An Automated Energy Detection Algorithm Based on Morphological Filter Processing with a Modified Watershed Transform

Science.gov (United States)

2018-01-01

ARL-TR-8270 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Morphological Filter...Automated Energy Detection Algorithm Based on Morphological Filter Processing with a Modified Watershed Transform by Kwok F Tom Sensors and Electron...1 October 2016–30 September 2017 4. TITLE AND SUBTITLE An Automated Energy Detection Algorithm Based on Morphological Filter Processing with a

A Novel Algorithm for Intrusion Detection Based on RASL Model Checking

Directory of Open Access Journals (Sweden)

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.

THE APPROACHING TRAIN DETECTION ALGORITHM

OpenAIRE

S. V. Bibikov

2015-01-01

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 t...

Species-specific audio detection: a comparison of three template-based detection algorithms using random forests

Directory of Open Access Journals (Sweden)

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.

Hardware Implementation of a Modified Delay-Coordinate Mapping-Based QRS Complex Detection Algorithm

Directory of Open Access Journals (Sweden)

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.

An Anomaly Detection Algorithm of Cloud Platform Based on Self-Organizing Maps

Directory of Open Access Journals (Sweden)

Jun Liu

2016-01-01

Full Text Available Virtual machines (VM on a Cloud platform can be influenced by a variety of factors which can lead to decreased performance and downtime, affecting the reliability of the Cloud platform. Traditional anomaly detection algorithms and strategies for Cloud platforms have some flaws in their accuracy of detection, detection speed, and adaptability. In this paper, a dynamic and adaptive anomaly detection algorithm based on Self-Organizing Maps (SOM for virtual machines is proposed. A unified modeling method based on SOM to detect the machine performance within the detection region is presented, which avoids the cost of modeling a single virtual machine and enhances the detection speed and reliability of large-scale virtual machines in Cloud platform. The important parameters that affect the modeling speed are optimized in the SOM process to significantly improve the accuracy of the SOM modeling and therefore the anomaly detection accuracy of the virtual machine.

A Novel Immune-Inspired Shellcode Detection Algorithm Based on Hyperellipsoid Detectors

Directory of Open Access Journals (Sweden)

Tianliang Lu

2018-01-01

Full Text Available Shellcodes are machine language codes injected into target programs in the form of network packets or malformed files. Shellcodes can trigger buffer overflow vulnerability and execute malicious instructions. Signature matching technology used by antivirus software or intrusion detection system has low detection rate for unknown or polymorphic shellcodes; to solve such problem, an immune-inspired shellcode detection algorithm was proposed, named ISDA. Static analysis and dynamic analysis were both applied. The shellcodes were disassembled to assembly instructions during static analysis and, for dynamic analysis, the API function sequences of shellcodes were obtained by simulation execution to get the behavioral features of polymorphic shellcodes. The extracted features of shellcodes were encoded to antigens based on n-gram model. Immature detectors become mature after immune tolerance based on negative selection algorithm. To improve nonself space coverage rate, the immune detectors were encoded to hyperellipsoids. To generate better antibody offspring, the detectors were optimized through clonal selection algorithm with genetic mutation. Finally, shellcode samples were collected and tested, and result shows that the proposed method has higher detection accuracy for both nonencoded and polymorphic shellcodes.

Detection of honeycomb cell walls from measurement data based on Harris corner detection algorithm

Science.gov (United States)

Qin, Yan; Dong, Zhigang; Kang, Renke; Yang, Jie; Ayinde, Babajide O.

2018-06-01

A honeycomb core is a discontinuous material with a thin-wall structure—a characteristic that makes accurate surface measurement difficult. This paper presents a cell wall detection method based on the Harris corner detection algorithm using laser measurement data. The vertexes of honeycomb cores are recognized with two different methods: one method is the reduction of data density, and the other is the optimization of the threshold of the Harris corner detection algorithm. Each cell wall is then identified in accordance with the neighboring relationships of its vertexes. Experiments were carried out for different types and surface shapes of honeycomb cores, where the proposed method was proved effective in dealing with noise due to burrs and/or deformation of cell walls.

Advanced Oil Spill Detection Algorithms For Satellite Based Maritime Environment Monitoring

Science.gov (United States)

Radius, Andrea; Azevedo, Rui; Sapage, Tania; Carmo, Paulo

2013-12-01

During the last years, the increasing pollution occurrence and the alarming deterioration of the environmental health conditions of the sea, lead to the need of global monitoring capabilities, namely for marine environment management in terms of oil spill detection and indication of the suspected polluter. The sensitivity of Synthetic Aperture Radar (SAR) to the different phenomena on the sea, especially for oil spill and vessel detection, makes it a key instrument for global pollution monitoring. The SAR performances in maritime pollution monitoring are being operationally explored by a set of service providers on behalf of the European Maritime Safety Agency (EMSA), which has launched in 2007 the CleanSeaNet (CSN) project - a pan-European satellite based oil monitoring service. EDISOFT, which is from the beginning a service provider for CSN, is continuously investing in R&D activities that will ultimately lead to better algorithms and better performance on oil spill detection from SAR imagery. This strategy is being pursued through EDISOFT participation in the FP7 EC Sea-U project and in the Automatic Oil Spill Detection (AOSD) ESA project. The Sea-U project has the aim to improve the current state of oil spill detection algorithms, through the informative content maximization obtained with data fusion, the exploitation of different type of data/ sensors and the development of advanced image processing, segmentation and classification techniques. The AOSD project is closely related to the operational segment, because it is focused on the automation of the oil spill detection processing chain, integrating auxiliary data, like wind information, together with image and geometry analysis techniques. The synergy between these different objectives (R&D versus operational) allowed EDISOFT to develop oil spill detection software, that combines the operational automatic aspect, obtained through dedicated integration of the processing chain in the existing open source NEST

Detection of Human Impacts by an Adaptive Energy-Based Anisotropic Algorithm

Directory of Open Access Journals (Sweden)

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.

A Detection Algorithm for the BOC Signal Based on Quadrature Channel Correlation

Directory of Open Access Journals (Sweden)

Bo Qian

2018-01-01

Full Text Available In order to solve the problem of detecting a BOC signal, which uses a long-period pseudo random sequence, an algorithm is presented based on quadrature channel correlation. The quadrature channel correlation method eliminates the autocorrelation component of the carrier wave, allowing for the extraction of the absolute autocorrelation peaks of the BOC sequence. If the same lag difference and height difference exist for the adjacent peaks, the BOC signal can be detected effectively using a statistical analysis of the multiple autocorrelation peaks. The simulation results show that the interference of the carrier wave component is eliminated and the autocorrelation peaks of the BOC sequence are obtained effectively without demodulation. The BOC signal can be detected effectively when the SNR is greater than −12 dB. The detection ability can be improved further by increasing the number of sampling points. The higher the ratio of the square wave subcarrier speed to the pseudo random sequence speed is, the greater the detection ability is with a lower SNR. The algorithm presented in this paper is superior to the algorithm based on the spectral correlation.

Optimization of an Accelerometer and Gyroscope-Based Fall Detection Algorithm

Directory of Open Access Journals (Sweden)

Quoc T. Huynh

2015-01-01

Full Text Available Falling is a common and significant cause of injury in elderly adults (>65 yrs old, often leading to disability and death. In the USA, one in three of the elderly suffers from fall injuries annually. This study’s purpose is to develop, optimize, and assess the efficacy of a falls detection algorithm based upon a wireless, wearable sensor system (WSS comprised of a 3-axis accelerometer and gyroscope. For this study, the WSS is placed at the chest center to collect real-time motion data of various simulated daily activities (i.e., walking, running, stepping, and falling. Tests were conducted on 36 human subjects with a total of 702 different movements collected in a laboratory setting. Half of the dataset was used for development of the fall detection algorithm including investigations of critical sensor thresholds and the remaining dataset was used for assessment of algorithm sensitivity and specificity. Experimental results show that the algorithm detects falls compared to other daily movements with a sensitivity and specificity of 96.3% and 96.2%, respectively. The addition of gyroscope information enhances sensitivity dramatically from results in the literature as angular velocity changes provide further delineation of a fall event from other activities that may also experience high acceleration peaks.

GA-DoSLD: Genetic Algorithm Based Denial-of-Sleep Attack Detection in WSN

Directory of Open Access Journals (Sweden)

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.

Vibration-Based Damage Detection in Beams by Cooperative Coevolutionary Genetic Algorithm

Directory of Open Access Journals (Sweden)

Kittipong Boonlong

2014-03-01

Full Text Available Vibration-based damage detection, a nondestructive method, is based on the fact that vibration characteristics such as natural frequencies and mode shapes of structures are changed when the damage happens. This paper presents cooperative coevolutionary genetic algorithm (CCGA, which is capable for an optimization problem with a large number of decision variables, as the optimizer for the vibration-based damage detection in beams. In the CCGA, a minimized objective function is a numerical indicator of differences between vibration characteristics of the actual damage and those of the anticipated damage. The damage detection in a uniform cross-section cantilever beam, a uniform strength cantilever beam, and a uniform cross-section simply supported beam is used as the test problems. Random noise in the vibration characteristics is also considered in the damage detection. In the simulation analysis, the CCGA provides the superior solutions to those that use standard genetic algorithms presented in previous works, although it uses less numbers of the generated solutions in solution search. The simulation results reveal that the CCGA can efficiently identify the occurred damage in beams for all test problems including the damage detection in a beam with a large number of divided elements such as 300 elements.

