasant; Soundararajan, Padmanabhan; Korzhova, Valentina; Boonstra, Matthew; Goldgof, Dmitry; Kasturi, Rangachar
2007-10-01
Establishing benchmark datasets, performance metrics and baseline algorithms have considerable research significance in gauging the progress in any application domain. These primarily allow both users and developers to compare the performance of various algorithms on a common platform. In our earlier works, we focused on developing performance metrics and establishing a substantial dataset with ground truth for object detection and tracking tasks (text and face) in two video domains -- broadcast news and meetings. In this paper, we present the results of a face detection and tracking algorithm on broadcast news videos with the objective of establishing a baseline performance for this task-domain pair. The detection algorithm uses a statistical approach that was originally developed by Viola and Jones and later extended by Lienhart. The algorithm uses a feature set that is Haar-like and a cascade of boosted decision tree classifiers as a statistical model. In this work, we used the Intel Open Source Computer Vision Library (OpenCV) implementation of the Haar face detection algorithm. The optimal values for the tunable parameters of this implementation were found through an experimental design strategy commonly used in statistical analyses of industrial processes. Tracking was accomplished as continuous detection with the detected objects in two frames mapped using a greedy algorithm based on the distances between the centroids of bounding boxes. Results on the evaluation set containing 50 sequences (~ 2.5 mins.) using the developed performance metrics show good performance of the algorithm reflecting the state-of-the-art which makes it an appropriate choice as the baseline algorithm for the problem.
Improved Genetic Algorithm Application in Textile Defect Detection
GENG Zhao-feng; Li Bei-bei; ZHAO Zhi-hong
2007-01-01
Based on an efficient improved genetic algorithm,a pattern recognition approach is represented for textile defects inspection. An image process is developed to automatically detect the drawbacks on textile caused by three circumstances: break, dual, and jump of yams. By statistic method, some texture feature values of the image with defects points can be achieved. Therefore, the textile defects are classified properly. The advanced process of the defect image is done. Image segmentation is realized by an improved genetic algorithm to detect the defects. This method can be used to automatically classify and detect textile defects. According to different users' requirements, ifferent types of textile material can be detected.
Plagiarism Detection Based on SCAM Algorithm
Anzelmi, Daniele; Carlone, Domenico; Rizzello, Fabio
2011-01-01
Plagiarism is a complex problem and considered one of the biggest in publishing of scientific, engineering and other types of documents. Plagiarism has also increased with the widespread use of the Internet as large amount of digital data is available. Plagiarism is not just direct copy but also...... paraphrasing, rewording, adapting parts, missing references or wrong citations. This makes the problem more difficult to handle adequately. Plagiarism detection techniques are applied by making a distinction between natural and programming languages. Our proposed detection process is based on natural language...... document. Our plagiarism detection system, like many Information Retrieval systems, is evaluated with metrics of precision and recall....
Algorithm for Detecting Significant Locations from Raw GPS Data
Kami, Nobuharu; Enomoto, Nobuyuki; Baba, Teruyuki; Yoshikawa, Takashi
We present a fast algorithm for probabilistically extracting significant locations from raw GPS data based on data point density. Extracting significant locations from raw GPS data is the first essential step of algorithms designed for location-aware applications. Assuming that a location is significant if users spend a certain time around that area, most current algorithms compare spatial/temporal variables, such as stay duration and a roaming diameter, with given fixed thresholds to extract significant locations. However, the appropriate threshold values are not clearly known in priori and algorithms with fixed thresholds are inherently error-prone, especially under high noise levels. Moreover, for N data points, they are generally O(N 2) algorithms since distance computation is required. We developed a fast algorithm for selective data point sampling around significant locations based on density information by constructing random histograms using locality sensitive hashing. Evaluations show competitive performance in detecting significant locations even under high noise levels.
Detection Algorithms for Hyperspectral Imaging Applications
2010-08-26
Schaum and A. Stocker. Spectrally-selective target detection. Proceedings oflSSSR, 1997. [54] R. A. Schowengerdt. Remote Sensing: Models and...Stein, S. Beaven, L. Hoff, E. Winter, A. Schaum , and A. Stocker. Anomaly detection from fyper- spectral imagery. Signal Processing Magazine, 2002. [58...adaptive processor or a structured covariance matrix. IEEE AES, 36(4): 1115-1125, Oct. 2000. [60] A.D. Stocker and A. Schaum . Application of
An Improved Moving Multi-Human Target Detection Algorithm
Liang Feng-Mei
2013-07-01
Full Text Available In the detection of moving multi-human targets, the major problems existing lie in the detection speed and precision. Fortunately, the HOG feature presents a very considerable effect on the detection accuracy. However, the problem of low detecting speed caused by its large amount of calculation prevents the HOG feature from being well applied in scenes where the real-time requirements are needed. Given this problem, this paper presents a method which combines the Gaussian mixture background model and HOG feature. This method solved firstly by the Gaussian mixture background model to detect the moving foreground in the video. And then use HOG+SVM to handle the moving foreground that has been detected. As a result, the amount of computation is reduced considerably and the real-time performance of the HOG algorithm is improved greatly. Verified by the experiment, the detection accuracy of this algorithm can reach 94%.
Plagiarism Detection Based on SCAM Algorithm
Anzelmi, Daniele; Carlone, Domenico; Rizzello, Fabio
2011-01-01
Plagiarism is a complex problem and considered one of the biggest in publishing of scientific, engineering and other types of documents. Plagiarism has also increased with the widespread use of the Internet as large amount of digital data is available. Plagiarism is not just direct copy but also...... paraphrasing, rewording, adapting parts, missing references or wrong citations. This makes the problem more difficult to handle adequately. Plagiarism detection techniques are applied by making a distinction between natural and programming languages. Our proposed detection process is based on natural language...
Line matching for automatic change detection algorithm
Dhollande, Jérôme; Monnin, David; Gond, Laetitia; Cudel, Christophe; Kohler, Sophie; Dieterlen, Alain
2012-06-01
During foreign operations, Improvised Explosive Devices (IEDs) are one of major threats that soldiers may unfortunately encounter along itineraries. Based on a vehicle-mounted camera, we propose an original approach by image comparison to detect signicant changes on these roads. The classic 2D-image registration techniques do not take into account parallax phenomena. The consequence is that the misregistration errors could be detected as changes. According to stereovision principles, our automatic method compares intensity proles along corresponding epipolar lines by extrema matching. An adaptive space warping compensates scale dierence in 3D-scene. When the signals are matched, the signal dierence highlights changes which are marked in current video.
Vehicle detection algorithm based on codebook and local binary patterns algorithms
许雪梅; 周立超; 墨芹; 郭巧云
2015-01-01
Detecting the moving vehicles in jittering traffic scenes is a very difficult problem because of the complex environment. Only by the color features of the pixel or only by the texture features of image cannot establish a suitable background model for the moving vehicles. In order to solve this problem, the Gaussian pyramid layered algorithm is proposed, combining with the advantages of the Codebook algorithm and the Local binary patterns (LBP) algorithm. Firstly, the image pyramid is established to eliminate the noises generated by the camera shake. Then, codebook model and LBP model are constructed on the low-resolution level and the high-resolution level of Gaussian pyramid, respectively. At last, the final test results are obtained through a set of operations according to the spatial relations of pixels. The experimental results show that this algorithm can not only eliminate the noises effectively, but also save the calculating time with high detection sensitivity and high detection accuracy.
Detecting Community Structure by Using a Constrained Label Propagation Algorithm.
Jia Hou Chin
Full Text Available Community structure is considered one of the most interesting features in complex networks. Many real-world complex systems exhibit community structure, where individuals with similar properties form a community. The identification of communities in a network is important for understanding the structure of said network, in a specific perspective. Thus, community detection in complex networks gained immense interest over the last decade. A lot of community detection methods were proposed, and one of them is the label propagation algorithm (LPA. The simplicity and time efficiency of the LPA make it a popular community detection method. However, the LPA suffers from instability detection due to randomness that is induced in the algorithm. The focus of this paper is to improve the stability and accuracy of the LPA, while retaining its simplicity. Our proposed algorithm will first detect the main communities in a network by using the number of mutual neighbouring nodes. Subsequently, nodes are added into communities by using a constrained LPA. Those constraints are then gradually relaxed until all nodes are assigned into groups. In order to refine the quality of the detected communities, nodes in communities can be switched to another community or removed from their current communities at various stages of the algorithm. We evaluated our algorithm on three types of benchmark networks, namely the Lancichinetti-Fortunato-Radicchi (LFR, Relaxed Caveman (RC and Girvan-Newman (GN benchmarks. We also apply the present algorithm to some real-world networks of various sizes. The current results show some promising potential, of the proposed algorithm, in terms of detecting communities accurately. Furthermore, our constrained LPA has a robustness and stability that are significantly better than the simple LPA as it is able to yield deterministic results.
Detecting Community Structure by Using a Constrained Label Propagation Algorithm
Ratnavelu, Kuru
2016-01-01
Community structure is considered one of the most interesting features in complex networks. Many real-world complex systems exhibit community structure, where individuals with similar properties form a community. The identification of communities in a network is important for understanding the structure of said network, in a specific perspective. Thus, community detection in complex networks gained immense interest over the last decade. A lot of community detection methods were proposed, and one of them is the label propagation algorithm (LPA). The simplicity and time efficiency of the LPA make it a popular community detection method. However, the LPA suffers from instability detection due to randomness that is induced in the algorithm. The focus of this paper is to improve the stability and accuracy of the LPA, while retaining its simplicity. Our proposed algorithm will first detect the main communities in a network by using the number of mutual neighbouring nodes. Subsequently, nodes are added into communities by using a constrained LPA. Those constraints are then gradually relaxed until all nodes are assigned into groups. In order to refine the quality of the detected communities, nodes in communities can be switched to another community or removed from their current communities at various stages of the algorithm. We evaluated our algorithm on three types of benchmark networks, namely the Lancichinetti-Fortunato-Radicchi (LFR), Relaxed Caveman (RC) and Girvan-Newman (GN) benchmarks. We also apply the present algorithm to some real-world networks of various sizes. The current results show some promising potential, of the proposed algorithm, in terms of detecting communities accurately. Furthermore, our constrained LPA has a robustness and stability that are significantly better than the simple LPA as it is able to yield deterministic results. PMID:27176470
Wideband Array Signal Detection Algorithm Based on Power Focusing
Gong Bin
2012-09-01
Full Text Available Aiming at the requirement of real-time signal detection in the passive surveillance system, a wideband array signal detection algorithm is proposed based on the concept of power focusing. By making use of the phase difference of the signal received by a uniform linear array, the algorithm makes the power of the received signal focused in the Direction Of Arrival (DOA with improved cascade FFT. Subsequently, the probability density function of the output noise at each angle is derived. Furthermore, a Constant False Alarm Rate (CFAR test statistic and the corresponding detection threshold are constructed. The theoretical probability of detection is also derived for different false alarm rate and Signal-to-Noise Ratio (SNR. The proposed algorithm is computationally efficient, and the detection process is independent of the prior information. Meanwhile, the results can act as the initial value for other algorithms with higher precision. Simulation results show that the proposed algorithm achieves good performance for weak signal detection.
New algorithm for moving object detection
Zeljković Vesna
2004-01-01
Full Text Available A new, simple, fast and effective method for moving object detection in outdoor environments, invariant to extreme illumination changes is presented as an improvement to the shading model method described in [8]. It is based on an analytical parameter introduced in the shading model, background updating technique and window processing.
Novel automatic eye detection and tracking algorithm
Ghazali, Kamarul Hawari; Jadin, Mohd Shawal; Jie, Ma; Xiao, Rui
2015-04-01
The eye is not only one of the most complex but also the most important sensory organ of the human body. Eye detection and eye tracking are basement and hot issue in image processing. A non-invasive eye location and eye tracking is promising for hands-off gaze-based human-computer interface, fatigue detection, instrument control by paraplegic patients and so on. For this purpose, an innovation work frame is proposed to detect and tracking eye in video sequence in this paper. The contributions of this work can be divided into two parts. The first contribution is that eye filters were trained which can detect eye location efficiently and accurately without constraints on the background and skin colour. The second contribution is that a framework of tracker based on sparse representation and LK optic tracker were built which can track eye without constraint on eye status. The experimental results demonstrate the accuracy aspects and the real-time applicability of the proposed approach.
QRS Detection Based on an Advanced Multilevel Algorithm
Wissam Jenkal
2016-01-01
Full Text Available This paper presents an advanced multilevel algorithm used for the QRS complex detection. This method is based on three levels. The first permits the extraction of higher peaks using an adaptive thresholding technique. The second allows the QRS region detection. The last level permits the detection of Q, R and S waves. The proposed algorithm shows interesting results compared to recently published methods. The perspective of this work is the implementation of this method on an embedded system for a real time ECG monitoring system.
The algorithm of malicious code detection based on data mining
Yang, Yubo; Zhao, Yang; Liu, Xiabi
2017-08-01
Traditional technology of malicious code detection has low accuracy and it has insufficient detection capability for new variants. In terms of malicious code detection technology which is based on the data mining, its indicators are not accurate enough, and its classification detection efficiency is relatively low. This paper proposed the information gain ratio indicator based on the N-gram to choose signature, this indicator can accurately reflect the detection weight of the signature, and helped by C4.5 decision tree to elevate the algorithm of classification detection.
An ellipse detection algorithm based on edge classification
Yu, Liu; Chen, Feng; Huang, Jianming; Wei, Xiangquan
2015-12-01
In order to enhance the speed and accuracy of ellipse detection, an ellipse detection algorithm based on edge classification is proposed. Too many edge points are removed by making edge into point in serialized form and the distance constraint between the edge points. It achieves effective classification by the criteria of the angle between the edge points. And it makes the probability of randomly selecting the edge points falling on the same ellipse greatly increased. Ellipse fitting accuracy is significantly improved by the optimization of the RED algorithm. It uses Euclidean distance to measure the distance from the edge point to the elliptical boundary. Experimental results show that: it can detect ellipse well in case of edge with interference or edges blocking each other. It has higher detecting precision and less time consuming than the RED algorithm.
Information dynamics algorithm for detecting communities in networks
Massaro, E; Bagnoli, F; Liò, P
2011-01-01
The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network - inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark ...
Face detection based on multiple kernel learning algorithm
Sun, Bo; Cao, Siming; He, Jun; Yu, Lejun
2016-09-01
Face detection is important for face localization in face or facial expression recognition, etc. The basic idea is to determine whether there is a face in an image or not, and also its location, size. It can be seen as a binary classification problem, which can be well solved by support vector machine (SVM). Though SVM has strong model generalization ability, it has some limitations, which will be deeply analyzed in the paper. To access them, we study the principle and characteristics of the Multiple Kernel Learning (MKL) and propose a MKL-based face detection algorithm. In the paper, we describe the proposed algorithm in the interdisciplinary research perspective of machine learning and image processing. After analyzing the limitation of describing a face with a single feature, we apply several ones. To fuse them well, we try different kernel functions on different feature. By MKL method, the weight of each single function is determined. Thus, we obtain the face detection model, which is the kernel of the proposed method. Experiments on the public data set and real life face images are performed. We compare the performance of the proposed algorithm with the single kernel-single feature based algorithm and multiple kernels-single feature based algorithm. The effectiveness of the proposed algorithm is illustrated. Keywords: face detection, feature fusion, SVM, MKL
Texture orientation-based algorithm for detecting infrared maritime targets.
Wang, Bin; Dong, Lili; Zhao, Ming; Wu, Houde; Xu, Wenhai
2015-05-20
Infrared maritime target detection is a key technology for maritime target searching systems. However, in infrared maritime images (IMIs) taken under complicated sea conditions, background clutters, such as ocean waves, clouds or sea fog, usually have high intensity that can easily overwhelm the brightness of real targets, which is difficult for traditional target detection algorithms to deal with. To mitigate this problem, this paper proposes a novel target detection algorithm based on texture orientation. This algorithm first extracts suspected targets by analyzing the intersubband correlation between horizontal and vertical wavelet subbands of the original IMI on the first scale. Then the self-adaptive wavelet threshold denoising and local singularity analysis of the original IMI is combined to remove false alarms further. Experiments show that compared with traditional algorithms, this algorithm can suppress background clutter much better and realize better single-frame detection for infrared maritime targets. Besides, in order to guarantee accurate target extraction further, the pipeline-filtering algorithm is adopted to eliminate residual false alarms. The high practical value and applicability of this proposed strategy is backed strongly by experimental data acquired under different environmental conditions.
Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model
Hsieh, Chia-Yeh; Liu, Kai-Chun; Huang, Chih-Ning; Chu, Woei-Chyn; Chan, Chia-Tai
2017-01-01
Falls are the primary cause of accidents for the elderly in the living environment. Reducing hazards in the living environment and performing exercises for training balance and muscles are the common strategies for fall prevention. However, falls cannot be avoided completely; fall detection provides an alarm that can decrease injuries or death caused by the lack of rescue. The automatic fall detection system has opportunities to provide real-time emergency alarms for improving the safety and quality of home healthcare services. Two common technical challenges are also tackled in order to provide a reliable fall detection algorithm, including variability and ambiguity. We propose a novel hierarchical fall detection algorithm involving threshold-based and knowledge-based approaches to detect a fall event. The threshold-based approach efficiently supports the detection and identification of fall events from continuous sensor data. A multiphase fall model is utilized, including free fall, impact, and rest phases for the knowledge-based approach, which identifies fall events and has the potential to deal with the aforementioned technical challenges of a fall detection system. Seven kinds of falls and seven types of daily activities arranged in an experiment are used to explore the performance of the proposed fall detection algorithm. The overall performances of the sensitivity, specificity, precision, and accuracy using a knowledge-based algorithm are 99.79%, 98.74%, 99.05% and 99.33%, respectively. The results show that the proposed novel hierarchical fall detection algorithm can cope with the variability and ambiguity of the technical challenges and fulfill the reliability, adaptability, and flexibility requirements of an automatic fall detection system with respect to the individual differences. PMID:28208694
An Improved Time Domain Pitch Detection Algorithm for Pathological Voice
Mohd R. Jamaludin
2012-01-01
Full Text Available Problem statement: The present study proposes a new pitch detection algorithm which could potentially be used to detect pitch for disordered or pathological voices. One of the parameters required for dysphonia diagnosis is pitch and this prompted the development of a new and reliable pitch detection algorithm capable of accurately detect pitch in disordered voices. Approach: The proposed method applies a technique where the frame size of the half wave rectified autocorrelation is adjusted to a smaller frame after two potential pitch candidates are identified within the preliminary frame. Results: The method is compared to PRAATs standard autocorrelation and the result shows a significant improvement in detecting pitch for pathological voices. Conclusion: The proposed method is more reliable way to detect pitch, either in low or high pitched voice without adjusting the window size, fixing the pitch candidate search range and predefining threshold like most of the standard autocorrelation do.
Cooperative detection algorithm of spectrum holes in cognitive radio
SHI Lei; YE Zhun; ZHANG Zhong-zhao
2009-01-01
To improve the detection performance of sensing users for primary users in the cognitive radio, an optimal cooperative detection algorithm for many sensing users is proposed. In this paper, optimal decision thresholds of each sensing user are discussed. Theoretical analysis and simulation results indicate that the detection probability of optimal decision threshold rules is better than that of determined threshold rules when the false alarm of the fusion center is constant. The proposed optimal cooperative detection algorithm improves the detection performance of primary users as the attendees grow. The 2 dB gain of detection probabihty can be obtained when a new sensing user joins in, and there is a 17 dB improvement when the accumulation number increases from 1 to 50.
Lidar detection algorithm for time and range anomalies
Ben-David, Avishai; Davidson, Charles E.; Vanderbeek, Richard G.
2007-10-01
A new detection algorithm for lidar applications has been developed. The detection is based on hyperspectral anomaly detection that is implemented for time anomaly where the question "is a target (aerosol cloud) present at range R within time t1 to t2" is addressed, and for range anomaly where the question "is a target present at time t within ranges R1 and R2" is addressed. A detection score significantly different in magnitude from the detection scores for background measurements suggests that an anomaly (interpreted as the presence of a target signal in space/time) exists. The algorithm employs an option for a preprocessing stage where undesired oscillations and artifacts are filtered out with a low-rank orthogonal projection technique. The filtering technique adaptively removes the one over range-squared dependence of the background contribution of the lidar signal and also aids visualization of features in the data when the signal-to-noise ratio is low. A Gaussian-mixture probability model for two hypotheses (anomaly present or absent) is computed with an expectation-maximization algorithm to produce a detection threshold and probabilities of detection and false alarm. Results of the algorithm for CO2 lidar measurements of bioaerosol clouds Bacillus atrophaeus (formerly known as Bacillus subtilis niger, BG) and Pantoea agglomerans, Pa (formerly known as Erwinia herbicola, Eh) are shown and discussed.
Evaluating Subpixel Target Detection Algorithms in Hyperspectral Imagery
Yuval Cohen
2012-01-01
Full Text Available Our goal in this work is to demonstrate that detectors behave differently for different images and targets and to propose a novel approach to proper detector selection. To choose the algorithm, we analyze image statistics, the target signature, and the target's physical size, but we do not need any type of ground truth. We demonstrate our ability to evaluate detectors and find the best settings for their free parameters by comparing our results using the following stochastic algorithms for target detection: the constrained energy minimization (CEM, generalized likelihood ratio test (GLRT, and adaptive coherence estimator (ACE algorithms. We test our concepts by using the dataset and scoring methodology of the Rochester Institute of Technology (RIT Target Detection Blind Test project. The results show that our concept correctly ranks algorithms for the particular images and targets including in the RIT dataset.
2-D MULTIRATE ALGORITHMS FOR EFFICIENT IMPLEMENTATION OF EDGE DETECTION
Chen Ken; Wang Ping
2005-01-01
Edge detection is a fundamental issue in image analysis. This paper proposes multirate algorithms for efficient implementation of edge detector, and a design example is illustrated.The multirate (decimation and/or interpolation) signal processing algorithms can achieve considerable savings in computation and storage. The proposed algorithms result in mapping relations of their z-transfer functions between non-multirate and multirate mathematical expressions in terms of time-varying coefficient instead of traditional polyphase decomposition counterparts.The mapping properties can be readily utilized to efficiently analyze and synthesize multirate edge detection filters. The Very high-speed Hardware Description Language (VHDL) simulation results verify efficiency of the algorithms for real-time Field Programmable Gate-Array (FPGA)implementation.
The weirdest SDSS galaxies: results from an outlier detection algorithm
Baron, Dalya
2016-01-01
How can we discover objects we did not know existed within the large datasets that now abound in astronomy? We present an outlier detection algorithm that we developed, based on an unsupervised Random Forest. We test the algorithm on more than two million galaxy spectra from the Sloan Digital Sky Survey and examine the 400 galaxies with the highest outlier score. We find objects which have extreme emission line ratios and abnormally strong absorption lines, objects with unusual continua, including extremely reddened galaxies. We find galaxy-galaxy gravitational lenses, double-peaked emission line galaxies, and close galaxy pairs. We find galaxies with high ionisation lines, galaxies which host supernovae, and galaxies with unusual gas kinematics. Only a fraction of the outliers we find were reported by previous studies that used specific and tailored algorithms to find a single class of unusual objects. Our algorithm is general and detects all of these classes, and many more, regardless of what makes them pec...
A layer reduction based community detection algorithm on multiplex networks
Wang, Xiaodong; Liu, Jing
2017-04-01
Detecting hidden communities is important for the analysis of complex networks. However, many algorithms have been designed for single layer networks (SLNs) while just a few approaches have been designed for multiplex networks (MNs). In this paper, we propose an algorithm based on layer reduction for detecting communities on MNs, which is termed as LRCD-MNs. First, we improve a layer reduction algorithm termed as neighaggre to combine similar layers and keep others separated. Then, we use neighaggre to find the community structure hidden in MNs. Experiments on real-life networks show that neighaggre can obtain higher relative entropy than the other algorithm. Moreover, we apply LRCD-MNs on some real-life and synthetic multiplex networks and the results demonstrate that, although LRCD-MNs does not have the advantage in terms of modularity, it can obtain higher values of surprise, which is used to evaluate the quality of partitions of a network.
A Modularity Degree Based Heuristic Community Detection Algorithm
Dongming Chen
2014-01-01
Full Text Available A community in a complex network can be seen as a subgroup of nodes that are densely connected. Discovery of community structures is a basic problem of research and can be used in various areas, such as biology, computer science, and sociology. Existing community detection methods usually try to expand or collapse the nodes partitions in order to optimize a given quality function. These optimization function based methods share the same drawback of inefficiency. Here we propose a heuristic algorithm (MDBH algorithm based on network structure which employs modularity degree as a measure function. Experiments on both synthetic benchmarks and real-world networks show that our algorithm gives competitive accuracy with previous modularity optimization methods, even though it has less computational complexity. Furthermore, due to the use of modularity degree, our algorithm naturally improves the resolution limit in community detection.
WeirdestGalaxies: Outlier Detection Algorithm on Galaxy Spectra
Baron, Dalya; Poznanski, Dovi
2017-05-01
WeirdestGalaxies finds the weirdest galaxies in the Sloan Digital Sky Survey (SDSS) by using a basic outlier detection algorithm. It uses an unsupervised Random Forest (RF) algorithm to assign a similarity measure (or distance) between every pair of galaxy spectra in the SDSS. It then uses the distance matrix to find the galaxies that have the largest distance, on average, from the rest of the galaxies in the sample, and defined them as outliers.
Algorithm of detecting structural variations in DNA sequences
Nałecz-Charkiewicz, Katarzyna; Nowak, Robert
2014-11-01
Whole genome sequencing enables to use the longest common subsequence algorithm to detect genetic structure variations. We propose to search position of short unique fragments, genetic markers, to achieve acceptable time and space complexity. The markers are generated by algorithms searching the genetic sequence or its Fourier transformation. The presented methods are checked on structural variations generated in silico on bacterial genomes giving the comparable or better results than other solutions.
Detection of combined occurrences. [computer algorithms
Zobrist, A. L.; Carlson, F. R., Jr.
1977-01-01
In this paper it is supposed that the variables x sub 1,...,x sub n each have finite range with the variable x sub i taking on p sub i possible values and that the values of the variables are changing with time. It is supposed further that it is desired to detect occurrences in which some subset of the variables achieve particular values. Finally, it is supposed that the problem involves the detection of a large number of combined occurrences for a large number of changes of values of variables. Two efficient solutions for this problem are described. Both methods have the unusual property of being faster for systems where the sum p sub 1 +...+ p sub n is larger. The first solution is error-free and suitable for most cases. The second solution is slightly more elegant and allows negation as well as conjunction, but is subject to the possibility of errors. An error analysis is given for the second method and an empirical study is reported.
Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral
Wenhui Li
2014-01-01
Full Text Available Vision-based multivehicle detection plays an important role in Forward Collision Warning Systems (FCWS and Blind Spot Detection Systems (BSDS. The performance of these systems depends on the real-time capability, accuracy, and robustness of vehicle detection methods. To improve the accuracy of vehicle detection algorithm, we propose a multifeature fusion vehicle detection algorithm based on Choquet integral. This algorithm divides the vehicle detection problem into two phases: feature similarity measure and multifeature fusion. In the feature similarity measure phase, we first propose a taillight-based vehicle detection method, and then vehicle taillight feature similarity measure is defined. Second, combining with the definition of Choquet integral, the vehicle symmetry similarity measure and the HOG + AdaBoost feature similarity measure are defined. Finally, these three features are fused together by Choquet integral. Being evaluated on public test collections and our own test images, the experimental results show that our method has achieved effective and robust multivehicle detection in complicated environments. Our method can not only improve the detection rate but also reduce the false alarm rate, which meets the engineering requirements of Advanced Driving Assistance Systems (ADAS.
Active Sonar Detection in Reverberation via Signal Subspace Extraction Algorithm
Ma Xiaochuan
2010-01-01
Full Text Available This paper presents a new algorithm called Signal Subspace Extraction (SSE for detecting and estimating target echoes in reverberation. The new algorithm can be taken as an extension of the Principal Component Inverse (PCI and maintains the benefit of PCI algorithm and moreover shows better performance due to a more reasonable reverberation model. In the SSE approach, a best low-rank estimate of a target echo is extracted by decomposing the returns into short duration subintervals and by invoking the Eckart-Young theorem twice. It was assumed that CW is less efficiency in lower Doppler than broadband waveforms in spectrum methods; however, the subspace methods show good performance in detection whatever the respective Doppler is. Hence, the signal emitted by active sonar is CW in the new algorithm which performs well in detection and estimation even when low Doppler is low. Further, a block forward matrix is proposed to extend the algorithm to the sensor array problem. The comparison among the block forward matrix, the conventional matrix, and the three-mode array is discussed. Echo separation is also provided by the new algorithm. Examples are presented using both real, active-sonar data and simulated data.
SIDRA: a blind algorithm for signal detection in photometric surveys
Mislis, D.; Bachelet, E.; Alsubai, K. A.; Bramich, D. M.; Parley, N.
2016-01-01
We present the Signal Detection using Random-Forest Algorithm (SIDRA). SIDRA is a detection and classification algorithm based on the Machine Learning technique (Random Forest). The goal of this paper is to show the power of SIDRA for quick and accurate signal detection and classification. We first diagnose the power of the method with simulated light curves and try it on a subset of the Kepler space mission catalogue. We use five classes of simulated light curves (CONSTANT, TRANSIT, VARIABLE, MLENS and EB for constant light curves, transiting exoplanet, variable, microlensing events and eclipsing binaries, respectively) to analyse the power of the method. The algorithm uses four features in order to classify the light curves. The training sample contains 5000 light curves (1000 from each class) and 50 000 random light curves for testing. The total SIDRA success ratio is ≥90 per cent. Furthermore, the success ratio reaches 95-100 per cent for the CONSTANT, VARIABLE, EB and MLENS classes and 92 per cent for the TRANSIT class with a decision probability of 60 per cent. Because the TRANSIT class is the one which fails the most, we run a simultaneous fit using SIDRA and a Box Least Square (BLS)-based algorithm for searching for transiting exoplanets. As a result, our algorithm detects 7.5 per cent more planets than a classic BLS algorithm, with better results for lower signal-to-noise light curves. SIDRA succeeds to catch 98 per cent of the planet candidates in the Kepler sample and fails for 7 per cent of the false alarms subset. SIDRA promises to be useful for developing a detection algorithm and/or classifier for large photometric surveys such as TESS and PLATO exoplanet future space missions.
Algorithmic Detection of Computer Generated Text
Lavoie, Allen
2010-01-01
Computer generated academic papers have been used to expose a lack of thorough human review at several computer science conferences. We assess the problem of classifying such documents. After identifying and evaluating several quantifiable features of academic papers, we apply methods from machine learning to build a binary classifier. In tests with two hundred papers, the resulting classifier correctly labeled papers either as human written or as computer generated with no false classifications of computer generated papers as human and a 2% false classification rate for human papers as computer generated. We believe generalizations of these features are applicable to similar classification problems. While most current text-based spam detection techniques focus on the keyword-based classification of email messages, a new generation of unsolicited computer-generated advertisements masquerade as legitimate postings in online groups, message boards and social news sites. Our results show that taking the formatti...
An Improved Local Community Detection Algorithm Using Selection Probability
Shixiong Xia
2014-01-01
Full Text Available In order to find the structure of local community more effectively, we propose an improved local community detection algorithm ILCDSP, which improves the node selection strategy, and sets selection probability value for every candidate node. ILCDSP assigns nodes with different selection probability values, which are equal to the degree of the nodes to be chosen. By this kind of strategy, the proposed algorithm can detect the local communities effectively, since it can ensure the best search direction and avoid the local optimal solution. Various experimental results on both synthetic and real networks demonstrate that the quality of the local communities detected by our algorithm is significantly superior to the state-of-the-art methods.
Moving Vehicle Detection and Tracking Algorithm in Traffic Video
Shisong Zhu
2013-06-01
Full Text Available Aiming at the defects and shortages of traditional moving vehicles detection algorithms, by the analysis and comparison of the existing detection algorithms, we propose an algorithm that combined with frames with symmetric difference and background difference to detect moving vehicle in this paper. First, two different difference images by using frames with symmetric difference and background difference are gained respectively and two binary images can be gained by the appropriate threshold, then the contour of moving vehicles can be extracted by applying OR operation in the two binary images. Finally, the precise moving vehicles will be gained by mathematic morphological methods. In this paper we use Harris operator, Feature Points such as edges and corners are extracted, followed by block-matching to track the Feature Points in successive viedo frames. Many vehicles can be tracked at the same time automatically since the information is obtained from video sequences.
SIDRA: a blind algorithm for signal detection in photometric surveys
Mislis, D; Alsubai, K A; Bramich, D M; Parley, N
2015-01-01
We present the Signal Detection using Random-Forest Algorithm (SIDRA). SIDRA is a detection and classification algorithm based on the Machine Learning technique (Random Forest). The goal of this paper is to show the power of SIDRA for quick and accurate signal detection and classification. We first diagnose the power of the method with simulated light curves and try it on a subset of the Kepler space mission catalogue. We use five classes of simulated light curves (CONSTANT, TRANSIT, VARIABLE, MLENS and EB for constant light curves, transiting exoplanet, variable, microlensing events and eclipsing binaries, respectively) to analyse the power of the method. The algorithm uses four features in order to classify the light curves. The training sample contains 5000 light curves (1000 from each class) and 50000 random light curves for testing. The total SIDRA success ratio is $\\geq 90\\%$. Furthermore, the success ratio reaches 95 - 100$\\%$ for the CONSTANT, VARIABLE, EB, and MLENS classes and 92$\\%$ for the TRANSIT...
Extended seizure detection algorithm for intracranial EEG recordings
Kjaer, T. W.; Remvig, L. S.; Henriksen, J.
2010-01-01
Objective: We implemented and tested an existing seizure detection algorithm for scalp EEG (sEEG) with the purpose of improving it to intracranial EEG (iEEG) recordings. Method: iEEG was obtained from 16 patients with focal epilepsy undergoing work up for resective epilepsy surgery. Each patient...... had 4 or 5 recorded seizures and 24 hours of non-ictal data were used for evaluation. Data from three electrodes placed at the ictal focus were used for the analysis. A wavelet based feature extraction algorithm delivered input to a support vector machine (SVM) classifier for distinction between ictal...... the original implementation a sensitivity of 92.8% and a false positive ratio (FPR) of 0.93/h were obtained. Our extension of the algorithm rendered a 95.9% sensitivity and only 0.65 false detections per hour. Conclusion: Better seizure detection can be performed when the higher frequencies in the iEEG were...
An Early Fire Detection Algorithm Using IP Cameras
Hector Perez-Meana
2012-05-01
Full Text Available The presence of smoke is the first symptom of fire; therefore to achieve early fire detection, accurate and quick estimation of the presence of smoke is very important. In this paper we propose an algorithm to detect the presence of smoke using video sequences captured by Internet Protocol (IP cameras, in which important features of smoke, such as color, motion and growth properties are employed. For an efficient smoke detection in the IP camera platform, a detection algorithm must operate directly in the Discrete Cosine Transform (DCT domain to reduce computational cost, avoiding a complete decoding process required for algorithms that operate in spatial domain. In the proposed algorithm the DCT Inter-transformation technique is used to increase the detection accuracy without inverse DCT operation. In the proposed scheme, firstly the candidate smoke regions are estimated using motion and color smoke properties; next using morphological operations the noise is reduced. Finally the growth properties of the candidate smoke regions are furthermore analyzed through time using the connected component labeling technique. Evaluation results show that a feasible smoke detection method with false negative and false positive error rates approximately equal to 4% and 2%, respectively, is obtained.
VIPR: A probabilistic algorithm for analysis of microbial detection microarrays
Holbrook Michael R
2010-07-01
Full Text Available Abstract Background All infectious disease oriented clinical diagnostic assays in use today focus on detecting the presence of a single, well defined target agent or a set of agents. In recent years, microarray-based diagnostics have been developed that greatly facilitate the highly parallel detection of multiple microbes that may be present in a given clinical specimen. While several algorithms have been described for interpretation of diagnostic microarrays, none of the existing approaches is capable of incorporating training data generated from positive control samples to improve performance. Results To specifically address this issue we have developed a novel interpretive algorithm, VIPR (Viral Identification using a PRobabilistic algorithm, which uses Bayesian inference to capitalize on empirical training data to optimize detection sensitivity. To illustrate this approach, we have focused on the detection of viruses that cause hemorrhagic fever (HF using a custom HF-virus microarray. VIPR was used to analyze 110 empirical microarray hybridizations generated from 33 distinct virus species. An accuracy of 94% was achieved as measured by leave-one-out cross validation. Conclusions VIPR outperformed previously described algorithms for this dataset. The VIPR algorithm has potential to be broadly applicable to clinical diagnostic settings, wherein positive controls are typically readily available for generation of training data.
The Study of Randomized Visual Saliency Detection Algorithm
Yuantao Chen
2013-01-01
Full Text Available Image segmentation process for high quality visual saliency map is very dependent on the existing visual saliency metrics. It is mostly only get sketchy effect of saliency map, and roughly based visual saliency map will affect the image segmentation results. The paper had presented the randomized visual saliency detection algorithm. The randomized visual saliency detection method can quickly generate the same size as the original input image and detailed results of the saliency map. The randomized saliency detection method can be applied to real-time requirements for image content-based scaling saliency results map. The randomization method for fast randomized video saliency area detection, the algorithm only requires a small amount of memory space can be detected detailed oriented visual saliency map, the presented results are shown that the method of visual saliency map used in image after the segmentation process can be an ideal segmentation results.
A Blind Detection Algorithm Utilizing Statistical Covariance in Cognitive Radio
Yingxue Li
2012-11-01
Full Text Available As the expression of performance parameters are obtained using asymptotic method in most blind covariance detection algorithm, the paper presented a new blind detection algorithm using cholesky factorization. Utilizing random matrix theory, we derived the performance parameters using non-asymptotic method. The proposed method overcomes the noise uncertainty problem and performs well without any information about the channel, primary user and noise. Numerical simulation results demonstrate that the performance parameters expressions are correct and the new detector outperforms the other blind covariance detectors.
Space Object Maneuver Detection Algorithms Using TLE Data
Pittelkau, M.
2016-09-01
An important aspect of Space Situational Awareness (SSA) is detection of deliberate and accidental orbit changes of space objects. Although space surveillance systems detect orbit maneuvers within their tracking algorithms, maneuver data are not readily disseminated for general use. However, two-line element (TLE) data is available and can be used to detect maneuvers of space objects. This work is an attempt to improve upon existing TLE-based maneuver detection algorithms. Three adaptive maneuver detection algorithms are developed and evaluated: The first is a fading-memory Kalman filter, which is equivalent to the sliding-window least-squares polynomial fit, but computationally more efficient and adaptive to the noise in the TLE data. The second algorithm is based on a sample cumulative distribution function (CDF) computed from a histogram of the magnitude-squared |V|2 of change-in-velocity vectors (V), which is computed from the TLE data. A maneuver detection threshold is computed from the median estimated from the CDF, or from the CDF and a specified probability of false alarm. The third algorithm is a median filter. The median filter is the simplest of a class of nonlinear filters called order statistics filters, which is within the theory of robust statistics. The output of the median filter is practically insensitive to outliers, or large maneuvers. The median of the |V|2 data is proportional to the variance of the V, so the variance is estimated from the output of the median filter. A maneuver is detected when the input data exceeds a constant times the estimated variance.
Vision-based vehicle detection and tracking algorithm design
Hwang, Junyeon; Huh, Kunsoo; Lee, Donghwi
2009-12-01
The vision-based vehicle detection in front of an ego-vehicle is regarded as promising for driver assistance as well as for autonomous vehicle guidance. The feasibility of vehicle detection in a passenger car requires accurate and robust sensing performance. A multivehicle detection system based on stereo vision has been developed for better accuracy and robustness. This system utilizes morphological filter, feature detector, template matching, and epipolar constraint techniques in order to detect the corresponding pairs of vehicles. After the initial detection, the system executes the tracking algorithm for the vehicles. The proposed system can detect front vehicles such as the leading vehicle and side-lane vehicles. The position parameters of the vehicles located in front are obtained based on the detection information. The proposed vehicle detection system is implemented on a passenger car, and its performance is verified experimentally.
Kersten, K.; Cattell, C. A.; Breneman, A.; Goetz, K.; Kellogg, P. J.; Wygant, J. R.; Wilson, L. B., III; Blake, J. B.; Looper, M. D.; Roth, I.
2011-01-01
We present multi-satellite observations of large amplitude radiation belt whistler-mode waves and relativistic electron precipitation. On separate occasions during the Wind petal orbits and STEREO phasing orbits, Wind and STEREO recorded intense whistler-mode waves in the outer nightside equatorial radiation belt with peak-to-peak amplitudes exceeding 300 mV/m. During these intervals of intense wave activity, SAMPEX recorded relativistic electron microbursts in near magnetic conjunction with Wind and STEREO. This evidence of microburst precipitation occurring at the same time and at nearly the same magnetic local time and L-shell with a bursty temporal structure similar to that of the observed large amplitude wave packets suggests a causal connection between the two phenomena. Simulation studies corroborate this idea, showing that nonlinear wave.particle interactions may result in rapid energization and scattering on timescales comparable to those of the impulsive relativistic electron precipitation.
Copyright Detection System for Videos Using TIRI-DCT Algorithm
S. Nirmal
2012-12-01
Full Text Available The copyright detection system is used to detect whether a video is copyrighted or not by extracting the features or fingerprints of a video and matching them with fingerprints other videos. The system is mainly used for copyright applications of multimedia content. The copyright detection system depends on an algorithm to extract fingerprints which is the TIRI-DCT Algorithm of a video followed by an approximate search algorithm which is the Inverted File Based Similarity Search. To find whether a video is copyrighted or not, the query video is taken and the feature values of the video are extracted using the fingerprint extraction algorithm, it extracts feature values from special images called frames constructed from the video. Each frame represents a part or a segment of the video and contains both temporal and spatial information of the video segment. These images are called Temporally Informative Representative Images (TIRI. The fingerprints of all the videos in the database are extracted and stored in advance. The approximate search algorithm searches the fingerprints which is stored in the database and produces the closest matches to the fingerprint of the query video and based on the match the query video is found whether it is a copyrighted video or not.
Multi-sources information fusion algorithm in airborne detection systems
Yang Yan; Jing Zhanrong; Gao Tian; Wang Huilong
2007-01-01
To aim at the multimode character of the data from the airplane detecting system, the paper combines DempsterSharer evidence theory and subjective Bayesian algorithm and makes to propose a mixed structure multimode data fusion algorithm. The algorithm adopts a prorated algorithm relate to the incertitude evaluation to convert the probability evaluation into the precognition probability in an identity frame, and ensures the adaptability of different data from different source to the mixed system. To guarantee real time fusion, a combination of time domain fusion and space domain fusion is established, this not only assure the fusion of data chain in different time of the same sensor, but also the data fusion from different sensors distributed in different platforms and the data fusion among different modes. The feasibility and practicability are approved through computer simulation.
Robust Algorithm for Face Detection in Color Images
Hlaing Htake Khaung Tin
2012-03-01
Full Text Available Robust Algorithm is presented for frontal face detection in color images. Face detection is an important task in facial analysis systems in order to have a priori localized faces in a given image. Applications such as face tracking, facial expression recognition, gesture recognition, etc., for example, have a pre-requisite that a face is already located in the given image or the image sequence. Facial features such as eyes, nose and mouth are automatically detected based on properties of the associated image regions. On detecting a mouth, a nose and two eyes, a face verification step based on Eigen face theory is applied to a normalized search space in the image relative to the distance between the eye feature points. The experiments were carried out on test images taken from the internet and various other randomly selected sources. The algorithm has also been tested in practice with a webcam, giving (near real-time performance and good extraction results.
Comparison Between Four Detection Algorithms for GEO Objects
Yanagisawa, T.; Uetsuhara, M.; Banno, H.; Kurosaki, H.; Kinoshita, D.; Kitazawa, Y.; Hanada, T.