Validation of vision-based obstacle detection algorithms for low-altitude helicopter flight

Science.gov (United States)

Suorsa, Raymond; Sridhar, Banavar

1991-01-01

A validation facility being used at the NASA Ames Research Center is described which is aimed at testing vision based obstacle detection and range estimation algorithms suitable for low level helicopter flight. The facility is capable of processing hundreds of frames of calibrated multicamera 6 degree-of-freedom motion image sequencies, generating calibrated multicamera laboratory images using convenient window-based software, and viewing range estimation results from different algorithms along with truth data using powerful window-based visualization software.

A Forest Early Fire Detection Algorithm Based on Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

CHENG Qiang

2014-03-01

Full Text Available Wireless Sensor Networks (WSN adopt GHz as their communication carrier, and it has been found that GHz carrier attenuation model in transmission path is associated with vegetation water content. In this paper, based on RSSI mechanism of WSN nodes we formed vegetation dehydration sensors. Through relationships between vegetation water content and carrier attenuation, we perceived forest vegetation water content variations and early fire gestation process, and established algorithms of early forest fires detection. Experiments confirm that wireless sensor networks can accurately perceive vegetation dehydration events and forest fire events. Simulation results show that, WSN dehydration perception channel (P2P representing 75 % amounts of carrier channel or more, it can meet the detection requirements, which presented a new algorithm of early forest fire detection.

Robust and unobtrusive algorithm based on position independence for step detection

Science.gov (United States)

Qiu, KeCheng; Li, MengYang; Luo, YiHan

2018-04-01

Running is becoming one of the most popular exercises among the people, monitoring steps can help users better understand their running process and improve exercise efficiency. In this paper, we design and implement a robust and unobtrusive algorithm based on position independence for step detection under real environment. It applies Butterworth filter to suppress high frequency interference and then employs the projection based on mathematics to transform system to solve the problem of unknown position of smartphone. Finally, using sliding window to suppress the false peak. The algorithm was tested for eight participants on the Android 7.0 platform. In our experiments, the results show that the proposed algorithm can achieve desired effect in spite of device pose.

Implementation of anomaly detection algorithms for detecting transmission control protocol synchronized flooding attacks

CSIR Research Space (South Africa)

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...

Detection of algorithmic trading

Science.gov (United States)

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.

A Robust Vision-based Runway Detection and Tracking Algorithm for Automatic UAV Landing

KAUST Repository

Abu Jbara, Khaled F.

2015-05-01

This work presents a novel real-time algorithm for runway detection and tracking applied to the automatic takeoff and landing of Unmanned Aerial Vehicles (UAVs). The algorithm is based on a combination of segmentation based region competition and the minimization of a specific energy function to detect and identify the runway edges from streaming video data. The resulting video-based runway position estimates are updated using a Kalman Filter, which can integrate other sensory information such as position and attitude angle estimates to allow a more robust tracking of the runway under turbulence. We illustrate the performance of the proposed lane detection and tracking scheme on various experimental UAV flights conducted by the Saudi Aerospace Research Center. Results show an accurate tracking of the runway edges during the landing phase under various lighting conditions. Also, it suggests that such positional estimates would greatly improve the positional accuracy of the UAV during takeoff and landing phases. The robustness of the proposed algorithm is further validated using Hardware in the Loop simulations with diverse takeoff and landing videos generated using a commercial flight simulator.

An Algorithm for Detection of DVB-T Signals Based on Their Second-Order Statistics

Directory of Open Access Journals (Sweden)

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 epileptic seizures detection algorithm based on the empirical mode decomposition of EEG.

Science.gov (United States)

Orosco, Lorena; Laciar, Eric; Correa, Agustina Garces; Torres, Abel; Graffigna, Juan P

2009-01-01

Epilepsy is a neurological disorder that affects around 50 million people worldwide. The seizure detection is an important component in the diagnosis of epilepsy. In this study, the Empirical Mode Decomposition (EMD) method was proposed on the development of an automatic epileptic seizure detection algorithm. The algorithm first computes the Intrinsic Mode Functions (IMFs) of EEG records, then calculates the energy of each IMF and performs the detection based on an energy threshold and a minimum duration decision. The algorithm was tested in 9 invasive EEG records provided and validated by the Epilepsy Center of the University Hospital of Freiburg. In 90 segments analyzed (39 with epileptic seizures) the sensitivity and specificity obtained with the method were of 56.41% and 75.86% respectively. It could be concluded that EMD is a promissory method for epileptic seizure detection in EEG records.

Optimal Seamline Detection for Orthoimage Mosaicking Based on DSM and Improved JPS Algorithm

Directory of Open Access Journals (Sweden)

Gang Chen

2018-05-01

Full Text Available Based on the digital surface model (DSM and jump point search (JPS algorithm, this study proposed a novel approach to detect the optimal seamline for orthoimage mosaicking. By threshold segmentation, DSM was first identified as ground regions and obstacle regions (e.g., buildings, trees, and cars. Then, the mathematical morphology method was used to make the edge of obstacles more prominent. Subsequently, the processed DSM was considered as a uniform-cost grid map, and the JPS algorithm was improved and employed to search for key jump points in the map. Meanwhile, the jump points would be evaluated according to an optimized function, finally generating a minimum cost path as the optimal seamline. Furthermore, the search strategy was modified to avoid search failure when the search map was completely blocked by obstacles in the search direction. Comparison of the proposed method and the Dijkstra’s algorithm was carried out based on two groups of image data with different characteristics. Results showed the following: (1 the proposed method could detect better seamlines near the centerlines of the overlap regions, crossing far fewer ground objects; (2 the efficiency and resource consumption were greatly improved since the improved JPS algorithm skips many image pixels without them being explicitly evaluated. In general, based on DSM, the proposed method combining threshold segmentation, mathematical morphology, and improved JPS algorithms was helpful for detecting the optimal seamline for orthoimage mosaicking.

Wavelet based edge detection algorithm for web surface inspection of coated board web

Energy Technology Data Exchange (ETDEWEB)

Barjaktarovic, M; Petricevic, S, E-mail: slobodan@etf.bg.ac.r [School of Electrical Engineering, Bulevar Kralja Aleksandra 73, 11000 Belgrade (Serbia)

2010-07-15

This paper presents significant improvement of the already installed vision system. System was designed for real time coated board inspection. The improvement is achieved with development of a new algorithm for edge detection. The algorithm is based on the redundant (undecimated) wavelet transform. Compared to the existing algorithm better delineation of edges is achieved. This yields to better defect detection probability and more accurate geometrical classification, which will provide additional reduction of waste. Also, algorithm will provide detailed classification and more reliably tracking of defects. This improvement requires minimal changes in processing hardware, only a replacement of the graphic card would be needed, adding only negligibly to the system cost. Other changes are accomplished entirely in the image processing software.

Algorithm for detecting violations of traffic rules based on computer vision approaches

Directory of Open Access Journals (Sweden)

Ibadov Samir

2017-01-01

Full Text Available We propose a new algorithm for automatic detect violations of traffic rules for improving the people safety on the unregulated pedestrian crossing. The algorithm uses multi-step proceedings. They are zebra detection, cars detection, and pedestrian detection. For car detection, we use faster R-CNN deep learning tool. The algorithm shows promising results in the detection violations of traffic rules.

Training nuclei detection algorithms with simple annotations

Directory of Open Access Journals (Sweden)

Henning Kost

2017-01-01

Full Text Available Background: Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible. Methods: We compared different approaches for training nuclei detection methods solely based on nucleus center markers. Such markers contain less accurate information, especially with regard to nuclear boundaries, but can be produced much easier and in greater quantities. The approaches use different automated sample extraction methods to derive image positions and class labels from nucleus center markers. In addition, the approaches use different automated sample selection methods to improve the detection quality of the classification algorithm and reduce the run time of the training process. We evaluated the approaches based on a previously published generic nuclei detection algorithm and a set of Ki-67-stained breast cancer images. Results: A Voronoi tessellation-based sample extraction method produced the best performing training sets. However, subsampling of the extracted training samples was crucial. Even simple class balancing improved the detection quality considerably. The incorporation of active learning led to a further increase in detection quality. Conclusions: With appropriate sample extraction and selection methods, nuclei detection algorithms trained on the basis of simple center marker annotations can produce comparable quality to algorithms trained on conventionally created training sets.

A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data.

Science.gov (United States)

Lieb, Florian; Stark, Hans-Georg; Thielemann, Christiane

2017-06-01

Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.

A Moving Object Detection Algorithm Based on Color Information

International Nuclear Information System (INIS)

Fang, X H; Xiong, W; Hu, B J; Wang, L T

2006-01-01

This paper designed a new algorithm of moving object detection for the aim of quick moving object detection and orientation, which used a pixel and its neighbors as an image vector to represent that pixel and modeled different chrominance component pixel as a mixture of Gaussians, and set up different mixture model of Gauss for different YUV chrominance components. In order to make full use of the spatial information, color segmentation and background model were combined. Simulation results show that the algorithm can detect intact moving objects even when the foreground has low contrast with background

An optimized outlier detection algorithm for jury-based grading of engineering design projects

DEFF Research Database (Denmark)

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...... (the base rule and the three additional conditions) play a role in the algorithm's performance and should be included in the algorithm. Because there is significant interaction between the base rule and the additional conditions, many acceptable combinations that balance the FPR and FNR can be found......, 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...

An Algorithm for Detection of DVB-T Signals Based on Their Second-Order Statistics

Directory of Open Access Journals (Sweden)

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 −12 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 −8 dB.

A Gaussian Process Based Online Change Detection Algorithm for Monitoring Periodic Time Series

Energy Technology Data Exchange (ETDEWEB)

Chandola, Varun [ORNL; Vatsavai, Raju [ORNL

2011-01-01

Online time series change detection is a critical component of many monitoring systems, such as space and air-borne remote sensing instruments, cardiac monitors, and network traffic profilers, which continuously analyze observations recorded by sensors. Data collected by such sensors typically has a periodic (seasonal) component. Most existing time series change detection methods are not directly applicable to handle such data, either because they are not designed to handle periodic time series or because they cannot operate in an online mode. We propose an online change detection algorithm which can handle periodic time series. The algorithm uses a Gaussian process based non-parametric time series prediction model and monitors the difference between the predictions and actual observations within a statistically principled control chart framework to identify changes. A key challenge in using Gaussian process in an online mode is the need to solve a large system of equations involving the associated covariance matrix which grows with every time step. The proposed algorithm exploits the special structure of the covariance matrix and can analyze a time series of length T in O(T^2) time while maintaining a O(T) memory footprint, compared to O(T^4) time and O(T^2) memory requirement of standard matrix manipulation methods. We experimentally demonstrate the superiority of the proposed algorithm over several existing time series change detection algorithms on a set of synthetic and real time series. Finally, we illustrate the effectiveness of the proposed algorithm for identifying land use land cover changes using Normalized Difference Vegetation Index (NDVI) data collected for an agricultural region in Iowa state, USA. Our algorithm is able to detect different types of changes in a NDVI validation data set (with ~80% accuracy) which occur due to crop type changes as well as disruptive changes (e.g., natural disasters).

IMPLEMENTATION OF INCIDENT DETECTION ALGORITHM BASED ON FUZZY LOGIC IN PTV VISSIM

Directory of Open Access Journals (Sweden)

Andrey Borisovich Nikolaev

2017-05-01

Full Text Available Traffic incident management is a major challenge in the management of movement, requiring constant attention and significant investment, as well as fast and accurate solutions in order to re-establish normal traffic conditions. Automatic control methods are becoming an important factor for the reduction of traffic congestion caused by an arising incident. In this paper, the algorithm of automatic detection incident based on fuzzy logic is implemented in the software PTV VISSIM. 9 different types of tests were conducted on the two lane road section segment with changing traffic conditions: the location of the road accident, loading of traffic. The main conclusion of the research is that the proposed algorithm for the incidents detection demonstrates good performance in the time of detection and false alarms

A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data

Science.gov (United States)

Lieb, Florian; Stark, Hans-Georg; Thielemann, Christiane

2017-06-01

Objective. Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. Approach. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. Main results. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. Significance. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.

A Pre-Detection Based Anti-Collision Algorithm with Adjustable Slot Size Scheme for Tag Identification

Directory of Open Access Journals (Sweden)

Chiu-Kuo LIANG

2015-06-01

Full Text Available One of the research areas in RFID systems is a tag anti-collision protocol; how to reduce identification time with a given number of tags in the field of an RFID reader. There are two types of tag anti-collision protocols for RFID systems: tree based algorithms and slotted aloha based algorithms. Many anti-collision algorithms have been proposed in recent years, especially in tree based protocols. However, there still have challenges on enhancing the system throughput and stability due to the underlying technologies had faced different limitation in system performance when network density is high. Particularly, the tree based protocols had faced the long identification delay. Recently, a Hybrid Hyper Query Tree (H2QT protocol, which is a tree based approach, was proposed and aiming to speedup tag identification in large scale RFID systems. The main idea of H2QT is to track the tag response and try to predict the distribution of tag IDs in order to reduce collisions. In this paper, we propose a pre-detection tree based algorithm, called the Adaptive Pre-Detection Broadcasting Query Tree algorithm (APDBQT, to avoid those unnecessary queries. Our proposed APDBQT protocol can reduce not only the collisions but the idle cycles as well by using pre-detection scheme and adjustable slot size mechanism. The simulation results show that our proposed technique provides superior performance in high density environments. It is shown that the APDBQT is effective in terms of increasing system throughput and minimizing identification delay.

On a Hopping-Points SVD and Hough Transform-Based Line Detection Algorithm for Robot Localization and Mapping

Directory of Open Access Journals (Sweden)

Abhijeet Ravankar

2016-05-01

Full Text Available Line detection is an important problem in computer vision, graphics and autonomous robot navigation. Lines detected using a laser range sensor (LRS mounted on a robot can be used as features to build a map of the environment, and later to localize the robot in the map, in a process known as Simultaneous Localization and Mapping (SLAM. We propose an efficient algorithm for line detection from LRS data using a novel hopping-points Singular Value Decomposition (SVD and Hough transform-based algorithm, in which SVD is applied to intermittent LRS points to accelerate the algorithm. A reverse-hop mechanism ensures that the end points of the line segments are accurately extracted. Line segments extracted from the proposed algorithm are used to form a map and, subsequently, LRS data points are matched with the line segments to localize the robot. The proposed algorithm eliminates the drawbacks of point-based matching algorithms like the Iterative Closest Points (ICP algorithm, the performance of which degrades with an increasing number of points. We tested the proposed algorithm for mapping and localization in both simulated and real environments, and found it to detect lines accurately and build maps with good self-localization.

A hardware-algorithm co-design approach to optimize seizure detection algorithms for implantable applications.

Science.gov (United States)

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. Copyright © 2010 Elsevier B.V. All rights reserved.

A density based algorithm to detect cavities and holes from planar points

Science.gov (United States)

Zhu, Jie; Sun, Yizhong; Pang, Yueyong

2017-12-01

Delaunay-based shape reconstruction algorithms are widely used in approximating the shape from planar points. However, these algorithms cannot ensure the optimality of varied reconstructed cavity boundaries and hole boundaries. This inadequate reconstruction can be primarily attributed to the lack of efficient mathematic formulation for the two structures (hole and cavity). In this paper, we develop an efficient algorithm for generating cavities and holes from planar points. The algorithm yields the final boundary based on an iterative removal of the Delaunay triangulation. Our algorithm is mainly divided into two steps, namely, rough and refined shape reconstructions. The rough shape reconstruction performed by the algorithm is controlled by a relative parameter. Based on the rough result, the refined shape reconstruction mainly aims to detect holes and pure cavities. Cavity and hole are conceptualized as a structure with a low-density region surrounded by the high-density region. With this structure, cavity and hole are characterized by a mathematic formulation called as compactness of point formed by the length variation of the edges incident to point in Delaunay triangulation. The boundaries of cavity and hole are then found by locating a shape gradient change in compactness of point set. The experimental comparison with other shape reconstruction approaches shows that the proposed algorithm is able to accurately yield the boundaries of cavity and hole with varying point set densities and distributions.

Analysis of the Chirplet Transform-Based Algorithm for Radar Detection of Accelerated Targets

Science.gov (United States)

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 (processing channels (elementary filters with respect to the frequency change rate) the gain in the signal-tonoise ratio exceeds 10 dB. A 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.

Improving Polyp Detection Algorithms for CT Colonography: Pareto Front Approach.

Science.gov (United States)

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.

WAVELET-BASED ALGORITHM FOR DETECTION OF BEARING FAULTS IN A GAS TURBINE ENGINE

Directory of Open Access Journals (Sweden)

Sergiy Enchev

2014-07-01

Full Text Available Presented is a gas turbine engine bearing diagnostic system that integrates information from various advanced vibration analysis techniques to achieve robust bearing health state awareness. This paper presents a computational algorithm for identifying power frequency variations and integer harmonics by using wavelet-based transform. The continuous wavelet transform with  the complex Morlet wavelet is adopted to detect the harmonics presented in a power signal. The algorithm based on the discrete stationary wavelet transform is adopted to denoise the wavelet ridges.

Geostationary Sensor Based Forest Fire Detection and Monitoring: An Improved Version of the SFIDE Algorithm

Directory of Open Access Journals (Sweden)

Valeria Di Biase

2018-05-01

Full Text Available The paper aims to present the results obtained in the development of a system allowing for the detection and monitoring of forest fires and the continuous comparison of their intensity when several events occur simultaneously—a common occurrence in European Mediterranean countries during the summer season. The system, called SFIDE (Satellite FIre DEtection, exploits a geostationary satellite sensor (SEVIRI, Spinning Enhanced Visible and InfraRed Imager, on board of MSG, Meteosat Second Generation, satellite series. The algorithm was developed several years ago in the framework of a project (SIGRI funded by the Italian Space Agency (ASI. This algorithm has been completely reviewed in order to enhance its efficiency by reducing false alarms rate preserving a high sensitivity. Due to the very low spatial resolution of SEVIRI images (4 × 4 km2 at Mediterranean latitude the sensitivity of the algorithm should be very high to detect even small fires. The improvement of the algorithm has been obtained by: introducing the sun elevation angle in the computation of the preliminary thresholds to identify potential thermal anomalies (hot spots, introducing a contextual analysis in the detection of clouds and in the detection of night-time fires. The results of the algorithm have been validated in the Sardinia region by using ground true data provided by the regional Corpo Forestale e di Vigilanza Ambientale (CFVA. A significant reduction of the commission error (less than 10% has been obtained with respect to the previous version of the algorithm and also with respect to fire-detection algorithms based on low earth orbit satellites.

A feature matching and fusion-based positive obstacle detection algorithm for field autonomous land vehicles

Directory of Open Access Journals (Sweden)

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.