2012-09-01
Four detection algorithms for GEO objects are being developed under the collaboration between Kyushu University, IHI corporation and JAXA. Each algorithm is designed to process CCD images to detect GEO objects. First one is PC based stacking method which has been developed in JAXA since 2000. Numerous CCD images are used to detect faint GEO objects below the limiting magnitude of a single CCD image. Sub-images are cropped from many CCD image to fit the movement of the objects. A median image of all the sub-images is then created. Although this method has an ability to detect faint objects, it takes time to analyze. Second one is the line-identifying technique which also uses many CCD frames and finds any series of objects that are arrayed on a straight line from the first frame to the last frame. This can analyze data faster than the stacking method, but cannot detect faint objects as the stacking method. Third one is the robust stacking method developed by IHI corporation which uses average instead of median to reduce analysis time. This has same analysis speed as the line-identifying technique and better detection capabilities in terms of the darkness. Forth one is the FPGA based stacking method which uses binalized images and a new algorithm installed in a FPGA board which reduce analysis time about one thousandth. All four algorithms analyzed the same sets of data to evaluate their advantages and disadvantages. By comparing their analysis times and results, an optimal usage of these algorithms are considered.
A Novel Real Time Motion Detection Algorithm For Videos
M. Nagaraju
2013-11-01
Full Text Available Real-time detection of moving objects is vital for video surveillance. Background subtraction serves as a basic method typically used to segment the moving objects in image sequences taken from a camera. Some existing algorithms cannot fine-tune changing circumstances and they need manual calibration in relation to specification of parameters or some hypotheses for dynamic changing background. An adaptive motion segmentation and detection strategy is developed by using motion variation and chromatic characteristics, which eliminates undesired corruption of the background model and it doesn't look on the adaptation coefficient. In this particular proposed work, a novel real-time motion detection algorithm is proposed for dynamic changing background features. The algorithm integrates the temporal differencing along with optical flow method, double background filtering method and morphological processing techniques to achieve better detection performance. Temporal differencing is designed to detect initial motion areas for the optical-flow calculation to produce real-time and accurate object motion vectors detection. The double background filtering method is obtain and keep a reliable background image to handle variations on environmental changing conditions that is designed to get rid of the background interference and separate the moving objects from it. The morphological processing methods are adopted and mixed with the double background filtering to obtain improved results. The most attractive benefit for this algorithm is that the algorithm does not require to figure out the background model from hundreds of images and can handle quick image variations without prior understanding of the object size and shape.
An Algorithm of Sensor Management Based on Dynamic Target Detection
LIUXianxing; ZHOULin; JINYong
2005-01-01
The probability density of stationary target is only evolved at measurement update, but the probability density of dynamic target is evolved not only at measurement update but also during measurements, this paper researches an algorithm of dynamic targets detection. Firstly, it presents the evolution of probability density at measurement update by Bayes' rule and the evolution of probability density during measurements by Fokker-Planck differential equations, respectively. Secondly, the method of obtaining information entropy by the probability density is given and sensor resources are distributed based on the evolution of information entropy viz. the maximization of information gain. Simulation results show that compared with the algorithm of serial search, this algorithm is feasible and effective when it is used to detect dynamic target.
Improved Snake algorithm for complex target's boundary detection
ZHAO Bao-jun; LI Dong
2006-01-01
The traditional Snake algorithm cannot effectively detect the object edge of an image with non-convex shapes or low SNR.This paper studies the characteristics of this type of image with complex shape target or noise and presents an improved Snake algorithm.The traditional Snake function model and operation strategy are improved by increasing new control energy functions,and the influencing weight of these energy factors is discussed.At the same time,a dynamic arrangement for the Snake points is used to adapt different target shapes.The simulation results indicate that the new Snake model greatly decreases the dependence on the Snake point's initial position and effectively overcomes noise influence.This method enhances the Snake algorithm's ability of detecting object edge.
A Supervised Classification Algorithm for Note Onset Detection
Douglas Eck
2007-01-01
Full Text Available This paper presents a novel approach to detecting onsets in music audio files. We use a supervised learning algorithm to classify spectrogram frames extracted from digital audio as being onsets or nononsets. Frames classified as onsets are then treated with a simple peak-picking algorithm based on a moving average. We present two versions of this approach. The first version uses a single neural network classifier. The second version combines the predictions of several networks trained using different hyperparameters. We describe the details of the algorithm and summarize the performance of both variants on several datasets. We also examine our choice of hyperparameters by describing results of cross-validation experiments done on a custom dataset. We conclude that a supervised learning approach to note onset detection performs well and warrants further investigation.
An Adaptive Immune Genetic Algorithm for Edge Detection
Li, Ying; Bai, Bendu; Zhang, Yanning
An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.
Performance Comparisions of ICA Algorithms to DS-CDMA Detection
Parmar, Sargam
2010-01-01
Commercial cellular networks, like the systems based on DS-CDMA, face many types of interferences such as multi-user interference inside each sector in a cell to interoperate interference. Independent Component Analysis (ICA) has been used as an advanced preprocessing tool for blind suppression of interfering signals in DS-CDMA communication systems. The role of ICA is to provide an interference-mitigated signal to the conventional detection. This paper evaluates the performance of some major ICA algorithms like Cardoso's joint approximate diagonalization of eigen matrices (JADE), Hyvarinen's fixed point algorithm and Comon's algorithm to solve the symbol estimation problem of the multi users in a DSCDMA communication system. The main focus is on blind separation of convolved CDMA mixture and the improvement of the downlink symbol estimation. The results of numerical experiment are compared with those obtained by the Single User Detection (SUD) receiver, ICA detector and combined SUD-ICA detector.
Comparison of Two Detection Combination Algorithms for Phased Array Radars
2015-07-01
weapon guidance. It can also be used effectively for secure communications [1]. In an MFR, the radar surveillance plays a critical role to optimize the...horizon/surface search, detection confirmation, multi-target tracking and cued search. The simulated radar has an aperture of 1 m2. The antennas...Comparison of Two Detection Combination Algorithms for Phased Array Radars Zhen Ding and Peter Moo Wide Area Surveillance Radar Group Radar
Plagiarism Detection Algorithm for Source Code in Computer Science Education
Liu, Xin; Xu, Chan; Ouyang, Boyu
2015-01-01
Nowadays, computer programming is getting more necessary in the course of program design in college education. However, the trick of plagiarizing plus a little modification exists among some students' home works. It's not easy for teachers to judge if there's plagiarizing in source code or not. Traditional detection algorithms cannot fit this…
Detection of outliers in reference distributions: performance of Horn's algorithm.
Solberg, Helge Erik; Lahti, Ari
2005-12-01
Medical laboratory reference data may be contaminated with outliers that should be eliminated before estimation of the reference interval. A statistical test for outliers has been proposed by Paul S. Horn and coworkers (Clin Chem 2001;47:2137-45). The algorithm operates in 2 steps: (a) mathematically transform the original data to approximate a gaussian distribution; and (b) establish detection limits (Tukey fences) based on the central part of the transformed distribution. We studied the specificity of Horn's test algorithm (probability of false detection of outliers), using Monte Carlo computer simulations performed on 13 types of probability distributions covering a wide range of positive and negative skewness. Distributions with 3% of the original observations replaced by random outliers were used to also examine the sensitivity of the test (probability of detection of true outliers). Three data transformations were used: the Box and Cox function (used in the original Horn's test), the Manly exponential function, and the John and Draper modulus function. For many of the probability distributions, the specificity of Horn's algorithm was rather poor compared with the theoretical expectation. The cause for such poor performance was at least partially related to remaining nongaussian kurtosis (peakedness). The sensitivity showed great variation, dependent on both the type of underlying distribution and the location of the outliers (upper and/or lower tail). Although Horn's algorithm undoubtedly is an improvement compared with older methods for outlier detection, reliable statistical identification of outliers in reference data remains a challenge.
Evaluation of feature detection algorithms for structure from motion
Govender, N
2009-11-01
Full Text Available such as Harris corner detectors and feature descriptors such as SIFT (Scale Invariant Feature Transform) and SURF (Speeded Up Robust Features) given a set of input images. This paper implements state-of-the art feature detection algorithms and evaluates...
Staff line detection and revision algorithm based on subsection projection and correlation algorithm
Yang, Yin-xian; Yang, Ding-li
2013-03-01
Staff line detection plays a key role in OMR technology, and is the precon-ditions of subsequent segmentation 1& recognition of music sheets. For the phenomena of horizontal inclination & curvature of staff lines and vertical inclination of image, which often occur in music scores, an improved approach based on subsection projection is put forward to realize the detection of original staff lines and revision in an effect to implement staff line detection more successfully. Experimental results show the presented algorithm can detect and revise staff lines fast and effectively.
J. J. Lee
2012-11-01
Full Text Available Electron microburst energy spectra in the range of 170 keV to 360 keV have been measured using two solid-state detectors onboard the low-altitude (680 km, polar-orbiting Korean STSAT-1 (Science and Technology SATellite-1. Applying a unique capability of the spacecraft attitude control system, microburst energy spectra have been accurately resolved into two components: perpendicular to and parallel to the geomagnetic field direction. The former measures trapped electrons and the latter those electrons with pitch angles in the loss cone and precipitating into atmosphere. It is found that the perpendicular component energy spectra are harder than the parallel component and the loss cone is not completely filled by the electrons in the energy range of 170 keV to 360 keV. These results have been modeled assuming a wave-particle cyclotron resonance mechanism, where higher energy electrons travelling within a magnetic flux tube interact with whistler mode waves at higher latitudes (lower altitudes. Our results suggest that because higher energy (relativistic microbursts do not fill the loss cone completely, only a small portion of electrons is able to reach low altitude (~100 km atmosphere. Thus assuming that low energy microbursts and relativistic microbursts are created by cyclotron resonance with chorus elements (but at different locations, the low energy portion of the microburst spectrum will dominate at low altitudes. This explains why relativistic microbursts have not been observed by balloon experiments, which typically float at altitudes of ~30 km and measure only X-ray flux produced by collisions between neutral atmospheric particles and precipitating electrons.
Radar Detection for Dim Moving Target Using DP Algorithm
MOLi; WUSiliang; MAOErke
2004-01-01
A lot of work has been done for applying Dynamic programming (DP) algorithm in weak moving target detection and tracking. Most of them are based on IR image sequence and a constant target velocity is assumed. The goal of this paper is to apply DP algorithm to radar Doppler detection problems. Radar returns from multiple range cells are arranged as a set of Doppler-range images to fit in DP imagery detection model. Trajectories in image sequence are modeled as states sets. Then De algorithm is applied to integrate measurements along possible target trajectories, returning as possible targets those trajectories for which the measurement sum, or merit function, exceeds a threshold. Low SNR target that maneuvers among several range cells can be detected through such long-term integration. Under a practical set of radar parameters, good detection performances are presented that at about 10070 detection probability, about 7dB SNR gain is achieved through 15-frame DP non-coherent integration.
Information dynamics algorithm for detecting communities in networks
Massaro, Emanuele; Bagnoli, Franco; Guazzini, Andrea; Lió, Pietro
2012-11-01
The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network-inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method [4] by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark and on computer generated networks with known community structure. Our approach has three important features: the capacity of detecting overlapping communities, the capability of identifying communities from an individual point of view and the fine tuning the community detectability with respect to prior knowledge of the data. Finally we discuss how to use a Shannon entropy measure for parameter estimation in complex networks.
Detecting microsatellites within genomes: significant variation among algorithms
Rivals Eric
2007-04-01
Full Text Available Abstract Background Microsatellites are short, tandemly-repeated DNA sequences which are widely distributed among genomes. Their structure, role and evolution can be analyzed based on exhaustive extraction from sequenced genomes. Several dedicated algorithms have been developed for this purpose. Here, we compared the detection efficiency of five of them (TRF, Mreps, Sputnik, STAR, and RepeatMasker. Results Our analysis was first conducted on the human X chromosome, and microsatellite distributions were characterized by microsatellite number, length, and divergence from a pure motif. The algorithms work with user-defined parameters, and we demonstrate that the parameter values chosen can strongly influence microsatellite distributions. The five algorithms were then compared by fixing parameters settings, and the analysis was extended to three other genomes (Saccharomyces cerevisiae, Neurospora crassa and Drosophila melanogaster spanning a wide range of size and structure. Significant differences for all characteristics of microsatellites were observed among algorithms, but not among genomes, for both perfect and imperfect microsatellites. Striking differences were detected for short microsatellites (below 20 bp, regardless of motif. Conclusion Since the algorithm used strongly influences empirical distributions, studies analyzing microsatellite evolution based on a comparison between empirical and theoretical size distributions should therefore be considered with caution. We also discuss why a typological definition of microsatellites limits our capacity to capture their genomic distributions.
An Efficient Conflict Detection Algorithm for Packet Filters
Lee, Chun-Liang; Lin, Guan-Yu; Chen, Yaw-Chung
Packet classification is essential for supporting advanced network services such as firewalls, quality-of-service (QoS), virtual private networks (VPN), and policy-based routing. The rules that routers use to classify packets are called packet filters. If two or more filters overlap, a conflict occurs and leads to ambiguity in packet classification. This study proposes an algorithm that can efficiently detect and resolve filter conflicts using tuple based search. The time complexity of the proposed algorithm is O(nW+s), and the space complexity is O(nW), where n is the number of filters, W is the number of bits in a header field, and s is the number of conflicts. This study uses the synthetic filter databases generated by ClassBench to evaluate the proposed algorithm. Simulation results show that the proposed algorithm can achieve better performance than existing conflict detection algorithms both in time and space, particularly for databases with large numbers of conflicts.
A bioinspired collision detection algorithm for VLSI implementation
Cuadri, J.; Linan, G.; Stafford, R.; Keil, M. S.; Roca, E.
2005-06-01
In this paper a bioinspired algorithm for collision detection is proposed, based on previous models of the locust (Locusta migratoria) visual system reported by F.C. Rind and her group, in the University of Newcastle-upon-Tyne. The algorithm is suitable for VLSI implementation in standard CMOS technologies as a system-on-chip for automotive applications. The working principle of the algorithm is to process a video stream that represents the current scenario, and to fire an alarm whenever an object approaches on a collision course. Moreover, it establishes a scale of warning states, from no danger to collision alarm, depending on the activity detected in the current scenario. In the worst case, the minimum time before collision at which the model fires the collision alarm is 40 msec (1 frame before, at 25 frames per second). Since the average time to successfully fire an airbag system is 2 msec, even in the worst case, this algorithm would be very helpful to more efficiently arm the airbag system, or even take some kind of collision avoidance countermeasures. Furthermore, two additional modules have been included: a "Topological Feature Estimator" and an "Attention Focusing Algorithm". The former takes into account the shape of the approaching object to decide whether it is a person, a road line or a car. This helps to take more adequate countermeasures and to filter false alarms. The latter centres the processing power into the most active zones of the input frame, thus saving memory and processing time resources.
Advanced defect detection algorithm using clustering in ultrasonic NDE
Gongzhang, Rui; Gachagan, Anthony
2016-02-01
A range of materials used in industry exhibit scattering properties which limits ultrasonic NDE. Many algorithms have been proposed to enhance defect detection ability, such as the well-known Split Spectrum Processing (SSP) technique. Scattering noise usually cannot be fully removed and the remaining noise can be easily confused with real feature signals, hence becoming artefacts during the image interpretation stage. This paper presents an advanced algorithm to further reduce the influence of artefacts remaining in A-scan data after processing using a conventional defect detection algorithm. The raw A-scan data can be acquired from either traditional single transducer or phased array configurations. The proposed algorithm uses the concept of unsupervised machine learning to cluster segmental defect signals from pre-processed A-scans into different classes. The distinction and similarity between each class and the ensemble of randomly selected noise segments can be observed by applying a classification algorithm. Each class will then be labelled as `legitimate reflector' or `artefacts' based on this observation and the expected probability of defection (PoD) and probability of false alarm (PFA) determined. To facilitate data collection and validate the proposed algorithm, a 5MHz linear array transducer is used to collect A-scans from both austenitic steel and Inconel samples. Each pulse-echo A-scan is pre-processed using SSP and the subsequent application of the proposed clustering algorithm has provided an additional reduction to PFA while maintaining PoD for both samples compared with SSP results alone.
Common pharmacophore identification using frequent clique detection algorithm.
Podolyan, Yevgeniy; Karypis, George
2009-01-01
The knowledge of a pharmacophore, or the 3D arrangement of features in the biologically active molecule that is responsible for its pharmacological activity, can help in the search and design of a new or better drug acting upon the same or related target. In this paper, we describe two new algorithms based on the frequent clique detection in the molecular graphs. The first algorithm mines all frequent cliques that are present in at least one of the conformers of each (or a portion of all) molecules. The second algorithm exploits the similarities among the different conformers of the same molecule and achieves an order of magnitude performance speedup compared to the first algorithm. Both algorithms are guaranteed to find all common pharmacophores in the data set, which is confirmed by the validation on the set of molecules for which pharmacophores have been determined experimentally. In addition, these algorithms are able to scale to data sets with arbitrarily large number of conformers per molecule and identify multiple ligand binding modes or multiple binding sites of the target.
Fall detection using supervised machine learning algorithms: A comparative study
Zerrouki, Nabil
2017-01-05
Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches which are: Naïve Bayes, K nearest neighbor, neural network, and support vector machine. The analysis of the classification power associated to these most widely utilized algorithms is conducted on two fall detection databases namely FDD and URFD. Since the performance of the classification algorithm is inherently dependent on the features, we extracted and used the same features for all classifiers. The classification evaluation is conducted using different state of the art statistical measures such as the overall accuracy, the F-measure coefficient, and the area under ROC curve (AUC) value.
DDoS Attack Detection Algorithms Based on Entropy Computing
Li, Liying; Zhou, Jianying; Xiao, Ning
Distributed Denial of Service (DDoS) attack poses a severe threat to the Internet. It is difficult to find the exact signature of attacking. Moreover, it is hard to distinguish the difference of an unusual high volume of traffic which is caused by the attack or occurs when a huge number of users occasionally access the target machine at the same time. The entropy detection method is an effective method to detect the DDoS attack. It is mainly used to calculate the distribution randomness of some attributes in the network packets' headers. In this paper, we focus on the detection technology of DDoS attack. We improve the previous entropy detection algorithm, and propose two enhanced detection methods based on cumulative entropy and time, respectively. Experiment results show that these methods could lead to more accurate and effective DDoS detection.
Charged System Search Algorithm Utilized for Structural Damage Detection
Zahra Tabrizian
2014-01-01
Full Text Available This paper presents damage detection and assessment methodology based on the changes in dynamic parameters of a structural system. The method is applied at an element level using a finite element model. According to continuum damage mechanics, damage is represented by a reduction factor of the element stiffness. A recently developed metaheuristic optimization algorithm known as the charged system search (CSS is utilized for locating and quantifying the damaged areas of the structure. In order to demonstrate the abilities of this method, three examples are included comprising of a 10-elements cantilever beam, a Bowstring plane truss, and a 39-element three-story three-bay plane frame. The possible damage types in structures by considering several damage scenarios and using incomplete modal data are modeled. Finally, results are obtained from the CSS algorithm by detecting damage in these structures and compared to the results of the PSOPC algorithm. In addition, the effect of noise is shown in the results of the CSS algorithm by suitable diagrams. As is illustrated, this method has acceptable results in the structural detection damage with low computational time.
Community Structure Detection Algorithm Based on the Node Belonging Degree
Jian Li
2013-07-01
Full Text Available In this paper, we propose a novel algorithm to identify communities in complex networks based on the node belonging degree. First, we give the concept of the node belonging degree, and then determine whether a node belongs to a community or not according to the belonging degree of the node with respect to the community. The experiment results of three real-world networks: a network with three communities with 19 nodes, Zachary Karate Club and network of American college football teams show that the proposed algorithm has satisfactory community structure detection.
Cooperative Automated Worm Response and Detection Immune Algorithm
Kim, Jungwon; Aickelin, Uwe; McLeod, Julie
2010-01-01
The role of T-cells within the immune system is to confirm and assess anomalous situations and then either respond to or tolerate the source of the effect. To illustrate how these mechanisms can be harnessed to solve real-world problems, we present the blueprint of a T-cell inspired algorithm for computer security worm detection. We show how the three central T-cell processes, namely T-cell maturation, differentiation and proliferation, naturally map into this domain and further illustrate how such an algorithm fits into a complete immune inspired computer security system and framework.
Multi-objective community detection based on memetic algorithm.
Peng Wu
Full Text Available Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.
Artifact removal algorithms for stroke detection using a multistatic MIST beamforming algorithm.
Ricci, E; Di Domenico, S; Cianca, E; Rossi, T
2015-01-01
Microwave imaging (MWI) has been recently proved as a promising imaging modality for low-complexity, low-cost and fast brain imaging tools, which could play a fundamental role to efficiently manage emergencies related to stroke and hemorrhages. This paper focuses on the UWB radar imaging approach and in particular on the processing algorithms of the backscattered signals. Assuming the use of the multistatic version of the MIST (Microwave Imaging Space-Time) beamforming algorithm, developed by Hagness et al. for the early detection of breast cancer, the paper proposes and compares two artifact removal algorithms. Artifacts removal is an essential step of any UWB radar imaging system and currently considered artifact removal algorithms have been shown not to be effective in the specific scenario of brain imaging. First of all, the paper proposes modifications of a known artifact removal algorithm. These modifications are shown to be effective to achieve good localization accuracy and lower false positives. However, the main contribution is the proposal of an artifact removal algorithm based on statistical methods, which allows to achieve even better performance but with much lower computational complexity.
Algorithm for detecting important changes in lidar point clouds
Korchev, Dmitriy; Owechko, Yuri
2014-06-01
Protection of installations in hostile environments is a very critical part of military and civilian operations that requires a significant amount of security personnel to be deployed around the clock. Any electronic change detection system for detection of threats must have high probability of detection and low false alarm rates to be useful in the presence of natural motion of trees and vegetation due to wind. We propose a 3D change detection system based on a LIDAR sensor that can reliably and robustly detect threats and intrusions in different environments including surrounding trees, vegetation, and other natural landscape features. Our LIDAR processing algorithm finds human activity and human-caused changes not only in open spaces but also in heavy vegetated areas hidden from direct observation by 2D imaging sensors. The algorithm processes a sequence of point clouds called frames. Every 3D frame is mapped into a 2D horizontal rectangular grid. Each cell of this grid is processed to calculate the distribution of the points mapped into it. The spatial differences are detected by analyzing the differences in distributions of the corresponding cells that belong to different frames. Several heuristic filters are considered to reduce false detections caused by natural changes in the environment.
Constructing Three-Dimension Space Graph for Outlier Detection Algorithms in Data Mining
ZHANG Jing; SUN Zhi-hui
2004-01-01
Outlier detection has very important applied value in data mining literature.Different outlier detection algorithms based on distinct theories have different definitions and mining processes.The three-dimensional space graph for constructing applied algorithms and an improved GridOf algorithm were proposed in terms of analyzing the existing outlier detection algorithms from criterion and theory.
Fast and Robust Stereo Vision Algorithm for Obstacle Detection
Yi-peng Zhou
2008-01-01
Binocular computer vision is based on bionics, after the calibration through the camera head by double-exposure image synchronization, access to the calculation of two-dimensional image pixels of the three-dimensional depth information. In this paper, a fast and robust stereo vision algorithm is described to perform in-vehicle obstacles detection and characterization. The stereo algorithm which provides a suitable representation of the geometric content of the road scene is described, and an in-vehicle embedded system is presented. We present the way in which the algorithm is used, and then report experiments on real situations which show that our solution is accurate, reliable and efficient. In particular, both processes are fast, generic,robust to noise and bad conditions, and work even with partial occlusion.
aTrunk—An ALS-Based Trunk Detection Algorithm
Sebastian Lamprecht
2015-08-01
Full Text Available This paper presents a rapid multi-return ALS-based (Airborne Laser Scanning tree trunk detection approach. The multi-core Divide & Conquer algorithm uses a CBH (Crown Base Height estimation and 3D-clustering approach to isolate points associated with single trunks. For each trunk, a principal-component-based linear model is fitted, while a deterministic modification of LO-RANSAC is used to identify an optimal model. The algorithm returns a vector-based model for each identified trunk while parameters like the ground position, zenith orientation, azimuth orientation and length of the trunk are provided. The algorithm performed well for a study area of 109 trees (about 2/3 Norway Spruce and 1/3 European Beech, with a point density of 7.6 points per m2, while a detection rate of about 75% and an overall accuracy of 84% were reached. Compared to crown-based tree detection methods, the aTrunk approach has the advantages of a high reliability (5% commission error and its high tree positioning accuracy (0.59m average difference and 0.78m RMSE. The usage of overlapping segments with parametrizable size allows a seamless detection of the tree trunks.
Evaluation of hybrids algorithms for mass detection in digitalized mammograms
Cordero, Jose; Garzon Reyes, Johnson, E-mail: josecorderog@hotmail.com [Grupo de Optica y Espectroscopia GOE, Centro de Ciencia Basica, Universidad Pontifica Bolivariana de Medellin (Colombia)
2011-01-01
The breast cancer remains being a significant public health problem, the early detection of the lesions can increase the success possibilities of the medical treatments. The mammography is an image modality effective to early diagnosis of abnormalities, where the medical image is obtained of the mammary gland with X-rays of low radiation, this allows detect a tumor or circumscribed mass between two to three years before that it was clinically palpable, and is the only method that until now achieved reducing the mortality by breast cancer. In this paper three hybrids algorithms for circumscribed mass detection on digitalized mammograms are evaluated. In the first stage correspond to a review of the enhancement and segmentation techniques used in the processing of the mammographic images. After a shape filtering was applied to the resulting regions. By mean of a Bayesian filter the survivors regions were processed, where the characteristics vector for the classifier was constructed with few measurements. Later, the implemented algorithms were evaluated by ROC curves, where 40 images were taken for the test, 20 normal images and 20 images with circumscribed lesions. Finally, the advantages and disadvantages in the correct detection of a lesion of every algorithm are discussed.
Circle Detection Using an Electromagnetism-Inspired Algorithm
Cuevas E.
2011-10-01
Full Text Available The Physic-inspired computation is becoming popular and has been acknowledged by the scientific community. This emerging area has developed a wide range of techniques and methods for dealing with complex problems. On the other hand, automatic circle detection in digital images has been considered as an important and complex task for the computer vision community that has devoted a tremendous amount of research seeking for an optimal circle detector. This article presents an algorithm for the automatic detection of circular shapes embedded into complicated and noisy images with no consideration of the conventional Hough transform techniques. The approach is based on a nature-inspired technique called the Electromagnetism- Like Optimization (EMO which is a heuristic method following electromagnetism principles for solving complex optimization problems. For the EMO algorithm, solutions are built considering the electromagnetic attraction and repulsion among charged particles with a charge representing the fitness solution for each particle. The algorithm uses the encoding of three non-collinear points as candidate circles over an edge-only image. Guided by the values of the objective function, the set of encoded candidate circles (charged particles are evolved using the EMO algorithm so that they can fit into the actual circles on the edge map of the image. Experimental results from several tests on synthetic and natural images with a varying range of complexity are included to validate the efficiency of the proposed technique regarding accuracy, speed, and robustness.
A Greedy Algorithm for Neighborhood Overlap-Based Community Detection
Natarajan Meghanathan
2016-01-01
Full Text Available The neighborhood overlap (NOVER of an edge u-v is defined as the ratio of the number of nodes who are neighbors for both u and v to that of the number of nodes who are neighbors of at least u or v. In this paper, we hypothesize that an edge u-v with a lower NOVER score bridges two or more sets of vertices, with very few edges (other than u-v connecting vertices from one set to another set. Accordingly, we propose a greedy algorithm of iteratively removing the edges of a network in the increasing order of their neighborhood overlap and calculating the modularity score of the resulting network component(s after the removal of each edge. The network component(s that have the largest cumulative modularity score are identified as the different communities of the network. We evaluate the performance of the proposed NOVER-based community detection algorithm on nine real-world network graphs and compare the performance against the multi-level aggregation-based Louvain algorithm, as well as the original and time-efficient versions of the edge betweenness-based Girvan-Newman (GN community detection algorithm.
Stochastic Resonance algorithms to enhance damage detection in bearing faults
Castiglione Roberto
2015-01-01
Full Text Available Stochastic Resonance is a phenomenon, studied and mainly exploited in telecommunication, which permits the amplification and detection of weak signals by the assistance of noise. The first papers on this technique are dated early 80 s and were developed to explain the periodically recurrent ice ages. Other applications mainly concern neuroscience, biology, medicine and obviously signal analysis and processing. Recently, some researchers have applied the technique for detecting faults in mechanical systems and bearings. In this paper, we try to better understand the conditions of applicability and which is the best algorithm to be adopted for these purposes. In fact, to get the methodology profitable and efficient to enhance the signal spikes due to fault in rings and balls/rollers of bearings, some parameters have to be properly selected. This is a problem since in system identification this procedure should be as blind as possible. Two algorithms are analysed: the first exploits classical SR with three parameters mutually dependent, while the other uses Woods-Saxon potential, with three parameters yet but holding a different meaning. The comparison of the performances of the two algorithms and the optimal choice of their parameters are the scopes of this paper. Algorithms are tested on simulated and experimental data showing an evident capacity of increasing the signal to noise ratio.
The weirdest SDSS galaxies: results from an outlier detection algorithm
Baron, Dalya; Poznanski, Dovi
2017-03-01
How can we discover objects we did not know existed within the large data sets that now abound in astronomy? We present an outlier detection algorithm that we developed, based on an unsupervised Random Forest. We test the algorithm on more than two million galaxy spectra from the Sloan Digital Sky Survey and examine the 400 galaxies with the highest outlier score. We find objects which have extreme emission line ratios and abnormally strong absorption lines, objects with unusual continua, including extremely reddened galaxies. We find galaxy-galaxy gravitational lenses, double-peaked emission line galaxies and close galaxy pairs. We find galaxies with high ionization lines, galaxies that host supernovae and galaxies with unusual gas kinematics. Only a fraction of the outliers we find were reported by previous studies that used specific and tailored algorithms to find a single class of unusual objects. Our algorithm is general and detects all of these classes, and many more, regardless of what makes them peculiar. It can be executed on imaging, time series and other spectroscopic data, operates well with thousands of features, is not sensitive to missing values and is easily parallelizable.
Comparison of machine learning algorithms for detecting coral reef
Eduardo Tusa
2014-09-01
Full Text Available (Received: 2014/07/31 - Accepted: 2014/09/23This work focuses on developing a fast coral reef detector, which is used for an autonomous underwater vehicle, AUV. A fast detection secures the AUV stabilization respect to an area of reef as fast as possible, and prevents devastating collisions. We use the algorithm of Purser et al. (2009 because of its precision. This detector has two parts: feature extraction that uses Gabor Wavelet filters, and feature classification that uses machine learning based on Neural Networks. Due to the extensive time of the Neural Networks, we exchange for a classification algorithm based on Decision Trees. We use a database of 621 images of coral reef in Belize (110 images for training and 511 images for testing. We implement the bank of Gabor Wavelets filters using C++ and the OpenCV library. We compare the accuracy and running time of 9 machine learning algorithms, whose result was the selection of the Decision Trees algorithm. Our coral detector performs 70ms of running time in comparison to 22s executed by the algorithm of Purser et al. (2009.
AN ENHANCED DETECTION ALGORITHM FOR V-BLAST SYSTEM
Su Xin; Yi Kechu; Tian Bin; Sun Yongjun
2006-01-01
A decoding method complemented by Maximum Likelihood (ML) detection for V-BLAST (Vertical Bell Labs Layered Space-Time) system is presented. The ranked layers are divided into several groups. ML decoding is performed jointly for the layers within the same group while the Decision Feedback Equalization (DFE) is performed for groups. Based on the assumption of QPSK modulation and the quasi-static flat fading channel, simulations are made to testify the performance of the proposed algorithm. The results show that the algorithm outperforms the original V-BLAST detection dramatically in Symbol Error Probability (SEP) performance. Specifically, Signal-to-Noise Ratio (SNR) improvement of 3.4dB is obtained for SEP of 10-2 (4×4case), with a reasonable complexity maintained.
Printing Detecting Algorithm Basing on Maximum Degree of Recognition
Hu Zhang
2013-04-01
Full Text Available In modern packaging, printing industry, due to effects of the properties of the strip itself and the ambient light, strip background color and the color of the printing line, the low contrast boundaries of the strip on both sides and so on, the traditional digital qualitative detection and control to the correction system does not meet the comprehensive requirements. This paper aims to study the detection of a continuous line, discontinuous line and color dividing line on the strip, and because of low contrast between background color and dividing line, we proposed an innovative solution and implementation. This article discusses a new algorithm basing on maximum degree of recognition and optimal light source search algorithm, and we simulated this in MATLAB, finally, we completed the physical testing of the overall system.
A Real-time Single Pulse Detection Algorithm for GPUs
Adámek, Karel
2016-01-01
The detection of non-repeating events in the radio spectrum has become an important area of study in radio astronomy over the last decade due to the discovery of fast radio bursts (FRBs). We have implemented a single pulse detection algorithm, for NVIDIA GPUs, which use boxcar filters of varying widths. Our code performs the calculation of standard deviation, matched filtering by using boxcar filters and thresholding based on the signal-to-noise ratio. We present our parallel implementation of our single pulse detection algorithm. Our GPU algorithm is approximately 17x faster than our current CPU OpenMP code (NVIDIA Titan XP vs Intel E5-2650v3). This code is part of the AstroAccelerate project which is a many-core accelerated time-domain signal processing code for radio astronomy. This work allows our AstroAccelerate code to perform a single pulse search on SKA-like data 4.3x faster than real-time.
3D face recognition algorithm based on detecting reliable components
Huang Wenjun; Zhou Xuebing; Niu Xiamu
2007-01-01
Fisherfaces algorithm is a popular method for face recognition. However, there exist some unstable components that degrade recognition performance. In this paper, we propose a method based on detecting reliable components to overcome the problem and introduce it to 3D face recognition. The reliable components are detected within the binary feature vector, which is generated from the Fisherfaces feature vector based on statistical properties, and is used for 3D face recognition as the final feature vector. Experimental results show that the reliable components feature vector is much more effective than the Fisherfaces feature vector for face recognition.
A Study of Lane Detection Algorithm for Personal Vehicle
Kobayashi, Kazuyuki; Watanabe, Kajiro; Ohkubo, Tomoyuki; Kurihara, Yosuke
By the word “Personal vehicle”, we mean a simple and lightweight vehicle expected to emerge as personal ground transportation devices. The motorcycle, electric wheelchair, motor-powered bicycle, etc. are examples of the personal vehicle and have been developed as the useful for transportation for a personal use. Recently, a new types of intelligent personal vehicle called the Segway has been developed which is controlled and stabilized by using on-board intelligent multiple sensors. The demand for needs for such personal vehicles are increasing, 1) to enhance human mobility, 2) to support mobility for elderly person, 3) reduction of environmental burdens. Since rapidly growing personal vehicles' market, a number of accidents caused by human error is also increasing. The accidents are caused by it's drive ability. To enhance or support drive ability as well as to prevent accidents, intelligent assistance is necessary. One of most important elemental functions for personal vehicle is robust lane detection. In this paper, we develop a robust lane detection method for personal vehicle at outdoor environments. The proposed lane detection method employing a 360 degree omni directional camera and unique robust image processing algorithm. In order to detect lanes, combination of template matching technique and Hough transform are employed. The validity of proposed lane detection algorithm is confirmed by actual developed vehicle at various type of sunshined outdoor conditions.
Automated microaneurysm detection algorithms applied to diabetic retinopathy retinal images
Akara Sopharak
2013-07-01
Full Text Available Diabetic retinopathy is the commonest cause of blindness in working age people. It is characterised and graded by the development of retinal microaneurysms, haemorrhages and exudates. The damage caused by diabetic retinopathy can be prevented if it is treated in its early stages. Therefore, automated early detection can limit the severity of the disease, improve the follow-up management of diabetic patients and assist ophthalmologists in investigating and treating the disease more efficiently. This review focuses on microaneurysm detection as the earliest clinically localised characteristic of diabetic retinopathy, a frequently observed complication in both Type 1 and Type 2 diabetes. Algorithms used for microaneurysm detection from retinal images are reviewed. A number of features used to extract microaneurysm are summarised. Furthermore, a comparative analysis of reported methods used to automatically detect microaneurysms is presented and discussed. The performance of methods and their complexity are also discussed.
Sparsity-based algorithm for detecting faults in rotating machines
He, Wangpeng; Ding, Yin; Zi, Yanyang; Selesnick, Ivan W.
2016-05-01
This paper addresses the detection of periodic transients in vibration signals so as to detect faults in rotating machines. For this purpose, we present a method to estimate periodic-group-sparse signals in noise. The method is based on the formulation of a convex optimization problem. A fast iterative algorithm is given for its solution. A simulated signal is formulated to verify the performance of the proposed approach for periodic feature extraction. The detection performance of comparative methods is compared with that of the proposed approach via RMSE values and receiver operating characteristic (ROC) curves. Finally, the proposed approach is applied to single fault diagnosis of a locomotive bearing and compound faults diagnosis of motor bearings. The processed results show that the proposed approach can effectively detect and extract the useful features of bearing outer race and inner race defect.
Hardware Design and Simulation of Sobel Edge Detection Algorithm
Sohag Kabir
2014-04-01
Full Text Available In this paper, a hardware system for Sobel Edge Detection Algorithm is designed and simulated for a 128 pixel, 8-bit monochrome line-scan camera. The system is designed to detect objects as they move along a conveyor belt in a manufacturing environment, the camera will observe dark objects on a light conveyor belt. The edge detector is required to detect horizontal and vertical edges using Sobel edge detection method. The Sobel operator requires 3 lines and takes 3 pixels per line, thus using a 3×3 input block to process each pixel. The centre pixel of the 3×3 pixel block can be classified as an edge point or otherwise by thresholding the value from the operator. The FPGA based Sobel edge detector is designed and simulated using Altera Quartus II 8.1 web edition by targeting Cyclone II development boards.
GPU based cloud system for high-performance arrhythmia detection with parallel k-NN algorithm.
Tae Joon Jun; Hyun Ji Park; Hyuk Yoo; Young-Hak Kim; Daeyoung Kim
2016-08-01
In this paper, we propose an GPU based Cloud system for high-performance arrhythmia detection. Pan-Tompkins algorithm is used for QRS detection and we optimized beat classification algorithm with K-Nearest Neighbor (K-NN). To support high performance beat classification on the system, we parallelized beat classification algorithm with CUDA to execute the algorithm on virtualized GPU devices on the Cloud system. MIT-BIH Arrhythmia database is used for validation of the algorithm. The system achieved about 93.5% of detection rate which is comparable to previous researches while our algorithm shows 2.5 times faster execution time compared to CPU only detection algorithm.
Oscillation Detection Algorithm Development Summary Report and Test Plan
Zhou, Ning; Huang, Zhenyu; Tuffner, Francis K.; Jin, Shuangshuang
2009-10-03
-based modal analysis algorithms have been developed. They include Prony analysis, Regularized Ro-bust Recursive Least Square (R3LS) algorithm, Yule-Walker algorithm, Yule-Walker Spectrum algorithm, and the N4SID algo-rithm. Each has been shown to be effective for certain situations, but not as effective for some other situations. For example, the traditional Prony analysis works well for disturbance data but not for ambient data, while Yule-Walker is designed for ambient data only. Even in an algorithm that works for both disturbance data and ambient data, such as R3LS, latency results from the time window used in the algorithm is an issue in timely estimation of oscillation modes. For ambient data, the time window needs to be longer to accumulate information for a reasonably accurate estimation; while for disturbance data, the time window can be significantly shorter so the latency in estimation can be much less. In addition, adding a known input signal such as noise probing signals can increase the knowledge of system oscillatory properties and thus improve the quality of mode estimation. System situations change over time. Disturbances can occur at any time, and probing signals can be added for a certain time period and then removed. All these observations point to the need to add intelligence to ModeMeter applications. That is, a ModeMeter needs to adaptively select different algorithms and adjust parameters for various situations. This project aims to develop systematic approaches for algorithm selection and parameter adjustment. The very first step is to detect occurrence of oscillations so the algorithm and parameters can be changed accordingly. The proposed oscillation detection approach is based on the signal-noise ratio of measurements.
A hierarchic collision detection algorithm for simple Brownian dynamics.
Katsimitsoulia, Zoe; Taylor, William R
2010-04-01
We describe an algorithm to avoid steric violation (bumps) between bodies arranged in a hierarchy. The algorithm recursively directs the focus of a bump-detector towards the interactions of children whose parents are in collision. This has the effect of concentrating available computer resources towards maintaining good steric interactions in the region where bodies are colliding. The algorithm was implemented and tested under two programming environments: a graphical environment, OpenGL under Java3D, and a non-graphical environment in "C". The former used a built-in collision detection system whereas the latter used a simple algorithm devised previously for the interaction of "soft" bodies. This simpler system was found to run much faster (by 50-fold) even after allowing for time spent on graphical activity and was also better at preventing steric violations. With a hierarchy of three levels of 100, the non-graphical implementation was able to simulate a million atomic bodies for 100,000 steps in 12h on a laptop computer.
Improved Genetic Algorithm Optimization for Forward Vehicle Detection Problems
Longhui Gang
2015-07-01
Full Text Available Automated forward vehicle detection is an integral component of many advanced driver-assistance systems. The method based on multi-visual information fusion, with its exclusive advantages, has become one of the important topics in this research field. During the whole detection process, there are two key points that should to be resolved. One is to find the robust features for identification and the other is to apply an efficient algorithm for training the model designed with multi-information. This paper presents an adaptive SVM (Support Vector Machine model to detect vehicle with range estimation using an on-board camera. Due to the extrinsic factors such as shadows and illumination, we pay more attention to enhancing the system with several robust features extracted from a real driving environment. Then, with the introduction of an improved genetic algorithm, the features are fused efficiently by the proposed SVM model. In order to apply the model in the forward collision warning system, longitudinal distance information is provided simultaneously. The proposed method is successfully implemented on a test car and evaluation experimental results show reliability in terms of both the detection rate and potential effectiveness in a real-driving environment.
BPDA - A Bayesian peptide detection algorithm for mass spectrometry
Braga-Neto Ulisses
2010-09-01
Full Text Available Abstract Background Mass spectrometry (MS is an essential analytical tool in proteomics. Many existing algorithms for peptide detection are based on isotope template matching and usually work at different charge states separately, making them ineffective to detect overlapping peptides and low abundance peptides. Results We present BPDA, a Bayesian approach for peptide detection in data produced by MS instruments with high enough resolution to baseline-resolve isotopic peaks, such as MALDI-TOF and LC-MS. We model the spectra as a mixture of candidate peptide signals, and the model is parameterized by MS physical properties. BPDA is based on a rigorous statistical framework and avoids problems, such as voting and ad-hoc thresholding, generally encountered in algorithms based on template matching. It systematically evaluates all possible combinations of possible peptide candidates to interpret a given spectrum, and iteratively finds the best fitting peptide signal in order to minimize the mean squared error of the inferred spectrum to the observed spectrum. In contrast to previous detection methods, BPDA performs deisotoping and deconvolution of mass spectra simultaneously, which enables better identification of weak peptide signals and produces higher sensitivities and more robust results. Unlike template-matching algorithms, BPDA can handle complex data where features overlap. Our experimental results indicate that BPDA performs well on simulated data and real MS data sets, for various resolutions and signal to noise ratios, and compares very favorably with commonly used commercial and open-source software, such as flexAnalysis, OpenMS, and Decon2LS, according to sensitivity and detection accuracy. Conclusion Unlike previous detection methods, which only employ isotopic distributions and work at each single charge state alone, BPDA takes into account the charge state distribution as well, thus lending information to better identify weak peptide
Actual Pathogen Detection: Sensors and Algorithms - a Review
Federico Hahn
2009-03-01
Full Text Available Pathogens feed on fruits and vegetables causing great food losses or at least reduction of their shelf life. These pathogens can cause losses of the final product or in the farms were the products are grown, attacking leaves, stems and trees. This review analyses disease detection sensors and algorithms for both the farm and postharvest management of fruit and vegetable quality. Mango, avocado, apple, tomato, potato, citrus and grapes were selected as the fruits and vegetables for study due to their world-wide consumption. Disease warning systems for predicting pathogens and insects on farms during fruit and vegetable production are commonly used for all the crops and are available where meteorological stations are present. It can be seen that these disease risk systems are being slowly replaced by remote sensing monitoring in developed countries. Satellite images have reduced their temporal resolution, but are expensive and must become cheaper for their use world-wide. In the last 30 years, a lot of research has been carried out in non-destructive sensors for food quality. Actually, non-destructive technology has been applied for sorting high quality fruit which is desired by the consumer. The sensors require algorithms to work properly; the most used being discriminant analysis and training neural networks. New algorithms will be required due to the high quantity of data acquired and its processing, and for disease warning strategies for disease detection.