Sum of the Magnitude for Hard Decision Decoding Algorithm Based on Loop Update Detection

Science.gov (United States)

Meng, Jiahui; Zhao, Danfeng; Tian, Hai; Zhang, Liang

2018-01-01

In order to improve the performance of non-binary low-density parity check codes (LDPC) hard decision decoding algorithm and to reduce the complexity of decoding, a sum of the magnitude for hard decision decoding algorithm based on loop update detection is proposed. This will also ensure the reliability, stability and high transmission rate of 5G mobile communication. The algorithm is based on the hard decision decoding algorithm (HDA) and uses the soft information from the channel to calculate the reliability, while the sum of the variable nodes’ (VN) magnitude is excluded for computing the reliability of the parity checks. At the same time, the reliability information of the variable node is considered and the loop update detection algorithm is introduced. The bit corresponding to the error code word is flipped multiple times, before this is searched in the order of most likely error probability to finally find the correct code word. Simulation results show that the performance of one of the improved schemes is better than the weighted symbol flipping (WSF) algorithm under different hexadecimal numbers by about 2.2 dB and 2.35 dB at the bit error rate (BER) of 10−5 over an additive white Gaussian noise (AWGN) channel, respectively. Furthermore, the average number of decoding iterations is significantly reduced. PMID:29342963

Sum of the Magnitude for Hard Decision Decoding Algorithm Based on Loop Update Detection.

Science.gov (United States)

Meng, Jiahui; Zhao, Danfeng; Tian, Hai; Zhang, Liang

2018-01-15

In order to improve the performance of non-binary low-density parity check codes (LDPC) hard decision decoding algorithm and to reduce the complexity of decoding, a sum of the magnitude for hard decision decoding algorithm based on loop update detection is proposed. This will also ensure the reliability, stability and high transmission rate of 5G mobile communication. The algorithm is based on the hard decision decoding algorithm (HDA) and uses the soft information from the channel to calculate the reliability, while the sum of the variable nodes' (VN) magnitude is excluded for computing the reliability of the parity checks. At the same time, the reliability information of the variable node is considered and the loop update detection algorithm is introduced. The bit corresponding to the error code word is flipped multiple times, before this is searched in the order of most likely error probability to finally find the correct code word. Simulation results show that the performance of one of the improved schemes is better than the weighted symbol flipping (WSF) algorithm under different hexadecimal numbers by about 2.2 dB and 2.35 dB at the bit error rate (BER) of 10 -5 over an additive white Gaussian noise (AWGN) channel, respectively. Furthermore, the average number of decoding iterations is significantly reduced.

Sum of the Magnitude for Hard Decision Decoding Algorithm Based on Loop Update Detection

Directory of Open Access Journals (Sweden)

Jiahui Meng

2018-01-01

Full Text Available In order to improve the performance of non-binary low-density parity check codes (LDPC hard decision decoding algorithm and to reduce the complexity of decoding, a sum of the magnitude for hard decision decoding algorithm based on loop update detection is proposed. This will also ensure the reliability, stability and high transmission rate of 5G mobile communication. The algorithm is based on the hard decision decoding algorithm (HDA and uses the soft information from the channel to calculate the reliability, while the sum of the variable nodes’ (VN magnitude is excluded for computing the reliability of the parity checks. At the same time, the reliability information of the variable node is considered and the loop update detection algorithm is introduced. The bit corresponding to the error code word is flipped multiple times, before this is searched in the order of most likely error probability to finally find the correct code word. Simulation results show that the performance of one of the improved schemes is better than the weighted symbol flipping (WSF algorithm under different hexadecimal numbers by about 2.2 dB and 2.35 dB at the bit error rate (BER of 10−5 over an additive white Gaussian noise (AWGN channel, respectively. Furthermore, the average number of decoding iterations is significantly reduced.

Detection algorithm for glass bottle mouth defect by continuous wavelet transform based on machine vision

Science.gov (United States)

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.

ANOMALY DETECTION IN NETWORKING USING HYBRID ARTIFICIAL IMMUNE ALGORITHM

Directory of Open Access Journals (Sweden)

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 community detection algorithm using greedy surprise maximization

International Nuclear Information System (INIS)

Jiang, Yawen; Jia, Caiyan; Yu, Jian

2014-01-01

Community detection is an important and crucial problem in complex network analysis. Although classical modularity function optimization approaches are widely used for identifying communities, the modularity function (Q) suffers from its resolution limit. Recently, the surprise function (S) was experimentally proved to be better than the Q function. However, up until now, there has been no algorithm available to perform searches to directly determine the maximal surprise values. In this paper, considering the superiority of the S function over the Q function, we propose an efficient community detection algorithm called AGSO (algorithm based on greedy surprise optimization) and its improved version FAGSO (fast-AGSO), which are based on greedy surprise optimization and do not suffer from the resolution limit. In addition, (F)AGSO does not need the number of communities K to be specified in advance. Tests on experimental networks show that (F)AGSO is able to detect optimal partitions in both simple and even more complex networks. Moreover, algorithms based on surprise maximization perform better than those algorithms based on modularity maximization, including Blondel–Guillaume–Lambiotte–Lefebvre (BGLL), Clauset–Newman–Moore (CNM) and the other state-of-the-art algorithms such as Infomap, order statistics local optimization method (OSLOM) and label propagation algorithm (LPA). (paper)

An Algorithm for Pedestrian Detection in Multispectral Image Sequences

Science.gov (United States)

Kniaz, V. V.; Fedorenko, V. V.

2017-05-01

The growing interest for self-driving cars provides a demand for scene understanding and obstacle detection algorithms. One of the most challenging problems in this field is the problem of pedestrian detection. Main difficulties arise from a diverse appearances of pedestrians. Poor visibility conditions such as fog and low light conditions also significantly decrease the quality of pedestrian detection. This paper presents a new optical flow based algorithm BipedDetet that provides robust pedestrian detection on a single-borad computer. The algorithm is based on the idea of simplified Kalman filtering suitable for realization on modern single-board computers. To detect a pedestrian a synthetic optical flow of the scene without pedestrians is generated using slanted-plane model. The estimate of a real optical flow is generated using a multispectral image sequence. The difference of the synthetic optical flow and the real optical flow provides the optical flow induced by pedestrians. The final detection of pedestrians is done by the segmentation of the difference of optical flows. To evaluate the BipedDetect algorithm a multispectral dataset was collected using a mobile robot.

Comparison of public peak detection algorithms for MALDI mass spectrometry data analysis.

Science.gov (United States)

Yang, Chao; He, Zengyou; Yu, Weichuan

2009-01-06

In mass spectrometry (MS) based proteomic data analysis, peak detection is an essential step for subsequent analysis. Recently, there has been significant progress in the development of various peak detection algorithms. However, neither a comprehensive survey nor an experimental comparison of these algorithms is yet available. The main objective of this paper is to provide such a survey and to compare the performance of single spectrum based peak detection methods. In general, we can decompose a peak detection procedure into three consequent parts: smoothing, baseline correction and peak finding. We first categorize existing peak detection algorithms according to the techniques used in different phases. Such a categorization reveals the differences and similarities among existing peak detection algorithms. Then, we choose five typical peak detection algorithms to conduct a comprehensive experimental study using both simulation data and real MALDI MS data. The results of comparison show that the continuous wavelet-based algorithm provides the best average performance.

Multisensors Cooperative Detection Task Scheduling Algorithm Based on Hybrid Task Decomposition and MBPSO

Directory of Open Access Journals (Sweden)

Changyun Liu

2017-01-01

Full Text Available A multisensor scheduling algorithm based on the hybrid task decomposition and modified binary particle swarm optimization (MBPSO is proposed. Firstly, aiming at the complex relationship between sensor resources and tasks, a hybrid task decomposition method is presented, and the resource scheduling problem is decomposed into subtasks; then the sensor resource scheduling problem is changed into the match problem of sensors and subtasks. Secondly, the resource match optimization model based on the sensor resources and tasks is established, which considers several factors, such as the target priority, detecting benefit, handover times, and resource load. Finally, MBPSO algorithm is proposed to solve the match optimization model effectively, which is based on the improved updating means of particle’s velocity and position through the doubt factor and modified Sigmoid function. The experimental results show that the proposed algorithm is better in terms of convergence velocity, searching capability, solution accuracy, and efficiency.

Improvement and implementation for Canny edge detection algorithm

Science.gov (United States)

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.

Linear feature detection algorithm for astronomical surveys - I. Algorithm description

Science.gov (United States)

Bektešević, Dino; Vinković, Dejan

2017-11-01

Computer vision algorithms are powerful tools in astronomical image analyses, especially when automation of object detection and extraction is required. Modern object detection algorithms in astronomy are oriented towards detection of stars and galaxies, ignoring completely the detection of existing linear features. With the emergence of wide-field sky surveys, linear features attract scientific interest as possible trails of fast flybys of near-Earth asteroids and meteors. In this work, we describe a new linear feature detection algorithm designed specifically for implementation in big data astronomy. The algorithm combines a series of algorithmic steps that first remove other objects (stars and galaxies) from the image and then enhance the line to enable more efficient line detection with the Hough algorithm. The rate of false positives is greatly reduced thanks to a step that replaces possible line segments with rectangles and then compares lines fitted to the rectangles with the lines obtained directly from the image. The speed of the algorithm and its applicability in astronomical surveys are also discussed.

Optimal algorithm for automatic detection of microaneurysms based on receiver operating characteristic curve

Science.gov (United States)

Xu, Lili; Luo, Shuqian

2010-11-01

Microaneurysms (MAs) are the first manifestations of the diabetic retinopathy (DR) as well as an indicator for its progression. Their automatic detection plays a key role for both mass screening and monitoring and is therefore in the core of any system for computer-assisted diagnosis of DR. The algorithm basically comprises the following stages: candidate detection aiming at extracting the patterns possibly corresponding to MAs based on mathematical morphological black top hat, feature extraction to characterize these candidates, and classification based on support vector machine (SVM), to validate MAs. Feature vector and kernel function of SVM selection is very important to the algorithm. We use the receiver operating characteristic (ROC) curve to evaluate the distinguishing performance of different feature vectors and different kernel functions of SVM. The ROC analysis indicates the quadratic polynomial SVM with a combination of features as the input shows the best discriminating performance.