Detection of cracks in shafts with the Approximated Entropy algorithm
Sampaio, Diego Luchesi; Nicoletti, Rodrigo
2016-05-01
The Approximate Entropy is a statistical calculus used primarily in the fields of Medicine, Biology, and Telecommunication for classifying and identifying complex signal data. In this work, an Approximate Entropy algorithm is used to detect cracks in a rotating shaft. The signals of the cracked shaft are obtained from numerical simulations of a de Laval rotor with breathing cracks modelled by the Fracture Mechanics. In this case, one analysed the vertical displacements of the rotor during run-up transients. The results show the feasibility of detecting cracks from 5% depth, irrespective of the unbalance of the rotating system and crack orientation in the shaft. The results also show that the algorithm can differentiate the occurrence of crack only, misalignment only, and crack + misalignment in the system. However, the algorithm is sensitive to intrinsic parameters p (number of data points in a sample vector) and f (fraction of the standard deviation that defines the minimum distance between two sample vectors), and good results are only obtained by appropriately choosing their values according to the sampling rate of the signal.
Incremental refinement of a multi-user-detection algorithm (II
M. Vollmer
2003-01-01
Full Text Available Multi-user detection is a technique proposed for mobile radio systems based on the CDMA principle, such as the upcoming UMTS. While offering an elegant solution to problems such as intra-cell interference, it demands very significant computational resources. In this paper, we present a high-level approach for reducing the required resources for performing multi-user detection in a 3GPP TDD multi-user system. This approach is based on a displacement representation of the parameters that describe the transmission system, and a generalized Schur algorithm that works on this representation. The Schur algorithm naturally leads to a highly parallel hardware implementation using CORDIC cells. It is shown that this hardware architecture can also be used to compute the initial displacement representation. It is very beneficial to introduce incremental refinement structures into the solution process, both at the algorithmic level and in the individual cells of the hardware architecture. We detail these approximations and present simulation results that confirm their effectiveness.
Ranganadh Narayanam*
2013-01-01
Voice Activity Detection (VAD) problem considers detecting the presence of speech in a noisy signal. The speech/non-speech classification task is not as trivial as it appears, and most of the VAD algorithms fail when the level of background noise increases. In this research we are presenting a new technique for Voice Activity Detection (VAD) in EEG collected brain stem speech evoked potentials data [7, 8, 9]. This one is spectral subtraction method in which we have developed ou...
A New Method for Intrusion Detection using Manifold Learning Algorithm
Guoping Hou
2013-07-01
Full Text Available Computer and network security has received and will still receive much attention. Any unexpected intrusion will damage the network. It is therefore imperative to detect the network intrusion to ensure the normal operation of the internet. There are many studies in the intrusion detection and intrusion patter recognition. The artificial neural network (ANN has proven to be powerful for the intrusion detection. However, very little work has discussed the optimization of the input intrusion features for the ANN. Generally, the intrusion features contain a certain number of useless features, which is useless for the intrusion detection. Large dimensions of the feature data will also affect the intrusion detection performance of the ANN. In order to improve the ANN performance, a new approach for network intrusion detection based on nonlinear feature dimension reduction and ANN is proposed in this work. The manifold learning algorithm was used to reduce the intrusion feature vector. Then an ANN classifier was employed to identify the intrusion. The efficiency of the proposed method was evaluated with the real intrusion data. The test result shows that the proposed approach has good intrusion detection performance.
The derivation of distributed termination detection algorithms from garbage collection schemes
Tel, G.; Mattern, F.
2001-01-01
It is shown that the termination detection problem for distributed computations can be modelled as an instance of the garbage collection problem. Consequently, algorithms for the termination detection problem are obtained by applying transformations to garbage collection algorithms. The transformati
The derivation of distributed termination detection algorithms from garbage collection schemes
Tel, G.; Mattern, F.
1990-01-01
It is shown that the termination detection problem for distributed computations can be modelled as an instance of the garbage collection problem. Consequently, algorithms for the termination detection problem are obtained by applying transformations to garbage collection algorithms. The
Novel space-time multiuser detection algorithm of WCDMA system
Zhang Xiaofei; Xu Dazhuan; Yang Bei
2005-01-01
The structure and performance of space-time multiuser detection receiver at base stations of WCDMA system is analyzed, in which smart antenna is employed. WCDMA uplink signal model is established in this paper. Space-time multiuser receiver presented in this paper combines 2D-RAKE with parallel interference cancellation (PIC), and the improved parallel interference cancellation methods are given. A novel space-time multiuser detection scheme,2DRAKE-GPPIC is proposed. This scheme employs smart antenna to suppress unexpected DOA (Direction Of Arrival) signal, uses RAKE receiver to combine different delays of expected signal, and utilizes grouped partial parallel interference cancellation (GPPIC) algorithm to suppress further the interference signal in the main lobe of array antennas. The simulation results reveal that the scheme of space-time multiuser detection presented in this paper has better performance for WCDMA system.
Performance of a community detection algorithm based on semidefinite programming
Javanmard, Adel; Ricci-Tersenghi, Federico
2016-01-01
The problem of detecting communities in a graph is maybe one the most studied inference problems, given its simplicity and widespread diffusion among several disciplines. A very common benchmark for this problem is the stochastic block model or planted partition problem, where a phase transition takes place in the detection of the planted partition by changing the signal-to-noise ratio. Optimal algorithms for the detection exist which are based on spectral methods, but we show these are extremely sensible to slight modification in the generative model. Recently Javanmard, Montanari and Ricci-Tersenghi (arXiv:1511.08769) have used statistical physics arguments, and numerical simulations to show that finding communities in the stochastic block model via semidefinite programming is quasi optimal. Further, the resulting semidefinite relaxation can be solved efficiently, and is very robust with respect to changes in the generative model. In this paper we study in detail several practical aspects of this new algori...
Practical Algorithms for Subgroup Detection in Covert Networks
Memon, Nasrullah; Wiil, Uffe Kock; Qureshi, Pir Abdul Rasool
2010-01-01
In this paper, we present algorithms for subgroup detection and demonstrated them with a real-time case study of USS Cole bombing terrorist network. The algorithms are demonstrated in an application by a prototype system. The system finds associations between terrorist and terrorist organisations...... and is capable of determining links between terrorism plots occurred in the past, their affiliation with terrorist camps, travel record, funds transfer, etc. The findings are represented by a network in the form of an Attributed Relational Graph (ARG). Paths from a node to any other node in the network indicate...... the relationships between individuals and organisations. The system also provides assistance to law enforcement agencies, indicating when the capture of a specific terrorist will more likely destabilise the terrorist network. In this paper, we discuss the important application area related to subgroups...
A novel dynamical community detection algorithm based on weighting scheme
Li, Ju; Yu, Kai; Hu, Ke
2015-12-01
Network dynamics plays an important role in analyzing the correlation between the function properties and the topological structure. In this paper, we propose a novel dynamical iteration (DI) algorithm, which incorporates the iterative process of membership vector with weighting scheme, i.e. weighting W and tightness T. These new elements can be used to adjust the link strength and the node compactness for improving the speed and accuracy of community structure detection. To estimate the optimal stop time of iteration, we utilize a new stability measure which is defined as the Markov random walk auto-covariance. We do not need to specify the number of communities in advance. It naturally supports the overlapping communities by associating each node with a membership vector describing the node's involvement in each community. Theoretical analysis and experiments show that the algorithm can uncover communities effectively and efficiently.
[Application of PSO algorithm in wavelength detection of FBG sensors].
Ding, Hui; Wu, Xiang-Nan; Liang, Jian-Qi; Li, Xian-Li
2010-02-01
In order to improve the measurement accuracy of FBG sensing system, particle swarm optimization (PSO) algorithm combined with reference FBGs array was applied to investigate the nonlinearity and hysteresis character of Fabry-Parot filter (FPF). A method of modeling the wavelength-voltage relationship of FPF online in each FPF scanning cycle was proposed in the present paper. The feature of particle swarm optimization algorithm such as fast convergence and simple implementation makes the process of modeling wavelength-voltage relationship of FPF be completed with low computing cost and high accuracy. With the set-up model, the absolute error in wavelength detection of FBG sensors was demonstrated by experiments to be as low as 0.03 nm. The structure of the system is compact and the proposed modeling approach has important meaning in FBG sensors system when FPF is used as wavelength demodulator.
A Duffing oscillator algorithm to detect the weak chromatographic signal.
Zhang, Wei; Xiang, Bing-Ren
2007-02-28
Based on the Duffing equation, a Duffing oscillator algorithm (DOA) to improve the signal-to-noise ratio (SNR) was presented. By simulated and experimental data sets, it was proven that the signal-to-noise ratio (SNR) of the weak signal could be greatly enhanced by this method. Using signal enhancement by DOA, this method extends the SNR of low concentrations of methylbenzene from 2.662 to 29.90 and the method can be used for quantitative analysis of methylbenzene, which are lower than detection limit of an analytical system. The Duffing oscillator algorithm (DOA) might be a promising tool to extend instrumental linear range and to improve the accuracy of trace analysis. The research enlarged the application scope of Duffing equation to chromatographic signal processing.
Microburst applications of brightness temperature difference between GOES Imager channels 3 and 4
Pryor, Kenneth L
2010-01-01
This paper presents a new application of brightness temperature difference (BTD) between Geostationary Operational Environmental Satellite (GOES) imager channels 3 and 4. It has been found recently that the BTD between GOES infrared channel 3 (water vapor) and channel 4 (thermal infrared) can highlight regions where severe outflow wind generation (i.e. downbursts, microbursts) is likely due to the channeling of dry mid-tropospheric air into the precipitation core of a deep, moist convective storm. Case studies demonstrating effective operational use of this image product are presented for two significant marine transportation accidents as well as a severe downburst event over the Washington, DC metropolitan area in April 2010.
HYBRID FEATURE SELECTION ALGORITHM FOR INTRUSION DETECTION SYSTEM
Seyed Reza Hasani
2014-01-01
Full Text Available Network security is a serious global concern. Usefulness Intrusion Detection Systems (IDS are increasing incredibly in Information Security research using Soft computing techniques. In the previous researches having irrelevant and redundant features are recognized causes of increasing the processing speed of evaluating the known intrusive patterns. In addition, an efficient feature selection method eliminates dimension of data and reduce redundancy and ambiguity caused by none important attributes. Therefore, feature selection methods are well-known methods to overcome this problem. There are various approaches being utilized in intrusion detections, they are able to perform their method and relatively they are achieved with some improvements. This work is based on the enhancement of the highest Detection Rate (DR algorithm which is Linear Genetic Programming (LGP reducing the False Alarm Rate (FAR incorporates with Bees Algorithm. Finally, Support Vector Machine (SVM is one of the best candidate solutions to settle IDSs problems. In this study four sample dataset containing 4000 random records are excluded randomly from this dataset for training and testing purposes. Experimental results show that the LGP_BA method improves the accuracy and efficiency compared with the previous related research and the feature subcategory offered by LGP_BA gives a superior representation of data.
EEG seizure detection and prediction algorithms: a survey
Alotaiby, Turkey N.; Alshebeili, Saleh A.; Alshawi, Tariq; Ahmad, Ishtiaq; Abd El-Samie, Fathi E.
2014-12-01
Epilepsy patients experience challenges in daily life due to precautions they have to take in order to cope with this condition. When a seizure occurs, it might cause injuries or endanger the life of the patients or others, especially when they are using heavy machinery, e.g., deriving cars. Studies of epilepsy often rely on electroencephalogram (EEG) signals in order to analyze the behavior of the brain during seizures. Locating the seizure period in EEG recordings manually is difficult and time consuming; one often needs to skim through tens or even hundreds of hours of EEG recordings. Therefore, automatic detection of such an activity is of great importance. Another potential usage of EEG signal analysis is in the prediction of epileptic activities before they occur, as this will enable the patients (and caregivers) to take appropriate precautions. In this paper, we first present an overview of seizure detection and prediction problem and provide insights on the challenges in this area. Second, we cover some of the state-of-the-art seizure detection and prediction algorithms and provide comparison between these algorithms. Finally, we conclude with future research directions and open problems in this topic.
Detecting circumbinary planets: A new quasi-periodic search algorithm
Pollacco D.
2013-04-01
Full Text Available We present a search method based around the grouping of data residuals, suitable for the detection of many quasi-periodic signals. Combined with an efficient and easily implemented method to predict the maximum transit timing variations of a transiting circumbinary exoplanet, we form a fast search algorithm for such planets. We here target the Kepler dataset in particular, where all the transiting examples of circumbinary planets have been found to date. The method is presented and demonstrated on two known systems in the Kepler data.
Detection of HIV and AIDS by Advance Algorithms
Amol Joglekar
2014-05-01
Full Text Available There are many serious diseases which causes harm on human body like cancer, HIV, H1N1 and so on. These Viruses entered in human body and start replicating automatically which disturbs the general activity of a human being. These viruses may give lots of symptoms which doctors need to identify and accordingly a treatment can be provided. Therefore early detection of such diseases is needed so that a person can live a normal life. Using data mining algorithms we can able to discover the type of disease and cure it
Detecting structural breaks in time series via genetic algorithms
Doerr, Benjamin; Fischer, Paul; Hilbert, Astrid
2016-01-01
Detecting structural breaks is an essential task for the statistical analysis of time series, for example, for fitting parametric models to it. In short, structural breaks are points in time at which the behaviour of the time series substantially changes. Typically, no solid background knowledge...... and mutation operations for this problem, we conduct extensive experiments to determine good choices for the parameters and operators of the genetic algorithm. One surprising observation is that use of uniform and one-point crossover together gave significantly better results than using either crossover...
Textural defect detect using a revised ant colony clustering algorithm
Zou, Chao; Xiao, Li; Wang, Bingwen
2007-11-01
We propose a totally novel method based on a revised ant colony clustering algorithm (ACCA) to explore the topic of textural defect detection. In this algorithm, our efforts are mainly made on the definition of local irregularity measurement and the implementation of the revised ACCA. The local irregular measurement defined evaluates the local textural inconsistency of each pixel against their mini-environment. In our revised ACCA, the behaviors of each ant are divided into two steps: release pheromone and act. The quantity of pheromone released is proportional to the irregularity measurement; the actions of the ants to act next are chosen independently of each other in a stochastic way according to some evaluated heuristic knowledge. The independency of ants implies the inherent parallel computation architecture of this algorithm. We apply the proposed method in some typical textural images with defects. From the series of pheromone distribution map (PDM), it can be clearly seen that the pheromone distribution approaches the textual defects gradually. By some post-processing, the final distribution of pheromone can demonstrate the shape and area of the defects well.
Efficient Algorithm for Railway Tracks Detection Using Satellite Imagery
Ali Javed
2012-10-01
Full Text Available Satellite imagery can produce maps including roads, railway tracks, buildings, bridges, oceans, lakes, rivers, etc. In developed countries like USA, Canada, Australia, Europe, images produced by Google map are of high resolution and good quality. On the other hand, mostly images of the third world countries like Pakistan, Asian and African countries are of poor quality and not clearly visible. Similarly railway tracks of these countries are hardly visible in Google map. We have developed an efficient algorithm for railway track detection from a low quality image of Google map. This would lead to detect damaged railway track, railway crossings and help to schedule/divert locomotive movements in order to avoid catastrophe.
Fast Parabola Detection Using Estimation of Distribution Algorithms
Sierra-Hernandez, Juan Manuel; Avila-Garcia, Maria Susana; Rojas-Laguna, Roberto
2017-01-01
This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about 93.61% on synthetic images and 89% on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications. PMID:28321264
Fast Parabola Detection Using Estimation of Distribution Algorithms.
Guerrero-Turrubiates, Jose de Jesus; Cruz-Aceves, Ivan; Ledesma, Sergio; Sierra-Hernandez, Juan Manuel; Velasco, Jonas; Avina-Cervantes, Juan Gabriel; Avila-Garcia, Maria Susana; Rostro-Gonzalez, Horacio; Rojas-Laguna, Roberto
2017-01-01
This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about 93.61% on synthetic images and 89% on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications.
Fast Parabola Detection Using Estimation of Distribution Algorithms
Jose de Jesus Guerrero-Turrubiates
2017-01-01
Full Text Available This paper presents a new method based on Estimation of Distribution Algorithms (EDAs to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about 93.61% on synthetic images and 89% on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications.
Analogue Simulation and Orbital Solving Algorithm of Astrometric Exoplanet Detection
Huang, P. H.; Ji, J. H.
2016-09-01
Astrometry is an effective method to detect exoplanets. It has many advantages that other detection methods do not bear, such as providing three dimensional planetary orbit and determining the planetary mass. Astrometry will enrich the sample of exoplanets. As the high-precision astrometric satellite Gaia (Global Astrometry interferometer for Astrophysics) was launched in 2013, there will be abundant long-period Jupiter-size planets to be discovered by Gaia. In this paper, we specify the α Centauri A, HD 62509, and GJ 876 systems, and generate the synthetic astrometric data with the single astrometric precision of Gaia. Then we use the Lomb-Scargle periodogram to analyse the signature of planets and the Markov Chain Monte Carlo (MCMC) algorithm to fit the orbit of planets. The simulation results are well coincide with the initial solutions.
Vibration-based damage detection algorithm for WTT structures
Nguyen, Tuan-Cuong; Kim, Tae-Hwan; Choi, Sang-Hoon; Ryu, Joo-Young; Kim, Jeong-Tae
2016-04-01
In this paper, the integrity of a wind turbine tower (WTT) structure is nondestructively estimated using its vibration responses. Firstly, a damage detection algorithm using changes in modal characteristics to predict damage locations and severities in structures is outlined. Secondly, a finite element (FE) model based on a real WTT structure is established by using a commercial software, Midas FEA. Thirdly, forced vibration tests are performed on the FE model of the WTT structure under various damage scenarios. The changes in modal parameters such as natural frequencies and mode shapes are examined for damage monitoring in the structure. Finally, the feasibility of the vibration-based damage detection method is numerically verified by predicting locations and severities of the damage in the FE model of the WTT structure.
Retinal Microaneursym Detection using Maximally Stable External Region Algorithm
Diana Tri Susetianingtias
2016-10-01
Full Text Available The growth of diabetics’ worldwide increased drastically. Diabetic can cause blindness due to retinopathy diabetic. Often the patients of retinopathy diabetic do not experience the sign and the symptoms at early stage of their symptoms, even in the severe stages where the bleeding start to occur. One indicator of patients that has diabetic retinopathy can be seen from the blood vessel that experienced microaneurysm and hemorrhage due to a swelling blood vessels in the retina. The study in this paper will implement the Maximally Stable External Region (MSER algorithm to detect microaneursym. Microaneursym is one of the main indicators that causes retinopathy diabetic. This study uses HRF dataset. The results are expected to improve the accuracy microaneursym detection.
Clone Detection Using DIFF Algorithm For Aspect Mining
Rowyda Mohammed Abd El-Aziz
2012-08-01
Full Text Available Aspect mining is a reverse engineering process that aims at mining legacy systems to discover crosscutting concerns to be refactored into aspects. This process improves system reusability and maintainability. But, locating crosscutting concerns in legacy systems manually is very difficult and causes many errors. So, there is a need for automated techniques that can discover crosscutting concerns in source code. Aspect mining approaches are automated techniques that vary according to the type of crosscutting concerns symptoms they search for. Code duplication is one of such symptoms which risks software maintenance and evolution. So, many code clone detection techniques have been proposed to find this duplicated code in legacy systems. In this paper, we present a clone detection technique to extract exact clones from object-oriented source code using Differential File Comparison Algorithm (DIFF to improve system reusability and maintainability which is a major objective of aspect mining.
Nonlinear Algorithms for Channel Equalization and Map Symbol Detection.
Giridhar, K.
The transfer of information through a communication medium invariably results in various kinds of distortion to the transmitted signal. In this dissertation, a feed -forward neural network-based equalizer, and a family of maximum a posteriori (MAP) symbol detectors are proposed for signal recovery in the presence of intersymbol interference (ISI) and additive white Gaussian noise. The proposed neural network-based equalizer employs a novel bit-mapping strategy to handle multilevel data signals in an equivalent bipolar representation. It uses a training procedure to learn the channel characteristics, and at the end of training, the multilevel symbols are recovered from the corresponding inverse bit-mapping. When the channel characteristics are unknown and no training sequences are available, blind estimation of the channel (or its inverse) and simultaneous data recovery is required. Convergence properties of several existing Bussgang-type blind equalization algorithms are studied through computer simulations, and a unique gain independent approach is used to obtain a fair comparison of their rates of convergence. Although simple to implement, the slow convergence of these Bussgang-type blind equalizers make them unsuitable for many high data-rate applications. Rapidly converging blind algorithms based on the principle of MAP symbol-by -symbol detection are proposed, which adaptively estimate the channel impulse response (CIR) and simultaneously decode the received data sequence. Assuming a linear and Gaussian measurement model, the near-optimal blind MAP symbol detector (MAPSD) consists of a parallel bank of conditional Kalman channel estimators, where the conditioning is done on each possible data subsequence that can convolve with the CIR. This algorithm is also extended to the recovery of convolutionally encoded waveforms in the presence of ISI. Since the complexity of the MAPSD algorithm increases exponentially with the length of the assumed CIR, a suboptimal
Data detection algorithms for multiplexed quantum dot encoding.
Goss, Kelly C; Messier, Geoff G; Potter, Mike E
2012-02-27
A group of quantum dots can be designed to have a unique spectral emission by varying the size of the quantum dots (wavelength) and number of quantum dots (intensity). This technique has been previously proposed for biological tags and object identification. The potential of this system lies in the ability to have a large number of distinguishable wavelengths and intensity levels. This paper presents a communications system model for MxQDs including the interference between neighbouring QD colours and detector noise. An analytical model of the signal-to-noise ratio of a Charge-Coupled Device (CCD) spectrometer is presented and confirmed with experimental results. We then apply a communications system perspective and propose data detection algorithms that increase the readability of the quantum dots tags. It is demonstrated that multiplexed quantum dot barcodes can be read with 99.7% accuracy using the proposed data detection algorithms in a system with 6 colours and 6 intensity values resulting in 46,655 unique spectral codes.
Cardio Vascular Detection with Neuro Computing and Genetic Algorithm
T. John Peter
2014-09-01
Full Text Available For human the most fundamental requirement is having a healthy life, which is being difficult to maintain day to day as we are getting more progress in technological era. Among the possible reasons of unnatural death, heart disease based causes are showing very significant part. The diagnosis of heart diseases is a vital and intricate job. The recognition of heart disease from diverse features or signs is a multi-layered problem that is highly sensitive with respect diagnostic tests and establishing the relationship with multiple parameters is very difficult. In result decision is not free from false assumptions and is frequently accompanied by impulsive effects. This encourages developing a more reliable and cost effective knowledge based algorithmic approach to detect the heart disease. From engineering point of view, solution for detecting the presence of heart diseases is developed with the concept of artificial intelligence in data mining in this study. Feed forward architecture of neural network technology is taken as platform of computation to generate the intelligence in association with well established field of genetic algorithm (GA. A comparative performance has presented between both learning concepts with various different size of architecture.
Enhancing time-series detection algorithms for automated biosurveillance.
Tokars, Jerome I; Burkom, Howard; Xing, Jian; English, Roseanne; Bloom, Steven; Cox, Kenneth; Pavlin, Julie A
2009-04-01
BioSense is a US national system that uses data from health information systems for automated disease surveillance. We studied 4 time-series algorithm modifications designed to improve sensitivity for detecting artificially added data. To test these modified algorithms, we used reports of daily syndrome visits from 308 Department of Defense (DoD) facilities and 340 hospital emergency departments (EDs). At a constant alert rate of 1%, sensitivity was improved for both datasets by using a minimum standard deviation (SD) of 1.0, a 14-28 day baseline duration for calculating mean and SD, and an adjustment for total clinic visits as a surrogate denominator. Stratifying baseline days into weekdays versus weekends to account for day-of-week effects increased sensitivity for the DoD data but not for the ED data. These enhanced methods may increase sensitivity without increasing the alert rate and may improve the ability to detect outbreaks by using automated surveillance system data.
Enhancing Time-Series Detection Algorithms for Automated Biosurveillance
Burkom, Howard; Xing, Jian; English, Roseanne; Bloom, Steven; Cox, Kenneth; Pavlin, Julie A.
2009-01-01
BioSense is a US national system that uses data from health information systems for automated disease surveillance. We studied 4 time-series algorithm modifications designed to improve sensitivity for detecting artificially added data. To test these modified algorithms, we used reports of daily syndrome visits from 308 Department of Defense (DoD) facilities and 340 hospital emergency departments (EDs). At a constant alert rate of 1%, sensitivity was improved for both datasets by using a minimum standard deviation (SD) of 1.0, a 14–28 day baseline duration for calculating mean and SD, and an adjustment for total clinic visits as a surrogate denominator. Stratifying baseline days into weekdays versus weekends to account for day-of-week effects increased sensitivity for the DoD data but not for the ED data. These enhanced methods may increase sensitivity without increasing the alert rate and may improve the ability to detect outbreaks by using automated surveillance system data. PMID:19331728
Detecting Intermittent Steering Activity ; Development of a Phase-detection Algorithm
Silva Peixoto de Aboim Chaves, H.M. da; Pauwelussen, J.J.A.; Mulder, M.; Paassen, M.M. van; Happee, R.; Mulder, M.
2012-01-01
Drivers usually maintain an error-neglecting control strategy (passive phase) in keeping their vehicle on the road, only to change to an error-correcting approach (active phase) when the vehicle state becomes inadequate. We developed an algorithm that is capable of detecting whether the driver is cu
Andersson, Richard; Larsson, Linnea; Holmqvist, Kenneth; Stridh, Martin; Nyström, Marcus
2016-05-18
Almost all eye-movement researchers use algorithms to parse raw data and detect distinct types of eye movement events, such as fixations, saccades, and pursuit, and then base their results on these. Surprisingly, these algorithms are rarely evaluated. We evaluated the classifications of ten eye-movement event detection algorithms, on data from an SMI HiSpeed 1250 system, and compared them to manual ratings of two human experts. The evaluation focused on fixations, saccades, and post-saccadic oscillations. The evaluation used both event duration parameters, and sample-by-sample comparisons to rank the algorithms. The resulting event durations varied substantially as a function of what algorithm was used. This evaluation differed from previous evaluations by considering a relatively large set of algorithms, multiple events, and data from both static and dynamic stimuli. The main conclusion is that current detectors of only fixations and saccades work reasonably well for static stimuli, but barely better than chance for dynamic stimuli. Differing results across evaluation methods make it difficult to select one winner for fixation detection. For saccade detection, however, the algorithm by Larsson, Nyström and Stridh (IEEE Transaction on Biomedical Engineering, 60(9):2484-2493,2013) outperforms all algorithms in data from both static and dynamic stimuli. The data also show how improperly selected algorithms applied to dynamic data misestimate fixation and saccade properties.
Multiuser Detection in MIMO-OFDM Wireless Communication System Using Hybrid Firefly Algorithm
B. Sathish Kumar
2014-05-01
Full Text Available In recent years, future generation wireless communication technologies are most the prominent fields in which many innovative techniques are used for effective communication. Orthogonal frequency-division multiplexing is one of the important technologies used for communication in future generation technologies. Although it gives efficient results, it has some problems during the implementation in real-time. MIMO and OFDM are integrated to have both their benefits. But, noise and interference are the major issues in the MIMO OFDM systems. To overcome these issues multiuser detection method is used in MIMO OFDM. Several algorithms and mathematical formulations have been presented for solving multiuser detection problem in MIMO OFDM systems. The algorithms such as genetic simulated annealing algorithm, hybrid ant colony optimization algorithm are used for multiuser detection problem in previous studies. But, due to the limitations of those optimization algorithms, the results obtained are not significant. In this research, to overcome the noise and interference problems, hybrid firefly optimization algorithm based on the evolutionary algorithm is proposed. The proposed algorithm is compared with the existing multiuser detection algorithm such as particle swarm optimization, CEFM-GADA [complementary error function mutation (CEFM and a differential algorithm (DA genetic algorithm (GA] and Hybrid firefly optimization algorithm based on evolutionary algorithm. The simulation results shows that performance of the proposed algorithm is better than the existing algorithm and it provides a satisfactory trade-off between computational complexity and detection performance
Improving the Attack Detection Rate in Network Intrusion Detection using Adaboost Algorithm
G. Gowrison
2012-01-01
Full Text Available Problem statement: Nowadays, the Internet plays an important role in communication between people. To ensure a secure communication between two parties, we need a security system to detect the attacks very effectively. Network intrusion detection serves as a major system to work with other security system to protect the computer networks. Approach: In this article, an Adaboost algorithm for network intrusion detection system with single weak classifier is proposed. The classifiers such as Bayes Net, Naive Bayes and Decision tree are used as weak classifiers. A benchmark data set is used in these experiments to demonstrate that boosting algorithm can greatly improve the classification accuracy of weak classification algorithms. Results: Our approach achieves a higher detection rate with low false alarm rates and is scalable for large data sets, resulting in an effective intrusion detection system. Conclusion: The Naive Bayes and Decision Tree Classifiers have comparatively better performance as a weak classifier with Adaboost, it should be considered for the building of IDS.
Cable Damage Detection System and Algorithms Using Time Domain Reflectometry
Clark, G A; Robbins, C L; Wade, K A; Souza, P R
2009-03-24
This report describes the hardware system and the set of algorithms we have developed for detecting damage in cables for the Advanced Development and Process Technologies (ADAPT) Program. This program is part of the W80 Life Extension Program (LEP). The system could be generalized for application to other systems in the future. Critical cables can undergo various types of damage (e.g. short circuits, open circuits, punctures, compression) that manifest as changes in the dielectric/impedance properties of the cables. For our specific problem, only one end of the cable is accessible, and no exemplars of actual damage are available. This work addresses the detection of dielectric/impedance anomalies in transient time domain reflectometry (TDR) measurements on the cables. The approach is to interrogate the cable using time domain reflectometry (TDR) techniques, in which a known pulse is inserted into the cable, and reflections from the cable are measured. The key operating principle is that any important cable damage will manifest itself as an electrical impedance discontinuity that can be measured in the TDR response signal. Machine learning classification algorithms are effectively eliminated from consideration, because only a small number of cables is available for testing; so a sufficient sample size is not attainable. Nonetheless, a key requirement is to achieve very high probability of detection and very low probability of false alarm. The approach is to compare TDR signals from possibly damaged cables to signals or an empirical model derived from reference cables that are known to be undamaged. This requires that the TDR signals are reasonably repeatable from test to test on the same cable, and from cable to cable. Empirical studies show that the repeatability issue is the 'long pole in the tent' for damage detection, because it is has been difficult to achieve reasonable repeatability. This one factor dominated the project. The two-step model
Cable Damage Detection System and Algorithms Using Time Domain Reflectometry
Clark, G A; Robbins, C L; Wade, K A; Souza, P R
2009-03-24
This report describes the hardware system and the set of algorithms we have developed for detecting damage in cables for the Advanced Development and Process Technologies (ADAPT) Program. This program is part of the W80 Life Extension Program (LEP). The system could be generalized for application to other systems in the future. Critical cables can undergo various types of damage (e.g. short circuits, open circuits, punctures, compression) that manifest as changes in the dielectric/impedance properties of the cables. For our specific problem, only one end of the cable is accessible, and no exemplars of actual damage are available. This work addresses the detection of dielectric/impedance anomalies in transient time domain reflectometry (TDR) measurements on the cables. The approach is to interrogate the cable using time domain reflectometry (TDR) techniques, in which a known pulse is inserted into the cable, and reflections from the cable are measured. The key operating principle is that any important cable damage will manifest itself as an electrical impedance discontinuity that can be measured in the TDR response signal. Machine learning classification algorithms are effectively eliminated from consideration, because only a small number of cables is available for testing; so a sufficient sample size is not attainable. Nonetheless, a key requirement is to achieve very high probability of detection and very low probability of false alarm. The approach is to compare TDR signals from possibly damaged cables to signals or an empirical model derived from reference cables that are known to be undamaged. This requires that the TDR signals are reasonably repeatable from test to test on the same cable, and from cable to cable. Empirical studies show that the repeatability issue is the 'long pole in the tent' for damage detection, because it is has been difficult to achieve reasonable repeatability. This one factor dominated the project. The two-step model
Robust and accurate detection algorithm for multimode polymer optical FBG sensor system
Ganziy, Denis; Jespersen, O.; Rose, B.
2015-01-01
We propose a novel dynamic gate algorithm (DGA) for robust and fast peak detection. The algorithm uses a threshold determined detection window and center of gravity algorithm with bias compensation. Our experiment demonstrates that the DGA method is fast and robust with better stability...
A Fast and Simple Algorithm for Detecting Large Scale Structures
Pillastrini, Giovanni C Baiesi
2013-01-01
Aims: we propose a gravitational potential method (GPM) as a supercluster finder based on the analysis of the local gravitational potential distribution measured by fast and simple algorithm applied to a spatial distribution of mass tracers. Methodology: the GPM performs a two-step exploratory data analysis: first, it measures the comoving local gravitational potential generated by neighboring mass tracers at the position of a test point-like mass tracer. The computation extended to all mass tracers of the sample provides a detailed map of the negative potential fluctuations. The most negative gravitational potential is provided by the highest mass density or, in other words, the deeper is a potential fluctuations in a certain region of space and denser are the mass tracers in that region. Therefore, from a smoothed potential distribution, the deepest potential well detects unambiguously a high concentration in the mass tracer distribution. Second, applying a density contrast criterion to that mass concentrat...
A new dwarf detection algorithm applied to M101
Bennet, Paul; Sand, David J.; Crnojevic, Denija
2017-01-01
The Lambda Cold Dark Matter model for structure formation has been very successful at reproducing observations of large scale structures. However, challenges emerge at sub-galactic scales, e.g. the number of dwarfs around the Milky Way show an order of magnitude difference with simulations (the 'missing satellites problem'). There are several theories to explain this apparent discrepancy but further observations of Local Volume galaxies and their substructure is required to constrain these models by better sampling halo to halo scatter. Here we report on a survey of the M101 group from archival data and a novel dwarf detection algorithm. This survey has discovered 26 new dwarf candidates in the M101 system, extending the dwarf luminosity function by two magnitudes, to M=-7.5. These dwarf candidates also show a distinct spatial asymmetry suggestive of an infalling dwarf group.
Yazan M. Alomari
2014-01-01
Full Text Available Segmentation and counting of blood cells are considered as an important step that helps to extract features to diagnose some specific diseases like malaria or leukemia. The manual counting of white blood cells (WBCs and red blood cells (RBCs in microscopic images is an extremely tedious, time consuming, and inaccurate process. Automatic analysis will allow hematologist experts to perform faster and more accurately. The proposed method uses an iterative structured circle detection algorithm for the segmentation and counting of WBCs and RBCs. The separation of WBCs from RBCs was achieved by thresholding, and specific preprocessing steps were developed for each cell type. Counting was performed for each image using the proposed method based on modified circle detection, which automatically counted the cells. Several modifications were made to the basic (RCD algorithm to solve the initialization problem, detecting irregular circles (cells, selecting the optimal circle from the candidate circles, determining the number of iterations in a fully dynamic way to enhance algorithm detection, and running time. The validation method used to determine segmentation accuracy was a quantitative analysis that included Precision, Recall, and F-measurement tests. The average accuracy of the proposed method was 95.3% for RBCs and 98.4% for WBCs.
Eu-Detect: An algorithm for detecting eukaryotic sequences in metagenomic data sets
Monzoorul Haque Mohammed; Sudha Chadaram Dinakar; Dinakar Komanduri; Tarini Shankar Ghosh; Sharmila S Mande
2011-09-01
Physical partitioning techniques are routinely employed (during sample preparation stage) for segregating the prokaryotic and eukaryotic fractions of metagenomic samples. In spite of these efforts, several metagenomic studies focusing on bacterial and archaeal populations have reported the presence of contaminating eukaryotic sequences inmetagenomic data sets. Contaminating sequences originate not only from genomes of micro-eukaryotic species but also from genomes of (higher) eukaryotic host cells. The latter scenario usually occurs in the case of host-associatedmetagenomes. Identification and removal of contaminating sequences is important, since these sequences not only impact estimates of microbial diversity but also affect the accuracy of several downstream analyses. Currently, the computational techniques used for identifying contaminating eukaryotic sequences, being alignment based, are slow, inefficient, and require huge computing resources. In this article, we present Eu-Detect, an alignment-free algorithm that can rapidly identify eukaryotic sequences contaminating metagenomic data sets. Validation results indicate that on a desktop with modest hardware specifications, the Eu-Detect algorithm is able to rapidly segregate DNA sequence fragments of prokaryotic and eukaryotic origin, with high sensitivity. A Web server for the Eu-Detect algorithm is available at http://metagenomics.atc.tcs.com/Eu-Detect/.
A Stereo-Vision Based Hazard-Detection Algorithm for Future Planetary Landers
Woicke, S.; Mooij, E.
2014-06-01
A hazard detection algorithm based on the stereo-vision principle is presented. A sensitivity analysis concerning the minimum baseline and the maximum altitude is discussed, based on which the limitations of this algorithm are investigated.
Long, CN; Gaustad, KL
2004-01-31
This document describes some specifics of the algorithm for detecting clear skies and fitting clear-sky shortwave (SW) functions described in Long and Ackerman (2000). This algorithm forms the basis of the ARM SW FLUX ANAL 1Long VAP. In the Atmospheric Radiation Measurement (ARM) case, the value added procedures (VAP) can be described as having three parts: a “front end,” a “black box,” and a “back end.” The “front end” handles the file management of the processing, what range of data files to process in the run, which configuration file to use for each site, extracting the data from the ARM NetCDF files into an ASCII format for the code to process, etc. The “back end” produces ARM-format NetCDF files of the output and other file management. The “black box” is the processing code(s), and is what is discussed in this document. Details on the “front” and “back” ends of the ARM VAP are presented elsewhere.
Joint Interference Detection Method for DSSS Communications Based on the OMP Algorithm and CA-CFAR
Zhang Yongshun
2016-01-01
Full Text Available The existing direct sequence spread spectrum (DSSS communications interference detection algorithms are confined to the high sampling rate. In order to solve this problem, algorithm for DSSS communications interference detection was designed based on compressive sensing (CS. First of all, the orthogonal matching pursuit (OMP algorithm was applied to the interference detection in DSSS communications, the advantages and weaknesses of the algorithm were analyzed; Secondly, according to the weaknesses of the OMP algorithm, a joint interference detection method based on the OMP algorithm and cell average constant false alarm rate (CA-CFAR was proposed. The theoretical analyze and computer simulation all proved the effectiveness of the new algorithm. The simulation results show that the new method not only could achieve the interference detection, but also could estimate the interference quantity effectively.
Low Complexity Multiuser Detection Algorithm for Multi-Beam Satellite Systems
Yang Wang; Danfeng Zhao; Xi Liao
2015-01-01
The minimum mean square error⁃successive interference cancellation ( MMSE⁃SIC ) multiuser detection algorithm has high complexity and long processing latency. A multiuser detection algorithm is proposed for multi⁃beam satellite systems in order to decrease the complexity and latency. The spot beams are grouped base on the distance between them in the proposed algorithm. Some groups are detected in parallel after a crucial group⁃wise interference cancellation. Furthermore, the multi⁃stage structure is introduced to improve the performance. Simulation results show that the proposed algorithm can achieve better performance with less complexity compared with the existing group detection algorithm. Moreover, the proposed algorithm using one stage can reduce the complexity over the fast MMSE⁃SIC and existing group detection algorithm by 9% and 20�9%. The processing latency is reduced significantly compared with the MMSE⁃SIC.
ASSESSMENT OF RELIABILITY AND COMPARISON OF TWO ALGORITHMS FOR HAIL HAZARD DETECTION FROM AIRCRAFT
I.M. Braun
2005-02-01
Full Text Available This paper presents and analyzes two algorithms for the detection of hail zones in clouds and precipitation: parametric algorithm and adaptive non-parametric algorithm. Reliability of detection of radar signals from hailstones is investigated by statistical simulation with application of experimental researches as initial data. The results demonstrate the limits of both algorithms as well as higher viability of non-parametric algorithm. Polarimetric algorithms are useful for the implementation in ground-based and airborne weather radars.
Comparison of different classification algorithms for landmine detection using GPR
Karem, Andrew; Fadeev, Aleksey; Frigui, Hichem; Gader, Paul
2010-04-01
The Edge Histogram Detector (EHD) is a landmine detection algorithm that has been developed for ground penetrating radar (GPR) sensor data. It has been tested extensively and has demonstrated excellent performance. The EHD consists of two main components. The first one maps the raw data to a lower dimension using edge histogram based feature descriptors. The second component uses a possibilistic K-Nearest Neighbors (pK-NN) classifier to assign a confidence value. In this paper we show that performance of the baseline EHD could be improved by replacing the pK-NN classifier with model based classifiers. In particular, we investigate two such classifiers: Support Vector Regression (SVR), and Relevance Vector Machines (RVM). We investigate the adaptation of these classifiers to the landmine detection problem with GPR, and we compare their performance to the baseline EHD with a pK-NN classifier. As in the baseline EHD, we treat the problem as a two class classification problem: mine vs. clutter. Model parameters for the SVR and the RVM classifiers are estimated from training data using logarithmic grid search. For testing, soft labels are assigned to the test alarms. A confidence of zero indicates the maximum probability of being a false alarm. Similarly, a confidence of one represents the maximum probability of being a mine. Results on large and diverse GPR data collections show that the proposed modification to the classifier component can improve the overall performance of the EHD significantly.
Bearing Fault Detection Using Artificial Neural Networks and Genetic Algorithm
Samanta B
2004-01-01
Full Text Available A study is presented to compare the performance of bearing fault detection using three types of artificial neural networks (ANNs, namely, multilayer perceptron (MLP, radial basis function (RBF network, and probabilistic neural network (PNN. The time domain vibration signals of a rotating machine with normal and defective bearings are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to all three ANN classifiers: MLP, RBF, and PNN for two-class (normal or fault recognition. The characteristic parameters like number of nodes in the hidden layer of MLP and the width of RBF, in case of RBF and PNN along with the selection of input features, are optimized using genetic algorithms (GA. For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions. The ANNs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine with and without bearing faults. The results show the relative effectiveness of three classifiers in detection of the bearing condition.
Alahyari, A.; Longmire, E. K.
1994-10-01
A fondamental difficulty in the experimental study of gravity-driven flows using particle image velocimetry (PIV) and other optical diagnostic techniques is the problem associated with variations in thé refractive index within the fluid. This paper discusses a method by which the refractive indices of two fluids are matched while maintaining density differences of up to 4%. Aqueous solutions of glycerol and potassium phosphate are used to achieve precise index matching in the presence of mixed and unmixed constituents. The effectiveness of the method is verified in a PIV study of a laboratory-scale model of an atmospheric microburst where planes of two-dimensional velocity vectors are obtained in thé evolving flow field.
Assessment of a GOES microburst product for two early cold season convective storms
Pryor, Kenneth L
2010-01-01
This paper presents an assessment of the new Geostationary Operational Environmental Satellite (GOES) imager channel 3 - 4 brightness temperature difference (BTD) product for two early cold season severe convective storm events that occurred over the Mid-Atlantic region on 17 November and 1 December 2010. Both of these events involved squall lines that produced strong downbursts as they tracked over the Tidal Potomac River and Chesapeake Bay regions. It has been found recently that the BTD between GOES infrared channel 3 (water vapor) and channel 4 (thermal infrared) can highlight regions where severe outflow wind generation (i.e. downbursts, microbursts) is likely due to the channeling of dry mid-tropospheric air into the precipitation core of a deep, moist convective storm. These two cases demonstrate effective operational use of this image product for cold-season convective storm events.
Application of a New Fuzzy Clustering Algorithm in Intrusion Detection
无
2008-01-01
This paper presents a new Section Set Adaptive FCM algorithm. The algorithm solved the shortcomings of localoptimality, unsure classification and clustering numbers ascertained previously. And it improved on the architecture of FCM al-gorithm, enhanced the analysis for effective clustering. During the clustering processing, it may adjust clustering numbers dy-namically. Finally, it used the method of section set decreasing the time of classification. By experiments, the algorithm can im-prove dependability of clustering and correctness of classification.
SURF IA Conflict Detection and Resolution Algorithm Evaluation
Jones, Denise R.; Chartrand, Ryan C.; Wilson, Sara R.; Commo, Sean A.; Barker, Glover D.
2012-01-01
The Enhanced Traffic Situational Awareness on the Airport Surface with Indications and Alerts (SURF IA) algorithm was evaluated in a fast-time batch simulation study at the National Aeronautics and Space Administration (NASA) Langley Research Center. SURF IA is designed to increase flight crew situation awareness of the runway environment and facilitate an appropriate and timely response to potential conflict situations. The purpose of the study was to evaluate the performance of the SURF IA algorithm under various runway scenarios, multiple levels of conflict detection and resolution (CD&R) system equipage, and various levels of horizontal position accuracy. This paper gives an overview of the SURF IA concept, simulation study, and results. Runway incursions are a serious aviation safety hazard. As such, the FAA is committed to reducing the severity, number, and rate of runway incursions by implementing a combination of guidance, education, outreach, training, technology, infrastructure, and risk identification and mitigation initiatives [1]. Progress has been made in reducing the number of serious incursions - from a high of 67 in Fiscal Year (FY) 2000 to 6 in FY2010. However, the rate of all incursions has risen steadily over recent years - from a rate of 12.3 incursions per million operations in FY2005 to a rate of 18.9 incursions per million operations in FY2010 [1, 2]. The National Transportation Safety Board (NTSB) also considers runway incursions to be a serious aviation safety hazard, listing runway incursion prevention as one of their most wanted transportation safety improvements [3]. The NTSB recommends that immediate warning of probable collisions/incursions be given directly to flight crews in the cockpit [4].
IMPROVEMENT OF ANOMALY DETECTION ALGORITHMS IN HYPERSPECTRAL IMAGES USING DISCRETE WAVELET TRANSFORM
Kamal Jamshidi; Mohsen Zare Baghbidi; Ahmad Reza Naghsh Nilchi; Saeid Homayouni
2012-01-01
Recently anomaly detection (AD) has become an important application for target detection in hyperspectral remotely sensed images. In many applications, in addition to high accuracy of detection we need a fast and reliable algorithm as well. This paper presents a novel method to improve the performance of current AD algorithms. The proposed method first calculates Discrete Wavelet Transform (DWT) of every pixel vector of image using Daubechies4 wavelet. Then, AD algorithm performs on four band...
A Scale and Pose Invariant Algorithm for Fast Detecting Human Faces in a Complex Background
XING Xin; SHEN Lansun; JIA Kebin
2001-01-01
Human face detection is an interesting and challenging task in computer vision. A scale and pose invariant algorithm is proposed in this paper.The algorithm is able to detect human faces in a complex background in about 400ms with a detection rate of 92%. The algorithm can be used in a wide range of applications such as human-computer interface, video coding, etc.
无
2007-01-01
The mismatch between echo and replica caused by underwater moving target(UMT)'s radial velocity degrades the detection performance of the matched filter(MF) for the linear frequency modulation(LFM) signal. By using the focusing property of fractional Fourier transform(FRFT) to that signal, a detection algorithm for UMT's LFM echo based on the discrete fractional Fourier transform(DFRFT) is proposed. This algorithm is less affected by the target's radial velocity compared with the other MF detection algorithm utilizing zero radial velocity replica(ZRVR), and the mathematical relation between the output peak positions of these two algorithms exists in the case of existence of target echo. The algorithm can also estimate the target distance by using this relation. The simulation and experiment show that this algorithm's detection performance is better than or equivalent to that of the other MF algorithm utilizing ZRVR for the LFM echo of UMT with unknown radial velocity under reverberation noise background.
National Aeronautics and Space Administration — This paper considers the problem of change detection using local distributed eigen monitoring algorithms for next generation of astronomy petascale data pipelines...
Yin, Jiale; Liu, Lei; Li, He; Liu, Qiankun
2016-07-01
This paper presents the infrared moving object detection and security detection related algorithms in video surveillance based on the classical W4 and frame difference algorithm. Classical W4 algorithm is one of the powerful background subtraction algorithms applying to infrared images which can accurately, integrally and quickly detect moving object. However, the classical W4 algorithm can only overcome the deficiency in the slight movement of background. The error will become bigger and bigger for long-term surveillance system since the background model is unchanged once established. In this paper, we present the detection algorithm based on the classical W4 and frame difference. It cannot only overcome the shortcoming of falsely detecting because of state mutations from background, but also eliminate holes caused by frame difference. Based on these we further design various security detection related algorithms such as illegal intrusion alarm, illegal persistence alarm and illegal displacement alarm. We compare our method with the classical W4, frame difference, and other state-of-the-art methods. Experiments detailed in this paper show the method proposed in this paper outperforms the classical W4 and frame difference and serves well for the security detection related algorithms.
An Algorithm to detect balancing of iterated line sigraph.
Sinha, Deepa; Sethi, Anshu
2015-01-01
A signedgraph (or sigraph in short) S is a graph G in which each edge x carries a value [Formula: see text] called its sign denoted specially as [Formula: see text]. Given a sigraph S, H = L(S) called the line sigraph of S is that sigraph in which edges of S are represented as vertices, two of these vertices are defined to be adjacent whenever the corresponding edges in S have a vertex in common and any such edge ef is defined to be negative whenever both e and f are negative edges in S. Here S is called root sigraph of H. Iterated signed line graphs [Formula: see text] = [Formula: see text] k [Formula: see text] [Formula: see text], S:= [Formula: see text] is defined similarly. In this paper, we give an algorithm to obtain iterated line sigraph and detect for which value of 'k' it is balanced and determine its complexity. In the end we will propose a technique that will use adjacency matrix of S and adjacency matrix of [Formula: see text] which is balanced for some 'k' as a parameter to encrypt a network and forward the data in the form of balanced [Formula: see text] and will decrypt it by applying inverse matrix operations.
Detection of malicious attacks by Meta classification algorithms
G.Michael
2015-03-01
Full Text Available We address the problem of malicious node detection in a network based on the characteristics in the behavior of the network. This issue brings out a challenging set of research papers in the recent contributing a critical component to secure the network. This type of work evolves with many changes in the solution strategies. In this work, we propose carefully the learning models with cautious selection of attributes, selection of parameter thresholds and number of iterations. In this research, appropriate approach to evaluate the performance of a set of meta classifier algorithms (Ad Boost, Attribute selected classifier, Bagging, Classification via Regression, Filtered classifier, logit Boost, multiclass classifier. The ratio between training and testing data is made such way that compatibility of data patterns in both the sets are same. Hence we consider a set of supervised machine learning schemes with meta classifiers were applied on the selected dataset to predict the attack risk of the network environment . The trained models were then used for predicting the risk of the attacks in a web server environment or by any network administrator or any Security Experts. The Prediction Accuracy of the Classifiers was evaluated using 10-fold Cross Validation and the results have been compared to obtain the accuracy.
An Efficient Hierarchy Algorithm for Community Detection in Complex Networks
Lili Zhang
2014-01-01
Full Text Available Community structure is one of the most fundamental and important topology characteristics of complex networks. The research on community structure has wide applications and is very important for analyzing the topology structure, understanding the functions, finding the hidden properties, and forecasting the time-varying of the networks. This paper analyzes some related algorithms and proposes a new algorithm—CN agglomerative algorithm based on graph theory and the local connectedness of network to find communities in network. We show this algorithm is distributed and polynomial; meanwhile the simulations show it is accurate and fine-grained. Furthermore, we modify this algorithm to get one modified CN algorithm and apply it to dynamic complex networks, and the simulations also verify that the modified CN algorithm has high accuracy too.
A Reliability-Based Multi-Algorithm Fusion Technique in Detecting Changes in Land Cover
Jiangping Chen
2013-03-01
Full Text Available Detecting land use or land cover changes is a challenging problem in analyzing images. Change-detection plays a fundamental role in most of land use or cover monitoring systems using remote-sensing techniques. The reliability of individual automatic change-detection algorithms is currently below operating requirements when considering the intrinsic uncertainty of a change-detection algorithm and the complexity of detecting changes in remote-sensing images. In particular, most of these algorithms are only suited for a specific image data source, study area and research purpose. Only a number of comprehensive change-detection methods that consider the reliability of the algorithm in different implementation situations have been reported. This study attempts to explore the advantages of combining several typical change-detection algorithms. This combination is specifically designed for a highly reliable change-detection task. Specifically, a fusion approach based on reliability is proposed for an exclusive land use or land cover change-detection. First, the reliability of each candidate algorithm is evaluated. Then, a fuzzy comprehensive evaluation is used to generate a reliable change-detection approach. This evaluation is a transformation between a one-way evaluation matrix and a weight vector computed using the reliability of each candidate algorithm. Experimental results reveal that the advantages of combining these distinct change-detection techniques are evident.
Frequency-Feedback Based Islanding Detection Algorithm for Micro-Grid
LI Yongli; LI Shengwei; BAI Shibin; NIU Chongxuan
2008-01-01
The unintentional islanding of micro-grid may cause negative impacts on distribution loads and distributed generations, so it must be detected within the acceptable duration. In this paper a new islanding detection algorithm is proposed. This algorithm introduces the frequency feedback method by the reactive power compensation to derive the frequency continuous shift. Accordingly, the islanding can be detected by monitoring the frequency within 0.1 s. The simulationresults prove that this algorithm has extremely small non-detection zone, and meanwhile it presents an excellent islanding detection speed as well.
Yutian Cao
2015-12-01
Full Text Available In this paper, by analyzing the characteristics of infrared moving targets, a Symmetric Frame Differencing Target Detection algorithm based on local clustering segmentation is proposed. In consideration of the high real-time performance and accuracy of traditional symmetric differencing, this novel algorithm uses local grayscale clustering to accomplish target detection after carrying out symmetric frame differencing to locate the regions of change. In addition, the mean shift tracking algorithm is also improved to solve the problem of missed targets caused by error convergence. As a result, a kernel-based mean shift target tracking algorithm based on detection updates is also proposed. This tracking algorithm makes use of the interaction between detection and tracking to correct the tracking errors in real time and to realize robust target tracking in complex scenes. In addition, the validity, robustness and stability of the proposed algorithms are all verified by experiments on mid-infrared aerial sequences with vehicles as targets.
Hardware Implementation of a Modified Delay-Coordinate Mapping-Based QRS Complex Detection Algorithm
Andrej Zemva
2007-01-01
Full Text Available We present a modified delay-coordinate mapping-based QRS complex detection algorithm, suitable for hardware implementation. In the original algorithm, the phase-space portrait of an electrocardiogram signal is reconstructed in a two-dimensional plane using the method of delays. Geometrical properties of the obtained phase-space portrait are exploited for QRS complex detection. In our solution, a bandpass filter is used for ECG signal prefiltering and an improved method for detection threshold-level calculation is utilized. We developed the algorithm on the MIT-BIH Arrhythmia Database (sensitivity of 99.82% and positive predictivity of 99.82% and tested it on the long-term ST database (sensitivity of 99.72% and positive predictivity of 99.37%. Our algorithm outperforms several well-known QRS complex detection algorithms, including the original algorithm.
Implementation of a high-impedance fault detection algorithm. Final report
Balser, S.J.; Lawrence, D.J.; Caprino, B.; Delaney, L.
1985-05-01
A digital computer based algorithm was developed to detect high impedance faults on distribution systems using statistical methods. The algorithm is written in PL/M 86 and PASCAL and implemented on an INTEL SYS380 microcomputer system, designed to operate in real time and interface with acquisition software. The report contains a description of the calculation procedures comprising the detection algorithm, implementation requirements, and test results for algorithm verification. A discussion of hardware limitations and an estimation of fault detection rate based on historical records is also presented.
Evaluation of subgraph searching algorithms for detecting network motifs in biological networks
Jialu HU; Lin GAO; Guimin QIN
2009-01-01
Despite several algorithms for searching sub-graphs in motif detection presented in the literature, no ef-fort has been done for characterizing their performance till now. This paper presents a methodology to evaluate the performance of three algorithms: edge sampling algorithm (ESA), enumerate subgraphs (ESU) and randomly enumer-ate subgraphs (RAND-ESU). A series of experiments are performed to test sampling speed and sampling quality. The results show that RAND-ESU is more efficient and has less computational cost than other algorithms for large-size mo-tif detection, and ESU has its own advantage in small-size motif detection.
Hirakawa, Satoshi; Nishio, Yoshifumi; Ushida, Akio; Ueno, Junji; Kasem, I.; Nishitani, Hiromu [Tokushima Univ. (Japan); Rekeczky, C.; Roska, T.
1997-07-01
In this article, a new type of diffusion template and an analogic CNN algorithm using this diffusion template for detecting some lung cancer symptoms in X-ray films are proposed. The performance of the diffusion template is investigated and our CNN algorithm is verified to detect some key lung cancer symptoms, successfully. (author)
Target Impact Detection Algorithm Using Computer-aided Design (CAD) Model Geometry
2014-09-01
UNCLASSIFIED AD-E403 558 Technical Report ARMET-TR-13024 TARGET IMPACT DETECTION ALGORITHM USING COMPUTER-AIDED DESIGN ( CAD ...DETECTION ALGORITHM USING COMPUTER-AIDED DESIGN ( CAD ) MODEL GEOMETRY 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6...This report documents a method and algorithm to export geometry from a three-dimensional, computer-aided design ( CAD ) model in a format that can be
Algorithms for the detection of hazelnut oil in olive oil
Moreda, W.
2000-06-01
Full Text Available The fraudulent addition of hazelnut oil to olive oil can be only detected in high proportions (20â25%, using the D7-stigmastenol and the difference between triacylglycerols of equivalent carbon number 42, determined experimentally by HPLC and calculated theoretically from the fatty acid composition (DECN42. A new method lies on a sequential comparison of the values of several algorithms with a database built with data obtained from genuine virgin olive oils. The algorithms are: LLLexp vs %L; (LLL/OLLnexp - (LLL/OLLntheor vs DECN44 and (ECN44/LLLexp vs %L; being LLLexp, OLLnexp, and ECN44exp the percentage of triacylglycerols determined by HPLC; LLLtheor, OLLntheor, and ECN44theor the percentage of those calculated theoretically from the fatty acid composition; DECN44 the difference between the experimental and theoretical value of ECN44; and finally %L the percentage of linoleic acid. The database has been built considering the values obtained from olive oils of different fatty acid composition and from admixtures between them. The method allows the detection of low percentages of hazelnut oil in olive oil (5 %.La adición fraudulenta de aceite de avellana en aceite de oliva puede ser detectada sólo en altas proporciones (20â25 %, usando el D7-estigmastenol y la diferencia entre los triglicéridos con número de carbono equivalente igual a 42, determinados experimentalmente por HPLC y teóricamente a partir de la composición de ácidos grasos (DECN42. Se propone un nuevo método que consiste en la comparación de los valores de varios algoritmos con una base de datos de valores experimentales obtenidos de aceites de oliva virgen genuinos. Estos algoritmos son: LLLexp en función de %L; (LLL/OLLnexp - (LLL/OLLnteor en función de DECN44 y (ECN44/LLLexp en función de %L; siendo LLLexp, OLLnexp, y ECN44exp los porcentajes de los triglicéridos obtenidos por HPLC; LLLteor , OLLnteor, y ECN44teor los
A community detection algorithm based on topology potential and spectral clustering.
Wang, Zhixiao; Chen, Zhaotong; Zhao, Ya; Chen, Shaoda
2014-01-01
Community detection is of great value for complex networks in understanding their inherent law and predicting their behavior. Spectral clustering algorithms have been successfully applied in community detection. This kind of methods has two inadequacies: one is that the input matrixes they used cannot provide sufficient structural information for community detection and the other is that they cannot necessarily derive the proper community number from the ladder distribution of eigenvector elements. In order to solve these problems, this paper puts forward a novel community detection algorithm based on topology potential and spectral clustering. The new algorithm constructs the normalized Laplacian matrix with nodes' topology potential, which contains rich structural information of the network. In addition, the new algorithm can automatically get the optimal community number from the local maximum potential nodes. Experiments results showed that the new algorithm gave excellent performance on artificial networks and real world networks and outperforms other community detection methods.
A Community Detection Algorithm Based on Topology Potential and Spectral Clustering
Zhixiao Wang
2014-01-01
Full Text Available Community detection is of great value for complex networks in understanding their inherent law and predicting their behavior. Spectral clustering algorithms have been successfully applied in community detection. This kind of methods has two inadequacies: one is that the input matrixes they used cannot provide sufficient structural information for community detection and the other is that they cannot necessarily derive the proper community number from the ladder distribution of eigenvector elements. In order to solve these problems, this paper puts forward a novel community detection algorithm based on topology potential and spectral clustering. The new algorithm constructs the normalized Laplacian matrix with nodes’ topology potential, which contains rich structural information of the network. In addition, the new algorithm can automatically get the optimal community number from the local maximum potential nodes. Experiments results showed that the new algorithm gave excellent performance on artificial networks and real world networks and outperforms other community detection methods.
Detection of Local/Regional Events in Kuwait Using Next-Generation Detection Algorithms
Gok, M. Rengin [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Al-Jerri, Farra [Kuwait Inst. for Scientific Research, Kuwait City (Kuwait); Dodge, Douglas [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Al-Enezi, Abdullah [Kuwait Inst. for Scientific Research, Kuwait City (Kuwait); Hauk, Terri [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Mellors, R. [Kuwait Inst. for Scientific Research, Kuwait City (Kuwait)
2014-12-10
Seismic networks around the world use conventional triggering algorithms to detect seismic signals in order to locate local/regional seismic events. Kuwait National Seismological Network (KNSN) of Kuwait Institute of Scientific Research (KISR) is operating seven broad-band and short-period three-component stations in Kuwait. The network is equipped with Nanometrics digitizers and uses Antelope and Guralp acquisition software for processing and archiving the data. In this study, we selected 10 days of archived hourly-segmented continuous data of five stations (Figure 1) and 250 days of continuous recording at MIB. For the temporary deployment our selection criteria was based on KNSN catalog intensity for the period of time we test the method. An autonomous event detection and clustering framework is employed to test a more complete catalog of this short period of time. The goal is to illustrate the effectiveness of the technique and pursue the framework for longer period of time.
Genetic algorithm for flood detection and evacuation route planning
Gomes, Rahul; Straub, Jeremy
2017-05-01
A genetic-type algorithm is presented that uses satellite geospatial data to determine the most probable path to safety for individuals in a disaster area, where a traditional routing system cannot be used. The algorithm uses geological features and disaster information to determine the shortest safe path. It predicts how a flood can change a landform over time and uses this data to predict alternate routes. It also predicts safe routes in rural locations where GPS/map-based routing data is unavailable or inaccurate. Reflectance and a supervised classification algorithm are used and the output is compared with RFPI and PCR-GLOBWB data.
A zero velocity detection algorithm using inertial sensors for pedestrian navigation systems.
Park, Sang Kyeong; Suh, Young Soo
2010-01-01
In pedestrian navigation systems, the position of a pedestrian is computed using an inertial navigation algorithm. In the algorithm, the zero velocity updating plays an important role, where zero velocity intervals are detected and the velocity error is reset. To use the zero velocity updating, it is necessary to detect zero velocity intervals reliably. A new zero detection algorithm is proposed in the paper, where only one gyroscope value is used. A Markov model is constructed using segmentation of gyroscope outputs instead of using gyroscope outputs directly, which makes the zero velocity detection more reliable.
Acquisition algorithm for direct-detection ladars with Geiger-mode avalanche photodiodes.
Milstein, Adam B; Jiang, Leaf A; Luu, Jane X; Hines, Eric L; Schultz, Kenneth I
2008-01-10
An optimal algorithm for detecting a target using a ladar system employing Geiger-mode avalanche photodiodes (GAPDs) is presented. The algorithm applies to any scenario where a ranging direct detection ladar is used to determine the presence of a target against a sky background within a specified range window. A complete statistical model of the detection process for GAPDs is presented, including GAPDs that are inactive for a fixed period of time each time they fire. The model is used to develop a constant false alarm rate detection algorithm that minimizes acquisition time. Numerical performance predictions, simulation results, and experimental results are presented.
A Zero Velocity Detection Algorithm Using Inertial Sensors for Pedestrian Navigation Systems
Young Soo Suh
2010-10-01
Full Text Available In pedestrian navigation systems, the position of a pedestrian is computed using an inertial navigation algorithm. In the algorithm, the zero velocity updating plays an important role, where zero velocity intervals are detected and the velocity error is reset. To use the zero velocity updating, it is necessary to detect zero velocity intervals reliably. A new zero detection algorithm is proposed in the paper, where only one gyroscope value is used. A Markov model is constructed using segmentation of gyroscope outputs instead of using gyroscope outputs directly, which makes the zero velocity detection more reliable.
Maximum-likelihood detection based on branch and bound algorithm for MIMO systems
LI Zi; CAI YueMing
2008-01-01
Maximum likelihood detection for MIMO systems can be formulated as an integer quadratic programming problem. In this paper, we introduce depth-first branch and bound algorithm with variable dichotomy into MIMO detection. More nodes may be pruned with this structure. At each stage of the branch and bound algorithm, active set algorithm is adopted to solve the dual subproblem. In order to reduce the com- plexity further, the Cholesky factorization update is presented to solve the linear system at each iteration of active set algorithm efficiently. By relaxing the pruning conditions, we also present the quasi branch and bound algorithm which imple- ments a good tradeoff between performance and complexity. Numerical results show that the complexity of MIMO detection based on branch and bound algorithm is very low, especially in low SNR and large constellations.
A Robust Algorithm for Real-time Endpoint Detection in the Noisy Mobile Environments
WUBian; RENXiaolin; LIUChongqing; ZHANGYaxin
2003-01-01
In speech recognition, the endpoint detection must be robust to noise. In low SNR situations, the conventional energy-based endpoint detection algorithms often fail and the performance of speech recognizer usually degrades distinctly, especially when in mobile environments, the background noise changes dramatically. In this paper, we propose a new algorithm that improves the endpoint detection for speech recognition in low SNR and in various noisy environments. The described algorithm not only uses multiple features but introduces a decision logic to increase the robustness in both low SNR and various noisy mobile environments. To evaluate the new algorithm, we carry out experiments in various noisy mobile environments (e.g. railway station, airport, street etc), and the performance of the algorithm is significantly improved, especially in low SNR situations. At the same time, the proposed algorithm has a low complexity and is suitable for real time embedded systems.
A general-purpose contact detection algorithm for nonlinear structural analysis codes
Heinstein, M.W.; Attaway, S.W.; Swegle, J.W.; Mello, F.J.
1993-05-01
A new contact detection algorithm has been developed to address difficulties associated with the numerical simulation of contact in nonlinear finite element structural analysis codes. Problems including accurate and efficient detection of contact for self-contacting surfaces, tearing and eroding surfaces, and multi-body impact are addressed. The proposed algorithm is portable between dynamic and quasi-static codes and can efficiently model contact between a variety of finite element types including shells, bricks, beams and particles. The algorithm is composed of (1) a location strategy that uses a global search to decide which slave nodes are in proximity to a master surface and (2) an accurate detailed contact check that uses the projected motions of both master surface and slave node. In this report, currently used contact detection algorithms and their associated difficulties are discussed. Then the proposed algorithm and how it addresses these problems is described. Finally, the capability of the new algorithm is illustrated with several example problems.
A Modified Energy Detection Based Spectrum Sensing Algorithm for Green Cognitive Radio Communication
Sidra Rajput
2015-10-01
Full Text Available Spectrum Sensing is the first and fundamental function of Cognitive Cycle which plays a vital role in the success of CRs (Cognitive Radios. Spectrum Sensing indicate the presence and absence of PUs (Primary Users in RF (Radio Frequency spectrum occupancy measurements. In order to correctly determine the presence and absence of Primary Users, the algorithms in practice include complex mathematics which increases the computational complexity of the algorithm, thus shifted the CRs to operate as ?green? communication systems. In this paper, an energy efficient and computationally less complex, energy detection based Spectrum Sensing algorithm have been proposed. The design goals of the proposed algorithm are to save the processing and sensing energies. At first, by using less MAC (Multiply and Accumulate operation, it saves the processing energy needed to determine the presence and absence of PUs. Secondly, it saves the sensing energy by providing a way to find lowest possible sensing time at which spectrum is to be sensed. Two scenarios have been defined for testing the proposed algorithm i.e. simulate detection capability of Primary Users in ideal and noisy scenarios. Detection of PUs in both of these scenarios have been compared to obtain the probability of detection. Energy Efficiency of the proposed algorithm has been proved by making performance comparison between the proposed (less complex algorithm and the legacy energy detection algorithm. With reduced complexity, the proposed spectrum sensing algorithm can be considered under the paradigm of Green Cognitive Radio Communication
Distributed edge detection algorithm based on wavelet transform for wireless video sensor network
Li, Qiulin; Hao, Qun; Song, Yong; Wang, Dongsheng
2011-05-01
Edge detection algorithms are critical to image processing and computer vision. Traditional edge detection algorithms are not suitable for wireless video sensor network (WVSN) in which the nodes are with in limited calculation capability and resources. In this paper, a distributed edge detection algorithm based on wavelet transform designed for WVSN is proposed. Wavelet transform decompose the image into several parts, then the parts are assigned to different nodes through wireless network separately. Each node performs sub-image edge detecting algorithm correspondingly, all the results are sent to sink node, Fusing and Synthesis which include image binary and edge connect are executed in it. And finally output the edge image. Lifting scheme and parallel distributed algorithm are adopted to improve the efficiency, simultaneously, decrease the computational complexity. Experimental results show that this method could achieve higher efficiency and better result.
Generalized Analysis of a Distributed Energy Efficient Algorithm for Change Detection
Banerjee, Taposh
2009-01-01
An energy efficient distributed Change Detection scheme based on Page's CUSUM algorithm was presented in \\cite{icassp}. In this paper we consider a nonparametric version of this algorithm. In the algorithm in \\cite{icassp}, each sensor runs 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. In this paper, we generalize the algorithm to also include nonparametric CUSUM and provide a unified analysis.
A Bubble Detection Algorithm Based on Sparse and Redundant Image Processing
Ye Tian
2013-06-01
Full Text Available Deinked pulp flotation column has been applied in wastepaper recycling. Bubble size in deinked pulp flotation column is very important during the flotation process. In this paper, bubble images of deinked pulp flotation column were first caught by digital camera, and then the bubbles were detected by using a detection algorithm based on sparse and redundant image processing. The results show the algorithms are very practical and effective on bubble detection in deinked pulp flotation column.
A Heuristic Clustering Algorithm for Intrusion Detection Based on Information Entropy
无
2006-01-01
This paper studied on the clustering problem for intrusion detection with the theory of information entropy, it was put forward that the clustering problem for exact intrusion detection based on information entropy is NP-complete, therefore, the heuristic algorithm to solve the clustering problem for intrusion detection was designed, this algorithm has the characteristic of incremental development, it can deal with the database with large connection records from the internet.
2013-10-01
Insider Threat Control: Using Plagiarism Detection Algorithms to Prevent Data Exfiltration in Near Real Time Todd Lewellen George J. Silowash...algorithms used in plagiarism detection software—to search the index for bodies of text similar to the text found in the outgoing web request. If the...2. REPORT DATE October 2013 3. REPORT TYPE AND DATES COVERED Final 4. TITLE AND SUBTITLE Insider Threat Control: Using Plagiarism Detection
A NEW ML DETECTION ALGORITHM FOR ORTHOGONAL MULTICODE SYSTEM IN NAKAGAMI FADING CHANNEL
无
2006-01-01
Based on the Maximum-Likelihood (ML) criterion, this paper proposes a novel noncoherent de-tection algorithm for Orthogonal Multicode (OM) system in Nakagami fading channel. Some theoreticalanalysis and simulation results are presented. It is shown that the proposed ML algorithm is at least 0.7 dBbetter than the conventional Matched-Filter (MF) algorithm for uncoded systems, in both non-fading and fad-ing channels. For the consideration of practical application, it is further simplified in complexity. Comparedwith the original ML algorithm, the simplified ML algorithm can provide significant reduction in complexitywith small degradation in performance.
Multi-View Algorithm for Face, Eyes and Eye State Detection in Human Image- Study Paper
Latesh Kumari
2014-07-01
Full Text Available For fatigue detection such as in the application of driver‟s fatigue monitoring system, the eye state analysis is one of the important and deciding steps to determine the fatigue of driver‟s eyes. In this study, algorithms for face detection, eye detection and eye state analysis have been studied and presented as well as an efficient algorithm for detection of face, eyes have been proposed. Firstly the efficient algorithm for face detection method has been presented which find the face area in the human images. Then, novel algorithms for detection of eye region and eye state are introduced. In this paper we propose a multi-view based eye state detection to determine the state of the eye. With the help of skin color model, the algorithm detects the face regions in an YCbCr color model. By applying the skin segmentation which normally separates the skin and non-skin pixels of the images, it detects the face regions of the image under various lighting and noise conditions. Then from these face regions, the eye regions are extracted within those extracted face regions. Our proposed algorithms are fast and robust as there is not pattern match.
Performance of the GLAS Onboard Surface Detection Algorithm
McGarry, Jan F.; Saba, Jack L.; Sun, Xiaoli; Abshire, James B.; Yi, Donghui; Brenner, Anita C.
2003-01-01
The Geoscience Laser Altimeter System (GLAS) determines the range from the satellite to the Earth's surface from the time of flight of the instrument's 1064 nm laser pulses, which are generated at a rate of 40 Hz. The time of flight is defined as the difference between the laser transmit time and the time of return of the surface echo. The detector output is digitized with a 1 ns sampling interval, starting before the laser fires and ending well after any possible surface return, for a total of 5.4 million points. Because there is not enough downlink bandwidth for the entire waveform, the algorithm must extract both the transmit and surface echo waveforms from the 5.4 million digitized points, and pass these waveforms on to be included in the science data packets. Results from orbit show the algorithm to be effective at finding the surface echoes. A few features of the algorithm, however, require post-launch modification. One is that cloud cover tends to cause the algorithm to raise the gain, which then causes saturation of surface echoes from clear regions immediately following the clouds. Details of the algorithm, along with specific examples and recent modifications, will be presented.
The Algorithm to Detect Color Gradation on Silk
Suyoto
2012-03-01
Full Text Available The process of silk dyeing with natural dye extracts will produce a certain color. Using extracts of wood, leaf and their combinations will give some color gradations. This paper aims to create a new algorithm which can help one, whose intention is to formulate the combination of coloring process to achieve the desired color through combining coloring materials on silk fabric. This algorithm will be expected to be able to formulate the combination of colors with more than 75 percent of accuracy. The natural dyes used were Ceriops candolleana arn wood for the red, Cudraina javanensis wood for the yellow, and indigofera leaf for the blue base color.
Hybrid Self-Adaptive Algorithm for Community Detection in Complex Networks
Bin Xu
2015-01-01
Full Text Available The study of community detection algorithms in complex networks has been very active in the past several years. In this paper, a Hybrid Self-adaptive Community Detection Algorithm (HSCDA based on modularity is put forward first. In HSCDA, three different crossover and two different mutation operators for community detection are designed and then combined to form a strategy pool, in which the strategies will be selected probabilistically based on statistical self-adaptive learning framework. Then, by adopting the best evolving strategy in HSCDA, a Multiobjective Community Detection Algorithm (MCDA based on kernel k-means (KKM and ratio cut (RC objective functions is proposed which efficiently make use of recommendation of strategy by statistical self-adaptive learning framework, thus assisting the process of community detection. Experimental results on artificial and real networks show that the proposed algorithms achieve a better performance compared with similar state-of-the-art approaches.
A Novel Multiuser Detection Algorithm for CDMA-Based MIMO-OFDM System
LIU Jun-shi; TANG Bi-hua; WANG Ya-chen; LIU Yua-nan
2006-01-01
This paper investigates QR matrix decomposition based successive interference cancellation multiuser detection algorithms in synchronous uplink for code division multiple access based multiple input multiple output orthogonal frequency division multiplexing system. The Symbol Error Rate(SER) performance of the optimal order and the suboptimal order QR-SIC MUD algorithms are compared with conventional zero forcing and minimum mean square error multiuser detection algorithms by Monte Carlo simulations. Complexity analysis is presented at the end of the paper. Both our simulation results and complexity analysis show that SER performance of QR-Successive Interference Cancellation (QR-SIC) algorithms is superior to those of zero forcing(ZF) and minimum mean square error algorithms, and the suboptimal order QR-SIC algorithm has a good trade-off between SER performance and computation complexity.
Cued search algorithm with uncertain detection performance for phased array radars
Jianbin Lu; Hui Xiao; Zemin Xi; Mingmin Zhang
2013-01-01
A cued search algorithm with uncertain detection per-formance is proposed for phased array radars. Firstly, a target search model based on the information gain criterion is presented with known detection performance, and the statistical characteris-tic of the detection probability is calculated by using the fluctuant model of the target radar cross section (RCS). Secondly, when the detection probability is completely unknown, its probability den-sity function is modeled with a beta distribution, and its posterior probability distribution with the radar observation is derived based on the Bayesian theory. Final y simulation results show that the cued search algorithm with a known RCS fluctuant model can achieve the best performance, and the algorithm with the detection probability modeled as a beta distribution is better than that with a random selected detection probability because the model parame-ters can be updated by the radar observation to approach to the real value of the detection probability.
Scale-space point spread function based framework to boost infrared target detection algorithms
Moradi, Saed; Moallem, Payman; Sabahi, Mohamad Farzan
2016-07-01
Small target detection is one of the major concern in the development of infrared surveillance systems. Detection algorithms based on Gaussian target modeling have attracted most attention from researchers in this field. However, the lack of accurate target modeling limits the performance of this type of infrared small target detection algorithms. In this paper, signal to clutter ratio (SCR) improvement mechanism based on the matched filter is described in detail and effect of Point Spread Function (PSF) on the intensity and spatial distribution of the target pixels is clarified comprehensively. In the following, a new parametric model for small infrared targets is developed based on the PSF of imaging system which can be considered as a matched filter. Based on this model, a new framework to boost model-based infrared target detection algorithms is presented. In order to show the performance of this new framework, the proposed model is adopted in Laplacian scale-space algorithms which is a well-known algorithm in the small infrared target detection field. Simulation results show that the proposed framework has better detection performance in comparison with the Gaussian one and improves the overall performance of IRST system. By analyzing the performance of the proposed algorithm based on this new framework in a quantitative manner, this new framework shows at least 20% improvement in the output SCR values in comparison with Laplacian of Gaussian (LoG) algorithm.
Pasta, Muhammad Qasim; Melançon, Guy
2016-01-01
One of the most widely studied problem in mining and analysis of complex networks is the detection of community structures. The problem has been extensively studied by researchers due to its high utility and numerous applications in various domains. Many algorithmic solutions have been proposed for the community detection problem but the quest to find the best algorithm is still on. More often than not, researchers focus on developing fast and accurate algorithms that can be generically applied to networks from a variety of domains without taking into consideration the structural and topological variations in these networks. In this paper, we evaluate the performance of different clustering algorithms as a function of varying network topology. Along with the well known LFR model to generate benchmark networks with communities,we also propose a new model named Naive Scale Free Model to study the behavior of community detection algorithms with respect to different topological features. More specifically, we are...
Double-Talk Detection Algorithm Based on the l2 Norm
Wang Shao-wei; Zhu Qiu-ping; Yang Yong
2004-01-01
Echo canceller generally needs a double-talk detector which is used to keep the adaptive filter from diverging in the appearance of near-end speech. In this paper we adopt a new double-talk detection algorithm based on l2 norm to detect the existence of near-end speech in an acoustic echo canceller. We analyze this algorithm from the point of view of functional analysis and point out that the proposed double-talk detection algorithm has the same performance as the classic one in a finite Banach space. The remarkable feature of this algorithm is its higher accuracy and better computation complexity. The fine properties of this algorithm are confirmed by computer simulation and the application in a multimedia communication system.
Enhancement of Fast Face Detection Algorithm Based on a Cascade of Decision Trees
Khryashchev, V. V.; Lebedev, A. A.; Priorov, A. L.
2017-05-01
Face detection algorithm based on a cascade of ensembles of decision trees (CEDT) is presented. The new approach allows detecting faces other than the front position through the use of multiple classifiers. Each classifier is trained for a specific range of angles of the rotation head. The results showed a high rate of productivity for CEDT on images with standard size. The algorithm increases the area under the ROC-curve of 13% compared to a standard Viola-Jones face detection algorithm. Final realization of given algorithm consist of 5 different cascades for frontal/non-frontal faces. One more thing which we take from the simulation results is a low computational complexity of CEDT algorithm in comparison with standard Viola-Jones approach. This could prove important in the embedded system and mobile device industries because it can reduce the cost of hardware and make battery life longer.
Improvement of Anomoly Detection Algorithms in Hyperspectral Images using Discrete Wavelet Transform
Baghbidi, Mohsen Zare; Nilchi, Ahmad Reza Naghsh; Homayouni, Saeid; 10.5121/sipij.2011.2402
2012-01-01
Recently anomaly detection (AD) has become an important application for target detection in hyperspectral remotely sensed images. In many applications, in addition to high accuracy of detection we need a fast and reliable algorithm as well. This paper presents a novel method to improve the performance of current AD algorithms. The proposed method first calculates Discrete Wavelet Transform (DWT) of every pixel vector of image using Daubechies4 wavelet. Then, AD algorithm performs on four bands of "Wavelet transform" matrix which are the approximation of main image. In this research some benchmark AD algorithms including Local RX, DWRX and DWEST have been implemented on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral datasets. Experimental results demonstrate significant improvement of runtime in proposed method. In addition, this method improves the accuracy of AD algorithms because of DWT's power in extracting approximation coefficients of signal, which contain the main behaviour of sig...
ALGORITHM FOR THE DETECTION AND PARAMETER ESTIMATION OF MULTICOMPONENT LFM SIGNALS
Yuan Weiming; Wang Min; Wu Shunjun
2005-01-01
A novel algorithm based on Radon-Ambiguity Transform (RAT) and Adaptive Signal Decomposition (ASD) is presented for the detection and parameter estimation of multicomponent Linear Frequency Modulated (LFM) signals. The key problem lies in the chirplet estimation.Genetic algorithm is employed to search for the optimization parameter of chirplet. High estimation accuracy can be obtained even at low Signal-to-Noise Ratio(SNR). Finally simulation results are provided to demonstrate the performance of the proposed algorithm.
A FEATURE SELECTION ALGORITHM DESIGN AND ITS IMPLEMENTATION IN INTRUSION DETECTION SYSTEM
杨向荣; 沈钧毅
2003-01-01
Objective Present a new features selection algorithm. Methods based on rule induction and field knowledge. Results This algorithm can be applied in catching dataflow when detecting network intrusions, only the sub-dataset including discriminating features is catched. Then the time spend in following behavior patterns mining is reduced and the patterns mined are more precise. Conclusion The experiment results show that the feature subset catched by this algorithm is more informative and the dataset's quantity is reduced significantly.
Lanying Lin; Sheng He; Feng Fu; Xiping Wang
2015-01-01
Wood failure percentage (WFP) is an important index for evaluating the bond strength of plywood. Currently, the method used for detecting WFP is visual inspection, which lacks efficiency. In order to improve it, image processing methods are applied to wood failure detection. The present study used thresholding and K-means clustering algorithms in wood failure detection...
[A Hyperspectral Imagery Anomaly Detection Algorithm Based on Gauss-Markov Model].
Gao, Kun; Liu, Ying; Wang, Li-jing; Zhu, Zhen-yu; Cheng, Hao-bo
2015-10-01
With the development of spectral imaging technology, hyperspectral anomaly detection is getting more and more widely used in remote sensing imagery processing. The traditional RX anomaly detection algorithm neglects spatial correlation of images. Besides, it does not validly reduce the data dimension, which costs too much processing time and shows low validity on hyperspectral data. The hyperspectral images follow Gauss-Markov Random Field (GMRF) in space and spectral dimensions. The inverse matrix of covariance matrix is able to be directly calculated by building the Gauss-Markov parameters, which avoids the huge calculation of hyperspectral data. This paper proposes an improved RX anomaly detection algorithm based on three-dimensional GMRF. The hyperspectral imagery data is simulated with GMRF model, and the GMRF parameters are estimated with the Approximated Maximum Likelihood method. The detection operator is constructed with GMRF estimation parameters. The detecting pixel is considered as the centre in a local optimization window, which calls GMRF detecting window. The abnormal degree is calculated with mean vector and covariance inverse matrix, and the mean vector and covariance inverse matrix are calculated within the window. The image is detected pixel by pixel with the moving of GMRF window. The traditional RX detection algorithm, the regional hypothesis detection algorithm based on GMRF and the algorithm proposed in this paper are simulated with AVIRIS hyperspectral data. Simulation results show that the proposed anomaly detection method is able to improve the detection efficiency and reduce false alarm rate. We get the operation time statistics of the three algorithms in the same computer environment. The results show that the proposed algorithm improves the operation time by 45.2%, which shows good computing efficiency.
Gonzaga, Adilson [Sao Paulo Univ., Sao Carlos, SP (Brazil). Escola de Engenharia. Dept. de Engenharia Eletrica; Franca, Celso Aparecido de [Sao Paulo Univ., SP (Brazil). Inst. de Fisica. Dept. de Fisica e Informatica
1996-12-31
Edge detecting techniques applied to radiographic digital images are discussed. Some algorithms have been implemented and the results are displayed to enhance boundary or hide details. An algorithm applied in a pre processed image with contrast enhanced is proposed and the results are discussed 5 refs., 4 figs.
A Simulated Annealing Algorithm for Maximum Common Edge Subgraph Detection in Biological Networks
Larsen, Simon; Alkærsig, Frederik G.; Ditzel, Henrik
2016-01-01
introduce a heuristic algorithm for the multiple maximum common edge subgraph problem that is able to detect large common substructures shared across multiple, real-world size networks efficiently. Our algorithm uses a combination of iterated local search, simulated annealing and a pheromone...
Saadi, Dorthe Bodholt; Egstrup, Kenneth; Branebjerg, Jens;
2012-01-01
We have designed and optimized an automatic QRS complex detection algorithm for electrocardiogram (ECG) signals recorded with the DELTA ePatch platform. The algorithm is able to automatically switch between single-channel and multi-channel analysis mode. This preliminary study includes data from ...
Comparison of fractal dimension estimation algorithms for epileptic seizure onset detection
Polychronaki, G. E.; Ktonas, P. Y.; Gatzonis, S.; Siatouni, A.; Asvestas, P. A.; Tsekou, H.; Sakas, D.; Nikita, K. S.
2010-08-01
Fractal dimension (FD) is a natural measure of the irregularity of a curve. In this study the performances of three waveform FD estimation algorithms (i.e. Katz's, Higuchi's and the k-nearest neighbour (k-NN) algorithm) were compared in terms of their ability to detect the onset of epileptic seizures in scalp electroencephalogram (EEG). The selection of parameters involved in FD estimation, evaluation of the accuracy of the different algorithms and assessment of their robustness in the presence of noise were performed based on synthetic signals of known FD. When applied to scalp EEG data, Katz's and Higuchi's algorithms were found to be incapable of producing consistent changes of a single type (either a drop or an increase) during seizures. On the other hand, the k-NN algorithm produced a drop, starting close to the seizure onset, in most seizures of all patients. The k-NN algorithm outperformed both Katz's and Higuchi's algorithms in terms of robustness in the presence of noise and seizure onset detection ability. The seizure detection methodology, based on the k-NN algorithm, yielded in the training data set a sensitivity of 100% with 10.10 s mean detection delay and a false positive rate of 0.27 h-1, while the corresponding values in the testing data set were 100%, 8.82 s and 0.42 h-1, respectively. The above detection results compare favourably to those of other seizure onset detection methodologies applied to scalp EEG in the literature. The methodology described, based on the k-NN algorithm, appears to be promising for the detection of the onset of epileptic seizures based on scalp EEG.