Automated seismic detection of landslides at regional scales: a Random Forest based detection algorithm

Science.gov (United States)

Hibert, C.; Michéa, D.; Provost, F.; Malet, J. P.; Geertsema, M.

2017-12-01

Detection of landslide occurrences and measurement of their dynamics properties during run-out is a high research priority but a logistical and technical challenge. Seismology has started to help in several important ways. Taking advantage of the densification of global, regional and local networks of broadband seismic stations, recent advances now permit the seismic detection and location of landslides in near-real-time. This seismic detection could potentially greatly increase the spatio-temporal resolution at which we study landslides triggering, which is critical to better understand the influence of external forcings such as rainfalls and earthquakes. However, detecting automatically seismic signals generated by landslides still represents a challenge, especially for events with small mass. The low signal-to-noise ratio classically observed for landslide-generated seismic signals and the difficulty to discriminate these signals from those generated by regional earthquakes or anthropogenic and natural noises are some of the obstacles that have to be circumvented. We present a new method for automatically constructing instrumental landslide catalogues from continuous seismic data. We developed a robust and versatile solution, which can be implemented in any context where a seismic detection of landslides or other mass movements is relevant. The method is based on a spectral detection of the seismic signals and the identification of the sources with a Random Forest machine learning algorithm. The spectral detection allows detecting signals with low signal-to-noise ratio, while the Random Forest algorithm achieve a high rate of positive identification of the seismic signals generated by landslides and other seismic sources. The processing chain is implemented to work in a High Performance Computers centre which permits to explore years of continuous seismic data rapidly. We present here the preliminary results of the application of this processing chain for years

A Space Object Detection Algorithm using Fourier Domain Likelihood Ratio Test

Science.gov (United States)

Becker, D.; Cain, S.

Space object detection is of great importance in the highly dependent yet competitive and congested space domain. Detection algorithms employed play a crucial role in fulfilling the detection component in the situational awareness mission to detect, track, characterize and catalog unknown space objects. Many current space detection algorithms use a matched filter or a spatial correlator to make a detection decision at a single pixel point of a spatial image based on the assumption that the data follows a Gaussian distribution. This paper explores the potential for detection performance advantages when operating in the Fourier domain of long exposure images of small and/or dim space objects from ground based telescopes. A binary hypothesis test is developed based on the joint probability distribution function of the image under the hypothesis that an object is present and under the hypothesis that the image only contains background noise. The detection algorithm tests each pixel point of the Fourier transformed images to make the determination if an object is present based on the criteria threshold found in the likelihood ratio test. Using simulated data, the performance of the Fourier domain detection algorithm is compared to the current algorithm used in space situational awareness applications to evaluate its value.

Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms : VISCERAL Anatomy Benchmarks

OpenAIRE

Jimenez-del-Toro, Oscar; Muller, Henning; Krenn, Markus; Gruenberg, Katharina; Taha, Abdel Aziz; Winterstein, Marianne; Eggel, Ivan; Foncubierta-Rodriguez, Antonio; Goksel, Orcun; Jakab, Andres; Kontokotsios, Georgios; Langs, Georg; Menze, Bjoern H.; Fernandez, Tomas Salas; Schaer, Roger

2016-01-01

Variations in the shape and appearance of anatomical structures in medical images are often relevant radiological signs of disease. Automatic tools can help automate parts of this manual process. A cloud-based evaluation framework is presented in this paper including results of benchmarking current state-of-the-art medical imaging algorithms for anatomical structure segmentation and landmark detection: the VISCERAL Anatomy benchmarks. The algorithms are implemented in virtual machines in the ...

Acoustic change detection algorithm using an FM radio

Science.gov (United States)

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 Formally Verified Conflict Detection Algorithm for Polynomial Trajectories

Science.gov (United States)

Narkawicz, Anthony; Munoz, Cesar

2015-01-01

In air traffic management, conflict detection algorithms are used to determine whether or not aircraft are predicted to lose horizontal and vertical separation minima within a time interval assuming a trajectory model. In the case of linear trajectories, conflict detection algorithms have been proposed that are both sound, i.e., they detect all conflicts, and complete, i.e., they do not present false alarms. In general, for arbitrary nonlinear trajectory models, it is possible to define detection algorithms that are either sound or complete, but not both. This paper considers the case of nonlinear aircraft trajectory models based on polynomial functions. In particular, it proposes a conflict detection algorithm that precisely determines whether, given a lookahead time, two aircraft flying polynomial trajectories are in conflict. That is, it has been formally verified that, assuming that the aircraft trajectories are modeled as polynomial functions, the proposed algorithm is both sound and complete.

A Robust Automated Cataract Detection Algorithm Using Diagnostic Opinion Based Parameter Thresholding for Telemedicine Application

Directory of Open Access Journals (Sweden)

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 3-Step Algorithm Using Region-Based Active Contours for Video Objects Detection

Directory of Open Access Journals (Sweden)

Stéphanie Jehan-Besson

2002-06-01

Full Text Available We propose a 3-step algorithm for the automatic detection of moving objects in video sequences using region-based active contours. First, we introduce a very full general framework for region-based active contours with a new Eulerian method to compute the evolution equation of the active contour from a criterion including both region-based and boundary-based terms. This framework can be easily adapted to various applications, thanks to the introduction of functions named descriptors of the different regions. With this new Eulerian method based on shape optimization principles, we can easily take into account the case of descriptors depending upon features globally attached to the regions. Second, we propose a 3-step algorithm for detection of moving objects, with a static or a mobile camera, using region-based active contours. The basic idea is to hierarchically associate temporal and spatial information. The active contour evolves with successively three sets of descriptors: a temporal one, and then two spatial ones. The third spatial descriptor takes advantage of the segmentation of the image in intensity homogeneous regions. User interaction is reduced to the choice of a few parameters at the beginning of the process. Some experimental results are supplied.

ID card number detection algorithm based on convolutional neural network

Science.gov (United States)

Zhu, Jian; Ma, Hanjie; Feng, Jie; Dai, Leiyan

2018-04-01

In this paper, a new detection algorithm based on Convolutional Neural Network is presented in order to realize the fast and convenient ID information extraction in multiple scenarios. The algorithm uses the mobile device equipped with Android operating system to locate and extract the ID number; Use the special color distribution of the ID card, select the appropriate channel component; Use the image threshold segmentation, noise processing and morphological processing to take the binary processing for image; At the same time, the image rotation and projection method are used for horizontal correction when image was tilting; Finally, the single character is extracted by the projection method, and recognized by using Convolutional Neural Network. Through test shows that, A single ID number image from the extraction to the identification time is about 80ms, the accuracy rate is about 99%, It can be applied to the actual production and living environment.

Structural Damage Detection using Frequency Response Function Index and Surrogate Model Based on Optimized Extreme Learning Machine Algorithm

Directory of Open Access Journals (Sweden)

R. Ghiasi

2017-09-01

Full Text Available Utilizing surrogate models based on artificial intelligence methods for detecting structural damages has attracted the attention of many researchers in recent decades. In this study, a new kernel based on Littlewood-Paley Wavelet (LPW is proposed for Extreme Learning Machine (ELM algorithm to improve the accuracy of detecting multiple damages in structural systems.  ELM is used as metamodel (surrogate model of exact finite element analysis of structures in order to efficiently reduce the computational cost through updating process. In the proposed two-step method, first a damage index, based on Frequency Response Function (FRF of the structure, is used to identify the location of damages. In the second step, the severity of damages in identified elements is detected using ELM. In order to evaluate the efficacy of ELM, the results obtained from the proposed kernel were compared with other kernels proposed for ELM as well as Least Square Support Vector Machine algorithm. The solved numerical problems indicated that ELM algorithm accuracy in detecting structural damages is increased drastically in case of using LPW kernel.

Adaptive sampling algorithm for detection of superpoints

Institute of Scientific and Technical Information of China (English)

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.

Leads Detection Using Mixture Statistical Distribution Based CRF Algorithm from Sentinel-1 Dual Polarization SAR Imagery

Science.gov (United States)

Zhang, Yu; Li, Fei; Zhang, Shengkai; Zhu, Tingting

2017-04-01

Synthetic Aperture Radar (SAR) is significantly important for polar remote sensing since it can provide continuous observations in all days and all weather. SAR can be used for extracting the surface roughness information characterized by the variance of dielectric properties and different polarization channels, which make it possible to observe different ice types and surface structure for deformation analysis. In November, 2016, Chinese National Antarctic Research Expedition (CHINARE) 33rd cruise has set sails in sea ice zone in Antarctic. Accurate leads spatial distribution in sea ice zone for routine planning of ship navigation is essential. In this study, the semantic relationship between leads and sea ice categories has been described by the Conditional Random Fields (CRF) model, and leads characteristics have been modeled by statistical distributions in SAR imagery. In the proposed algorithm, a mixture statistical distribution based CRF is developed by considering the contexture information and the statistical characteristics of sea ice for improving leads detection in Sentinel-1A dual polarization SAR imagery. The unary potential and pairwise potential in CRF model is constructed by integrating the posteriori probability estimated from statistical distributions. For mixture statistical distribution parameter estimation, Method of Logarithmic Cumulants (MoLC) is exploited for single statistical distribution parameters estimation. The iteration based Expectation Maximal (EM) algorithm is investigated to calculate the parameters in mixture statistical distribution based CRF model. In the posteriori probability inference, graph-cut energy minimization method is adopted in the initial leads detection. The post-processing procedures including aspect ratio constrain and spatial smoothing approaches are utilized to improve the visual result. The proposed method is validated on Sentinel-1A SAR C-band Extra Wide Swath (EW) Ground Range Detected (GRD) imagery with a

A simple fall detection algorithm for Powered Two Wheelers

OpenAIRE

BOUBEZOUL, Abderrahmane; ESPIE, Stéphane; LARNAUDIE, Bruno; BOUAZIZ, Samir

2013-01-01

The aim of this study is to evaluate a low-complexity fall detection algorithm, that use both acceleration and angular velocity signals to trigger an alert-system or to inflate an airbag jacket. The proposed fall detection algorithm is a threshold-based algorithm, using data from 3-accelerometers and 3-gyroscopes sensors mounted on the motorcycle. During the first step, the commonly fall accident configurations were selected and analyzed in order to identify the main causation factors. On the...

A novel ship CFAR detection algorithm based on adaptive parameter enhancement and wake-aided detection in SAR images

Science.gov (United States)

Meng, Siqi; Ren, Kan; Lu, Dongming; Gu, Guohua; Chen, Qian; Lu, Guojun

2018-03-01

Synthetic aperture radar (SAR) is an indispensable and useful method for marine monitoring. With the increase of SAR sensors, high resolution images can be acquired and contain more target structure information, such as more spatial details etc. This paper presents a novel adaptive parameter transform (APT) domain constant false alarm rate (CFAR) to highlight targets. The whole method is based on the APT domain value. Firstly, the image is mapped to the new transform domain by the algorithm. Secondly, the false candidate target pixels are screened out by the CFAR detector to highlight the target ships. Thirdly, the ship pixels are replaced by the homogeneous sea pixels. And then, the enhanced image is processed by Niblack algorithm to obtain the wake binary image. Finally, normalized Hough transform (NHT) is used to detect wakes in the binary image, as a verification of the presence of the ships. Experiments on real SAR images validate that the proposed transform does enhance the target structure and improve the contrast of the image. The algorithm has a good performance in the ship and ship wake detection.

An Improved Topology-Potential-Based Community Detection Algorithm for Complex Network

Directory of Open Access Journals (Sweden)

Zhixiao Wang

2014-01-01

Full Text Available Topology potential theory is a new community detection theory on complex network, which divides a network into communities by spreading outward from each local maximum potential node. At present, almost all topology-potential-based community detection methods ignore node difference and assume that all nodes have the same mass. This hypothesis leads to inaccuracy of topology potential calculation and then decreases the precision of community detection. Inspired by the idea of PageRank algorithm, this paper puts forward a novel mass calculation method for complex network nodes. A node’s mass obtained by our method can effectively reflect its importance and influence in complex network. The more important the node is, the bigger its mass is. Simulation experiment results showed that, after taking node mass into consideration, the topology potential of node is more accurate, the distribution of topology potential is more reasonable, and the results of community detection are more precise.

Anomaly Detection for Aviation Safety Based on an Improved KPCA Algorithm

Directory of Open Access Journals (Sweden)

Xiaoyu Zhang

2017-01-01

Full Text Available Thousands of flights datasets should be analyzed per day for a moderate sized fleet; therefore, flight datasets are very large. In this paper, an improved kernel principal component analysis (KPCA method is proposed to search for signatures of anomalies in flight datasets through the squared prediction error statistics, in which the number of principal components and the confidence for the confidence limit are automatically determined by OpenMP-based K-fold cross-validation algorithm and the parameter in the radial basis function (RBF is optimized by GPU-based kernel learning method. Performed on Nvidia GeForce GTX 660, the computation of the proposed GPU-based RBF parameter is 112.9 times (average 82.6 times faster than that of sequential CPU task execution. The OpenMP-based K-fold cross-validation process for training KPCA anomaly detection model becomes 2.4 times (average 1.5 times faster than that of sequential CPU task execution. Experiments show that the proposed approach can effectively detect the anomalies with the accuracy of 93.57% and false positive alarm rate of 1.11%.

Mobile Phone Based Falling Detection Sensor and Computer-Aided Algorithm for Elderly People

Directory of Open Access Journals (Sweden)

Lee Jong-Ha

2016-01-01

Full Text Available Falls are dangerous for the elderly population; therefore many fall detection systems have been developed. However, previous methods are bulky for elderly people or only use a single sensor to isolate falls from daily living activities, which makes a fall difficult to distinguish. In this paper, we present a cost-effective and easy-to-use portable fall-detection sensor and algorithm. Specifically, to detect human falls, we used a three-axis accelerator and a three-axis gyroscope in a mobile phone. We used the Fourier descriptor-based frequency analysis method to classify both normal and falling status. From the experimental results, the proposed method detects falling status with 96.14% accuracy.

Lining seam elimination algorithm and surface crack detection in concrete tunnel lining

Science.gov (United States)

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.

Decision algorithms in fire detection systems

Directory of Open Access Journals (Sweden)

Ristić Jovan D.

2011-01-01

Full Text Available Analogue (and addressable fire detection systems enables a new quality in improving sensitivity to real fires and reducing susceptibility to nuisance alarm sources. Different decision algorithms types were developed with intention to improve sensitivity and reduce false alarm occurrence. At the beginning, it was free alarm level adjustment based on preset level. Majority of multi-criteria decision work was based on multi-sensor (multi-signature decision algorithms - using different type of sensors on the same location or, rather, using different aspects (level and rise of one sensor measured value. Our idea is to improve sensitivity and reduce false alarm occurrence by forming groups of sensors that work in similar conditions (same world side in the building, same or similar technology or working time. Original multi-criteria decision algorithms based on level, rise and difference of level and rise from group average are discussed in this paper.

Prefiltering Model for Homology Detection Algorithms on GPU.

Science.gov (United States)

Retamosa, Germán; de Pedro, Luis; González, Ivan; Tamames, Javier

2016-01-01

Homology detection has evolved over the time from heavy algorithms based on dynamic programming approaches to lightweight alternatives based on different heuristic models. However, the main problem with these algorithms is that they use complex statistical models, which makes it difficult to achieve a relevant speedup and find exact matches with the original results. Thus, their acceleration is essential. The aim of this article was to prefilter a sequence database. To make this work, we have implemented a groundbreaking heuristic model based on NVIDIA's graphics processing units (GPUs) and multicore processors. Depending on the sensitivity settings, this makes it possible to quickly reduce the sequence database by factors between 50% and 95%, while rejecting no significant sequences. Furthermore, this prefiltering application can be used together with multiple homology detection algorithms as a part of a next-generation sequencing system. Extensive performance and accuracy tests have been carried out in the Spanish National Centre for Biotechnology (NCB). The results show that GPU hardware can accelerate the execution times of former homology detection applications, such as National Centre for Biotechnology Information (NCBI), Basic Local Alignment Search Tool for Proteins (BLASTP), up to a factor of 4.

Breadth-First Search-Based Single-Phase Algorithms for Bridge Detection in Wireless Sensor Networks

Science.gov (United States)

Akram, Vahid Khalilpour; Dagdeviren, Orhan

2013-01-01

Wireless sensor networks (WSNs) are promising technologies for exploring harsh environments, such as oceans, wild forests, volcanic regions and outer space. Since sensor nodes may have limited transmission range, application packets may be transmitted by multi-hop communication. Thus, connectivity is a very important issue. A bridge is a critical edge whose removal breaks the connectivity of the network. Hence, it is crucial to detect bridges and take preventions. Since sensor nodes are battery-powered, services running on nodes should consume low energy. In this paper, we propose energy-efficient and distributed bridge detection algorithms for WSNs. Our algorithms run single phase and they are integrated with the Breadth-First Search (BFS) algorithm, which is a popular routing algorithm. Our first algorithm is an extended version of Milic's algorithm, which is designed to reduce the message length. Our second algorithm is novel and uses ancestral knowledge to detect bridges. We explain the operation of the algorithms, analyze their proof of correctness, message, time, space and computational complexities. To evaluate practical importance, we provide testbed experiments and extensive simulations. We show that our proposed algorithms provide less resource consumption, and the energy savings of our algorithms are up by 5.5-times. PMID:23845930

Research on the algorithm of infrared target detection based on the frame difference and background subtraction method

Science.gov (United States)

Liu, Yun; Zhao, Yuejin; Liu, Ming; Dong, Liquan; Hui, Mei; Liu, Xiaohua; Wu, Yijian

2015-09-01

As an important branch of infrared imaging technology, infrared target tracking and detection has a very important scientific value and a wide range of applications in both military and civilian areas. For the infrared image which is characterized by low SNR and serious disturbance of background noise, an innovative and effective target detection algorithm is proposed in this paper, according to the correlation of moving target frame-to-frame and the irrelevance of noise in sequential images based on OpenCV. Firstly, since the temporal differencing and background subtraction are very complementary, we use a combined detection method of frame difference and background subtraction which is based on adaptive background updating. Results indicate that it is simple and can extract the foreground moving target from the video sequence stably. For the background updating mechanism continuously updating each pixel, we can detect the infrared moving target more accurately. It paves the way for eventually realizing real-time infrared target detection and tracking, when transplanting the algorithms on OpenCV to the DSP platform. Afterwards, we use the optimal thresholding arithmetic to segment image. It transforms the gray images to black-white images in order to provide a better condition for the image sequences detection. Finally, according to the relevance of moving objects between different frames and mathematical morphology processing, we can eliminate noise, decrease the area, and smooth region boundaries. Experimental results proves that our algorithm precisely achieve the purpose of rapid detection of small infrared target.