A New Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Complex Networks
Guoqiang Chen
2013-01-01
Full Text Available Community detection in dynamic networks is an important research topic and has received an enormous amount of attention in recent years. Modularity is selected as a measure to quantify the quality of the community partition in previous detection methods. But, the modularity has been exposed to resolution limits. In this paper, we propose a novel multiobjective evolutionary algorithm for dynamic networks community detection based on the framework of nondominated sorting genetic algorithm. Modularity density which can address the limitations of modularity function is adopted to measure the snapshot cost, and normalized mutual information is selected to measure temporal cost, respectively. The characteristics knowledge of the problem is used in designing the genetic operators. Furthermore, a local search operator was designed, which can improve the effectiveness and efficiency of community detection. Experimental studies based on synthetic datasets show that the proposed algorithm can obtain better performance than the compared algorithms.
Night-Time Vehicle Detection Algorithm Based on Visual Saliency and Deep Learning
Yingfeng Cai
2016-01-01
Full Text Available Night vision systems get more and more attention in the field of automotive active safety field. In this area, a number of researchers have proposed far-infrared sensor based night-time vehicle detection algorithm. However, existing algorithms have low performance in some indicators such as the detection rate and processing time. To solve this problem, we propose a far-infrared image vehicle detection algorithm based on visual saliency and deep learning. Firstly, most of the nonvehicle pixels will be removed with visual saliency computation. Then, vehicle candidate will be generated by using prior information such as camera parameters and vehicle size. Finally, classifier trained with deep belief networks will be applied to verify the candidates generated in last step. The proposed algorithm is tested in around 6000 images and achieves detection rate of 92.3% and processing time of 25 Hz which is better than existing methods.
LNN Blind Multi-user Detection Algorithm for Multi-path-fading CDMA Channels
LI Yan-ping; WANG Hua-kui; MIAO Rui-qing
2006-01-01
A blind multi-user detection algorithm (BMUD) which is suitable for multi-path-fading Channels based on Lagrange neural network (LNN) is proposed. Based on the minimum output energy (MOE) criterion, the blind detection algorithm is formulated as a constrained optimization problem inherently and is then resolved efficiently using the neural network. Compared with the previous RLS(recursive least squares )-MOE blind detection algorithm or for multi-path channel, the BMUD based on LNN has better performances: lower computational complexity, faster convergence speed and capability in the multi-path-fading channel. The bit error rate (BER) and signal-to-interference-and-noise ratio(SINR) performances of the detection algorithm in multi-path channel are close to that in single path channel.
Bio-Inspired Distributed Decision Algorithms for Anomaly Detection
2017-03-01
NUMBER RU 5f. WORK UNIT NUMBER TG 7. PERFORMING ORGANIZATION NAME( S ) AND ADDRESS(ES) Rutgers University New Brunswick, NJ 08901 8. PERFORMING... ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING AGENCY NAME( S ) AND ADDRESS(ES) Air Force Research Laboratory/RIG 525 Brooks Road Rome NY 13441-4505...algorithm ia with thresholds tist , . We then defined four different test functions tic , for all nodes in order to explore the impact of different
Comparison of Object Detection Algorithms on Maritime Vessels
2014-01-01
1Department of Electrical and Computer Engineering; University of Nevada, Las Vegas; Las Vegas, NV 89154 2Adaptive Systems Section; Naval Research...These algorithms performed extremely well in their given submission years. The HOG/SIFT representation has several advantages . It captures edge or...a bicycle object (a). Image (b) shows the HOG representation of the root filter while (c) and (e) shows part models being generated at twice the
Study of Computational Intelligence Algorithms to Detect Behaviour Patterns
Palero Molina, Fernando
2014-01-01
In order to achieve the game flow and increase player retention, it is important that games difficulty matches player skills. As a consequence, to evaluate how people play a game is a crucial component, because detecting gamers strategies in video-games, it is possible to fix the game difficulty. The main problem to detect the strategies is whether attributes selected to define the strategies correctly detect the actions of the player. To study the player strategies, we will us...
Predictive algorithms for early detection of retinopathy of prematurity.
Piermarocchi, Stefano; Bini, Silvia; Martini, Ferdinando; Berton, Marianna; Lavini, Anna; Gusson, Elena; Marchini, Giorgio; Padovani, Ezio Maria; Macor, Sara; Pignatto, Silvia; Lanzetta, Paolo; Cattarossi, Luigi; Baraldi, Eugenio; Lago, Paola
2017-03-01
To evaluate sensitivity, specificity and the safest cut-offs of three predictive algorithms (WINROP, ROPScore and CHOP ROP) for retinopathy of prematurity (ROP). A retrospective study was conducted in three centres from 2012 to 2014; 445 preterms with gestational age (GA) ≤ 30 weeks and/or birthweight (BW) ≤ 1500 g, and additional unstable cases, were included. No-ROP, mild and type 1 ROP were categorized. The algorithms were analysed for infants with all parameters (GA, BW, weight gain, oxygen therapy, blood transfusion) needed for calculation (399 babies). Retinopathy of prematurity (ROP) was identified in both eyes in 116 patients (26.1%), and 44 (9.9%) had type 1 ROP. Gestational age and BW were significantly lower in ROP group compared with no-ROP subjects (GA: 26.7 ± 2.2 and 30.2 ± 1.9, respectively, p algorithms are a reliable tool for early identification of infants requiring referral to an ophthalmologist, for reorganizing resources and reducing stressful procedures to preterm babies. © 2016 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.
Detection of Human Head Direction Based on Facial Normal Algorithm
Lam Thanh Hien
2015-01-01
Full Text Available Many scholars worldwide have paid special efforts in searching for advance approaches to efficiently estimate human head direction which has been successfully applied in numerous applications such as human-computer interaction, teleconferencing, virtual reality, and 3D audio rendering. However, one of the existing shortcomings in the current literature is the violation of some ideal assumptions in practice. Hence, this paper aims at proposing a novel algorithm based on the normal of human face to recognize human head direction by optimizing a 3D face model combined with the facial normal model. In our experiments, a computational program was also developed based on the proposed algorithm and integrated with the surveillance system to alert the driver drowsiness. The program intakes data from either video or webcam, and then automatically identify the critical points of facial features based on the analysis of major components on the faces; and it keeps monitoring the slant angle of the head closely and makes alarming signal whenever the driver dozes off. From our empirical experiments, we found that our proposed algorithm effectively works in real-time basis and provides highly accurate results
Evolutionary Algorithms for the Detection of Structural Breaks in Time Series
Doerr, Benjamin; Fischer, Paul; Hilbert, Astrid
2013-01-01
series under consideration is available. Therefore, a black-box optimization approach is our method of choice for detecting structural breaks. We describe a evolutionary algorithm framework which easily adapts to a large number of statistical settings. The experiments on artificial and real-world time...... series show that the algorithm detects break points with high precision and is computationally very efficient. A reference implementation is availble at the following address: http://www2.imm.dtu.dk/~pafi/SBX/launch.html...
A hierarchical lazy smoking detection algorithm using smartwatch sensors
Shoaib, Muhammad; Scholten, Hans; Havinga, Paul J.M.; Durmaz Incel, Ozlem
2016-01-01
Smoking is known to be one of the main causes for premature deaths. A reliable smoking detection method can enable applications for an insight into a user’s smoking behaviour and for use in smoking cessation programs. However, it is difficult to accurately detect smoking because it can be performed
Lining seam elimination algorithm and surface crack detection in concrete tunnel lining
Qu, Zhong; Bai, Ling; An, Shi-Quan; Ju, Fang-Rong; Liu, Ling
2016-11-01
Due to the particularity of the surface of concrete tunnel lining and the diversity of detection environments such as uneven illumination, smudges, localized rock falls, water leakage, and the inherent seams of the lining structure, existing crack detection algorithms cannot detect real cracks accurately. This paper proposed an algorithm that combines lining seam elimination with the improved percolation detection algorithm based on grid cell analysis for surface crack detection in concrete tunnel lining. First, check the characteristics of pixels within the overlapping grid to remove the background noise and generate the percolation seed map (PSM). Second, cracks are detected based on the PSM by the accelerated percolation algorithm so that the fracture unit areas can be scanned and connected. Finally, the real surface cracks in concrete tunnel lining can be obtained by removing the lining seam and performing percolation denoising. Experimental results show that the proposed algorithm can accurately, quickly, and effectively detect the real surface cracks. Furthermore, it can fill the gap in the existing concrete tunnel lining surface crack detection by removing the lining seam.
High Density Impulse Noise Detection using Fuzzy C-means Algorithm
Isha Singh
2016-01-01
Full Text Available A new technique for detecting the high density impulse noise from corrupted images using Fuzzy C-means algorithm is proposed. The algorithm is iterative in nature and preserves more image details in high noise environment. Fuzzy C-means is initially used to cluster the image data. The application of Fuzzy C-means algorithm in the detection phase provides an optimum classification of noisy data and uncorrupted image data so that the pictorial information remains well preserved. Experimental results show that the proposed algorithm significantly outperforms existing well-known techniques. Results show that with the increase in percentage of noise density, the performance of the algorithm is not degraded. Furthermore, the varying window size in the two detection stages provides more efficient results in terms of low false alarm rate and miss detection rate. The simple structure of the algorithm to detect impulse noise makes it useful for various applications like satellite imaging, remote sensing, medical imaging diagnosis and military survillance. After the efficient detection of noise, the existing filtering techniques can be used for the removal of noise.Defence Science Journal, Vol. 66, No. 1, January 2016, pp. 30-36, DOI: http://dx.doi.org/10.14429/dsj.66.8722
Nguyen, Lien B; Nguyen, Anh V; Ling, Sai Ho; Nguyen, Hung T
2013-01-01
Hypoglycemia is the most common but highly feared complication induced by the intensive insulin therapy in patients with type 1 diabetes mellitus (T1DM). Nocturnal hypoglycemia is dangerous because sleep obscures early symptoms and potentially leads to severe episodes which can cause seizure, coma, or even death. It is shown that the hypoglycemia onset induces early changes in electroencephalography (EEG) signals which can be detected non-invasively. In our research, EEG signals from five T1DM patients during an overnight clamp study were measured and analyzed. By applying a method of feature extraction using Fast Fourier Transform (FFT) and classification using neural networks, we establish that hypoglycemia can be detected efficiently using EEG signals from only two channels. This paper demonstrates that by implementing a training process of combining genetic algorithm and Levenberg-Marquardt algorithm, the classification results are improved markedly up to 75% sensitivity and 60% specificity on a separate testing set.
Dariusz Ceglarek
2013-05-01
Full Text Available This work presents results of the ongoing novel research in the area of natural language processing focusing on plagiarism detection, semantic networks and semantic compression. The results demon strate that the seman tic compression is a valuable addition to the existing methods used in plagiary detection. The application of the seman tic compression boosts the efficiency of Sentence Hashing Algorithm for Plagiarism Detection2 (SHAPD2 and authors ’implementation of the w-shingling algorithm. Experiments were performed on Clough & Stephenson corpusas well as an available PAN – PC - 10plagiarism corpus used to evaluate plagiarism detection methods, so the results can be compared with other research teams.
The research of moving objects behavior detection and tracking algorithm in aerial video
Yang, Le-le; Li, Xin; Yang, Xiao-ping; Li, Dong-hui
2015-12-01
The article focuses on the research of moving target detection and tracking algorithm in Aerial monitoring. Study includes moving target detection, moving target behavioral analysis and Target Auto tracking. In moving target detection, the paper considering the characteristics of background subtraction and frame difference method, using background reconstruction method to accurately locate moving targets; in the analysis of the behavior of the moving object, using matlab technique shown in the binary image detection area, analyzing whether the moving objects invasion and invasion direction; In Auto Tracking moving target, A video tracking algorithm that used the prediction of object centroids based on Kalman filtering was proposed.
AsteroidZoo: A New Zooniverse project to detect asteroids and improve asteroid detection algorithms
Beasley, M.; Lewicki, C. A.; Smith, A.; Lintott, C.; Christensen, E.
2013-12-01
We present a new citizen science project: AsteroidZoo. A collaboration between Planetary Resources, Inc., the Zooniverse Team, and the Catalina Sky Survey, we will bring the science of asteroid identification to the citizen scientist. Volunteer astronomers have proved to be a critical asset in identification and characterization of asteroids, especially potentially hazardous objects. These contributions, to date, have required that the volunteer possess a moderate telescope and the ability and willingness to be responsive to observing requests. Our new project will use data collected by the Catalina Sky Survey (CSS), currently the most productive asteroid survey, to be used by anyone with sufficient interest and an internet connection. As previous work by the Zooniverse has demonstrated, the capability of the citizen scientist is superb at classification of objects. Even the best automated searches require human intervention to identify new objects. These searches are optimized to reduce false positive rates and to prevent a single operator from being overloaded with requests. With access to the large number of people in Zooniverse, we will be able to avoid that problem and instead work to produce a complete detection list. Each frame from CSS will be searched in detail, generating a large number of new detections. We will be able to evaluate the completeness of the CSS data set and potentially provide improvements to the automated pipeline. The data corpus produced by AsteroidZoo will be used as a training environment for machine learning challenges in the future. Our goals include a more complete asteroid detection algorithm and a minimum computation program that skims the cream of the data suitable for implemention on small spacecraft. Our goal is to have the site become live in the Fall 2013.
Mousse, Mikaël A.; Motamed, Cina; Ezin, Eugène C.
2016-11-01
The detection of moving objects in a video sequence is the first step in an automatic video surveillance system. This work proposes an enhancement of a codebook-based algorithm for moving objects extraction. The proposed algorithm used a perceptual-based approach to optimize foreground information extraction complexity by using a modified codebook algorithm. The purpose of the adaptive strategy is to reduce the computational complexity of the foreground detection algorithm while maintaining its global accuracy. In this algorithm, we use a superpixels segmentation approach to model the spatial dependencies between pixels. The processing of the superpixels is controlled to focus it on the superpixels that are near to the possible location of foreground objects. The performance of the proposed algorithm is evaluated and compared to other algorithms of the state of the art using a public dataset that proposes sequences with a dynamic background. Experimental results prove that the proposed algorithm obtained the best the frame processing rate during the foreground detection.
Building test data from real outbreaks for evaluating detection algorithms.
Texier, Gaetan; Jackson, Michael L; Siwe, Leonel; Meynard, Jean-Baptiste; Deparis, Xavier; Chaudet, Herve
2017-01-01
Benchmarking surveillance systems requires realistic simulations of disease outbreaks. However, obtaining these data in sufficient quantity, with a realistic shape and covering a sufficient range of agents, size and duration, is known to be very difficult. The dataset of outbreak signals generated should reflect the likely distribution of authentic situations faced by the surveillance system, including very unlikely outbreak signals. We propose and evaluate a new approach based on the use of historical outbreak data to simulate tailored outbreak signals. The method relies on a homothetic transformation of the historical distribution followed by resampling processes (Binomial, Inverse Transform Sampling Method-ITSM, Metropolis-Hasting Random Walk, Metropolis-Hasting Independent, Gibbs Sampler, Hybrid Gibbs Sampler). We carried out an analysis to identify the most important input parameters for simulation quality and to evaluate performance for each of the resampling algorithms. Our analysis confirms the influence of the type of algorithm used and simulation parameters (i.e. days, number of cases, outbreak shape, overall scale factor) on the results. We show that, regardless of the outbreaks, algorithms and metrics chosen for the evaluation, simulation quality decreased with the increase in the number of days simulated and increased with the number of cases simulated. Simulating outbreaks with fewer cases than days of duration (i.e. overall scale factor less than 1) resulted in an important loss of information during the simulation. We found that Gibbs sampling with a shrinkage procedure provides a good balance between accuracy and data dependency. If dependency is of little importance, binomial and ITSM methods are accurate. Given the constraint of keeping the simulation within a range of plausible epidemiological curves faced by the surveillance system, our study confirms that our approach can be used to generate a large spectrum of outbreak signals.
Building test data from real outbreaks for evaluating detection algorithms
Texier, Gaetan; Jackson, Michael L.; Siwe, Leonel; Meynard, Jean-Baptiste; Deparis, Xavier; Chaudet, Herve
2017-01-01
Benchmarking surveillance systems requires realistic simulations of disease outbreaks. However, obtaining these data in sufficient quantity, with a realistic shape and covering a sufficient range of agents, size and duration, is known to be very difficult. The dataset of outbreak signals generated should reflect the likely distribution of authentic situations faced by the surveillance system, including very unlikely outbreak signals. We propose and evaluate a new approach based on the use of historical outbreak data to simulate tailored outbreak signals. The method relies on a homothetic transformation of the historical distribution followed by resampling processes (Binomial, Inverse Transform Sampling Method—ITSM, Metropolis-Hasting Random Walk, Metropolis-Hasting Independent, Gibbs Sampler, Hybrid Gibbs Sampler). We carried out an analysis to identify the most important input parameters for simulation quality and to evaluate performance for each of the resampling algorithms. Our analysis confirms the influence of the type of algorithm used and simulation parameters (i.e. days, number of cases, outbreak shape, overall scale factor) on the results. We show that, regardless of the outbreaks, algorithms and metrics chosen for the evaluation, simulation quality decreased with the increase in the number of days simulated and increased with the number of cases simulated. Simulating outbreaks with fewer cases than days of duration (i.e. overall scale factor less than 1) resulted in an important loss of information during the simulation. We found that Gibbs sampling with a shrinkage procedure provides a good balance between accuracy and data dependency. If dependency is of little importance, binomial and ITSM methods are accurate. Given the constraint of keeping the simulation within a range of plausible epidemiological curves faced by the surveillance system, our study confirms that our approach can be used to generate a large spectrum of outbreak signals. PMID
Unsupervised unstained cell detection by SIFT keypoint clustering and self-labeling algorithm.
Muallal, Firas; Schöll, Simon; Sommerfeldt, Björn; Maier, Andreas; Steidl, Stefan; Buchholz, Rainer; Hornegger, Joachim
2014-01-01
We propose a novel unstained cell detection algorithm based on unsupervised learning. The algorithm utilizes the scale invariant feature transform (SIFT), a self-labeling algorithm, and two clustering steps in order to achieve high performance in terms of time and detection accuracy. Unstained cell imaging is dominated by phase contrast and bright field microscopy. Therefore, the algorithm was assessed on images acquired using these two modalities. Five cell lines having in total 37 images and 7250 cells were considered for the evaluation: CHO, L929, Sf21, HeLa, and Bovine cells. The obtained F-measures were between 85.1 and 89.5. Compared to the state-of-the-art, the algorithm achieves very close F-measure to the supervised approaches in much less time.
Algorithm for detection of the broken phase conductor in the radial networks
Ostojić Mladen M.
2016-01-01
Full Text Available The paper presents an algorithm for a directional relay to be used for a detection of the broken phase conductor in the radial networks. The algorithm would use synchronized voltages, measured at the beginning and at the end of the line, as input signals. During the process, the measured voltages would be phase-compared. On the basis of the normalized energy, the direction of the phase conductor, with a broken point, would be detected. Software tool Matlab/Simulink package has developed a radial network model which simulates the broken phase conductor. The simulations generated required input signals by which the algorithm was tested. Development of the algorithm along with the formation of the simulation model and the test results of the proposed algorithm are presented in this paper.
Combined Dust Detection Algorithm by Using MODIS Infrared Channels over East Asia
Park, Sang Seo; Kim, Jhoon; Lee, Jaehwa; Lee, Sukjo; Kim, Jeong Soo; Chang, Lim Seok; Ou, Steve
2014-01-01
A new dust detection algorithm is developed by combining the results of multiple dust detectionmethods using IR channels onboard the MODerate resolution Imaging Spectroradiometer (MODIS). Brightness Temperature Difference (BTD) between two wavelength channels has been used widely in previous dust detection methods. However, BTDmethods have limitations in identifying the offset values of the BTDto discriminate clear-sky areas. The current algorithm overcomes the disadvantages of previous dust detection methods by considering the Brightness Temperature Ratio (BTR) values of the dual wavelength channels with 30-day composite, the optical properties of the dust particles, the variability of surface properties, and the cloud contamination. Therefore, the current algorithm shows improvements in detecting the dust loaded region over land during daytime. Finally, the confidence index of the current dust algorithm is shown in 10 × 10 pixels of the MODIS observations. From January to June, 2006, the results of the current algorithm are within 64 to 81% of those found using the fine mode fraction (FMF) and aerosol index (AI) from the MODIS and Ozone Monitoring Instrument (OMI). The agreement between the results of the current algorithm and the OMI AI over the non-polluted land also ranges from 60 to 67% to avoid errors due to the anthropogenic aerosol. In addition, the developed algorithm shows statistically significant results at four AErosol RObotic NETwork (AERONET) sites in East Asia.
Different Algorithms for Improving Detection Power of Atomic Fluorescence Spectrometry
Jian Cui
2012-11-01
Full Text Available The purpose of detecting trace concentrations of analytes often is hindered by occurring noise in the signal curves of analytical methods. This is also a problem when different arsenic species (organic arsenic species such as arsanilic acid, nitarsone and roxarsone are to be determined in animal meat by HPLC-UV-HG-AFS, which is the basis of this work. In order to improve the detection power, methods of signal treatment may be applied. We show a comparison of convolution with Gaussian distribution curves, Fourier transform, and wavelet transform. It is illustrated how to estimate decisive parameters for these techniques. All methods result in improved limits of detection. Furthermore, applying baselines and evaluating peaks thoroughly is facilitated. However, there are differences. Fourier transform may be applied, but convolution with Gaussian distribution curves shows better results of improvement. The best of the three is wavelet transform, whereby the detection power is improved by factors of about 2.4
Algorithms for Speeding up Distance-Based Outlier Detection
National Aeronautics and Space Administration — The problem of distance-based outlier detection is difficult to solve efficiently in very large datasets because of potential quadratic time complexity. We address...
A Harmony Search Based Algorithm for Detecting Distributed Predicates
Eslam Al Maghayreh
2012-10-01
Full Text Available Detection of distributed predicates (also referred to as runtime verification can be used to verify that a particular run of a given distributed program satisfies certain properties (represented as predicates. Consequently, distributed predicates detection techniques can be used to effectively improve the dependability of a given distributed application. Due to concurrency, the detection of distributed predicates can incur significant overhead. Most of the effective techniques developed to solve this problem work efficiently for certain classes of predicates, like conjunctive predicates. In this paper, we have presented a technique based on harmony search to efficiently detect the satisfaction of a predicate under the possibly modality. We have implemented the proposed technique and we have conducted several experiments to demonstrate its effectiveness.
Performance evaluation of spot detection algorithms in fluorescence microscopy images
Mabaso, M
2012-10-01
Full Text Available Detection of messenger Ribonucleic Acid (mRNA) spots in fluorescence microscopy images is of great importance for biologists seeking better understanding of cell functionality. Fluorescence microscopy and specific staining methods make biological...
An optimized outlier detection algorithm for jury-based grading of engineering design projects
Thompson, Mary Kathryn; Espensen, Christina; Clemmensen, Line Katrine Harder
2016-01-01
This work characterizes and optimizes an outlier detection algorithm to identify potentially invalid scores produced by jury members while grading engineering design projects. The paper describes the original algorithm and the associated adjudication process in detail. The impact of the various...... conditions in the algorithm on the false positive and false negative rates is explored. Aresponse surface design is performed to optimize the algorithm using a data set from Fall 2010. Finally, the results are tested against a data set from Fall 2011. It is shown that all elements of the original algorithm......, but no true optimum seems to exist. The performance of the best optimizations and the original algorithm are similar. Therefore, it should be possible to choose new coefficient values for jury populations in other cultures and contexts logically and empirically without a full optimization as long...
Karlsson, Jonny; Dooley, Laurence S; Pulkkis, Göran
2013-05-17
Traversal time and hop count analysis (TTHCA) is a recent wormhole detection algorithm for mobile ad hoc networks (MANET) which provides enhanced detection performance against all wormhole attack variants and network types. TTHCA involves each node measuring the processing time of routing packets during the route discovery process and then delivering the measurements to the source node. In a participation mode (PM) wormhole where malicious nodes appear in the routing tables as legitimate nodes, the time measurements can potentially be altered so preventing TTHCA from successfully detecting the wormhole. This paper analyses the prevailing conditions for time tampering attacks to succeed for PM wormholes, before introducing an extension to the TTHCA detection algorithm called ∆T Vector which is designed to identify time tampering, while preserving low false positive rates. Simulation results confirm that the ∆T Vector extension is able to effectively detect time tampering attacks, thereby providing an important security enhancement to the TTHCA algorithm.
T. Sree Kala,
2016-04-01
Full Text Available Nowadays the organizations are facing the number of threats every day in the form of viruses and attack etc. Since many different mechanisms were preferred by organizations in the form of intrusion detection and prevention system to protect its organizations from these kinds of attacks. Intrusion Detection System (IDS is considered as a system integrated with intelligent subsystems. In this paper the signature based intrusion detection system is discussed. There are different pattern matching algorithms available to detect intrusion. Brute force and Knuth-Morris-Pratt are the single keyword pattern matching algorithms. If one or more occurrence of pattern present in the input text, then there is an intrusion and the intrusion alarm will be sent. The occurrence of false alarm will be high in intrusion detection. In this paper the string matching algorithm to reduce the percentage of false alarm will be discussed.
Jonny Karlsson
2013-05-01
Full Text Available Traversal time and hop count analysis (TTHCA is a recent wormhole detection algorithm for mobile ad hoc networks (MANET which provides enhanced detection performance against all wormhole attack variants and network types. TTHCA involves each node measuring the processing time of routing packets during the route discovery process and then delivering the measurements to the source node. In a participation mode (PM wormhole where malicious nodes appear in the routing tables as legitimate nodes, the time measurements can potentially be altered so preventing TTHCA from successfully detecting the wormhole. This paper analyses the prevailing conditions for time tampering attacks to succeed for PM wormholes, before introducing an extension to the TTHCA detection algorithm called ∆T Vector which is designed to identify time tampering, while preserving low false positive rates. Simulation results confirm that the ∆T Vector extension is able to effectively detect time tampering attacks, thereby providing an important security enhancement to the TTHCA algorithm.
MAP Support Detection for Greedy Sparse Signal Recovery Algorithms in Compressive Sensing
Lee, Namyoon
2016-10-01
A reliable support detection is essential for a greedy algorithm to reconstruct a sparse signal accurately from compressed and noisy measurements. This paper proposes a novel support detection method for greedy algorithms, which is referred to as "\\textit{maximum a posteriori (MAP) support detection}". Unlike existing support detection methods that identify support indices with the largest correlation value in magnitude per iteration, the proposed method selects them with the largest likelihood ratios computed under the true and null support hypotheses by simultaneously exploiting the distributions of sensing matrix, sparse signal, and noise. Leveraging this technique, MAP-Matching Pursuit (MAP-MP) is first presented to show the advantages of exploiting the proposed support detection method, and a sufficient condition for perfect signal recovery is derived for the case when the sparse signal is binary. Subsequently, a set of iterative greedy algorithms, called MAP-generalized Orthogonal Matching Pursuit (MAP-gOMP), MAP-Compressive Sampling Matching Pursuit (MAP-CoSaMP), and MAP-Subspace Pursuit (MAP-SP) are presented to demonstrate the applicability of the proposed support detection method to existing greedy algorithms. From empirical results, it is shown that the proposed greedy algorithms with highly reliable support detection can be better, faster, and easier to implement than basis pursuit via linear programming.
A computerized algorithm for arousal detection in healthy adults and patients with Parkinson disease
Sørensen, Gertrud Laura; Jennum, Poul; Kempfner, Jacob
2012-01-01
Arousals occur from all sleep stages and can be identified as abrupt electroencephalogram (EEG) and electromyogram (EMG) changes. Manual scoring of arousals is time consuming with low interscore agreement. The aim of this study was to design an arousal detection algorithm capable of detecting...... arousals from non-rapid eye movement (REM) and REM sleep, independent of the subject's age and disease. The proposed algorithm uses features from EEG, EMG, and the manual sleep stage scoring as input to a feed-forward artificial neural network (ANN). The performance of the algorithm has been assessed using...... compared with those of previously presented arousal detection algorithms and especially compared with the high interscore variability of manual scorings....
Uy, D.L.
1996-02-01
An algorithm for detection and identification of image clusters or {open_quotes}blobs{close_quotes} based on color information for an autonomous mobile robot is developed. The input image data are first processed using a crisp color fuszzyfier, a binary smoothing filter, and a median filter. The processed image data is then inputed to the image clusters detection and identification program. The program employed the concept of {open_quotes}elastic rectangle{close_quotes}that stretches in such a way that the whole blob is finally enclosed in a rectangle. A C-program is develop to test the algorithm. The algorithm is tested only on image data of 8x8 sizes with different number of blobs in them. The algorithm works very in detecting and identifying image clusters.
Outbreak detection algorithms for seasonal disease data: a case study using ross river virus disease
Gatton Michelle L
2010-11-01
Full Text Available Abstract Background Detection of outbreaks is an important part of disease surveillance. Although many algorithms have been designed for detecting outbreaks, few have been specifically assessed against diseases that have distinct seasonal incidence patterns, such as those caused by vector-borne pathogens. Methods We applied five previously reported outbreak detection algorithms to Ross River virus (RRV disease data (1991-2007 for the four local government areas (LGAs of Brisbane, Emerald, Redland and Townsville in Queensland, Australia. The methods used were the Early Aberration Reporting System (EARS C1, C2 and C3 methods, negative binomial cusum (NBC, historical limits method (HLM, Poisson outbreak detection (POD method and the purely temporal SaTScan analysis. Seasonally-adjusted variants of the NBC and SaTScan methods were developed. Some of the algorithms were applied using a range of parameter values, resulting in 17 variants of the five algorithms. Results The 9,188 RRV disease notifications that occurred in the four selected regions over the study period showed marked seasonality, which adversely affected the performance of some of the outbreak detection algorithms. Most of the methods examined were able to detect the same major events. The exception was the seasonally-adjusted NBC methods that detected an excess of short signals. The NBC, POD and temporal SaTScan algorithms were the only methods that consistently had high true positive rates and low false positive and false negative rates across the four study areas. The timeliness of outbreak signals generated by each method was also compared but there was no consistency across outbreaks and LGAs. Conclusions This study has highlighted several issues associated with applying outbreak detection algorithms to seasonal disease data. In lieu of a true gold standard, a quantitative comparison is difficult and caution should be taken when interpreting the true positives, false positives
Wang, Bin; Dong, Lili; Zhao, Ming; Xu, Wenhai
2015-12-01
In order to realize accurate detection for small dim infrared maritime target, this paper proposes a target detection algorithm based on local peak detection and pipeline-filtering. This method firstly extracts some suspected targets through local peak detection and removes most of non-target peaks with self-adaptive threshold process. And then pipeline-filtering is used to eliminate residual interferences so that only real target can be retained. The experiment results prove that this method has high performance on target detection, and its missing alarm rate and false alarm rate can basically meet practical requirements.
In-depth performance analysis of an EEG based neonatal seizure detection algorithm
Mathieson, S.; Rennie, J.; Livingstone, V.; Temko, A.; Low, E.; Pressler, R.M.; Boylan, G.B.
2016-01-01
Objective To describe a novel neurophysiology based performance analysis of automated seizure detection algorithms for neonatal EEG to characterize features of detected and non-detected seizures and causes of false detections to identify areas for algorithmic improvement. Methods EEGs of 20 term neonates were recorded (10 seizure, 10 non-seizure). Seizures were annotated by an expert and characterized using a novel set of 10 criteria. ANSeR seizure detection algorithm (SDA) seizure annotations were compared to the expert to derive detected and non-detected seizures at three SDA sensitivity thresholds. Differences in seizure characteristics between groups were compared using univariate and multivariate analysis. False detections were characterized. Results The expert detected 421 seizures. The SDA at thresholds 0.4, 0.5, 0.6 detected 60%, 54% and 45% of seizures. At all thresholds, multivariate analyses demonstrated that the odds of detecting seizure increased with 4 criteria: seizure amplitude, duration, rhythmicity and number of EEG channels involved at seizure peak. Major causes of false detections included respiration and sweat artefacts or a highly rhythmic background, often during intermediate sleep. Conclusion This rigorous analysis allows estimation of how key seizure features are exploited by SDAs. Significance This study resulted in a beta version of ANSeR with significantly improved performance. PMID:27072097
The Inverse Bagging Algorithm: Anomaly Detection by Inverse Bootstrap Aggregating
Vischia, Pietro
2016-01-01
For data sets populated by a very well modeled process and by another process of unknown probability density function (PDF), a desired feature when manipulating the fraction of the unknown process (either for enhancing it or suppressing it) consists in avoiding to modify the kinematic distributions of the well modeled one. A bootstrap technique is used to identify sub-samples rich in the well modeled process, and classify each event according to the frequency of it being part of such sub-samples. Comparisons with general MVA algorithms will be shown, as well as a study of the asymptotic properties of the method, making use of a public domain data set that models a typical search for new physics as performed at hadronic colliders such as the Large Hadron Collider (LHC).
A local algorithm for detecting community structures in dynamic networks
Massaro, Emanuele; Guazzini, Andrea; Passarella, Andrea; Bagnoli, Franco
2013-01-01
The emergence and the global adaptation of mobile devices has influenced human interactions at the individual, community, and social levels leading to the so called Cyber-Physical World (CPW) convergence scenario [1]. One of the most important features of CPW is the possibility of exploiting information about the structure of social communities of users, that manifest through joint movement patterns and frequency of physical co-location: mobile devices of users that belong to the same social community are likely to "see" each other (and thus be able to communicate through ad hoc networking techniques) more frequently and regularly than devices outside of the community. In mobile opportunistic networks, this fact can be exploited, for example, to optimize networking operations such as forwarding and dissemination of messages. In this paper we present a novel local cognitive-inspired algorithm for revealing the structure of these dynamic social networks by exploiting information about physical encounters, logge...
Unsupervised Anomalous Vertices Detection Utilizing Link Prediction Algorithms
Kagan, Dima; Elovici, amd Yuval
2016-01-01
In the past decade, complex network structures have penetrated nearly every aspect of our lives. The detection of anomalous vertices in these networks can uncover important insights, such as exposing intruders in a computer network. In this study, we present a novel unsupervised two-layered meta classifier that can be employed to detect irregular vertices in complex networks using solely features extracted from the network topology. Our method is based on the hypothesis that a vertex having many links with low probabilities of existing has a higher likelihood of being anomalous. We evaluated our method on ten networks, using three fully simulated, five semi-simulated, and two real world datasets. In all the scenarios, our method was able to identify anomalous and irregular vertices with low false positive rates and high AUCs. Moreover, we demonstrated that our method can be applied to security-related use cases and is able to detect malicious profiles in online social networks.
A Night Time Application for a Real-Time Vehicle Detection Algorithm Based on Computer Vision
Shifu Zhou
2013-03-01
Full Text Available Vehicle detection technology is the key technology of intelligent transportation systems, attracting the attention of many researchers. Although much literature has been published concerning daytime vehicle detection, little has been published concerning nighttime vehicle detection. In this study, a nighttime vehicle detection algorithm, consisting of headlight segmentation, headlight pairing and headlight tracking, is proposed. First, the pixels of the headlights are segmented in nighttime traffic images, through the use of the thresholding method. Then the pixels of the headlights are grouped and labeled, to analyze the characteristics of related components, such as area, location and size. Headlights are paired based on their location and size and then tracked via a tracking procedure designed to detect vehicles. Vehicles with only one headlight or those with three or four headlights are also detected. Experimental results show that the proposed algorithm is robust and effective in detecting vehicles in nighttime traffic.
A Contextual Fire Detection Algorithm for Simulated HJ-1B Imagery
Xiangsheng Kong
2009-02-01
Full Text Available The HJ-1B satellite, which was launched on September 6, 2008, is one of the small ones placed in the constellation for disaster prediction and monitoring. HJ-1B imagery was simulated in this paper, which contains fires of various sizes and temperatures in a wide range of terrestrial biomes and climates, including RED, NIR, MIR and TIR channels. Based on the MODIS version 4 contextual algorithm and the characteristics of HJ-1B sensor, a contextual fire detection algorithm was proposed and tested using simulated HJ-1B data. It was evaluated by the probability of fire detection and false alarm as functions of fire temperature and fire area. Results indicate that when the simulated fire area is larger than 45 m2 and the simulated fire temperature is larger than 800 K, the algorithm has a higher probability of detection. But if the simulated fire area is smaller than 10 m2, only when the simulated fire temperature is larger than 900 K, may the fire be detected. For fire areas about 100 m2, the proposed algorithm has a higher detection probability than that of the MODIS product. Finally, the omission and commission error were evaluated which are important factors to affect the performance of this algorithm. It has been demonstrated that HJ-1B satellite data are much sensitive to smaller and cooler fires than MODIS or AVHRR data and the improved capabilities of HJ-1B data will offer a fine opportunity for the fire detection.
Clique-detection algorithms for matching three-dimensional molecular structures.
Gardiner, E J; Artymiuk, P J; Willett, P
1997-08-01
The representation of chemical and biological molecules by means of graphs permits the use of a maximum common subgraph (MCS) isomorphism algorithm to identify the structural relationships existing between pairs of such molecular graphs. Clique detection provides an efficient way of implementing MCS detection, and this article reports a comparison of several different clique-detection algorithms when used for this purpose. Experiments with both small molecules and proteins demonstrate that the most efficient of these particular applications, which typically involve correspondence graphs with low edge densities, is the algorithm described by Carraghan and Pardalos. This is shown to be two to three times faster than the Bron-Kerbosch algorithm that has been used previously for MCS applications in chemistry and biology. However, the latter algorithm enables all substructures common to a pair of molecules to be identified, and not just the largest ones, as with the other algorithms considered here. The two algorithms can usefully be combined to increase the efficiency of database-searching systems that use the MCS as a measure of structural similarity.
Detecting Resource Consumption Attack over MANET using an Artificial Immune Algorithm
Daud Israf
2011-09-01
Full Text Available The Human Immune System (HIS is considered as a bank of models, functions, and concepts from where Artificial Immune algorithms are inspired. These algorithms are used to secure both host-based and network-based systems. However, it is not only important to utilize the HIS in producing AIS-based algorithms as much as it is important to introduce an algorithm with high performance. Therefore, creating a balance between utilizing HIS on one side and introducing the required AIS-based intrusion detection algorithm on the other side is a crucial issue which would be valuable to investigate. Securing the mobile ad hoc network (MANET which is a collection of mobile, decentralized, and self organized nodes is another problem, which adds more challenges to the research. This is because MANET properties make it harder to be secured than the other types of static networks. We claim that AISs’ properties such as being self-healing, self-defensive and self-organizing can meet the challenges of securing the MANET environment. This paper’s objective is to utilize the biological model used in the dendritic cell algorithm (DCA to introduce a Dendritic Cell Inspired Intrusion Detection Algorithm (DCIIDA. DCIIDA is introduced to detect the Resource Consumption Attack (RCA over MANET. Furthermore, this paper proposes a DCIIDA architecture which should be applied by each node in MANET.
Stride Search: a general algorithm for storm detection in high-resolution climate data
Bosler, Peter A.; Roesler, Erika L.; Taylor, Mark A.; Mundt, Miranda R.
2016-04-01
This article discusses the problem of identifying extreme climate events such as intense storms within large climate data sets. The basic storm detection algorithm is reviewed, which splits the problem into two parts: a spatial search followed by a temporal correlation problem. Two specific implementations of the spatial search algorithm are compared: the commonly used grid point search algorithm is reviewed, and a new algorithm called Stride Search is introduced. The Stride Search algorithm is defined independently of the spatial discretization associated with a particular data set. Results from the two algorithms are compared for the application of tropical cyclone detection, and shown to produce similar results for the same set of storm identification criteria. Differences between the two algorithms arise for some storms due to their different definition of search regions in physical space. The physical space associated with each Stride Search region is constant, regardless of data resolution or latitude, and Stride Search is therefore capable of searching all regions of the globe in the same manner. Stride Search's ability to search high latitudes is demonstrated for the case of polar low detection. Wall clock time required for Stride Search is shown to be smaller than a grid point search of the same data, and the relative speed up associated with Stride Search increases as resolution increases.
Sonar Image Detection Algorithm Based on Two-Phase Manifold Partner Clustering
Xingmei Wang; Zhipeng Liu; Jianchuang Sun; Shu Liu
2015-01-01
According to the characteristics of sonar image data with manifold feature, the sonar image detection method based on two⁃phase manifold partner clustering algorithm is proposed. Firstly, K⁃means block clustering based on euclidean distance is proposed to reduce the data set. Mean value, standard deviation, and gray minimum value are considered as three features based on the relatinship between clustering model and data structure. Then K⁃means clustering algorithm based on manifold distance is utilized clustering again on the reduced data set to improve the detection efficiency. In K⁃means clustering algorithm based on manifold distance, line segment length on the manifold is analyzed, and a new power function line segment length is proposed to decrease the computational complexity. In order to quickly calculate the manifold distance, new all⁃source shortest path as the pretreatment of efficient algorithm is proposed. Based on this, the spatial feature of the image block is added in the three features to get the final precise partner clustering algorithm. The comparison with the other typical clustering algorithms demonstrates that the proposed algorithm gets good detection result. And it has better adaptability by experiments of the different real sonar images.
Comparing the biological coherence of network clusters identified by different detection algorithms
无
2007-01-01
Protein-protein interaction networks serve to carry out basic molecular activity in the cell. Detecting the modular structures from the protein-protein interaction network is important for understanding the organization, function and dynamics of a biological system. In order to identify functional neighborhoods based on network topology, many network cluster identification algorithms have been developed. However, each algorithm might dissect a network from a different aspect and may provide different insight on the network partition. In order to objectively evaluate the performance of four commonly used cluster detection algorithms: molecular complex detection (MCODE), NetworkBlast, shortest-distance clustering (SDC) and Girvan-Newman (G-N) algorithm, we compared the biological coherence of the network clusters found by these algorithms through a uniform evaluation framework. Each algorithm was utilized to find network clusters in two different protein-protein interaction networks with various parameters. Comparison of the resulting network clusters indicates that clusters found by MCODE and SDC are of higher biological coherence than those by NetworkBlast and G-N algorithm.
Assessment of anovulation in eumenorrheic women: comparison of ovulation detection algorithms.
Lynch, Kristine E; Mumford, Sunni L; Schliep, Karen C; Whitcomb, Brian W; Zarek, Shvetha M; Pollack, Anna Z; Bertone-Johnson, Elizabeth R; Danaher, Michelle; Wactawski-Wende, Jean; Gaskins, Audrey J; Schisterman, Enrique F
2014-08-01
To compare previously used algorithms to identify anovulatory menstrual cycles in women self-reporting regular menses. Prospective cohort study. Western New York. Two hundred fifty-nine healthy, regularly menstruating women followed for one (n=9) or two (n=250) menstrual cycles (2005-2007). None. Prevalence of sporadic anovulatory cycles identified using 11 previously defined algorithms that use E2, P, and LH concentrations. Algorithms based on serum LH, E2, and P levels detected a prevalence of anovulation across the study period of 5.5%-12.8% (concordant classification for 91.7%-97.4% of cycles). The prevalence of anovulatory cycles varied from 3.4% to 18.6% using algorithms based on urinary LH alone or with the primary E2 metabolite, estrone-3-glucuronide, levels. The prevalence of anovulatory cycles among healthy women varied by algorithm. Mid-cycle LH surge urine-based algorithms used in over-the-counter fertility monitors tended to classify a higher proportion of anovulatory cycles compared with luteal-phase P serum-based algorithms. Our study demonstrates that algorithms based on the LH surge, or in conjunction with estrone-3-glucuronide, potentially estimate a higher percentage of anovulatory episodes. Addition of measurements of postovulatory serum P or urine pregnanediol may aid in detecting ovulation. Published by Elsevier Inc.