An efficient and fast detection algorithm for multimode FBG sensing

DEFF Research Database (Denmark)

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...

A Cluster-Based Fuzzy Fusion Algorithm for Event Detection in Heterogeneous Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

ZiQi Hao

2015-01-01

Full Text Available As limited energy is one of the tough challenges in wireless sensor networks (WSN, energy saving becomes important in increasing the lifecycle of the network. Data fusion enables combining information from several sources thus to provide a unified scenario, which can significantly save sensor energy and enhance sensing data accuracy. In this paper, we propose a cluster-based data fusion algorithm for event detection. We use k-means algorithm to form the nodes into clusters, which can significantly reduce the energy consumption of intracluster communication. Distances between cluster heads and event and energy of clusters are fuzzified, thus to use a fuzzy logic to select the clusters that will participate in data uploading and fusion. Fuzzy logic method is also used by cluster heads for local decision, and then the local decision results are sent to the base station. Decision-level fusion for final decision of event is performed by base station according to the uploaded local decisions and fusion support degree of clusters calculated by fuzzy logic method. The effectiveness of this algorithm is demonstrated by simulation results.

Linear segmentation algorithm for detecting layer boundary with lidar.

Science.gov (United States)

Mao, Feiyue; Gong, Wei; Logan, Timothy

2013-11-04

The automatic detection of aerosol- and cloud-layer boundary (base and top) is important in atmospheric lidar data processing, because the boundary information is not only useful for environment and climate studies, but can also be used as input for further data processing. Previous methods have demonstrated limitations in defining the base and top, window-size setting, and have neglected the in-layer attenuation. To overcome these limitations, we present a new layer detection scheme for up-looking lidars based on linear segmentation with a reasonable threshold setting, boundary selecting, and false positive removing strategies. Preliminary results from both real and simulated data show that this algorithm cannot only detect the layer-base as accurate as the simple multi-scale method, but can also detect the layer-top more accurately than that of the simple multi-scale method. Our algorithm can be directly applied to uncalibrated data without requiring any additional measurements or window size selections.

Adaptive algorithm of magnetic heading detection

Science.gov (United States)

Liu, Gong-Xu; Shi, Ling-Feng

2017-11-01

Magnetic data obtained from a magnetic sensor usually fluctuate in a certain range, which makes it difficult to estimate the magnetic heading accurately. In fact, magnetic heading information is usually submerged in noise because of all kinds of electromagnetic interference and the diversity of the pedestrian’s motion states. In order to solve this problem, a new adaptive algorithm based on the (typically) right-angled corridors of a building or residential buildings is put forward to process heading information. First, a 3D indoor localization platform is set up based on MPU9250. Then, several groups of data are measured by changing the experimental environment and pedestrian’s motion pace. The raw data from the attached inertial measurement unit are calibrated and arranged into a time-stamped array and written to a data file. Later, the data file is imported into MATLAB for processing and analysis using the proposed adaptive algorithm. Finally, the algorithm is verified by comparison with the existing algorithm. The experimental results show that the algorithm has strong robustness and good fault tolerance, which can detect the heading information accurately and in real-time.

Spectrum sensing algorithm based on autocorrelation energy in cognitive radio networks

Science.gov (United States)

Ren, Shengwei; Zhang, Li; Zhang, Shibing

2016-10-01

Cognitive radio networks have wide applications in the smart home, personal communications and other wireless communication. Spectrum sensing is the main challenge in cognitive radios. This paper proposes a new spectrum sensing algorithm which is based on the autocorrelation energy of signal received. By taking the autocorrelation energy of the received signal as the statistics of spectrum sensing, the effect of the channel noise on the detection performance is reduced. Simulation results show that the algorithm is effective and performs well in low signal-to-noise ratio. Compared with the maximum generalized eigenvalue detection (MGED) algorithm, function of covariance matrix based detection (FMD) algorithm and autocorrelation-based detection (AD) algorithm, the proposed algorithm has 2 11 dB advantage.

Vibration based algorithm for crack detection in cantilever beam containing two different types of cracks

Science.gov (United States)

Behzad, Mehdi; Ghadami, Amin; Maghsoodi, Ameneh; Michael Hale, Jack

2013-11-01

In this paper, a simple method for detection of multiple edge cracks in Euler-Bernoulli beams having two different types of cracks is presented based on energy equations. Each crack is modeled as a massless rotational spring using Linear Elastic Fracture Mechanics (LEFM) theory, and a relationship among natural frequencies, crack locations and stiffness of equivalent springs is demonstrated. In the procedure, for detection of m cracks in a beam, 3m equations and natural frequencies of healthy and cracked beam in two different directions are needed as input to the algorithm. The main accomplishment of the presented algorithm is the capability to detect the location, severity and type of each crack in a multi-cracked beam. Concise and simple calculations along with accuracy are other advantages of this method. A number of numerical examples for cantilever beams including one and two cracks are presented to validate the method.

Model-based fault detection algorithm for photovoltaic system monitoring

KAUST Repository

Harrou, Fouzi

2018-02-12

Reliable detection of faults in PV systems plays an important role in improving their reliability, productivity, and safety. This paper addresses the detection of faults in the direct current (DC) side of photovoltaic (PV) systems using a statistical approach. Specifically, a simulation model that mimics the theoretical performances of the inspected PV system is designed. Residuals, which are the difference between the measured and estimated output data, are used as a fault indicator. Indeed, residuals are used as the input for the Multivariate CUmulative SUM (MCUSUM) algorithm to detect potential faults. We evaluated the proposed method by using data from an actual 20 MWp grid-connected PV system located in the province of Adrar, Algeria.

A simulation study comparing aberration detection algorithms for syndromic surveillance

Directory of Open Access Journals (Sweden)

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.

On the performance of pre-microRNA detection algorithms

DEFF Research Database (Denmark)

Saçar Demirci, Müşerref Duygu; Baumbach, Jan; Allmer, Jens

2017-01-01

assess 13 ab initio pre-miRNA detection approaches using all relevant, published, and novel data sets while judging algorithm performance based on ten intrinsic performance measures. We present an extensible framework, izMiR, which allows for the unbiased comparison of existing algorithms, adding new...

A review of feature detection and match algorithms for localization and mapping

Science.gov (United States)

Li, Shimiao

2017-09-01

Localization and mapping is an essential ability of a robot to keep track of its own location in an unknown environment. Among existing methods for this purpose, vision-based methods are more effective solutions for being accurate, inexpensive and versatile. Vision-based methods can generally be categorized as feature-based approaches and appearance-based approaches. The feature-based approaches prove higher performance in textured scenarios. However, their performance depend highly on the applied feature-detection algorithms. In this paper, we surveyed algorithms for feature detection, which is an essential step in achieving vision-based localization and mapping. In this pater, we present mathematical models of the algorithms one after another. To compare the performances of the algorithms, we conducted a series of experiments on their accuracy, speed, scale invariance and rotation invariance. The results of the experiments showed that ORB is the fastest algorithm in detecting and matching features, the speed of which is more than 10 times that of SURF and approximately 40 times that of SIFT. And SIFT, although with no advantage in terms of speed, shows the most correct matching pairs and proves its accuracy.

Automatic Laser Pointer Detection Algorithm for Environment Control Device Systems Based on Template Matching and Genetic Tuning of Fuzzy Rule-Based Systems

Directory of Open Access Journals (Sweden)

F.

2012-04-01

Full Text Available In this paper we propose a new approach for laser-based environment device control systems based on the automatic design of a Fuzzy Rule-Based System for laser pointer detection. The idea is to improve the success rate of the previous approaches decreasing as much as possible the false offs and increasing the success rate in images with laser spot, i.e., the detection of a false laser spot (since this could lead to dangerous situations. To this end, we propose to analyze both, the morphology and color of a laser spot image together, thus developing a new robust algorithm. Genetic Fuzzy Systems have also been employed to improve the laser spot system detection by means of a fine tuning of the involved membership functions thus reducing the system false offs, which is the main objective in this problem. The system presented in this paper, makes use of a Fuzzy Rule-Based System adjusted by a Genetic Algorithm, which, based on laser morphology and color analysis, shows a better success rate than previous approaches.

Validation of a knowledge-based boundary detection algorithm: a multicenter study

International Nuclear Information System (INIS)

Groch, M.W.; Erwin, W.D.; Murphy, P.H.; Ali, A.; Moore, W.; Ford, P.; Qian Jianzhong; Barnett, C.A.; Lette, J.

1996-01-01

A completely operator-independent boundary detection algorithm for multigated blood pool (MGBP) studies has been evaluated at four medical centers. The knowledge-based boundary detector (KBBD) algorithm is nondeterministic, utilizing a priori domain knowledge in the form of rule sets for the localization of cardiac chambers and image features, providing a case-by-case method for the identification and boundary definition of the left ventricle (LV). The nondeterministic algorithm employs multiple processing pathways, where KBBD rules have been designed for conventional (CONV) imaging geometries (nominal 45 LAO, nonzoom) as well as for highly zoomed and/or caudally tilted (ZOOM) studies. The resultant ejection fractions (LVEF) from the KBBD program have been compared with the standard LVEF calculations in 253 total cases in four institutions, 157 utilizing CONV geometry and 96 utilizing ZOOM geometries. The criteria for success was a KBBD boundary adequately defined over the LV as judged by an experienced observer, and the correlation of KBBD LVEFs to the standard calculation of LVEFs for the institution. The overall success rate for all institutions combined was 99.2%, with an overall correlation coefficient of r=0.95 (P<0.001). The individual success rates and EF correlations (r), for CONV and ZOOM geometers were: 98%, r=0.93 (CONV) and 100%, r=0.95 (ZOOM). The KBBD algorithm can be adapted to varying clinical situations, employing automatic processing using artificial intelligence, with performance close to that of a human operator. (orig.)