Change Detection Algorithms for Information Assurance of Computer Networks
2002-01-01
LIST OF FIGURES 2.1 Code Red I Infection (source CAIDA ) . . . . . . . . . . . . . . . . . 17 2.2 Number of probes due to the w32.Leave worm...16 Figure 2.1: Code Red I Infection (source CAIDA ) 2.3.2 Detection of an exponential signal in noise The i.i.d. assumption of the observations after
Microwave detection of breast tumors: comparison of skin subtraction algorithms
Fear, Elise C.; Stuchly, Maria A.
2000-07-01
Early detection of breast cancer is an important part of effective treatment. Microwave detection of breast cancer is of interest due to the contrast in dielectric properties of normal and malignant breast tissues. We are investigating a confocal microwave imaging system that adapts ideas from ground penetrating radar to breast cancer detection. In the proposed system, the patient lies prone with the breast extending through a hole in the examining table and encircled by an array of antennas. The breast is illuminated sequentially by each antenna with an ultrawideband signal, and the returns are recorded at the same antenna. Because the antennas are offset from the breast, the dominant component of the recorded returns is the reflection from the thin layer of breast skin. Two methods of reducing this reflection are compared, namely approximation of the signal with two time shifted, scaled and summed returns from a cylinder of skin, and subtraction of the mean of the set of aligned returns. Both approaches provide effective decrease of the skin signal, allowing for tumor detection.
Algorithms, nomograms and the detection of indolent prostate cancer
M.J. Roobol-Bouts (Monique)
2008-01-01
textabstractPurpose: Prostate cancer is the most commonly diagnosed cancer in men. However, only about 12% of the men diagnosed with prostate cancer will die of their disease. Result: The serum PSA test can detect prostate cancers early, but using a PSA based cut-off indication for prostate biopsy r
An unsupervised learning algorithm for fatigue crack detection in waveguides
Rizzo, Piervincenzo; Cammarata, Marcello; Dutta, Debaditya; Sohn, Hoon; Harries, Kent
2009-02-01
Ultrasonic guided waves (UGWs) are a useful tool in structural health monitoring (SHM) applications that can benefit from built-in transduction, moderately large inspection ranges, and high sensitivity to small flaws. This paper describes an SHM method based on UGWs and outlier analysis devoted to the detection and quantification of fatigue cracks in structural waveguides. The method combines the advantages of UGWs with the outcomes of the discrete wavelet transform (DWT) to extract defect-sensitive features aimed at performing a multivariate diagnosis of damage. In particular, the DWT is exploited to generate a set of relevant wavelet coefficients to construct a uni-dimensional or multi-dimensional damage index vector. The vector is fed to an outlier analysis to detect anomalous structural states. The general framework presented in this paper is applied to the detection of fatigue cracks in a steel beam. The probing hardware consists of a National Instruments PXI platform that controls the generation and detection of the ultrasonic signals by means of piezoelectric transducers made of lead zirconate titanate. The effectiveness of the proposed approach to diagnose the presence of defects as small as a few per cent of the waveguide cross-sectional area is demonstrated.
A Novel Detection and Classification Algorithm for Power Quality Disturbances using Wavelets
C. Sharmeela
2006-01-01
Full Text Available This study presents a novel method to detect and classify power quality disturbances using wavelets. The proposed algorithm uses different wavelets each for a particular class of disturbance. The method uses wavelet filter banks in an effective way and does multiple filtering to detect the disturbances. A qualitative comparison of results shows the advantages and drawbacks of each wavelet when applied to the detection of the disturbances. This method is tested for a large class of test conditions simulated in MATLAB. Power quality monitoring together with the ability of the proposed algorithm to classify the disturbances will be a powerful tool for the power system engineers.
Development of moving target detection algorithm using ADSP TS201 DSP Processor
Babu rao Kodavati
2010-08-01
Full Text Available This paper presents detect the presence of a target within a specified range(2 to 30m. The present work generally relates to a radar system and more particularly, to improve range resolution (3 m and minimum detection time (2 msec. Speed and accuracy are two important evaluation indicators in target detecting system. The challenges in developing the algorithm is finding the Doppler frequency and give caution signal to chief at an optimum instant of time to cause target kill. Time management serves to maintain a priority queue of all the tasks. In this work we have taken up issue of developing an algorithm using ADSP TS 201 DSP Processor.
A new algorithm of edge detection for color image: Generalized fuzzy operator
陈武凡; 鲁贤庆; 陈建军; 吴国雄
1995-01-01
The definition of the generalized fuzzy set is presented for the first time, and a generalized fuzzy operator is proposed to transform a generalized fuzzy set into a normal fuzzy set. The algorithm theory of the operator, as the newest method of the edge detection of a 2-D image, is successfully established. Many experiments haw proved that the algorithm is simpler, more rapid and more precise in location than other edge detection methods. And a schedule of the concrete performance has been given additionally about the edge detection of color images.
Gude, A.; Maraschek, M.; Kardaun, O.; the ASDEX Upgrade Team
2017-09-01
A sawtooth crash algorithm that can automatically detect irregular sawteeth with strongly varying crash characteristics, including inverted crashes with central signal increase, has been developed. Such sawtooth behaviour is observed in ASDEX Upgrade with its tungsten wall, especially in phases with central ECRH. This application of ECRH for preventing impurity accumulation is envisaged also for ITER. The detection consists of three steps: a sensitive edge detection, a multichannel combination to increase detection performance, and a profile analysis that tests generic sawtooth crash features. The effect of detection parameters on the edge detection results has been investigated using synthetic signals and tested in an application to ASDEX Upgrade soft x-ray data.
Incremental Density-Based Link Clustering Algorithm for Community Detection in Dynamic Networks
Fanrong Meng
2016-01-01
Full Text Available Community detection in complex networks has become a research hotspot in recent years. However, most of the existing community detection algorithms are designed for the static networks; namely, the connections between the nodes are invariable. In this paper, we propose an incremental density-based link clustering algorithm for community detection in dynamic networks, iDBLINK. This algorithm is an extended version of DBLINK which is proposed in our previous work. It can update the local link community structure in the current moment through the change of similarity between the edges at the adjacent moments, which includes the creation, growth, merging, deletion, contraction, and division of link communities. Extensive experimental results demonstrate that iDBLINK not only has a great time efficiency, but also maintains a high quality community detection performance when the network topology is changing.
A Gibbs Sampling Based MAP Detection Algorithm for OFDM Over Rapidly Varying Mobile Radio Channels
Panayirci, Erdal; Poor, H Vincent
2009-01-01
In orthogonal frequency-division multiplexing (OFDM) systems operating over rapidly time-varying channels, the orthogonality between subcarriers is destroyed leading to inter-carrier interference (ICI) and resulting in an irreducible error floor. In this paper, a new and low-complexity maximum {\\em a posteriori} probability (MAP) detection algorithm is proposed for OFDM systems operating over rapidly time-varying multipath channels. The detection algorithm exploits the banded structure of the frequency-domain channel matrix whose bandwidth is a parameter to be adjusted according to the speed of the mobile terminal. Based on this assumption, the received signal vector is decomposed into reduced dimensional sub-observations in such a way that all components of the observation vector contributing to the symbol to be detected are included in the decomposed observation model. The data symbols are then detected by the MAP algorithm by means of a Markov chain Monte Carlo (MCMC) technique in an optimal and computatio...
Kim, Goo; Kim, Dae Sun; Lee, Yang-Won
2013-10-01
The forest fires do much damage to our life in ecological and economic aspects. South Korea is probably more liable to suffer from the forest fire because mountain area occupies more than half of land in South Korea. They have recently launched the COMS(Communication Ocean and Meteorological Satellite) which is a geostationary satellite. In this paper, we developed forest fire detection algorithm using COMS data. Generally, forest fire detection algorithm uses characteristics of 4 and 11 micrometer brightness temperature. Our algorithm additionally uses LST(Land Surface Temperature). We confirmed the result of our fire detection algorithm using statistical data of Korea Forest Service and ASTER(Advanced Spaceborne Thermal Emission and Reflection Radiometer) images. We used the data in South Korea On April 1 and 2, 2011 because there are small and big forest fires at that time. The detection rate was 80% in terms of the frequency of the forest fires and was 99% in terms of the damaged area. Considering the number of COMS's channels and its low resolution, this result is a remarkable outcome. To provide users with the result of our algorithm, we developed a smartphone application for users JSP(Java Server Page). This application can work regardless of the smartphone's operating system. This study can be unsuitable for other areas and days because we used just two days data. To improve the accuracy of our algorithm, we need analysis using long-term data as future work.
Hungenahally, S K; Willis, R J
1994-11-01
An algorithm was devised to detect low incidence arrhythmic events in electrocardiograms obtained during ambulatory monitoring. The algorithm incorporated baseline correction and R wave detection. The RR interval was used to identify tachycardia, bradycardia, and premature ventricular beats. Only a few beats before and after the arrhythmic event were stored. The software was evaluated on a prototype hardware system which consisted of an Intel 86/30 single board computer with a suitable analog pre-processor and an analog to digital converter. The algorithm was used to determine the incidence and type of arrhythmia in records from an ambulatory electrocardiogram (ECG) database and from a cardiac exercise laboratory. These results were compared to annotations on the records which were assumed to be correct. Standard criteria used previously to evaluate algorithms designed for arrhythmia detection were sensitivity, specificity, and diagnostic accuracy. Sensitivities ranging from 77 to 100%, specificities from 94 to 100%, and diagnostic accuracies from 92 to 100% were obtained on the different data sets. These results compare favourably with published results based on more elaborate algorithms. By circumventing the need to make a continuous record of the ECG, the algorithm could form the basis for a compact monitoring device for the detection of arrhythmic events which are so infrequent that standard 24-h Holter monitoring is insufficient.
A STUDY OF SPAM DETECTION ALGORITHM ON SOCIAL MEDIA NETWORKS
Saini Jacob Soman
2014-01-01
Full Text Available In today’s world, the issue of identifying spammers has received increasing attention because of its practical relevance in the ﬁeld of social network analysis. The growing popularity of social networking sites has made them prime targets for spammers. By allowing users to publicize and share their independently generated content, online social networks become susceptible to different types of malicious and opportunistic user actions. Social network community users are fed with irrelevant information while surfing, due to spammer’s activity. Spam pervades any information system such as e-mail or web, social, blog or reviews platform. Therefore, this study attempts to review various spam detection frameworks which deals about the detection and elimination of spams in various sources.
Performance Assessment Method for a Forged Fingerprint Detection Algorithm
Shin, Yong Nyuo; Jun, In-Kyung; Kim, Hyun; Shin, Woochang
The threat of invasion of privacy and of the illegal appropriation of information both increase with the expansion of the biometrics service environment to open systems. However, while certificates or smart cards can easily be cancelled and reissued if found to be missing, there is no way to recover the unique biometric information of an individual following a security breach. With the recognition that this threat factor may disrupt the large-scale civil service operations approaching implementation, such as electronic ID cards and e-Government systems, many agencies and vendors around the world continue to develop forged fingerprint detection technology, but no objective performance assessment method has, to date, been reported. Therefore, in this paper, we propose a methodology designed to evaluate the objective performance of the forged fingerprint detection technology that is currently attracting a great deal of attention.
A Cost Constrained Boosting Algorithm for Fast Object Detection
Militzer, Arne; Tietjen, Christian; Hornegger, Joachim
2013-01-01
Boosting methods are among the most widely used machine learning techniques in practice for various reasons. In many scenarios, however, their use is prevented by runtime constraints. In this paper we propose a novel technique for reducing the computational complexity of hierarchical classifiers based on AdaBoost, such as the probabilistic boosting tree, which are often used for object detection. We modify AdaBoost training so that the hypothesis generation is no longer based solely on the...
Practical comparison of aberration detection algorithms for biosurveillance systems.
Zhou, Hong; Burkom, Howard; Winston, Carla A; Dey, Achintya; Ajani, Umed
2015-10-01
National syndromic surveillance systems require optimal anomaly detection methods. For method performance comparison, we injected multi-day signals stochastically drawn from lognormal distributions into time series of aggregated daily visit counts from the U.S. Centers for Disease Control and Prevention's BioSense syndromic surveillance system. The time series corresponded to three different syndrome groups: rash, upper respiratory infection, and gastrointestinal illness. We included a sample of facilities with data reported every day and with median daily syndromic counts ⩾1 over the entire study period. We compared anomaly detection methods of five control chart adaptations, a linear regression model and a Poisson regression model. We assessed sensitivity and timeliness of these methods for detection of multi-day signals. At a daily background alert rate of 1% and 2%, the sensitivities and timeliness ranged from 24 to 77% and 3.3 to 6.1days, respectively. The overall sensitivity and timeliness increased substantially after stratification by weekday versus weekend and holiday. Adjusting the baseline syndromic count by the total number of facility visits gave consistently improved sensitivity and timeliness without stratification, but it provided better performance when combined with stratification. The daily syndrome/total-visit proportion method did not improve the performance. In general, alerting based on linear regression outperformed control chart based methods. A Poisson regression model obtained the best sensitivity in the series with high-count data.
Network Intrusion Detection System Based On Machine Learning Algorithms
Vipin Das
2010-12-01
Full Text Available Network and system security is of paramount importance in the present data communication environment. Hackers and intruders can create many successful attempts to cause the crash of the networks and web services by unauthorized intrusion. New threats and associated solutions to prevent these threats are emerging together with the secured system evolution. Intrusion Detection Systems (IDS are one of these solutions. The main function of Intrusion Detection System is to protect the resources from threats. It analyzes and predicts the behaviours of users, and then these behaviours will be considered an attack or a normal behaviour. We use Rough Set Theory (RST and Support Vector Machine (SVM to detect network intrusions. First, packets are captured from the network, RST is used to pre-process the data and reduce the dimensions. The features selected by RST will be sent to SVM model to learn and test respectively. The method is effective to decrease the space density of data. The experiments compare the results with Principal Component Analysis (PCA and show RST and SVM schema could reduce the false positive rate and increase the accuracy.
Application of Improved HMM Algorithm in Slag Detection System
TAN Da-peng; LI Pei-yu; PAN Xiao-hong
2009-01-01
To solve the problems of ladle slag detection system (SDS),such as high cost,short service life,and inconvenient maintenance,a new SDS realization method based on hidden Markov model (HMM) was put forward.The physical process of continuous casting was analyzed,and vibration signal was considered as the main detecting signal according to the difference in shock vibration generated by molten steel and slag because of their difference in density.Automatic control experiment platform oriented to SDS was established,and vibration sensor was installed far away from molten steel,which could solve the problem of easy power consumption by the sensor.The combination of vector quantization technology with learning process parameters of HMM was optimized,and its revaluation formula was revised to enhance its recognition effectiveness.Industrial field experiments proved that this system requires low cost and little rebuilding for current devices,and its slag detection rate can exceed 95 %.
Comparison of algorithms for automatic border detection of melanoma in dermoscopy images
Srinivasa Raghavan, Sowmya; Kaur, Ravneet; LeAnder, Robert
2016-09-01
Melanoma is one of the most rapidly accelerating cancers in the world [1]. Early diagnosis is critical to an effective cure. We propose a new algorithm for more accurately detecting melanoma borders in dermoscopy images. Proper border detection requires eliminating occlusions like hair and bubbles by processing the original image. The preprocessing step involves transforming the RGB image to the CIE L*u*v* color space, in order to decouple brightness from color information, then increasing contrast, using contrast-limited adaptive histogram equalization (CLAHE), followed by artifacts removal using a Gaussian filter. After preprocessing, the Chen-Vese technique segments the preprocessed images to create a lesion mask which undergoes a morphological closing operation. Next, the largest central blob in the lesion is detected, after which, the blob is dilated to generate an image output mask. Finally, the automatically-generated mask is compared to the manual mask by calculating the XOR error [3]. Our border detection algorithm was developed using training and test sets of 30 and 20 images, respectively. This detection method was compared to the SRM method [4] by calculating the average XOR error for each of the two algorithms. Average error for test images was 0.10, using the new algorithm, and 0.99, using SRM method. In comparing the average error values produced by the two algorithms, it is evident that the average XOR error for our technique is lower than the SRM method, thereby implying that the new algorithm detects borders of melanomas more accurately than the SRM algorithm.
An Optional Threshold with Svm Cloud Detection Algorithm and Dsp Implementation
Zhou, Guoqing; Zhou, Xiang; Yue, Tao; Liu, Yilong
2016-06-01
This paper presents a method which combines the traditional threshold method and SVM method, to detect the cloud of Landsat-8 images. The proposed method is implemented using DSP for real-time cloud detection. The DSP platform connects with emulator and personal computer. The threshold method is firstly utilized to obtain a coarse cloud detection result, and then the SVM classifier is used to obtain high accuracy of cloud detection. More than 200 cloudy images from Lansat-8 were experimented to test the proposed method. Comparing the proposed method with SVM method, it is demonstrated that the cloud detection accuracy of each image using the proposed algorithm is higher than those of SVM algorithm. The results of the experiment demonstrate that the implementation of the proposed method on DSP can effectively realize the real-time cloud detection accurately.
An Enhancement of the Replacement Steady State Genetic Algorithm for Intrusion Detection
Reyadh Naoum
2014-06-01
Full Text Available In these days, Internet and computer systems face many intrusions, thus for this purpose we need to build a detection or prevention security system. Intrusion Detection System (IDS is a system used to detect attacks, Steady State Genetic Algorithm (SSGA is applied to support IDS by supplying the rule pool with additional data, these data can be used in testing phase to detect the attacks. The main goal of this research is to enhance Replacement steady state genetic algorithm to detect intrusions. This enhancement has been achieved by comparing replacement methods. This research proved that the Triple Tournament Replacement is better than Binary Tournament Replacement to increase Detection Rate and there are no effects on False Positive Rate. In this research represent the results of DR equal 100% for three types of attack (DoS, Probe and R2T, and 53% for U2R.
Lesion detection in magnetic resonance brain images by hyperspectral imaging algorithms
Xue, Bai; Wang, Lin; Li, Hsiao-Chi; Chen, Hsian Min; Chang, Chein-I.
2016-05-01
Magnetic Resonance (MR) images can be considered as multispectral images so that MR imaging can be processed by multispectral imaging techniques such as maximum likelihood classification. Unfortunately, most multispectral imaging techniques are not particularly designed for target detection. On the other hand, hyperspectral imaging is primarily developed to address subpixel detection, mixed pixel classification for which multispectral imaging is generally not effective. This paper takes advantages of hyperspectral imaging techniques to develop target detection algorithms to find lesions in MR brain images. Since MR images are collected by only three image sequences, T1, T2 and PD, if a hyperspectral imaging technique is used to process MR images it suffers from the issue of insufficient dimensionality. To address this issue, two approaches to nonlinear dimensionality expansion are proposed, nonlinear correlation expansion and nonlinear band ratio expansion. Once dimensionality is expanded hyperspectral imaging algorithms are readily applied. The hyperspectral detection algorithm to be investigated for lesion detection in MR brain is the well-known subpixel target detection algorithm, called Constrained Energy Minimization (CEM). In order to demonstrate the effectiveness of proposed CEM in lesion detection, synthetic images provided by BrainWeb are used for experiments.
Military target detection using spectrally modeled algorithms and independent component analysis
Tiwari, Kailash Chandra; Arora, Manoj K.; Singh, Dharmendra; Yadav, Deepti
2013-02-01
Most military targets of strategic importance are very small in size. Though some of them may get spatially resolved, most cannot be detected due to lack of adequate spectral resolution. Hyperspectral data, acquired over hundreds of narrow contiguous wavelength bands, are extremely suitable for most military target detection applications. Target detection, however, still remains complicated due to a host of other issues. These include, first, the heavy volume of hyperspectral data, which leads to computational complexities; second, most materials in nature exhibit spectral variability and remain unpredictable; and third, most target detection algorithms are based on spectral modeling and availability of a priori target spectra is an essential requirement, a condition difficult to meet in practice. Independent component analysis (ICA) is a new evolving technique that aims at finding components that are statistically independent or as independent as possible. It does not have any requirement of a priori availability of target spectra and is an attractive alternative. This paper, presents a study of military target detection using four spectral matching algorithms, namely, orthogonal subspace projection (OSP), constrained energy minimisation, spectral angle mapper and spectral correlation mapper, four anomaly detection algorithms, namely, OSP anomaly detector (OSPAD), Reed-Xiaoli anomaly detector (RXD), uniform target detector (UTD), a combination of RXD-UTD. The performances of these spectrally modeled algorithms are then also compared with ICA using receiver operating characteristic analysis. The superior performance of ICA indicates that it may be considered a viable alternative for military target detection.
Application of edge detection algorithm for vision guided robotics assembly system
Balabantaray, Bunil Kumar; Jha, Panchanand; Biswal, Bibhuti Bhusan
2013-12-01
Machine vision system has a major role in making robotic assembly system autonomous. Part detection and identification of the correct part are important tasks which need to be carefully done by a vision system to initiate the process. This process consists of many sub-processes wherein, the image capturing, digitizing and enhancing, etc. do account for reconstructive the part for subsequent operations. Edge detection of the grabbed image, therefore, plays an important role in the entire image processing activity. Thus one needs to choose the correct tool for the process with respect to the given environment. In this paper the comparative study of edge detection algorithm with grasping the object in robot assembly system is presented. The proposed work is performed on the Matlab R2010a Simulink. This paper proposes four algorithms i.e. Canny's, Robert, Prewitt and Sobel edge detection algorithm. An attempt has been made to find the best algorithm for the problem. It is found that Canny's edge detection algorithm gives better result and minimum error for the intended task.
Analysis of the Chirplet Transform-Based Algorithm for Radar Detection of Accelerated Targets
Galushko, V. G.; Vavriv, D. M.
2017-06-01
Purpose: Efficiency analysis of an optimal algorithm of chirp signal processing based on the chirplet transform as applied to detection of radar targets in uniformly accelerated motion. Design/methodology/approach: Standard methods of the optimal filtration theory are used to investigate the ambiguity function of chirp signals. Findings: An analytical expression has been derived for the ambiguity function of chirp signals that is analyzed with respect to detection of radar targets moving at a constant acceleration. Sidelobe level and characteristic width of the ambiguity function with respect to the coordinates frequency and rate of its change have been estimated. The gain in the signal-to-noise ratio has been assessed that is provided by the algorithm under consideration as compared with application of the standard Fourier transform to detection of chirp signals against a “white” noise background. It is shown that already with a comparatively small (block diagram of implementation of the algorithm under consideration is suggested on the basis of a multichannel weighted Fourier transform. Recommendations as for selection of the detection algorithm parameters have been developed. Conclusions: The obtained results testify to efficiency of application of the algorithm under consideration to detection of radar targets moving at a constant acceleration. Nevertheless, it seems expedient to perform computer simulations of its operability with account for the noise impact along with trial measurements in real conditions.
Sánchez, Clara I; Hornero, Roberto; López, María I; Aboy, Mateo; Poza, Jesús; Abásolo, Daniel
2008-04-01
We present an automatic image processing algorithm to detect hard exudates. Automatic detection of hard exudates from retinal images is an important problem since hard exudates are associated with diabetic retinopathy and have been found to be one of the most prevalent earliest signs of retinopathy. The algorithm is based on Fisher's linear discriminant analysis and makes use of colour information to perform the classification of retinal exudates. We prospectively assessed the algorithm performance using a database containing 58 retinal images with variable colour, brightness, and quality. Our proposed algorithm obtained a sensitivity of 88% with a mean number of 4.83+/-4.64 false positives per image using the lesion-based performance evaluation criterion, and achieved an image-based classification accuracy of 100% (sensitivity of 100% and specificity of 100%).
Lü, Li-hui; Liu, Wen-qing; Zhang, Tian-shu; Lu, Yi-huai; Dong, Yun-sheng; Chen, Zhen-yi; Fan, Guang-qiang; Qi, Shao-shuai
2015-07-01
Atmospheric aerosols have important impacts on human health, the environment and the climate system. Micro Pulse Lidar (MPL) is a new effective tool for detecting atmosphere aerosol horizontal distribution. And the extinction coefficient inversion and error analysis are important aspects of data processing. In order to detect the horizontal distribution of atmospheric aerosol near the ground, slope and Fernald algorithms were both used to invert horizontal MPL data and then the results were compared. The error analysis showed that the error of the slope algorithm and Fernald algorithm were mainly from theoretical model and some assumptions respectively. Though there still some problems exist in those two horizontal extinction coefficient inversions, they can present the spatial and temporal distribution of aerosol particles accurately, and the correlations with the forward-scattering visibility sensor are both high with the value of 95%. Furthermore relatively speaking, Fernald algorithm is more suitable for the inversion of horizontal extinction coefficient.
Anomaly Detection Algorithm for Stay Cable Monitoring Data Based on Data Fusion
Xiaoling Liu,Qiao Huang∗; Yuan Ren
2016-01-01
In order to improve the accuracy and consistency of data in health monitoring system, an anomaly detection algorithm for stay cables based on data fusion is proposed. The monitoring data of Nanjing No. 3 Yangtze River Bridge is used as the basis of study. Firstly, an adaptive processing framework with feedback control is established based on the concept of data fusion. The data processing contains four steps: data specification, data cleaning, data conversion and data fusion. Data processing information offers feedback to the original data system, which further gives guidance for the sensor maintenance or replacement. Subsequently, the algorithm steps based on the continuous data distortion is investigated,which integrates the inspection data and the distribution test method. Finally, a group of cable force data is utilized as an example to verify the established framework and algorithm. Experimental results show that the proposed algorithm can achieve high detection accuracy, providing a valuable reference for other monitoring data processing.
Bohui Zhu
2013-01-01
Full Text Available This paper presents a novel maximum margin clustering method with immune evolution (IEMMC for automatic diagnosis of electrocardiogram (ECG arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias.
Detection of Human Impacts by an Adaptive Energy-Based Anisotropic Algorithm
Manuel Prado-Velasco
2013-10-01
Full Text Available Boosted by health consequences and the cost of falls in the elderly, this work develops and tests a novel algorithm and methodology to detect human impacts that will act as triggers of a two-layer fall monitor. The two main requirements demanded by socio-healthcare providers—unobtrusiveness and reliability—defined the objectives of the research. We have demonstrated that a very agile, adaptive, and energy-based anisotropic algorithm can provide 100% sensitivity and 78% specificity, in the task of detecting impacts under demanding laboratory conditions. The algorithm works together with an unsupervised real-time learning technique that addresses the adaptive capability, and this is also presented. The work demonstrates the robustness and reliability of our new algorithm, which will be the basis of a smart falling monitor. This is shown in this work to underline the relevance of the results.
ADAPTIVE STEP-SIZE CONSTANT MODULUS ALGORITHM FOR BLIND MULTIUSER DETECTION IN DS-CDMA SYSTEMS
Sun Liping; Hu Guangrui
2004-01-01
Blind Adaptive Step-size Constant Modulus Algorithm (AS-CMA) for multiuser detection in DS-CDMA systems is presented. It combines the CMA and the concept of variable step-size, uses a second LMS algorithm for the step size. It adjusts the step-size according to the minimum output-energy principle within a specified range, thus overcomes the problems of bad effect of fixed step-size LMS algorithm. Compared with Adaptive Step-size LMS (AS-LMS) algorithm, through simulations, this algorithm can adapt the changes of the environment, suppress multiple access interference in the dynamic environment and the stability of Signal to Interference Ratio (SIR) is superior to that of AS-LMS.
QR Code Image Correction based on Corner Detection and Convex Hull Algorithm
Suran Kong
2013-12-01
Full Text Available Since the angular deviation produced when shooting a QR code image by a camera would cause geometric distortion of the QR code image, the traditional algorithm of QR code image correction would produce distortion. Therefore this paper puts forward the algorithm which combines corner detection with convex hull algorithm. Firstly, binaryzation of the collected QR code image with uneven light is obtained by the methods of local threshold and mathematical morphology. Next, the outline of the QR code and the dots on it are found and the distorted image is recovered by perspective collineation, according to the algorithm raised by this paper. Finally, experimental verification is made that the algorithm raised by this paper can correctly find the four apexes of QR code and achieves good effects of geometric correction. It will also significantly increase the recognition rate of seriously distorted QR code images
Tramacere, A; Dubath, P; Kneib, J -P; Courbin, F
2016-01-01
We present a study on galaxy detection and shape classification using topometric clustering algorithms. We first use the DBSCAN algorithm to extract, from CCD frames, groups of adjacent pixels with significant fluxes and we then apply the DENCLUE algorithm to separate the contributions of overlapping sources. The DENCLUE separation is based on the localization of pattern of local maxima, through an iterative algorithm which associates each pixel to the closest local maximum. Our main classification goal is to take apart elliptical from spiral galaxies. We introduce new sets of features derived from the computation of geometrical invariant moments of the pixel group shape and from the statistics of the spatial distribution of the DENCLUE local maxima patterns. Ellipticals are characterized by a single group of local maxima, related to the galaxy core, while spiral galaxies have additional ones related to segments of spiral arms. We use two different supervised ensemble classification algorithms, Random Forest,...
Target detection algorithm for airborne thermal hyperspectral data
Marwaha, R.; Kumar, A.; Raju, P. L. N.; Krishna Murthy, Y. V. N.
2014-11-01
Airborne hyperspectral imaging is constantly being used for classification purpose. But airborne thermal hyperspectral image usually is a challenge for conventional classification approaches. The Telops Hyper-Cam sensor is an interferometer-based imaging system that helps in the spatial and spectral analysis of targets utilizing a single sensor. It is based on the technology of Fourier-transform which yields high spectral resolution and enables high accuracy radiometric calibration. The Hypercam instrument has 84 spectral bands in the 868 cm-1 to 1280 cm-1 region (7.8 μm to 11.5 μm), at a spectral resolution of 6 cm-1 (full-width-half-maximum) for LWIR (long wave infrared) range. Due to the Hughes effect, only a few classifiers are able to handle high dimensional classification task. MNF (Minimum Noise Fraction) rotation is a data dimensionality reducing approach to segregate noise in the data. In this, the component selection of minimum noise fraction (MNF) rotation transformation was analyzed in terms of classification accuracy using constrained energy minimization (CEM) algorithm as a classifier for Airborne thermal hyperspectral image and for the combination of airborne LWIR hyperspectral image and color digital photograph. On comparing the accuracy of all the classified images for airborne LWIR hyperspectral image and combination of Airborne LWIR hyperspectral image with colored digital photograph, it was found that accuracy was highest for MNF component equal to twenty. The accuracy increased by using the combination of airborne LWIR hyperspectral image with colored digital photograph instead of using LWIR data alone.
Fusion Schemes for Ensembles of Hyperspectral Anomaly Detection Algorithms
2011-03-01
57 Appendix B: Storyboard ...infrared images taken from a tower experiment conducted at the White Sands Missile Range in New Mexico within hundreds of meters of the targets...confidence in the resulting identity declarations. 58 Appendix B: Storyboard Fusion Schemes for Ensembles of Hyperspectral Anomaly Detection
Carlos J. Corrada Bravo
2017-04-01
Full Text Available We developed a web-based cloud-hosted system that allow users to archive, listen, visualize, and annotate recordings. The system also provides tools to convert these annotations into datasets that can be used to train a computer to detect the presence or absence of a species. The algorithm used by the system was selected after comparing the accuracy and efficiency of three variants of a template-based detection. The algorithm computes a similarity vector by comparing a template of a species call with time increments across the spectrogram. Statistical features are extracted from this vector and used as input for a Random Forest classifier that predicts presence or absence of the species in the recording. The fastest algorithm variant had the highest average accuracy and specificity; therefore, it was implemented in the ARBIMON web-based system.
Robust Mean Change-Point Detecting through Laplace Linear Regression Using EM Algorithm
Fengkai Yang
2014-01-01
normal distribution, we developed the expectation maximization (EM algorithm to estimate the position of mean change-point. We investigated the performance of the algorithm through different simulations, finding that our methods is robust to the distributions of errors and is effective to estimate the position of mean change-point. Finally, we applied our method to the classical Holbert data and detected a change-point.
A new approach to optic disc detection in human retinal images using the firefly algorithm.
Rahebi, Javad; Hardalaç, Fırat
2016-03-01
There are various methods and algorithms to detect the optic discs in retinal images. In recent years, much attention has been given to the utilization of the intelligent algorithms. In this paper, we present a new automated method of optic disc detection in human retinal images using the firefly algorithm. The firefly intelligent algorithm is an emerging intelligent algorithm that was inspired by the social behavior of fireflies. The population in this algorithm includes the fireflies, each of which has a specific rate of lighting or fitness. In this method, the insects are compared two by two, and the less attractive insects can be observed to move toward the more attractive insects. Finally, one of the insects is selected as the most attractive, and this insect presents the optimum response to the problem in question. Here, we used the light intensity of the pixels of the retinal image pixels instead of firefly lightings. The movement of these insects due to local fluctuations produces different light intensity values in the images. Because the optic disc is the brightest area in the retinal images, all of the insects move toward brightest area and thus specify the location of the optic disc in the image. The results of implementation show that proposed algorithm could acquire an accuracy rate of 100 % in DRIVE dataset, 95 % in STARE dataset, and 94.38 % in DiaRetDB1 dataset. The results of implementation reveal high capability and accuracy of proposed algorithm in the detection of the optic disc from retinal images. Also, recorded required time for the detection of the optic disc in these images is 2.13 s for DRIVE dataset, 2.81 s for STARE dataset, and 3.52 s for DiaRetDB1 dataset accordingly. These time values are average value.
High Efficient QMC Detection Algorithm%一种高效的QMC检测算法
刘顺兰; 钱帅军
2011-01-01
基于垂直分层空时码的MIMO-OFDM系统提出一种高效的QMC检测算法,该算法对信道矩阵进行一次排序QR分解,对最先检测的信号层采用ML-OSIC算法,用M算法检测中间的信号层,逐层增加保留值M以提高算法有效性,利用串行干扰消除检测余下的信号层.与QRD-M算法相比,QMC检测算法能降低计算复杂度.仿真结果表明,该算法以更低的计算复杂度获得更接近最大似然检测的性能,取得性能与复杂度之间的折中更理想.%This paper proposes a new high efficient algorithm based on Vertical Bell Laboratories Layered Space-Time coding(V-BLAST) for MIMO-OFDM systems. The proposed QMC algorithm sorts the channel matrix, and uses the QR decomposition. At the beginning of the detection, the Maximum Likelihood-Ordered Serial Interference Cancellation(ML-OSIC) algorithm is utilized, and the M algorithm is applied to detect the middle symbol layers with M gradually increasing for more efficiency, then the SIC is used for detecting the remained symbol layers. Compared with the conventional QRD-M algorithm, the proposed algorithm significantly reduces the complexity and its performance gets closer to Maximum Likelihood Detection(MLD) performance. Simulation results show that the performance of the proposed QMC algorithm is very close to the MLD algorithm, it obtains a better tradeoff between complexity and performance.
An optimized outlier detection algorithm for jury-based grading of engineering design projects
Thompson, Mary Kathryn; Espensen, Christina; Clemmensen, Line Katrine Harder
2016-01-01
This work characterizes and optimizes an outlier detection algorithm to identify potentially invalid scores produced by jury members while grading engineering design projects. The paper describes the original algorithm and the associated adjudication process in detail. The impact of the various......, but no true optimum seems to exist. The performance of the best optimizations and the original algorithm are similar. Therefore, it should be possible to choose new coefficient values for jury populations in other cultures and contexts logically and empirically without a full optimization as long...
Egholm, Gro; Madsen, Morten; Thim, Troels;
2016-01-01
BACKGROUND: Registry-based monitoring of the safety and efficacy of interventions in patients with ischemic heart disease requires validated algorithms. OBJECTIVE: We aimed to evaluate algorithms to identify acute myocardial infarction (AMI) in the Danish National Patient Registry following...... additional information from the Danish National Patient Registry yield different sensitivities, specificities, and predictive values in registry-based detection of AMI following PCI. We were able to identify AMI following PCI with moderate-to-high validity. However, the choice of algorithm will depend...
CHEN Jun-jie; SONG Han-tao; LU Yu-chang
2005-01-01
A new classification algorithm for web mining is proposed on the basis of general classification algorithm for data mining in order to implement personalized information services. The building tree method of detecting class threshold is used for construction of decision tree according to the concept of user expectation so as to find classification rules in different layers. Compared with the traditional C4. 5 algorithm, the disadvantage of excessive adaptation in C4. 5 has been improved so that classification results not only have much higher accuracy but also statistic meaning.
A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data.
Markus Goldstein
Full Text Available Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks.
A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data.
Goldstein, Markus; Uchida, Seiichi
2016-01-01
Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks.
GA-DoSLD: Genetic Algorithm Based Denial-of-Sleep Attack Detection in WSN
Mahalakshmi Gunasekaran
2017-01-01
Full Text Available Denial-of-sleep (DoSL attack is a special category of denial-of-service attack that prevents the battery powered sensor nodes from going into the sleep mode, thus affecting the network performance. The existing schemes used for the DoSL attack detection do not provide an optimal energy conservation and key pairing operation. Hence, in this paper, an efficient Genetic Algorithm (GA based denial-of-sleep attack detection (GA-DoSLD algorithm is suggested for analyzing the misbehaviors of the nodes. The suggested algorithm implements a Modified-RSA (MRSA algorithm in the base station (BS for generating and distributing the key pair among the sensor nodes. Before sending/receiving the packets, the sensor nodes determine the optimal route using Ad Hoc On-Demand Distance Vector Routing (AODV protocol and then ensure the trustworthiness of the relay node using the fitness calculation. The crossover and mutation operations detect and analyze the methods that the attackers use for implementing the attack. On determining an attacker node, the BS broadcasts the blocked information to all the other sensor nodes in the network. Simulation results prove that the suggested algorithm is optimal compared to the existing algorithms such as X-MAC, ZKP, and TE2P schemes.
Suitable triggering algorithms for detecting strong ground motions using MEMS accelerometers
Jakka, Ravi Sankar; Garg, Siddharth
2015-03-01
With the recent development of digital Micro Electro Mechanical System (MEMS) sensors, the cost of monitoring and detecting seismic events in real time can be greatly reduced. Ability of MEMS accelerograph to record a seismic event depends upon the efficiency of triggering algorithm, apart from the sensor's sensitivity. There are several classic triggering algorithms developed to detect seismic events, ranging from basic amplitude threshold to more sophisticated pattern recognition. Algorithms based on STA/LTA are reported to be computationally efficient for real time monitoring. In this paper, we analyzed several STA/LTA algorithms to check their efficiency and suitability using data obtained from the Quake Catcher Network (network of MEMS accelerometer stations). We found that most of the STA/LTA algorithms are suitable for use with MEMS accelerometer data to accurately detect seismic events. However, the efficiency of any particular algorithm is found to be dependent on the parameter set used (i.e., window width of STA, LTA and threshold level).
Iravanian, Shahriar; Kanu, Uche B; Christini, David J
2012-07-01
Cardiac repolarization alternans is an electrophysiologic condition identified by a beat-to-beat fluctuation in action potential waveform. It has been mechanistically linked to instances of T-wave alternans, a clinically defined ECG alternation in T-wave morphology, and associated with the onset of cardiac reentry and sudden cardiac death. Many alternans detection algorithms have been proposed in the past, but the majority have been designed specifically for use with T-wave alternans. Action potential duration (APD) signals obtained from experiments (especially those derived from optical mapping) possess unique characteristics, which requires the development and use of a more appropriate alternans detection method. In this paper, we present a new class of algorithms, based on the Monte Carlo method, for the detection and quantitative measurement of alternans. Specifically, we derive a set of algorithms (one an analytical and more efficient version of the other) and compare its performance with the standard spectral method and the generalized likelihood ratio test algorithm using synthetic APD sequences and optical mapping data obtained from an alternans control experiment. We demonstrate the benefits of the new algorithm in the presence of Gaussian and Laplacian noise and frame-shift errors. The proposed algorithms are well suited for experimental applications, and furthermore, have low complexity and are implementable using fixed-point arithmetic, enabling potential use with implantable cardiac devices.
On the Analysis of a Label Propagation Algorithm for Community Detection
Kothapalli, Kishore; Sardeshmukh, Vivek
2012-01-01
This paper initiates formal analysis of a simple, distributed algorithm for community detection on networks. We analyze an algorithm that we call \\textsc{Max-LPA}, both in terms of its convergence time and in terms of the "quality" of the communities detected. \\textsc{Max-LPA} is an instance of a class of community detection algorithms called \\textit{label propagation} algorithms. As far as we know, most analysis of label propagation algorithms thus far has been empirical in nature and in this paper we seek a theoretical understanding of label propagation algorithms. In our main result, we define a clustered version of \\er random graphs with clusters $V_1, V_2,..., V_k$ where the probability $p$, of an edge connecting nodes within a cluster $V_i$ is higher than $p'$, the probability of an edge connecting nodes in distinct clusters. We show that even with fairly general restrictions on $p$ and $p'$ ($p = \\Omega(\\frac{1}{n^{1/4-\\epsilon}})$ for any $\\epsilon > 0$, $p' = O(p^2)$, where $n$ is the number of nodes...
New algorithm to detect modules in a fault tree for a PSA
Jung, Woo Sik [Sejong University, Seoul (Korea, Republic of)
2015-05-15
A module or independent subtree is a part of a fault tree whose child gates or basic events are not repeated in the remaining part of the fault tree. Modules are necessarily employed in order to reduce the computational costs of fault tree quantification. This paper presents a new linear time algorithm to detect modules of large fault trees. The size of cut sets can be substantially reduced by replacing independent subtrees in a fault tree with super-components. Chatterjee and Birnbaum developed properties of modules, and demonstrated their use in the fault tree analysis. Locks expanded the concept of modules to non-coherent fault trees. Independent subtrees were manually identified while coding a fault tree for computer analysis. However, nowadays, the independent subtrees are automatically identified by the fault tree solver. A Dutuit and Rauzy (DR) algorithm to detect modules of a fault tree for coherent or non-coherent fault tree was proposed in 1996. It has been well known that this algorithm quickly detects modules since it is a linear time algorithm. The new algorithm minimizes computational memory and quickly detects modules. Furthermore, it can be easily implemented into industry fault tree solvers that are based on traditional Boolean algebra, binary decision diagrams (BDDs), or Zero-suppressed BDDs. The new algorithm employs only two scalar variables in Eqs. to that are volatile information. After finishing the traversal and module detection of each node, the volatile information is destroyed. Thus, the new algorithm does not employ any other additional computational memory and operations. It is recommended that this method be implemented into fault tree solvers for efficient probabilistic safety assessment (PSA) of nuclear power plants.
IMPROVEMENT OF ANOMALY DETECTION ALGORITHMS IN HYPERSPECTRAL IMAGES USING DISCRETE WAVELET TRANSFORM
Kamal Jamshidi
2012-01-01
Full Text Available Recently anomaly detection (AD has become an important application for target detection in hyperspectralremotely sensed images. In many applications, in addition to high accuracy of detection we need a fast andreliable algorithm as well. This paper presents a novel method to improve the performance of current ADalgorithms. The proposed method first calculates Discrete Wavelet Transform (DWT of every pixel vectorof image using Daubechies4 wavelet. Then, AD algorithm performs on four bands of “Wavelet transform”matrix which are the approximation of main image. In this research some benchmark AD algorithmsincluding Local RX, DWRX and DWEST have been implemented on Airborne Visible/Infrared ImagingSpectrometer (AVIRIS hyperspectral datasets. Experimental results demonstrate significant improvementof runtime in proposed method. In addition, this method improves the accuracy of AD algorithms becauseof DWT’s power in extracting approximation coefficients of signal, which contain the main behaviour ofsignal, and abandon the redundant information in hyperspectral image data.
Amooee, Golriz; Bagheri-Dehnavi, Malihe
2012-01-01
In the current competitive world, industrial companies seek to manufacture products of higher quality which can be achieved by increasing reliability, maintainability and thus the availability of products. On the other hand, improvement in products lifecycle is necessary for achieving high reliability. Typically, maintenance activities are aimed to reduce failures of industrial machinery and minimize the consequences of such failures. So the industrial companies try to improve their efficiency by using different fault detection techniques. One strategy is to process and analyze previous generated data to predict future failures. The purpose of this paper is to detect wasted parts using different data mining algorithms and compare the accuracy of these algorithms. A combination of thermal and physical characteristics has been used and the algorithms were implemented on Ahanpishegan's current data to estimate the availability of its produced parts. Keywords: Data Mining, Fault Detection, Availability, Predictio...
A Novel Robust Scene Change Detection Algorithm for Autonomous Robots Using Mixtures of Gaussians
Luis J. Manso
2014-02-01
Full Text Available Interest in change detection techniques has considerably increased during recent years in the field of autonomous robotics. This is partly because changes in a robot's working environment are useful for several robotic skills (e.g., spatial cognition, modelling or navigation and applications (e.g., surveillance or guidance robots. Changes are usually detected by comparing current data provided by the robot's sensors with a previously known map or model of the environment. When the data consists of a large point cloud, dealing with it is a computationally expensive task, mainly due to the amount of points and the redundancy. Using Gaussian Mixture Models (GMM instead of raw point clouds leads to a more compact feature space that can be used to efficiently process the input data. This allows us to successfully segment the set of 3D points acquired by the sensor and reduce the computational load of the change detection algorithm. However, the segmentation of the environment as a Mixture of Gaussians has some problems that need to be properly addressed. In this paper, a novel change detection algorithm is described in order to improve the robustness and computational cost of previous approaches. The proposal is based on the classic Expectation Maximization (EM algorithm, for which different selection criteria are evaluated. As demonstrated in the experimental results section, the proposed change detection algorithm achieves the detection of changes in the robot's working environment faster and more accurately than similar approaches.
A Novel Robust Scene Change Detection Algorithm for Autonomous Robots Using Mixtures of Gaussians
Luis J. Manso
2014-02-01
Full Text Available Interest in change detection techniques has considerably increased during recent years in the field of autonomous robotics. This is partly because changes in a robot’s working environment are useful for several robotic skills (e.g., spatial cognition, modelling or navigation and applications (e.g., surveillance or guidance robots. Changes are usually detected by comparing current data provided by the robot’s sensors with a previously known map or model of the environment. When the data consists of a large point cloud, dealing with it is a computationally expensive task, mainly due to the amount of points and the redundancy. Using Gaussian Mixture Models (GMM instead of raw point clouds leads to a more compact feature space that can be used to efficiently process the input data. This allows us to successfully segment the set of 3D points acquired by the sensor and reduce the computational load of the change detection algorithm. However, the segmentation of the environment as a Mixture of Gaussians has some problems that need to be properly addressed. In this paper, a novel change detection algorithm is described in order to improve the robustness and computational cost of previous approaches. The proposal is based on the classic Expectation Maximization (EM algorithm, for which different selection criteria are evaluated. As demonstrated in the experimental results section, the proposed change detection algorithm achieves the detection of changes in the robot’s working environment faster and more accurately than similar approaches.
Integration of a Self-Coherence Algorithm into DISAT for Forced Oscillation Detection
Follum, James D. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Tuffner, Francis K. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Amidan, Brett G. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
2015-03-03
With the increasing number of phasor measurement units on the power system, behaviors typically not observable on the power system are becoming more apparent. Oscillatory behavior on the power system, notably forced oscillations, are one such behavior. However, the large amounts of data coming from the PMUs makes manually detecting and locating these oscillations difficult. To automate portions of the process, an oscillation detection routine was coded into the Data Integrity and Situational Awareness Tool (DISAT) framework. Integration into the DISAT framework allows forced oscillations to be detected and information about the event provided to operational engineers. The oscillation detection algorithm integrates with the data handling and atypical data detecting capabilities of DISAT, building off of a standard library of functions. This report details that integration with information on the algorithm, some implementation issues, and some sample results from the western United States’ power grid.
Yongsheng Liu; Yansong Yang; Chang Liu; Yu Gu
2015-01-01
A forest fire is a severe threat to forest resources and human life. In this paper, we propose a forest⁃fire detection system that has an artificial neural network algorithm implemented in a wireless sensor network (WSN). The proposed detection system mitigates the threat of forest fires by provide accurate fire alarm with low maintenance cost. The accuracy is increased by the novel multi⁃criteria detection, referred to as an alarm decision depends on multiple attributes of a forest fire. The multi⁃criteria detection is im⁃plemented by the artificial neural network algorithm. Meanwhile, we have developed a prototype of the proposed system consisting of the solar batter module, the fire detection module and the user interface module.
Raw data based image processing algorithm for fast detection of surface breaking cracks
Sruthi Krishna K., P.; Puthiyaveetil, Nithin; Kidangan, Renil; Unnikrishnakurup, Sreedhar; Zeigler, Mathias; Myrach, Philipp; Balasubramaniam, Krishnan; Biju, P.
2017-02-01
The aim of this work is to illustrate the contribution of signal processing techniques in the field of Non-Destructive Evaluation. A component's life evaluation is inevitably related to the presence of flaws in it. The detection and characterization of cracks prior to damage is a technologically and economically significant task and is of very importance when it comes to safety-relevant measures. The Laser Thermography is the most effective and advanced thermography method for Non-Destructive Evaluation. High capability for the detection of surface cracks and for the characterization of the geometry of artificial surface flaws in metallic samples of laser thermography is particularly encouraging. This is one of the non-contacting, fast and real time detection method. The presence of a vertical surface breaking crack will disturb the thermal footprint. The data processing method plays vital role in fast detection of the surface and sub-surface cracks. Currently in laser thermographic inspection lacks a compromising data processing algorithm which is necessary for the fast crack detection and also the analysis of data is done as part of post processing. In this work we introduced a raw data based image processing algorithm which results precise, better and fast crack detection. The algorithm we developed gives better results in both experimental and modeling data. By applying this algorithm we carried out a detailed investigation variation of thermal contrast with crack parameters like depth and width. The algorithm we developed is applied for various surface temperature data from the 2D scanning model and also validated credibility of algorithm with experimental data.
An effective hair detection algorithm for dermoscopic melanoma images of skin lesions
Chakraborti, Damayanti; Kaur, Ravneet; Umbaugh, Scott; LeAnder, Robert
2016-09-01
Dermoscopic images are obtained using the method of skin surface microscopy. Pigmented skin lesions are evaluated in terms of texture features such as color and structure. Artifacts, such as hairs, bubbles, black frames, ruler-marks, etc., create obstacles that prevent accurate detection of skin lesions by both clinicians and computer-aided diagnosis. In this article, we propose a new algorithm for the automated detection of hairs, using an adaptive, Canny edge-detection method, followed by morphological filtering and an arithmetic addition operation. The algorithm was applied to 50 dermoscopic melanoma images. In order to ascertain this method's relative detection accuracy, it was compared to the Razmjooy hair-detection method [1], using segmentation error (SE), true detection rate (TDR) and false positioning rate (FPR). The new method produced 6.57% SE, 96.28% TDR and 3.47% FPR, compared to 15.751% SE, 86.29% TDR and 11.74% FPR produced by the Razmjooy method [1]. Because of the 7.27-9.99% improvement in those parameters, we conclude that the new algorithm produces much better results for detecting thick, thin, dark and light hairs. The new method proposed here, shows an appreciable difference in the rate of detecting bubbles, as well.
无
2007-01-01
In this paper, a novel concept of the joint state of input and output of encoder is proposed. Based on it, a recursive algorithm that implements the multi-symbol differential detection in criterion of maximum likelihood is proposed. Simulation results show that its performance can approach to the theoretical boundary of multi-symbol differential detection without increasing the complexity per symbol, and it is effective in practical systems. It is also an optimal algorithm to solve the problems of estimating the state sequence of finite-state Markov process observed in memoryless noise.
A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection
Dalton Meitei Thounaojam
2016-01-01
Full Text Available This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter.
Evaluation of landmine detection performance applying two different algorithms to GPR field data
Mendez-Rial, Roi; Uschkerat, U.; Rial, F. I.; Gonzalez-Huici, Maria A.
2013-06-01
This paper evaluates and compares the performance of two algorithms that have previously demonstrated their potential in underground target detection. Field data was obtained on specially prepared test fields, where various mine simulants, reference objects, and mine-like clutter where placed at precise locations in different soil types. The efficiency of both algorithms in terms of detection accuracies (ROC curves) and computational burden is compared, as well as the impact of preprocessing strategies. Based on the results, we discuss the convenience of both methods to be integrated in a real - time signal processing system considering their benefits and drawbacks.
Stable algorithm for event detection in event-driven particle dynamics: logical states
Strobl, Severin; Bannerman, Marcus N.; Pöschel, Thorsten
2016-07-01
Following the recent development of a stable event-detection algorithm for hard-sphere systems, the implications of more complex interaction models are examined. The relative location of particles leads to ambiguity when it is used to determine the interaction state of a particle in stepped potentials, such as the square-well model. To correctly predict the next event in these systems, the concept of an additional state that is tracked separately from the particle position is introduced and integrated into the stable algorithm for event detection.
Stable algorithm for event detection in event-driven particle dynamics: Logical states
Strobl, Severin; Poeschel, Thorsten
2015-01-01
Following the recent development of a stable event-detection algorithm for hard-sphere systems, the implications of more complex interaction models are examined. The relative location of particles leads to ambiguity when it is used to determine the interaction state of a particle in stepped potentials, such as the square-well model. To correctly predict the next event in these systems, the concept of an additional state that is tracked separately from the particle position is introduced and integrated into the stable algorithm for event detection.
A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection.
Thounaojam, Dalton Meitei; Khelchandra, Thongam; Manglem Singh, Kh; Roy, Sudipta
2016-01-01
This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter.
Burhan Ergen
2014-01-01
Full Text Available This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT and Magnetic Resonance Imaging (MRI devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.
Ergen, Burhan
2014-01-01
This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT) and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM) clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.
Islanding Detection for Microgrid Based on Frequency Tracking Using Extended Kalman Filter Algorithm
Bin Li
2014-01-01
Full Text Available Islanding detection is essential for secure and reliable operation of microgrids. Considering the relationship between the power generation and the load in microgrids, frequency may vary with time when islanding occurs. As a common approach, frequency measurement is widely used to detect islanding condition. In this paper, a novel frequency calculation algorithm based on extended Kalman filter was proposed to track dynamic frequency of the microgrid. Taylor series expansion was introduced to solve nonlinear state equations. In addition, a typical microgrid model was built using MATLAB/SIMULINK. Simulation results demonstrated that the proposed algorithm achieved great stability and strong robustness in of tracking dynamic frequency.
Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm.
Naebi, Mohammad; Saberi, Eshaghali; Risbaf Fakour, Sirous; Naebi, Ahmad; Hosseini Tabatabaei, Somayeh; Ansari Moghadam, Somayeh; Bozorgmehr, Elham; Davtalab Behnam, Nasim; Azimi, Hamidreza
2016-01-01
Background/Purpose. In terms of the detection of tooth diagnosis, no intelligent detection has been done up till now. Dentists just look at images and then they can detect the diagnosis position in tooth based on their experiences. Using new technologies, scientists will implement detection and repair of tooth diagnosis intelligently. In this paper, we have introduced one intelligent method for detection using particle swarm optimization (PSO) and our mathematical formulation. This method was applied to 2D special images. Using developing of our method, we can detect tooth diagnosis for all of 2D and 3D images. Materials and Methods. In recent years, it is possible to implement intelligent processing of images by high efficiency optimization algorithms in many applications especially for detection of dental caries and restoration without human intervention. In the present work, we explain PSO algorithm with our detection formula for detection of dental caries and restoration. Also image processing helped us to implement our method. And to do so, pictures taken by digital radiography systems of tooth are used. Results and Conclusion. We implement some mathematics formula for fitness of PSO. Our results show that this method can detect dental caries and restoration in digital radiography pictures with the good convergence. In fact, the error rate of this method was 8%, so that it can be implemented for detection of dental caries and restoration. Using some parameters, it is possible that the error rate can be even reduced below 0.5%.
Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm
Mohammad Naebi
2016-01-01
Full Text Available Background/Purpose. In terms of the detection of tooth diagnosis, no intelligent detection has been done up till now. Dentists just look at images and then they can detect the diagnosis position in tooth based on their experiences. Using new technologies, scientists will implement detection and repair of tooth diagnosis intelligently. In this paper, we have introduced one intelligent method for detection using particle swarm optimization (PSO and our mathematical formulation. This method was applied to 2D special images. Using developing of our method, we can detect tooth diagnosis for all of 2D and 3D images. Materials and Methods. In recent years, it is possible to implement intelligent processing of images by high efficiency optimization algorithms in many applications especially for detection of dental caries and restoration without human intervention. In the present work, we explain PSO algorithm with our detection formula for detection of dental caries and restoration. Also image processing helped us to implement our method. And to do so, pictures taken by digital radiography systems of tooth are used. Results and Conclusion. We implement some mathematics formula for fitness of PSO. Our results show that this method can detect dental caries and restoration in digital radiography pictures with the good convergence. In fact, the error rate of this method was 8%, so that it can be implemented for detection of dental caries and restoration. Using some parameters, it is possible that the error rate can be even reduced below 0.5%.
QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases.
Saini, Indu; Singh, Dilbag; Khosla, Arun
2013-07-01
The performance of computer aided ECG analysis depends on the precise and accurate delineation of QRS-complexes. This paper presents an application of K-Nearest Neighbor (KNN) algorithm as a classifier for detection of QRS-complex in ECG. The proposed algorithm is evaluated on two manually annotated standard databases such as CSE and MIT-BIH Arrhythmia database. In this work, a digital band-pass filter is used to reduce false detection caused by interference present in ECG signal and further gradient of the signal is used as a feature for QRS-detection. In addition the accuracy of KNN based classifier is largely dependent on the value of K and type of distance metric. The value of K = 3 and Euclidean distance metric has been proposed for the KNN classifier, using fivefold cross-validation. The detection rates of 99.89% and 99.81% are achieved for CSE and MIT-BIH databases respectively. The QRS detector obtained a sensitivity Se = 99.86% and specificity Sp = 99.86% for CSE database, and Se = 99.81% and Sp = 99.86% for MIT-BIH Arrhythmia database. A comparison is also made between proposed algorithm and other published work using CSE and MIT-BIH Arrhythmia databases. These results clearly establishes KNN algorithm for reliable and accurate QRS-detection.
A F M Saifuddin Saif
Full Text Available Fast and computationally less complex feature extraction for moving object detection using aerial images from unmanned aerial vehicles (UAVs remains as an elusive goal in the field of computer vision research. The types of features used in current studies concerning moving object detection are typically chosen based on improving detection rate rather than on providing fast and computationally less complex feature extraction methods. Because moving object detection using aerial images from UAVs involves motion as seen from a certain altitude, effective and fast feature extraction is a vital issue for optimum detection performance. This research proposes a two-layer bucket approach based on a new feature extraction algorithm referred to as the moment-based feature extraction algorithm (MFEA. Because a moment represents the coherent intensity of pixels and motion estimation is a motion pixel intensity measurement, this research used this relation to develop the proposed algorithm. The experimental results reveal the successful performance of the proposed MFEA algorithm and the proposed methodology.
T. Venkat Narayana Rao
2011-11-01
Full Text Available Edge detection is the most important feature of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms/operators. Computer vision is rapidly expanding field that depends on the capability to perform faster segments and thus to classify and infer images. Segmentation is central to the successful extraction of image features and their ensuing classification. Powerful segmentation techniques are available; however each technique is ad hoc. In this paper, the computer vision investigates the sub regions of the composite image, brings out commonly used and most important edge detection algorithms/operators with a wide-ranging comparative along with the statistical approach. This paper implements popular algorithms such as Sobel, Roberts, Prewitt, Laplacian of Gaussian and canny. A standard metric is used for evaluating the performance degradation of edge detection algorithms as a function of Peak Signal to Noise Ratio (PSNR along with the elapsed time for generating the segmented output image. A statistical approach to evaluate the variance among the PSNR and the time elapsed in output image is also incorporated. This paper provides a basis for objectively comparing the performance of different techniques and quantifies relative noise tolerance. Results shown allow selection of the most optimum method for application to image.
Algorithms for the detection of chewing behavior in dietary monitoring applications
Schmalz, Mark S.; Helal, Abdelsalam; Mendez-Vasquez, Andres
2009-08-01
The detection of food consumption is key to the implementation of successful behavior modification in support of dietary monitoring and therapy, for example, during the course of controlling obesity, diabetes, or cardiovascular disease. Since the vast majority of humans consume food via mastication (chewing), we have designed an algorithm that automatically detects chewing behaviors in surveillance video of a person eating. Our algorithm first detects the mouth region, then computes the spatiotemporal frequency spectrum of a small perioral region (including the mouth). Spectral data are analyzed to determine the presence of periodic motion that characterizes chewing. A classifier is then applied to discriminate different types of chewing behaviors. Our algorithm was tested on seven volunteers, whose behaviors included chewing with mouth open, chewing with mouth closed, talking, static face presentation (control case), and moving face presentation. Early test results show that the chewing behaviors induce a temporal frequency peak at 0.5Hz to 2.5Hz, which is readily detected using a distance-based classifier. Computational cost is analyzed for implementation on embedded processing nodes, for example, in a healthcare sensor network. Complexity analysis emphasizes the relationship between the work and space estimates of the algorithm, and its estimated error. It is shown that chewing detection is possible within a computationally efficient, accurate, and subject-independent framework.
Flach, Milan; Gans, Fabian; Brenning, Alexander; Denzler, Joachim; Reichstein, Markus; Rodner, Erik; Bathiany, Sebastian; Bodesheim, Paul; Guanche, Yanira; Sippel, Sebastian; Mahecha, Miguel D.
2017-08-01
Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of extreme climatic events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations like sudden changes in basic characteristics of time series such as the sample mean, the variance, changes in the cycle amplitude, and trends. This artificial experiment is needed as there is no gold standard for the identification of anomalies in real Earth observations. Our results show that a well-chosen feature extraction step (e.g., subtracting seasonal cycles, or dimensionality reduction) is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify three detection algorithms (k-nearest neighbors mean distance, kernel density estimation, a recurrence approach) and their combinations (ensembles) that outperform other multivariate approaches as well as univariate extreme-event detection methods. Our results therefore provide an effective workflow to automatically detect anomalies
Qian, Jinfang; Zhang, Changjiang
2014-11-01
An efficient algorithm based on continuous wavelet transform combining with pre-knowledge, which can be used to detect the defect of glass bottle mouth, is proposed. Firstly, under the condition of ball integral light source, a perfect glass bottle mouth image is obtained by Japanese Computar camera through the interface of IEEE-1394b. A single threshold method based on gray level histogram is used to obtain the binary image of the glass bottle mouth. In order to efficiently suppress noise, moving average filter is employed to smooth the histogram of original glass bottle mouth image. And then continuous wavelet transform is done to accurately determine the segmentation threshold. Mathematical morphology operations are used to get normal binary bottle mouth mask. A glass bottle to be detected is moving to the detection zone by conveyor belt. Both bottle mouth image and binary image are obtained by above method. The binary image is multiplied with normal bottle mask and a region of interest is got. Four parameters (number of connected regions, coordinate of centroid position, diameter of inner cycle, and area of annular region) can be computed based on the region of interest. Glass bottle mouth detection rules are designed by above four parameters so as to accurately detect and identify the defect conditions of glass bottle. Finally, the glass bottles of Coca-Cola Company are used to verify the proposed algorithm. The experimental results show that the proposed algorithm can accurately detect the defect conditions of the glass bottles and have 98% detecting accuracy.
Community Detection in Dynamic Social Networks Based on Multiobjective Immune Algorithm
Mao-Guo Gong; Ling-Jun Zhang; Jing-Jing Ma; Li-Cheng Jiao
2012-01-01
Community structure is one of the most important properties in social networks,and community detection has received an enormous amount of attention in recent years.In dynamic networks,the communities may evolve over time so that pose more challenging tasks than in static ones.Community detection in dynamic networks is a problem which can naturally be formulated with two contradictory objectives and consequently be solved by multiobjective optimization algorithms.In this paper,a novel multiobjective immune algorithm is proposed to solve the community detection problem in dynamic networks.It employs the framework of nondominated neighbor immune algorithm to simultaneously optimize the modularity and normalized mutual information,which quantitatively measure the quality of the community partitions and temporal cost,respectively.The problem-specific knowledge is incorporated in genetic operators and local search to improve the effectiveness and efficiency of our method.Experimental studies based on four synthetic datasets and two real-world social networks demonstrate that our algorithm can not only find community structure and capture community evolution more accurately but also be more steadily than the state-of-the-art algorithms.
Zainuddin, Zarita; Lai, Kee Huong; Ong, Pauline
2013-04-01
Artificial neural networks (ANNs) are powerful mathematical models that are used to solve complex real world problems. Wavelet neural networks (WNNs), which were developed based on the wavelet theory, are a variant of ANNs. During the training phase of WNNs, several parameters need to be initialized; including the type of wavelet activation functions, translation vectors, and dilation parameter. The conventional k-means and fuzzy c-means clustering algorithms have been used to select the translation vectors. However, the solution vectors might get trapped at local minima. In this regard, the evolutionary harmony search algorithm, which is capable of searching for near-optimum solution vectors, both locally and globally, is introduced to circumvent this problem. In this paper, the conventional k-means and fuzzy c-means clustering algorithms were hybridized with the metaheuristic harmony search algorithm. In addition to obtaining the estimation of the global minima accurately, these hybridized algorithms also offer more than one solution to a particular problem, since many possible solution vectors can be generated and stored in the harmony memory. To validate the robustness of the proposed WNNs, the real world problem of epileptic seizure detection was presented. The overall classification accuracy from the simulation showed that the hybridized metaheuristic algorithms outperformed the standard k-means and fuzzy c-means clustering algorithms.
Chan, S L; Tham, M Y; Tan, S H; Loke, C; Foo, Bpq; Fan, Y; Ang, P S; Brunham, L R; Sung, C
2017-05-01
The purpose of this study was to develop and validate sensitive algorithms to detect hospitalized statin-induced myopathy (SIM) cases from electronic medical records (EMRs). We developed four algorithms on a training set of 31,211 patient records from a large tertiary hospital. We determined the performance of these algorithms against manually curated records. The best algorithm used a combination of elevated creatine kinase (>4× the upper limit of normal (ULN)), discharge summary, diagnosis, and absence of statin in discharge medications. This algorithm achieved a positive predictive value of 52-71% and a sensitivity of 72-78% on two validation sets of >30,000 records each. Using this algorithm, the incidence of SIM was estimated at 0.18%. This algorithm captured three times more rhabdomyolysis cases than spontaneous reports (95% vs. 30% of manually curated gold standard cases). Our results show the potential power of utilizing data and text mining of EMRs to enhance pharmacovigilance activities. © 2016 American Society for Clinical Pharmacology and Therapeutics.
Ma, Tianren; Xia, Zhengyou
2017-05-01
Currently, with the rapid development of information technology, the electronic media for social communication is becoming more and more popular. Discovery of communities is a very effective way to understand the properties of complex networks. However, traditional community detection algorithms consider the structural characteristics of a social organization only, with more information about nodes and edges wasted. In the meanwhile, these algorithms do not consider each node on its merits. Label propagation algorithm (LPA) is a near linear time algorithm which aims to find the community in the network. It attracts many scholars owing to its high efficiency. In recent years, there are more improved algorithms that were put forward based on LPA. In this paper, an improved LPA based on random walk and node importance (NILPA) is proposed. Firstly, a list of node importance is obtained through calculation. The nodes in the network are sorted in descending order of importance. On the basis of random walk, a matrix is constructed to measure the similarity of nodes and it avoids the random choice in the LPA. Secondly, a new metric IAS (importance and similarity) is calculated by node importance and similarity matrix, which we can use to avoid the random selection in the original LPA and improve the algorithm stability. Finally, a test in real-world and synthetic networks is given. The result shows that this algorithm has better performance than existing methods in finding community structure.
CenLP: A centrality-based label propagation algorithm for community detection in networks
Sun, Heli; Liu, Jiao; Huang, Jianbin; Wang, Guangtao; Yang, Zhou; Song, Qinbao; Jia, Xiaolin
2015-10-01
Community detection is an important work for discovering the structure and features of complex networks. Many existing methods are sensitive to critical user-dependent parameters or time-consuming in practice. In this paper, we propose a novel label propagation algorithm, called CenLP (Centrality-based Label Propagation). The algorithm introduces a new function to measure the centrality of nodes quantitatively without any user interaction by calculating the local density and the similarity with higher density neighbors for each node. Based on the centrality of nodes, we present a new label propagation algorithm with specific update order and node preference to uncover communities in large-scale networks automatically without imposing any prior restriction. Experiments on both real-world and synthetic networks manifest our algorithm retains the simplicity, effectiveness, and scalability of the original label propagation algorithm and becomes more robust and accurate. Extensive experiments demonstrate the superior performance of our algorithm over the baseline methods. Moreover, our detailed experimental evaluation on real-world networks indicates that our algorithm can effectively measure the centrality of nodes in social networks.
Fast recognition algorithm of underwater micro-terrain based on ultrasonic detection
LUO Bo-wen; ZHOU Zhi-jin; BU Ying-yong; ZHAO Hai-ming
2008-01-01
An algorithm was proposed to fast recognize three types of underwater micro-terrain, i.e. the level, the gradient and the uneven. With pendulum single beam bathymeter, the hard level concrete floor, the random uneven floor and the gradient wood panel (8°) were ultrasonically detected 20 times, respectively. The results show that the algorithm is right from fact that the first clustering values of the uneven are all less than the threshold value of 60.0% that is obtained by the level and gradient samples. The algorithm based on the dynamic clustering theory can effectively eliminate the influences of the exceptional elevation values produced by the disturbances resulted from the grazing angle, the characteristic of bottom material and environmental noises, and its real-time capability is good. Thus, the algorithm provides a foundation for the next restructuring of the micro-terrain.
Adaptive Weighted Morphology Detection Algorithm of Plane Object in Docking Guidance System
Guo yan-ying
2010-09-01
Full Text Available In this paper, we presented an image segmentation algorithm based on adaptive weighted mathematical morphology edge detectors. The performance of the proposed algorithm has been demonstrated on the Lena image. The input of the proposed algorithm is a grey level image. The image was first processed by the mathematical morphological closing and dilation residue edge detector to enhance the edge features and sketch out the contour of the image, respectively. Then the adaptive weight SE operation was applied to the edge-extracted image to fuse edge gaps and hill up holds. Experimental results show it can not only primely extract detail edge, but also superbly preserve integer effect comparative to classical edge detection algorithm.
Automatic face detection and tracking based on Adaboost with camshift algorithm
Lin, Hui; Long, JianFeng
2011-10-01
With the development of information technology, video surveillance is widely used in security monitoring and identity recognition. For most of pure face tracking algorithms are hard to specify the initial location and scale of face automatically, this paper proposes a fast and robust method to detect and track face by combining adaboost with camshift algorithm. At first, the location and scale of face is specified by adaboost algorithm based on Haar-like features and it will be conveyed to the initial search window automatically. Then, we apply camshift algorithm to track face. The experimental results based on OpenCV software yield good results, even in some special circumstances, such as light changing and face rapid movement. Besides, by drawing out the tracking trajectory of face movement, some abnormal behavior events can be analyzed.
CHEN Yunkai; LU Zhengding; LI Ruixuan; LI Yuhua; SUN Xiaolin
2006-01-01
Considering the constantly increasing of data in large databases such as wire transfer database, incremental clustering algorithms play a more and more important role in Data Mining (DM). However, Few of the traditional clustering algorithms can not only handle the categorical data, but also explain its output clearly. Based on the idea of dynamic clustering, an incremental conceptive clustering algorithm is proposed in this paper. Which introduces the Semantic Core Tree (SCT) to deal with large volume of categorical wire transfer data for the detecting money laundering. In addition, the rule generation algorithm is presented here to express the clustering result by the format of knowledge. When we apply this idea in financial data mining, the efficiency of searching the characters of money laundering data will be improved.
An enhanced iterative joint channel estimationand symbol detection algorithm for OFDM systems
HanBing; GaoXiqi; YouXiaohu
2003-01-01
For orthogonal frequency division multiplexing (OFDM) wireless communication, the system throughput and datarate are usually limited by pilots, especially in a high mobility environment. In this paper, an enhanced iterative joint channelestimation and symbol detection algorithm is .proposed to enhance the system throughput and data rate. With lower pilotpower, the proposed scheme increases system throughput firstly, and then the channel estimation and symbol detectionproceed iteratively within one OFDM symbol to improve the BER performance. In the proposed algorithm, the original channelestimate of each OFDM symbol is based on the channel estimate of the previous OFDM symbol, thus the variation of themobile channel is traced efficiently, so the number of pilots in the time domain can be reduced greatly. Besides reducing thesystem overhead, the proposed algorithm is also shown by simulation to give much better BER performance than theconventional iterative algorithm does.
Detectability thresholds and optimal algorithms for community structure in dynamic networks
Ghasemian, Amir; Clauset, Aaron; Moore, Cristopher; Peel, Leto
2015-01-01
We study the fundamental limits on learning latent community structure in dynamic networks. Specifically, we study dynamic stochastic block models where nodes change their community membership over time, but where edges are generated independently at each time step. In this setting (which is a special case of several existing models), we are able to derive the detectability threshold exactly, as a function of the rate of change and the strength of the communities. Below this threshold, we claim that no algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this limit. The first uses belief propagation (BP), which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the BP equations. We verify our analytic and algorithmic results via numerical simulation, and close with a brief discussion of extensions and open questions.
Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomoly Detection
Greensmith, Julie; Cayzer, Steve
2010-01-01
Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological concepts.
You, Tao; Cheng, Hui-Min; Ning, Yi-Zi; Shia, Ben-Chang; Zhang, Zhong-Yuan
2016-12-01
Like clustering analysis, community detection aims at assigning nodes in a network into different communities. Fdp is a recently proposed density-based clustering algorithm which does not need the number of clusters as prior input and the result is insensitive to its parameter. However, Fdp cannot be directly applied to community detection due to its inability to recognize the community centers in the network. To solve the problem, a new community detection method (named IsoFdp) is proposed in this paper. First, we use IsoMap technique to map the network data into a low dimensional manifold which can reveal diverse pair-wised similarity. Then Fdp is applied to detect the communities in the network. An improved partition density function is proposed to select the proper number of communities automatically. We test our method on both synthetic and real-world networks, and the results demonstrate the effectiveness of our algorithm over the state-of-the-art methods.
Shot Boundary Detection in Soccer Video using Twin-comparison Algorithm and Dominant Color Region
Matko Šarić
2008-06-01
Full Text Available The first step in generic video processing is temporal segmentation, i.e. shot boundary detection. Camera shot transitions can be either abrupt (e.g. cuts or gradual (e.g. fades, dissolves, wipes. Sports video is one of the most challenging domains for robust shot boundary detection. We proposed a shot boundary detection algorithm for soccer video based on the twin-comparison method and the absolute difference between frames in their ratios of dominant colored pixels to total number of pixels. With this approach the detection of gradual transitions is improved by decreasing the number of false positives caused by some camera operations. We also compared performances of our algorithm and the standard twin-comparison method.
Cloud detection algorithm comparison and validation for operational Landsat data products
Foga, Steven Curtis; Scaramuzza, Pat; Guo, Song; Zhu, Zhe; Dilley, Ronald; Beckmann, Tim; Schmidt, Gail L.; Dwyer, John L.; Hughes, MJ; Laue, Brady
2017-01-01
Clouds are a pervasive and unavoidable issue in satellite-borne optical imagery. Accurate, well-documented, and automated cloud detection algorithms are necessary to effectively leverage large collections of remotely sensed data. The Landsat project is uniquely suited for comparative validation of cloud assessment algorithms because the modular architecture of the Landsat ground system allows for quick evaluation of new code, and because Landsat has the most comprehensive manual truth masks of any current satellite data archive. Currently, the Landsat Level-1 Product Generation System (LPGS) uses separate algorithms for determining clouds, cirrus clouds, and snow and/or ice probability on a per-pixel basis. With more bands onboard the Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) satellite, and a greater number of cloud masking algorithms, the U.S. Geological Survey (USGS) is replacing the current cloud masking workflow with a more robust algorithm that is capable of working across multiple Landsat sensors with minimal modification. Because of the inherent error from stray light and intermittent data availability of TIRS, these algorithms need to operate both with and without thermal data. In this study, we created a workflow to evaluate cloud and cloud shadow masking algorithms using cloud validation masks manually derived from both Landsat 7 Enhanced Thematic Mapper Plus (ETM +) and Landsat 8 OLI/TIRS data. We created a new validation dataset consisting of 96 Landsat 8 scenes, representing different biomes and proportions of cloud cover. We evaluated algorithm performance by overall accuracy, omission error, and commission error for both cloud and cloud shadow. We found that CFMask, C code based on the Function of Mask (Fmask) algorithm, and its confidence bands have the best overall accuracy among the many algorithms tested using our validation data. The Artificial Thermal-Automated Cloud Cover Algorithm (AT-ACCA) is the most accurate
Kazeem I. Rufai
2014-03-01
Full Text Available Despite the great benefits accruable from the debut of computer and the internet, efforts are constantly being put up by fraudulent and mischievous individuals to compromise the integrity, confidentiality or availability of electronic information systems. In Cyber-security parlance, this is termed ‘intrusion’. Hence, this has necessitated the introduction of Intrusion Detection Systems (IDS to help detect and curb different types of attack. However, based on the high volume of data traffic involved in a network system, effects of redundant and irrelevant data should be minimized if a qualitative intrusion detection mechanism is genuinely desirous. Several attempts, especially feature subset selection approach using Bee Algorithm (BA, Linear Genetic Programming (LGP, Support Vector Decision Function Ranking (SVDF, Rough, Rough-DPSO, and Mutivariate Regression Splines (MARS have been advanced in the past to measure the dependability and quality of a typical IDS. The observed problem among these approaches has to do with their general performance. This has therefore motivated this research work. We hereby propose a new but robust algorithm called membrane algorithm to improve the Bee Algorithm based feature subset selection technique. This Membrane computing paradigm is a class of parallel computing devices. Data used were taken from KDD-Cup 99 Dataset which is the acceptable standard benchmark for intrusion detection. When the final results were compared to those of the existing approaches, using the three standard IDS measurements-Attack Detection, False Alarm and Classification Accuracy Rates, it was discovered that Bee Algorithm-Membrane Computing (BA-MC approach is a better technique. This is because our approach produced very high attack detection rate of 89.11%, classification accuracy of 95.60% and also generated a reasonable decrease in false alarm rate of 0.004. Receiver Operating Characteristic (ROC curve was used for results
Syndromic algorithms for detection of gambiense human African trypanosomiasis in South Sudan.
Jennifer J Palmer
Full Text Available Active screening by mobile teams is considered the best method for detecting human African trypanosomiasis (HAT caused by Trypanosoma brucei gambiense but the current funding context in many post-conflict countries limits this approach. As an alternative, non-specialist health care workers (HCWs in peripheral health facilities could be trained to identify potential cases who need testing based on their symptoms. We explored the predictive value of syndromic referral algorithms to identify symptomatic cases of HAT among a treatment-seeking population in Nimule, South Sudan.Symptom data from 462 patients (27 cases presenting for a HAT test via passive screening over a 7 month period were collected to construct and evaluate over 14,000 four item syndromic algorithms considered simple enough to be used by peripheral HCWs. For comparison, algorithms developed in other settings were also tested on our data, and a panel of expert HAT clinicians were asked to make referral decisions based on the symptom dataset. The best performing algorithms consisted of three core symptoms (sleep problems, neurological problems and weight loss, with or without a history of oedema, cervical adenopathy or proximity to livestock. They had a sensitivity of 88.9-92.6%, a negative predictive value of up to 98.8% and a positive predictive value in this context of 8.4-8.7%. In terms of sensitivity, these out-performed more complex algorithms identified in other studies, as well as the expert panel. The best-performing algorithm is predicted to identify about 9/10 treatment-seeking HAT cases, though only 1/10 patients referred would test positive.In the absence of regular active screening, improving referrals of HAT patients through other means is essential. Systematic use of syndromic algorithms by peripheral HCWs has the potential to increase case detection and would increase their participation in HAT programmes. The algorithms proposed here, though promising, should be
A novel time-domain signal processing algorithm for real time ventricular fibrillation detection
Monte, G. E.; Scarone, N. C.; Liscovsky, P. O.; Rotter S/N, P.
2011-12-01
This paper presents an application of a novel algorithm for real time detection of ECG pathologies, especially ventricular fibrillation. It is based on segmentation and labeling process of an oversampled signal. After this treatment, analyzing sequence of segments, global signal behaviours are obtained in the same way like a human being does. The entire process can be seen as a morphological filtering after a smart data sampling. The algorithm does not require any ECG digital signal pre-processing, and the computational cost is low, so it can be embedded into the sensors for wearable and permanent applications. The proposed algorithms could be the input signal description to expert systems or to artificial intelligence software in order to detect other pathologies.
An infrared target detection algorithm based on lateral inhibition and singular value decomposition
Li, Yun; Song, Yong; Zhao, Yufei; Zhao, Shangnan; Li, Xu; Li, Lin; Tang, Songyuan
2017-09-01
This paper proposes an infrared target detection algorithm based on lateral inhibition (LI) and singular value decomposition (SVD). Firstly, a local structure descriptor based on SVD of gradient domain is constructed, which reflects basic structures of the local regions of an infrared image. Then, LI network is modified by combining LI with the local structure descriptor for enhancing target and suppressing background. Meanwhile, to calculate lateral inhibition coefficients adaptively, the direction parameters are determined by the dominant orientations obtained from SVD. Experimental results show that, compared with the typical algorithms, the proposed algorithm not only can detect small target or area target under complex backgrounds, but also has excellent abilities of background suppression and target enhancement.
Shashwat Pathak
2016-09-01
Full Text Available This paper proposes and evaluates an algorithm to automatically detect the cataracts from color images in adult human subjects. Currently, methods available for cataract detection are based on the use of either fundus camera or Digital Single-Lens Reflex (DSLR camera; both are very expensive. The main motive behind this work is to develop an inexpensive, robust and convenient algorithm which in conjugation with suitable devices will be able to diagnose the presence of cataract from the true color images of an eye. An algorithm is proposed for cataract screening based on texture features: uniformity, intensity and standard deviation. These features are first computed and mapped with diagnostic opinion by the eye expert to define the basic threshold of screening system and later tested on real subjects in an eye clinic. Finally, a tele-ophthamology model using our proposed system has been suggested, which confirms the telemedicine application of the proposed system.
A Node Influence Based Label Propagation Algorithm for Community Detection in Networks
Yan Xing
2014-01-01
Full Text Available Label propagation algorithm (LPA is an extremely fast community detection method and is widely used in large scale networks. In spite of the advantages of LPA, the issue of its poor stability has not yet been well addressed. We propose a novel node influence based label propagation algorithm for community detection (NIBLPA, which improves the performance of LPA by improving the node orders of label updating and the mechanism of label choosing when more than one label is contained by the maximum number of nodes. NIBLPA can get more stable results than LPA since it avoids the complete randomness of LPA. The experimental results on both synthetic and real networks demonstrate that NIBLPA maintains the efficiency of the traditional LPA algorithm, and, at the same time, it has a superior performance to some representative methods.
A new, fast algorithm for detecting protein coevolution using maximum compatible cliques
Rose Jonathan
2011-06-01
Full Text Available Abstract Background The MatrixMatchMaker algorithm was recently introduced to detect the similarity between phylogenetic trees and thus the coevolution between proteins. MMM finds the largest common submatrices between pairs of phylogenetic distance matrices, and has numerous advantages over existing methods of coevolution detection. However, these advantages came at the cost of a very long execution time. Results In this paper, we show that the problem of finding the maximum submatrix reduces to a multiple maximum clique subproblem on a graph of protein pairs. This allowed us to develop a new algorithm and program implementation, MMMvII, which achieved more than 600× speedup with comparable accuracy to the original MMM. Conclusions MMMvII will thus allow for more more extensive and intricate analyses of coevolution. Availability An implementation of the MMMvII algorithm is available at: http://www.uhnresearch.ca/labs/tillier/MMMWEBvII/MMMWEBvII.php
The utility of MAS5 expression summary and detection call algorithms
Wilson Claire L
2007-07-01
Full Text Available Abstract Background Used alone, the MAS5.0 algorithm for generating expression summaries has been criticized for high False Positive rates resulting from exaggerated variance at low intensities. Results Here we show, with replicated cell line data, that, when used alongside detection calls, MAS5 can be both selective and sensitive. A set of differentially expressed transcripts were identified that were found to be changing by MAS5, but unchanging by RMA and GCRMA. Subsequent analysis by real time PCR confirmed these changes. In addition, with the Latin square datasets often used to assess expression summary algorithms, filtered MAS5.0 was found to have performance approaching that of its peers. Conclusion When used alongside detection calls, MAS5 is a sensitive and selective algorithm for identifying differentially expressed genes.
A reliable cluster detection technique using photometric redshifts: introducing the 2TecX algorithm
van Breukelen, Caroline
2009-01-01
We present a new cluster detection algorithm designed for finding high-redshift clusters using optical/infrared imaging data. The algorithm has two main characteristics. First, it utilises each galaxy's full redshift probability function, instead of an estimate of the photometric redshift based on the peak of the probability function and an associated Gaussian error. Second, it identifies cluster candidates through cross-checking the results of two substantially different selection techniques (the name 2TecX representing the cross-check of the two techniques). These are adaptations of the Voronoi Tesselations and Friends-Of-Friends methods. Monte-Carlo simulations of mock catalogues show that cross-checking the cluster candidates found by the two techniques significantly reduces the detection of spurious sources. Furthermore, we examine the selection effects and relative strengths and weaknesses of either method. The simulations also allow us to fine-tune the algorithm's parameters, and define completeness an...
Detection and Estimation of Damage in Structures Using Imperialist Competitive Algorithm
A. Bagheri
2012-01-01
Full Text Available This paper presents a method for detection and estimation of structural damage on the basis of modal parameters of a damaged structure using imperialist competitive algorithm. The imperialist competitive algorithm was developed over the last few years in an attempt to overcome inherent limitations of traditional optimize method. In this research, imperialist competitive algorithm has been employed due to its favorable performance in detection of structural damages. The performance of the proposed method has been verified through using a benchmark problem provided by the IASC-ASCE Task Group on Structural Health Monitoring and a number of numerical examples. By way of comparison between location and amount of damage obtained from the proposed method and simulation model, it was concluded that the method is sensitive to the location and amount of damage. The results clearly revealed the superiority of the presented method in comparison with energy index method.
A New Algorithm for Detection of Cloudiness and Moon Affect Area
Dindar, Murat; Helhel, Selcuk; Ünal Akdemir, Kemal
2016-07-01
Cloud detection is a crucial issue for observatories already operating and during phase of the site selection. Sky Quality Meter (SQM) devices mostly use to determine parameters of the quality of sky such as cloudiness, light flux. But, those parameters do not give us exact information about the cloudiness and moon affects. In this study we improved a new cloudiness and moon affects area detection algorithm. The algorithm is based on image processing methods and different approaches applied to both day time and night time images to calculate the sky coverage. The new algorithm also implemented with Matlab by using the images taken by all sky camera located at TÜBİTAK National Observatory and results were given.
Azman Hamzah
2013-09-01
Full Text Available Computer vision systems have found wide application in foods processing industry to perform quality evaluation. The systems enable to replace human inspectors for the evaluation of a variety of quality attributes. This paper describes the implementation of the Fast Fourier Transform and Kalman filtering algorithms to detect the glutinous rice flour slurry (GRFS gelatinization in an enzymatic „dodol. processing. The onset of the GRFS gelatinization is critical in determining the quality of an enzymatic „dodol.. Combinations of these two algorithms were able to detect the gelatinization of the GRFS. The result shows that the gelatinization of the GRFS was at the time range of 11.75 minutes to 14.75 minutes for 24 batches of processing. This paper will highlight the capability of computer vision using our proposed algorithms in monitoring and controlling of an enzymatic „dodol. processing via image processing technology.
Azman Hamzah
2007-11-01
Full Text Available Computer vision systems have found wide application in foods processing industry to perform the quality evaluation. The systems enable to replace human inspectors for the evaluation of a variety of quality attributes. This paper describes the implementation of the Fast Fourier Transform and Kalman filtering algorithms to detect the glutinous rice flour slurry (GRFS gelatinization in an enzymatic ‘dodol’ processing. The onset of the GRFS gelatinization is critical in determining the quality of an enzymatic ‘dodol’. Combinations of these two algorithms were able to detect the gelatinization of the GRFS. The result shows that the gelatinization of the GRFS was at the time range of 11.75 minutes to 15.33 minutes for 20 batches of processing. This paper will highlight the capability of computer vision using our proposed algorithms in monitoring and controlling of an enzymatic ‘dodol’ processing via image processing technology.
Indra Kanta Maitra
2011-06-01
Full Text Available Many image processing techniques have been developed over the past two decades to help radiologists in diagnosing breast cancer. At the same time, many studies proven that an early diagnosis of breastcancer can increase the survival rate, thus making screening programmes a mandatory step for females.Radiologists have to examine a large number of images. Digital Mammogram has emerged as the most popular screening technique for early detection of Breast Cancer and other abnormalities. Raw digital mammograms are medical images that are difficult to interpret so we need to develop Computer Aided Diagnosis (CAD systems that will improve detection of abnormalities in mammogram images. Extraction of the breast region by delineation of the breast contour and pectoral muscle allows the search for abnormalities to be limited to the region of the breast without undue influence from the background of the mammogram. We need to performessential pre-processing steps to suppress artifacts, enhance the breast region and then extract breast region by the process of segmentation. In this paper we present a fully automated scheme for detection of abnormal masses by anatomical segmentation of Breast Region of Interest (ROI. We are using medio-lateral oblique (MLO view of mammograms. We have proposed a new homogeneity enhancement process namely Binary Homogeneity Enhancement Algorithm (BHEA, followed by an innovative approach for edge detection (EDA. Then obtain the breast boundary by using our proposed Breast Boundary Detection Algorithm (BBDA. After we use our proposed Pectoral Muscle Detection Algorithm (PMDA to suppress the pectoral muscle thus obtaining the breast ROI, we use our proposed Anatomical Segmentation of Breast ROI (ASB algorithm to differentiate various regions within the breast. After segregating the different breast regions we use our proposed Seeded Region Growing Algorithm (SRGA to isolate normal and abnormal regions in the breast tissue. If any
A Fast Inspection of Tool Electrode and Drilling Depth in EDM Drilling by Detection Line Algorithm
Kuo-Yi Huang
2008-01-01
The purpose of this study was to develop a novel measurement method using a machine vision system. Besides using image processing techniques, the proposed system employs a detection line algorithm that detects the tool electrode length and drilling depth of a workpiece accurately and effectively. Different boundaries of areas on the tool electrode are defined: a baseline between base and normal areas, a ND-line between normal and drilling areas (accumulating carbon area), and a DD-line betwee...
An Algorithm to Improve Test Answer Copying Detection Using the Omega Statistic
Maeda, Hotaka; Zhang, Bo
2017-01-01
The omega (?) statistic is reputed to be one of the best indices for detecting answer copying on multiple choice tests, but its performance relies on the accurate estimation of copier ability, which is challenging because responses from the copiers may have been contaminated. We propose an algorithm that aims to identify and delete the suspected…
Shao-Fei Jiang
2014-01-01
Full Text Available Optimization techniques have been applied to structural health monitoring and damage detection of civil infrastructures for two decades. The standard particle swarm optimization (PSO is easy to fall into the local optimum and such deficiency also exists in the multiparticle swarm coevolution optimization (MPSCO. This paper presents an improved MPSCO algorithm (IMPSCO firstly and then integrates it with Newmark’s algorithm to localize and quantify the structural damage by using the damage threshold proposed. To validate the proposed method, a numerical simulation and an experimental study of a seven-story steel frame were employed finally, and a comparison was made between the proposed method and the genetic algorithm (GA. The results show threefold: (1 the proposed method not only is capable of localization and quantification of damage, but also has good noise-tolerance; (2 the damage location can be accurately detected using the damage threshold proposed in this paper; and (3 compared with the GA, the IMPSCO algorithm is more efficient and accurate for damage detection problems in general. This implies that the proposed method is applicable and effective in the community of damage detection and structural health monitoring.
Robust Mokken Scale Analysis by Means of the Forward Search Algorithm for Outlier Detection
Zijlstra, Wobbe P.; van der Ark, L. Andries; Sijtsma, Klaas
2011-01-01
Exploratory Mokken scale analysis (MSA) is a popular method for identifying scales from larger sets of items. As with any statistical method, in MSA the presence of outliers in the data may result in biased results and wrong conclusions. The forward search algorithm is a robust diagnostic method for outlier detection, which we adapt here to…
Detection of the arcuate fasciculus in congenital amusia depends on the tractography algorithm
Joyce L Chen
2015-01-01
Full Text Available The advent of diffusion magnetic resonance imaging allows researchers to virtually dissect white matter fibre pathways in the brain in vivo. This, for example, allows us to characterize and quantify how fibre tracts differ across populations in health and disease, and change as a function of training. Based on diffusion MRI, prior literature reports the absence of the arcuate fasciculus (AF in some control individuals and as well in those with congenital amusia. The complete absence of such a major anatomical tract is surprising given the subtle impairments that characterize amusia. Thus, we hypothesize that failure to detect the AF in this population may relate to the tracking algorithm used, and is not necessarily reflective of their phenotype. Diffusion data in control and amusic individuals were analyzed using three different tracking algorithms: deterministic and probabilistic, the latter either modeling two or one fibre populations. Across the three algorithms, we replicate prior findings of a left greater than right AF volume, but do not find group differences or an interaction. We detect the AF in all individuals using the probabilistic 2-fibre model, however, tracking failed in some control and amusic individuals when deterministic tractography was applied. These findings show that the ability to detect the AF in our sample is dependent on the type of tractography algorithm. This raises the question of whether failure to detect the AF in prior studies may be unrelated to the underlying anatomy or phenotype.
Hanxiao Wu
2012-10-01
Full Text Available In this paper, we propose an application of a compressive imaging system to the problem of wide-area video surveillance systems. A parallel coded aperture compressive imaging system is proposed to reduce the needed high resolution coded mask requirements and facilitate the storage of the projection matrix. Random Gaussian, Toeplitz and binary phase coded masks are utilized to obtain the compressive sensing images. The corresponding motion targets detection and tracking algorithms directly using the compressive sampling images are developed. A mixture of Gaussian distribution is applied in the compressive image space to model the background image and for foreground detection. For each motion target in the compressive sampling domain, a compressive feature dictionary spanned by target templates and noises templates is sparsely represented. An l1 optimization algorithm is used to solve the sparse coefficient of templates. Experimental results demonstrate that low dimensional compressed imaging representation is sufficient to determine spatial motion targets. Compared with the random Gaussian and Toeplitz phase mask, motion detection algorithms using a random binary phase mask can yield better detection results. However using random Gaussian and Toeplitz phase mask can achieve high resolution reconstructed image. Our tracking algorithm can achieve a real time speed that is up to 10 times faster than that of the l1 tracker without any optimization.
Assessment of algorithms for mitosis detection in breast cancer histopathology images
Veta, Mitko; van Diest, Paul J.; Willems, Stefan M.
2014-01-01
inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues.In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described...
MEMS-based sensing and algorithm development for fall detection and gait analysis
Gupta, Piyush; Ramirez, Gabriel; Lie, Donald Y. C.; Dallas, Tim; Banister, Ron E.; Dentino, Andrew
2010-02-01
Falls by the elderly are highly detrimental to health, frequently resulting in injury, high medical costs, and even death. Using a MEMS-based sensing system, algorithms are being developed for detecting falls and monitoring the gait of elderly and disabled persons. In this study, wireless sensors utilize Zigbee protocols were incorporated into planar shoe insoles and a waist mounted device. The insole contains four sensors to measure pressure applied by the foot. A MEMS based tri-axial accelerometer is embedded in the insert and a second one is utilized by the waist mounted device. The primary fall detection algorithm is derived from the waist accelerometer. The differential acceleration is calculated from samples received in 1.5s time intervals. This differential acceleration provides the quantification via an energy index. From this index one may ascertain different gait and identify fall events. Once a pre-determined index threshold is exceeded, the algorithm will classify an event as a fall or a stumble. The secondary algorithm is derived from frequency analysis techniques. The analysis consists of wavelet transforms conducted on the waist accelerometer data. The insole pressure data is then used to underline discrepancies in the transforms, providing more accurate data for classifying gait and/or detecting falls. The range of the transform amplitude in the fourth iteration of a Daubechies-6 transform was found sufficient to detect and classify fall events.
CoSMOS: Performance of Kurtosis Algorithm for Radio Frequency Interference Detection and Mitigation
Misra, Sidharth; Kristensen, Steen Savstrup; Skou, Niels
2007-01-01
The performance of a previously developed algorithm for Radio Frequency Interference (RFI) detection and mitigation is experimentally evaluated. Results obtained from CoSMOS, an airborne campaign using a fully polarimetric L-band radiometer are analyzed for this purpose. Data is collected using two...
NIC: a robust background extraction algorithm for foreground detection in dynamic scenes
Huynh-The, Thien; Banos, Oresti; Lee, Sungyoung; Kang, Byeong Ho; Kim, Eun-Soo; Le-Tien, Thuong
2016-01-01
This paper presents a robust foreground detection method capable of adapting to different motion speeds in scenes. A key contribution of this paper is the background estimation using a proposed novel algorithm, neighbor-based intensity correction (NIC), that identifies and modifies the motion pixels
SEGMENTATION OF CT SCAN LUMBAR SPINE IMAGE USING MEDIAN FILTER AND CANNY EDGE DETECTION ALGORITHM
E.Punarselvam
2013-09-01
Full Text Available The lumbar vertebrae are the largest segments of the movable part of the vertebral column, they are elected L1 to L5, starting at the top. The spinal column, more commonly called the backbone, is made up primarily of vertebrae discs, and the spinal cord. Acting as a communication conduit for the brain, signals are transmitted and received through the spinal cord. It is otherwise known as vertebralcolumn consists of 24 separate bony vertebrae together with 5 fused vertebrae, it is the unique interaction between the solid and fluid components that provides the disc strength and flexibility required to bear loading of the lumbar spine. In this work the Segmentation of Spine Image using Median Filter and Canny Edge Detection Algorithm between lumbar spine CT scan spine disc image. The result shows thatthe canny edge detection algorithm produced better result when compared other edge detection algorithm. Finding the correct boundary in a noisy image of spine disc is still a difficult one. To find outabsolute edges from noisy images, the comparative result can be verified and validated with the standard medical values. The result shows that the canny edge detection algorithm performs well and produced a solution very nearer to the optimal solution. This method is vigorous for all kinds of noisy images.
Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis: Preprint
Zappala, D.; Tavner, P.; Crabtree, C.; Sheng, S.
2013-01-01
Improving the availability of wind turbines (WT) is critical to minimize the cost of wind energy, especially for offshore installations. As gearbox downtime has a significant impact on WT availabilities, the development of reliable and cost-effective gearbox condition monitoring systems (CMS) is of great concern to the wind industry. Timely detection and diagnosis of developing gear defects within a gearbox is an essential part of minimizing unplanned downtime of wind turbines. Monitoring signals from WT gearboxes are highly non-stationary as turbine load and speed vary continuously with time. Time-consuming and costly manual handling of large amounts of monitoring data represent one of the main limitations of most current CMSs, so automated algorithms are required. This paper presents a fault detection algorithm for incorporation into a commercial CMS for automatic gear fault detection and diagnosis. The algorithm allowed the assessment of gear fault severity by tracking progressive tooth gear damage during variable speed and load operating conditions of the test rig. Results show that the proposed technique proves efficient and reliable for detecting gear damage. Once implemented into WT CMSs, this algorithm can automate data interpretation reducing the quantity of information that WT operators must handle.
Subspace-Based Algorithms for Structural Identification, Damage Detection, and Sensor Data Fusion
Goursat Maurice
2007-01-01
Full Text Available This paper reports on the theory and practice of covariance-driven output-only and input/output subspace-based identification and detection algorithms. The motivating and investigated application domain is vibration-based structural analysis and health monitoring of mechanical, civil, and aeronautic structures.
An Efficient Algorithm for the Detection of Exposed and Hidden Wormhole Attack
ZUBAIR AHMED KHAN
2016-07-01
Full Text Available MANETs (Mobile Ad Hoc Networks are slowly integrating into our everyday lives, their most prominent uses are visible in the disaster and war struck areas where physical infrastructure is almost impossible or very hard to build. MANETs like other networks are facing the threat of malicious users and their activities. A number of attacks have been identified but the most severe of them is the wormhole attack which has the ability to succeed even in case of encrypted traffic and secure networks. Once wormhole is launched successfully, the severity increases by the fact that attackers can launch other attacks too. This paper presents a comprehensive algorithm for the detection of exposed as well as hidden wormhole attack while keeping the detection rate to maximum and at the same reducing false alarms. The algorithm does not require any extra hardware, time synchronization or any special type of nodes. The architecture consists of the combination of Routing Table, RTT (Round Trip Time and RSSI (Received Signal Strength Indicator for comprehensive detection of wormhole attack. The proposed technique is robust, light weight, has low resource requirements and provides real-time detection against the wormhole attack. Simulation results show that the algorithm is able to provide a higher detection rate, packet delivery ratio, negligible false alarms and is also better in terms of Ease of Implementation, Detection Accuracy/ Speed and processing overhead.
An Algorithm for Detection of DVB-T Signals Based on Their Second-Order Statistics
Jallon Pierre
2008-01-01
Full Text Available Abstract We propose in this paper a detection algorithm based on a cost function that jointly tests the correlation induced by the cyclic prefix and the fact that this correlation is time-periodic. In the first part of the paper, the cost function is introduced and some analytical results are given. In particular, the noise and multipath channel impacts on its values are theoretically analysed. In a second part of the paper, some asymptotic results are derived. A first exploitation of these results is used to build a detection test based on the false alarm probability. These results are also used to evaluate the impact of the number of cycle frequencies taken into account in the cost function on the detection performances. Thanks to numerical estimations, we have been able to estimate that the proposed algorithm detects DVB-T signals with an SNR of dB. As a comparison, and in the same context, the detection algorithm proposed by the 802.22 WG in 2006 is able to detect these signals with an SNR of dB.
An Algorithm for Detection of DVB-T Signals Based on Their Second-Order Statistics
Pierre Jallon
2008-03-01
Full Text Available We propose in this paper a detection algorithm based on a cost function that jointly tests the correlation induced by the cyclic prefix and the fact that this correlation is time-periodic. In the first part of the paper, the cost function is introduced and some analytical results are given. In particular, the noise and multipath channel impacts on its values are theoretically analysed. In a second part of the paper, some asymptotic results are derived. A first exploitation of these results is used to build a detection test based on the false alarm probability. These results are also used to evaluate the impact of the number of cycle frequencies taken into account in the cost function on the detection performances. Thanks to numerical estimations, we have been able to estimate that the proposed algorithm detects DVB-T signals with an SNR of Ã¢ÂˆÂ’12Ã¢Â€Â‰dB. As a comparison, and in the same context, the detection algorithm proposed by the 802.22 WG in 2006 is able to detect these signals with an SNR of Ã¢ÂˆÂ’8Ã¢Â€Â‰dB.
Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks
Ghasemian, Amir; Zhang, Pan; Clauset, Aaron; Moore, Cristopher; Peel, Leto
2016-07-01
The detection of communities within a dynamic network is a common means for obtaining a coarse-grained view of a complex system and for investigating its underlying processes. While a number of methods have been proposed in the machine learning and physics literature, we lack a theoretical analysis of their strengths and weaknesses, or of the ultimate limits on when communities can be detected. Here, we study the fundamental limits of detecting community structure in dynamic networks. Specifically, we analyze the limits of detectability for a dynamic stochastic block model where nodes change their community memberships over time, but where edges are generated independently at each time step. Using the cavity method, we derive a precise detectability threshold as a function of the rate of change and the strength of the communities. Below this sharp threshold, we claim that no efficient algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this threshold. The first uses belief propagation, which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the belief propagation equations. These results extend our understanding of the limits of community detection in an important direction, and introduce new mathematical tools for similar extensions to networks with other types of auxiliary information.
Dongha Lim
2014-01-01
Full Text Available Falls are a serious medical and social problem among the elderly. This has led to the development of automatic fall-detection systems. To detect falls, a fall-detection algorithm that combines a simple threshold method and hidden Markov model (HMM using 3-axis acceleration is proposed. To apply the proposed fall-detection algorithm and detect falls, a wearable fall-detection device has been designed and produced. Several fall-feature parameters of 3-axis acceleration are introduced and applied to a simple threshold method. Possible falls are chosen through the simple threshold and are applied to two types of HMM to distinguish between a fall and an activity of daily living (ADL. The results using the simple threshold, HMM, and combination of the simple method and HMM were compared and analyzed. The combination of the simple threshold method and HMM reduced the complexity of the hardware and the proposed algorithm exhibited higher accuracy than that of the simple threshold method.
Cremers, Charlotte H P; Dankbaar, Jan Willem; Vergouwen, Mervyn D I; Vos, Pieter C; Bennink, Edwin; Rinkel, Gabriel J E; Velthuis, Birgitta K; van der Schaaf, Irene C
2015-05-01
Tracer delay-sensitive perfusion algorithms in CT perfusion (CTP) result in an overestimation of the extent of ischemia in thromboembolic stroke. In diagnosing delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH), delayed arrival of contrast due to vasospasm may also overestimate the extent of ischemia. We investigated the diagnostic accuracy of tracer delay-sensitive and tracer delay-insensitive algorithms for detecting DCI. From a prospectively collected series of aSAH patients admitted between 2007-2011, we included patients with any clinical deterioration other than rebleeding within 21 days after SAH who underwent NCCT/CTP/CTA imaging. Causes of clinical deterioration were categorized into DCI and no DCI. CTP maps were calculated with tracer delay-sensitive and tracer delay-insensitive algorithms and were visually assessed for the presence of perfusion deficits by two independent observers with different levels of experience. The diagnostic value of both algorithms was calculated for both observers. Seventy-one patients were included. For the experienced observer, the positive predictive values (PPVs) were 0.67 for the delay-sensitive and 0.66 for the delay-insensitive algorithm, and the negative predictive values (NPVs) were 0.73 and 0.74. For the less experienced observer, PPVs were 0.60 for both algorithms, and NPVs were 0.66 for the delay-sensitive and 0.63 for the delay-insensitive algorithm. Test characteristics are comparable for tracer delay-sensitive and tracer delay-insensitive algorithms for the visual assessment of CTP in diagnosing DCI. This indicates that both algorithms can be used for this purpose.
Cremers, Charlotte H.P. [University Medical Center Utrecht, Department of Neurology and Neurosurgery, Room G03.232, Brain Center Rudolf Magnus Department of Neurology and Neurosurgery, PO Box 85500, Utrecht (Netherlands); University Medical Center Utrecht, Department of Radiology, Utrecht (Netherlands); Dankbaar, Jan Willem; Bennink, Edwin; Velthuis, Birgitta K.; Schaaf, Irene C. van der [University Medical Center Utrecht, Department of Radiology, Utrecht (Netherlands); Vergouwen, Mervyn D.I.; Rinkel, Gabriel J.E. [University Medical Center Utrecht, Department of Neurology and Neurosurgery, Room G03.232, Brain Center Rudolf Magnus Department of Neurology and Neurosurgery, PO Box 85500, Utrecht (Netherlands); Vos, Pieter C. [University Medical Center Utrecht, Image Sciences Institute, Utrecht (Netherlands)
2015-05-01
Tracer delay-sensitive perfusion algorithms in CT perfusion (CTP) result in an overestimation of the extent of ischemia in thromboembolic stroke. In diagnosing delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH), delayed arrival of contrast due to vasospasm may also overestimate the extent of ischemia. We investigated the diagnostic accuracy of tracer delay-sensitive and tracer delay-insensitive algorithms for detecting DCI. From a prospectively collected series of aSAH patients admitted between 2007-2011, we included patients with any clinical deterioration other than rebleeding within 21 days after SAH who underwent NCCT/CTP/CTA imaging. Causes of clinical deterioration were categorized into DCI and no DCI. CTP maps were calculated with tracer delay-sensitive and tracer delay-insensitive algorithms and were visually assessed for the presence of perfusion deficits by two independent observers with different levels of experience. The diagnostic value of both algorithms was calculated for both observers. Seventy-one patients were included. For the experienced observer, the positive predictive values (PPVs) were 0.67 for the delay-sensitive and 0.66 for the delay-insensitive algorithm, and the negative predictive values (NPVs) were 0.73 and 0.74. For the less experienced observer, PPVs were 0.60 for both algorithms, and NPVs were 0.66 for the delay-sensitive and 0.63 for the delay-insensitive algorithm. Test characteristics are comparable for tracer delay-sensitive and tracer delay-insensitive algorithms for the visual assessment of CTP in diagnosing DCI. This indicates that both algorithms can be used for this purpose. (orig.)
Guo, Wei; Li, Qiang
2014-09-01
The purpose of this study is to reveal how the performance of lung nodule segmentation algorithm impacts the performance of lung nodule detection, and to provide guidelines for choosing an appropriate segmentation algorithm with appropriate parameters in a computer-aided detection (CAD) scheme. The database consisted of 85 CT scans with 111 nodules of 3 mm or larger in diameter from the standard CT lung nodule database created by the Lung Image Database Consortium. The initial nodule candidates were identified as those with strong response to a selective nodule enhancement filter. A uniform viewpoint reformation technique was applied to a three-dimensional nodule candidate to generate 24 two-dimensional (2D) reformatted images, which would be used to effectively distinguish between true nodules and false positives. Six different algorithms were employed to segment the initial nodule candidates in the 2D reformatted images. Finally, 2D features from the segmented areas in the 24 reformatted images were determined, selected, and classified for removal of false positives. Therefore, there were six similar CAD schemes, in which only the segmentation algorithms were different. The six segmentation algorithms included the fixed thresholding (FT), Otsu thresholding (OTSU), fuzzy C-means (FCM), Gaussian mixture model (GMM), Chan and Vese model (CV), and local binary fitting (LBF). The mean Jaccard index and the mean absolute distance (Dmean) were employed to evaluate the performance of segmentation algorithms, and the number of false positives at a fixed sensitivity was employed to evaluate the performance of the CAD schemes. For the segmentation algorithms of FT, OTSU, FCM, GMM, CV, and LBF, the highest mean Jaccard index between the segmented nodule and the ground truth were 0.601, 0.586, 0.588, 0.563, 0.543, and 0.553, respectively, and the corresponding Dmean were 1.74, 1.80, 2.32, 2.80, 3.48, and 3.18 pixels, respectively. With these segmentation results of the six
Tummala Pradeep
2011-11-01
Full Text Available This paper investigates the use of variable learning rate back-propagation algorithm and Levenberg-Marquardt back-propagation algorithm in Intrusion detection system for detecting attacks. Inthe present study, these 2 neural network (NN algorithms are compared according to their speed,accuracy and, performance using mean squared error (MSE (Closer the value of MSE to 0, higher willbe the performance. Based on the study and test results, the Levenberg-Marquardt algorithm has been found to be faster and having more accuracy and performance than variable learning rate backpropagation algorithm.
Al-Kaff, Abdulla; García, Fernando; Martín, David; De La Escalera, Arturo; Armingol, José María
2017-01-01
One of the most challenging problems in the domain of autonomous aerial vehicles is the designing of a robust real-time obstacle detection and avoidance system. This problem is complex, especially for the micro and small aerial vehicles, that is due to the Size, Weight and Power (SWaP) constraints. Therefore, using lightweight sensors (i.e., Digital camera) can be the best choice comparing with other sensors; such as laser or radar.For real-time applications, different works are based on stereo cameras in order to obtain a 3D model of the obstacles, or to estimate their depth. Instead, in this paper, a method that mimics the human behavior of detecting the collision state of the approaching obstacles using monocular camera is proposed. The key of the proposed algorithm is to analyze the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. During the Aerial Vehicle (UAV) motion, the detection algorithm estimates the changes in the size of the area of the approaching obstacles. First, the method detects the feature points of the obstacles, then extracts the obstacles that have the probability of getting close toward the UAV. Secondly, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, by estimating the obstacle 2D position in the image and combining with the tracked waypoints, the UAV performs the avoidance maneuver. The proposed algorithm was evaluated by performing real indoor and outdoor flights, and the obtained results show the accuracy of the proposed algorithm compared with other related works. PMID:28481277
Al-Kaff, Abdulla; García, Fernando; Martín, David; De La Escalera, Arturo; Armingol, José María
2017-05-07
One of the most challenging problems in the domain of autonomous aerial vehicles is the designing of a robust real-time obstacle detection and avoidance system. This problem is complex, especially for the micro and small aerial vehicles, that is due to the Size, Weight and Power (SWaP) constraints. Therefore, using lightweight sensors (i.e., Digital camera) can be the best choice comparing with other sensors; such as laser or radar.For real-time applications, different works are based on stereo cameras in order to obtain a 3D model of the obstacles, or to estimate their depth. Instead, in this paper, a method that mimics the human behavior of detecting the collision state of the approaching obstacles using monocular camera is proposed. The key of the proposed algorithm is to analyze the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. During the Aerial Vehicle (UAV) motion, the detection algorithm estimates the changes in the size of the area of the approaching obstacles. First, the method detects the feature points of the obstacles, then extracts the obstacles that have the probability of getting close toward the UAV. Secondly, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, by estimating the obstacle 2D position in the image and combining with the tracked waypoints, the UAV performs the avoidance maneuver. The proposed algorithm was evaluated by performing real indoor and outdoor flights, and the obtained results show the accuracy of the proposed algorithm compared with other related works.
Kanaya Shigehiko
2006-04-01
Full Text Available Abstract Background After complete sequencing of a number of genomes the focus has now turned to proteomics. Advanced proteomics technologies such as two-hybrid assay, mass spectrometry etc. are producing huge data sets of protein-protein interactions which can be portrayed as networks, and one of the burning issues is to find protein complexes in such networks. The enormous size of protein-protein interaction (PPI networks warrants development of efficient computational methods for extraction of significant complexes. Results This paper presents an algorithm for detection of protein complexes in large interaction networks. In a PPI network, a node represents a protein and an edge represents an interaction. The input to the algorithm is the associated matrix of an interaction network and the outputs are protein complexes. The complexes are determined by way of finding clusters, i. e. the densely connected regions in the network. We also show and analyze some protein complexes generated by the proposed algorithm from typical PPI networks of Escherichia coli and Saccharomyces cerevisiae. A comparison between a PPI and a random network is also performed in the context of the proposed algorithm. Conclusion The proposed algorithm makes it possible to detect clusters of proteins in PPI networks which mostly represent molecular biological functional units. Therefore, protein complexes determined solely based on interaction data can help us to predict the functions of proteins, and they are also useful to understand and explain certain biological processes.
Fuhrmann Alpert, Galit; Manor, Ran; Spanier, Assaf B; Deouell, Leon Y; Geva, Amir B
2014-08-01
Brain computer interface applications, developed for both healthy and clinical populations, critically depend on decoding brain activity in single trials. The goal of the present study was to detect distinctive spatiotemporal brain patterns within a set of event related responses. We introduce a novel classification algorithm, the spatially weighted FLD-PCA (SWFP), which is based on a two-step linear classification of event-related responses, using fisher linear discriminant (FLD) classifier and principal component analysis (PCA) for dimensionality reduction. As a benchmark algorithm, we consider the hierarchical discriminant component Analysis (HDCA), introduced by Parra, et al. 2007. We also consider a modified version of the HDCA, namely the hierarchical discriminant principal component analysis algorithm (HDPCA). We compare single-trial classification accuracies of all the three algorithms, each applied to detect target images within a rapid serial visual presentation (RSVP, 10 Hz) of images from five different object categories, based on single-trial brain responses. We find a systematic superiority of our classification algorithm in the tested paradigm. Additionally, HDPCA significantly increases classification accuracies compared to the HDCA. Finally, we show that presenting several repetitions of the same image exemplars improve accuracy, and thus may be important in cases where high accuracy is crucial.
rSW-seq: Algorithm for detection of copy number alterations in deep sequencing data
Kim Tae-Min
2010-08-01
Full Text Available Abstract Background Recent advances in sequencing technologies have enabled generation of large-scale genome sequencing data. These data can be used to characterize a variety of genomic features, including the DNA copy number profile of a cancer genome. A robust and reliable method for screening chromosomal alterations would allow a detailed characterization of the cancer genome with unprecedented accuracy. Results We develop a method for identification of copy number alterations in a tumor genome compared to its matched control, based on application of Smith-Waterman algorithm to single-end sequencing data. In a performance test with simulated data, our algorithm shows >90% sensitivity and >90% precision in detecting a single copy number change that contains approximately 500 reads for the normal sample. With 100-bp reads, this corresponds to a ~50 kb region for 1X genome coverage of the human genome. We further refine the algorithm to develop rSW-seq, (recursive Smith-Waterman-seq to identify alterations in a complex configuration, which are commonly observed in the human cancer genome. To validate our approach, we compare our algorithm with an existing algorithm using simulated and publicly available datasets. We also compare the sequencing-based profiles to microarray-based results. Conclusion We propose rSW-seq as an efficient method for detecting copy number changes in the tumor genome.
Tao Wu
2017-03-01
Full Text Available Positive obstacles will cause damage to field robotics during traveling in field. Field autonomous land vehicle is a typical field robotic. This article presents a feature matching and fusion-based algorithm to detect obstacles using LiDARs for field autonomous land vehicles. There are three main contributions: (1 A novel setup method of compact LiDAR is introduced. This method improved the LiDAR data density and reduced the blind region of the LiDAR sensor. (2 A mathematical model is deduced under this new setup method. The ideal scan line is generated by using the deduced mathematical model. (3 Based on the proposed mathematical model, a feature matching and fusion (FMAF-based algorithm is presented in this article, which is employed to detect obstacles. Experimental results show that the performance of the proposed algorithm is robust and stable, and the computing time is reduced by an order of two magnitudes by comparing with other exited algorithms. This algorithm has been perfectly applied to our autonomous land vehicle, which has won the champion in the challenge of Chinese “Overcome Danger 2014” ground unmanned vehicle.
何建军; 任震; 黄雯莹; 周宏; 林涛
1999-01-01
With a complex wavelet function, a new real-time recursive algorithm of wavelet transform (WT) is analyzed in detail. Compared with the existing recursive algorithm in two directions, the computing time is greatly redueed in response to faults signals in power systems, and the same recursive algorithm can be generalized to other wavelet functions. With the phases and magnitudes of complex WT coefficients under the fast recursive algorithm, a method to detect faults signals of power systems is presented. Lastly, the analyzing results of some signals show that it is effective and practical for the complex wavelet and its real-time recursive algorithm to detect faults of power systems.
A Novel Algorithm for Intrusion Detection Based on RASL Model Checking
Weijun Zhu
2013-01-01
Full Text Available The interval temporal logic (ITL model checking (MC technique enhances the power of intrusion detection systems (IDSs to detect concurrent attacks due to the strong expressive power of ITL. However, an ITL formula suffers from difficulty in the description of the time constraints between different actions in the same attack. To address this problem, we formalize a novel real-time interval temporal logic—real-time attack signature logic (RASL. Based on such a new logic, we put forward a RASL model checking algorithm. Furthermore, we use RASL formulas to describe attack signatures and employ discrete timed automata to create an audit log. As a result, RASL model checking algorithm can be used to automatically verify whether the automata satisfy the formulas, that is, whether the audit log coincides with the attack signatures. The simulation experiments show that the new approach effectively enhances the detection power of the MC-based intrusion detection methods for a number of telnet attacks, p-trace attacks, and the other sixteen types of attacks. And these experiments indicate that the new algorithm can find several types of real-time attacks, whereas the existing MC-based intrusion detection approaches cannot do that.
Raghuram, Jayaram; Miller, David J; Kesidis, George
2014-07-01
We propose a method for detecting anomalous domain names, with focus on algorithmically generated domain names which are frequently associated with malicious activities such as fast flux service networks, particularly for bot networks (or botnets), malware, and phishing. Our method is based on learning a (null hypothesis) probability model based on a large set of domain names that have been white listed by some reliable authority. Since these names are mostly assigned by humans, they are pronounceable, and tend to have a distribution of characters, words, word lengths, and number of words that are typical of some language (mostly English), and often consist of words drawn from a known lexicon. On the other hand, in the present day scenario, algorithmically generated domain names typically have distributions that are quite different from that of human-created domain names. We propose a fully generative model for the probability distribution of benign (white listed) domain names which can be used in an anomaly detection setting for identifying putative algorithmically generated domain names. Unlike other methods, our approach can make detections without considering any additional (latency producing) information sources, often used to detect fast flux activity. Experiments on a publicly available, large data set of domain names associated with fast flux service networks show encouraging results, relative to several baseline methods, with higher detection rates and low false positive rates.
Community Detection Algorithm Combining Stochastic Block Model and Attribute Data Clustering
Kataoka, Shun; Kobayashi, Takuto; Yasuda, Muneki; Tanaka, Kazuyuki
2016-11-01
We propose a new algorithm to detect the community structure in a network that utilizes both the network structure and vertex attribute data. Suppose we have the network structure together with the vertex attribute data, that is, the information assigned to each vertex associated with the community to which it belongs. The problem addressed this paper is the detection of the community structure from the information of both the network structure and the vertex attribute data. Our approach is based on the Bayesian approach that models the posterior probability distribution of the community labels. The detection of the community structure in our method is achieved by using belief propagation and an EM algorithm. We numerically verified the performance of our method using computer-generated networks and real-world networks.
Community Detection Algorithm Combining Stochastic Block Model and Attribute Data Clustering
Kataoka, Shun; Yasuda, Muneki; Tanaka, Kazuyuki
2016-01-01
We propose a new algorithm to detect the community structure in a network that utilizes both the network structure and vertex attribute data. Suppose we have the network structure together with the vertex attribute data, that is, the information assigned to each vertex associated with the community to which it belongs. The problem addressed this paper is the detection of the community structure from the information of both the network structure and the vertex attribute data. Our approach is based on the Bayesian approach that models the posterior probability distribution of the community labels. The detection of the community structure in our method is achieved by using belief propagation and an EM algorithm. We numerically verified the performance of our method using computer-generated networks and real-world networks.
A Real-Time Lane Detection Algorithm Based on Intelligent CCD Parameters Regulation
Ping-shu Ge
2012-01-01
Full Text Available Lane departure warning system (LDWS has been regarded as an efficient method to lessen the damages of road traffic accident resulting from driver fatigue or inattention. Lane detection is one of the key techniques for LDWS. To overcome the contradiction between complexity of algorithm and the real-time requirement for vehicle onboard system, this paper introduces a new lane detection method based on intelligent CCD parameters regulation. In order to improve the real-time capability of the system, a CCD parameters regulating method is proposed which enhances the contrast between lane line and road surfaces and reduces image noise, so it lays a good foundation for the following lane detection. Hough transform algorithm is improved by selection and classification of seed points. Finally the lane line is extracted through some restrictions. Experimental results verify the effectiveness of the proposed method, which improves not only real-time capability but also the accuracy of the system.
Evaluation of novel algorithm embedded in a wearable sEMG device for seizure detection
Conradsen, Isa; Beniczky, Sandor; Wolf, Peter;
2012-01-01
We implemented a modified version of a previously published algorithm for detection of generalized tonic-clonic seizures into a prototype wireless surface electromyography (sEMG) recording device. The method was modified to require minimum computational load, and two parameters were trained...... on prior sEMG data recorded with the device. Along with the normal sEMG recording, the device is able to set an alarm whenever the implemented algorithm detects a seizure. These alarms are annotated in the data file along with the signal. The device was tested at the Epilepsy Monitoring Unit (EMU......) at the Danish Epilepsy Center. Five patients were included in the study and two of them had generalized tonic-clonic seizures. All patients were monitored for 2–5 days. A double-blind study was made on the five patients. The overall result showed that the device detected four of seven seizures and had a false...
Clustering-boundary-detection algorithm based on center-of-gravity of neighborhood
Wang Gui Zhi
2013-07-01
Full Text Available The cluster boundary is a useful model, in order to identify the boundary effectively, according to the uneven distribution of data points int the epsilon neighborhood of boundary objects, this paper proposes a boundary detection algorithm ---- S-BOUND. Firstly, all the points in the epsilon neighborhood of the data objects are projected onto the boundary of the convex hull of the neighborhood, and then calculate the center of gravity of the neighborhood. Finally, detect the boundary object according to the degree of deviation of the center of gravity of the neighborhood with the object. The experimental results show that the S-BOUND algorithm can accurately detect a variety of clustering boundary and remove the noises, the time of performance is also better.
Reduced complexity and latency for a massive MIMO system using a parallel detection algorithm
Shoichi Higuchi
2017-09-01
Full Text Available In recent years, massive MIMO systems have been widely researched to realize high-speed data transmission. Since massive MIMO systems use a large number of antennas, these systems require huge complexity to detect the signal. In this paper, we propose a novel detection method for massive MIMO using parallel detection with maximum likelihood detection with QR decomposition and M-algorithm (QRM-MLD to reduce the complexity and latency. The proposed scheme obtains an R matrix after permutation of an H matrix and QR decomposition. The R matrix is also eliminated using a Gauss–Jordan elimination method. By using a modified R matrix, the proposed method can detect the transmitted signal using parallel detection. From the simulation results, the proposed scheme can achieve a reduced complexity and latency with a little degradation of the bit error rate (BER performance compared with the conventional method.
Hortos, William S.
2009-05-01
In previous work by the author, parameters across network protocol layers were selected as features in supervised algorithms that detect and identify certain intrusion attacks on wireless ad hoc sensor networks (WSNs) carrying multisensor data. The algorithms improved the residual performance of the intrusion prevention measures provided by any dynamic key-management schemes and trust models implemented among network nodes. The approach of this paper does not train algorithms on the signature of known attack traffic, but, instead, the approach is based on unsupervised anomaly detection techniques that learn the signature of normal network traffic. Unsupervised learning does not require the data to be labeled or to be purely of one type, i.e., normal or attack traffic. The approach can be augmented to add any security attributes and quantified trust levels, established during data exchanges among nodes, to the set of cross-layer features from the WSN protocols. A two-stage framework is introduced for the security algorithms to overcome the problems of input size and resource constraints. The first stage is an unsupervised clustering algorithm which reduces the payload of network data packets to a tractable size. The second stage is a traditional anomaly detection algorithm based on a variation of support vector machines (SVMs), whose efficiency is improved by the availability of data in the packet payload. In the first stage, selected algorithms are adapted to WSN platforms to meet system requirements for simple parallel distributed computation, distributed storage and data robustness. A set of mobile software agents, acting like an ant colony in securing the WSN, are distributed at the nodes to implement the algorithms. The agents move among the layers involved in the network response to the intrusions at each active node and trustworthy neighborhood, collecting parametric values and executing assigned decision tasks. This minimizes the need to move large amounts
Performance of Hull-Detection Algorithms For Proton Computed Tomography Reconstruction
Schultze, Blake; Censor, Yair; Schulte, Reinhard; Schubert, Keith Evan
2014-01-01
Proton computed tomography (pCT) is a novel imaging modality developed for patients receiving proton radiation therapy. The purpose of this work was to investigate hull-detection algorithms used for preconditioning of the large and sparse linear system of equations that needs to be solved for pCT image reconstruction. The hull-detection algorithms investigated here included silhouette/space carving (SC), modified silhouette/space carving (MSC), and space modeling (SM). Each was compared to the cone-beam version of filtered backprojection (FBP) used for hull-detection. Data for testing these algorithms included simulated data sets of a digital head phantom and an experimental data set of a pediatric head phantom obtained with a pCT scanner prototype at Loma Linda University Medical Center. SC was the fastest algorithm, exceeding the speed of FBP by more than 100 times. FBP was most sensitive to the presence of noise. Ongoing work will focus on optimizing threshold parameters in order to define a fast and effic...
Hadoop DDos攻击检测算法分析%Hadoop DDos Attack Detection Algorithm Analysis
赵晶玲; 张乃斌; 崔宝江; 兰芸
2013-01-01
Hadoop DDos attack detection algorithm should be researched for the condition that Hadoop platform may be suffered from DDos attack. Therefore the algorithms such as SVM, KNN, neural networks, Decision Tree, Naive Bayesian are to be researched. Resource usage information of Hadoop hosts on normal operation and under attack should be collected as a data set at ifrst. The above algorithms are used to analysis the data set and SVM shows the highest accuracy, which is up to 91.75%,for Hadoop DDos attack detection. Experimental results show that SVM is the most suitable algorithm for Hadoop platform DDos attack detection.%针对Hadoop平台可能遭受的DDos攻击，需要对Hadoop DDos攻击的检测算法进行研究需要对常用的SVM、KNN、神经网络、Decision Tree、Naive Bayesian算法进行研究。文章通过搜集主机正常运行时和遭受攻击时的资源使用信息作为数据集，运用上述算法进行分析后发现，SVM对Hadoop DDos攻击检测具有高达91.75%的准确率。实验结果表明，SVM是最适合Hadoop平台DDos攻击检测的算法。
Algorithms for detection of objects in image sequences captured from an airborne imaging system
Kasturi, Rangachar; Camps, Octavia; Tang, Yuan-Liang; Devadiga, Sadashiva; Gandhi, Tarak
1995-01-01
This research was initiated as a part of the effort at the NASA Ames Research Center to design a computer vision based system that can enhance the safety of navigation by aiding the pilots in detecting various obstacles on the runway during critical section of the flight such as a landing maneuver. The primary goal is the development of algorithms for detection of moving objects from a sequence of images obtained from an on-board video camera. Image regions corresponding to the independently moving objects are segmented from the background by applying constraint filtering on the optical flow computed from the initial few frames of the sequence. These detected regions are tracked over subsequent frames using a model based tracking algorithm. Position and velocity of the moving objects in the world coordinate is estimated using an extended Kalman filter. The algorithms are tested using the NASA line image sequence with six static trucks and a simulated moving truck and experimental results are described. Various limitations of the currently implemented version of the above algorithm are identified and possible solutions to build a practical working system are investigated.
A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features
P. Amudha
2015-01-01
Full Text Available Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC with Enhanced Particle Swarm Optimization (EPSO to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup’99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different.
Penalty Dynamic Programming Algorithm for Dim Targets Detection in Sensor Systems
2012-01-01
In order to detect and track multiple maneuvering dim targets in sensor systems, an improved dynamic programming track-before-detect algorithm (DP-TBD) called penalty DP-TBD (PDP-TBD) is proposed. The performances of tracking techniques are used as a feedback to the detection part. The feedback is constructed by a penalty term in the merit function, and the penalty term is a function of the possible target state estimation, which can be obtained by the tracking methods. With this feedback, th...
Inversion Algorithms and PS Detection in SAR Tomography, Case Study of Bucharest City
C. Dănişor
2016-06-01
Full Text Available Synthetic Aperture Radar (SAR tomography can reconstruct the elevation profile of each pixel based on a set of co-registered complex images of a scene. Its main advantage over classical interferometric methods consists in the capability to improve the detection of single persistent scatterers as well as to enable the detection of multiple scatterers interfering within the same pixel. In this paper, three tomographic algorithms are compared and applied to a dataset of 32 images to generate the elevation map of dominant scatterers from a scene. Targets which present stable proprieties over time - Persistent Scatterers (PS are then detected based on reflectivity functions reconstructed with Capon filtering.
A New Algorithm for Detecting the Transition Region on Noise Image
Wang Hui; Luo Jianping
2003-01-01
An algorithm of ramp width reduction based on the gray information of neighborhood pixels is proposed, which can sharpen the ramp edge effectively Then, a new gray-weighted gradient operator and the automatic method to determine its parameter are introduced when detecting the transition region of images. Gray-weighted gradient operator can not only make the correlation of gradient and gray information as big as possible, but also resist the noise in the images. Some experiments show that the algorithm in this paper can extract the transition regaon more effectively.
Detection and positioning of radioactive sources using a four-detector response algorithm
Willis, Michael J.; Skutnik, Steven E.; Hall, Howard L.
2014-12-11
A method for detecting and identifying radioactive gamma-ray sources along with determining a directional bearing is described. This method is based upon comparing the relative intensities of four detectors placed side by side in a four-quadrant formation, allowing for the system to take advantage of shadowing in the occluded array of detectors. Based on this shielding principle, a fuzzy logic algorithm is used to analyze the gross count response of each individual detector with respect to the other three. The result of this algorithm is a numerical result that can be converted to a directional bearing in a 360° x–y plane.