Test of TEDA, Tsunami Early Detection Algorithm

Science.gov (United States)

Bressan, Lidia; Tinti, Stefano

2010-05-01

Tsunami detection in real-time, both offshore and at the coastline, plays a key role in Tsunami Warning Systems since it provides so far the only reliable and timely proof of tsunami generation, and is used to confirm or cancel tsunami warnings previously issued on the basis of seismic data alone. Moreover, in case of submarine or coastal landslide generated tsunamis, which are not announced by clear seismic signals and are typically local, real-time detection at the coastline might be the fastest way to release a warning, even if the useful time for emergency operations might be limited. TEDA is an algorithm for real-time detection of tsunami signal on sea-level records, developed by the Tsunami Research Team of the University of Bologna. The development and testing of the algorithm has been accomplished within the framework of the Italian national project DPC-INGV S3 and the European project TRANSFER. The algorithm is to be implemented at station level, and it is based therefore only on sea-level data of a single station, either a coastal tide-gauge or an offshore buoy. TEDA's principle is to discriminate the first tsunami wave from the previous background signal, which implies the assumption that the tsunami waves introduce a difference in the previous sea-level signal. Therefore, in TEDA the instantaneous (most recent) and the previous background sea-level elevation gradients are characterized and compared by proper functions (IS and BS) that are updated at every new data acquisition. Detection is triggered when the instantaneous signal function passes a set threshold and at the same time it is significantly bigger compared to the previous background signal. The functions IS and BS depend on temporal parameters that allow the algorithm to be adapted different situations: in general, coastal tide-gauges have a typical background spectrum depending on the location where the instrument is installed, due to local topography and bathymetry, while offshore buoys are

A Fast Detection Algorithm for the X-Ray Pulsar Signal

Directory of Open Access Journals (Sweden)

Hao Liang

2017-01-01

Full Text Available The detection of the X-ray pulsar signal is important for the autonomous navigation system using X-ray pulsars. In the condition of short observation time and limited number of photons for detection, the noise does not obey the Gaussian distribution. This fact has been little considered extant. In this paper, the model of the X-ray pulsar signal is rebuilt as the nonhomogeneous Poisson distribution and, in the condition of a fixed false alarm rate, a fast detection algorithm based on maximizing the detection probability is proposed. Simulation results show the effectiveness of the proposed detection algorithm.

Community detection algorithm evaluation with ground-truth data

Science.gov (United States)

Jebabli, Malek; Cherifi, Hocine; Cherifi, Chantal; Hamouda, Atef

2018-02-01

Community structure is of paramount importance for the understanding of complex networks. Consequently, there is a tremendous effort in order to develop efficient community detection algorithms. Unfortunately, the issue of a fair assessment of these algorithms is a thriving open question. If the ground-truth community structure is available, various clustering-based metrics are used in order to compare it versus the one discovered by these algorithms. However, these metrics defined at the node level are fairly insensitive to the variation of the overall community structure. To overcome these limitations, we propose to exploit the topological features of the 'community graphs' (where the nodes are the communities and the links represent their interactions) in order to evaluate the algorithms. To illustrate our methodology, we conduct a comprehensive analysis of overlapping community detection algorithms using a set of real-world networks with known a priori community structure. Results provide a better perception of their relative performance as compared to classical metrics. Moreover, they show that more emphasis should be put on the topology of the community structure. We also investigate the relationship between the topological properties of the community structure and the alternative evaluation measures (quality metrics and clustering metrics). It appears clearly that they present different views of the community structure and that they must be combined in order to evaluate the effectiveness of community detection algorithms.

Statistical Algorithm for the Adaptation of Detection Thresholds

DEFF Research Database (Denmark)

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...... remains constant regardless of engine age and changing detection threshold values. This, in turn, guarantees the same event detection performance for new and aged engines/sensors. Adaptation of the engine knock detection threshold is given as an example. Udgivelsesdato: 2008...

Regression algorithm for emotion detection

OpenAIRE

Berthelon , Franck; Sander , Peter

2013-01-01

International audience; We present here two components of a computational system for emotion detection. PEMs (Personalized Emotion Maps) store links between bodily expressions and emotion values, and are individually calibrated to capture each person's emotion profile. They are an implementation based on aspects of Scherer's theoretical complex system model of emotion~\\cite{scherer00, scherer09}. We also present a regression algorithm that determines a person's emotional feeling from sensor m...

Improved QRD-M Detection Algorithm for Generalized Spatial Modulation Scheme

Directory of Open Access Journals (Sweden)

Xiaorong Jing

2017-01-01

Full Text Available Generalized spatial modulation (GSM is a spectral and energy efficient multiple-input multiple-output (MIMO transmission scheme. It will lead to imperfect detection performance with relatively high computational complexity by directly applying the original QR-decomposition with M algorithm (QRD-M to the GSM scheme. In this paper an improved QRD-M algorithm is proposed for GSM signal detection, which achieves near-optimal performance but with relatively low complexity. Based on the QRD, the improved algorithm firstly transforms the maximum likelihood (ML detection of the GSM signals into searching an inverted tree structure. Then, in the searching process of the M branches, the branches corresponding to the illegitimate transmit antenna combinations (TACs and related to invalid number of active antennas are cut in order to improve the validity of the resultant branches at each level by taking advantage of characteristics of GSM signals. Simulation results show that the improved QRD-M detection algorithm provides similar performance to maximum likelihood (ML with the reduced computational complexity compared to the original QRD-M algorithm, and the optimal value of parameter M of the improved QRD-M algorithm for detection of the GSM scheme is equal to modulation order plus one.

Detecting lung cancer symptoms with analogic CNN algorithms based on a constrained diffusion template

International Nuclear Information System (INIS)

Hirakawa, Satoshi; Nishio, Yoshifumi; Ushida, Akio; Ueno, Junji; Kasem, I.; Nishitani, Hiromu; Rekeczky, C.; Roska, T.

1997-01-01

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)

Performances of the New Real Time Tsunami Detection Algorithm applied to tide gauges data

Science.gov (United States)

Chierici, F.; Embriaco, D.; Morucci, S.

2017-12-01

Real-time tsunami detection algorithms play a key role in any Tsunami Early Warning System. We have developed a new algorithm for tsunami detection (TDA) based on the real-time tide removal and real-time band-pass filtering of seabed pressure time series acquired by Bottom Pressure Recorders. The TDA 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. In this work we present the performance of the TDA algorithm applied to tide gauge data. We have adapted the new tsunami detection algorithm and the Monte Carlo test methodology to tide gauges. Sea level data acquired by coastal tide gauges 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 generated by Tohoku earthquake on March 11th 2011, using data recorded by several tide gauges scattered all over the Pacific area.

A Dynamic Enhancement With Background Reduction Algorithm: Overview and Application to Satellite-Based Dust Storm Detection

Science.gov (United States)

Miller, Steven D.; Bankert, Richard L.; Solbrig, Jeremy E.; Forsythe, John M.; Noh, Yoo-Jeong; Grasso, Lewis D.

2017-12-01

This paper describes a Dynamic Enhancement Background Reduction Algorithm (DEBRA) applicable to multispectral satellite imaging radiometers. DEBRA uses ancillary information about the clear-sky background to reduce false detections of atmospheric parameters in complex scenes. Applied here to the detection of lofted dust, DEBRA enlists a surface emissivity database coupled with a climatological database of surface temperature to approximate the clear-sky equivalent signal for selected infrared-based multispectral dust detection tests. This background allows for suppression of false alarms caused by land surface features while retaining some ability to detect dust above those problematic surfaces. The algorithm is applicable to both day and nighttime observations and enables weighted combinations of dust detection tests. The results are provided quantitatively, as a detection confidence factor [0, 1], but are also readily visualized as enhanced imagery. Utilizing the DEBRA confidence factor as a scaling factor in false color red/green/blue imagery enables depiction of the targeted parameter in the context of the local meteorology and topography. In this way, the method holds utility to both automated clients and human analysts alike. Examples of DEBRA performance from notable dust storms and comparisons against other detection methods and independent observations are presented.

A Supervised Classification Algorithm for Note Onset Detection

Directory of Open Access Journals (Sweden)

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.

Gear Tooth Wear Detection Algorithm

Science.gov (United States)

Delgado, Irebert R.

2015-01-01

Vibration-based condition indicators continue to be developed for Health Usage Monitoring of rotorcraft gearboxes. Testing performed at NASA Glenn Research Center have shown correlations between specific condition indicators and specific types of gear wear. To speed up the detection and analysis of gear teeth, an image detection program based on the Viola-Jones algorithm was trained to automatically detect spiral bevel gear wear pitting. The detector was tested using a training set of gear wear pictures and a blind set of gear wear pictures. The detector accuracy for the training set was 75 percent while the accuracy for the blind set was 15 percent. Further improvements on the accuracy of the detector are required but preliminary results have shown its ability to automatically detect gear tooth wear. The trained detector would be used to quickly evaluate a set of gear or pinion pictures for pits, spalls, or abrasive wear. The results could then be used to correlate with vibration or oil debris data. In general, the program could be retrained to detect features of interest from pictures of a component taken over a period of time.

Detecting microsatellites within genomes: significant variation among algorithms

Directory of Open Access Journals (Sweden)

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 improved label propagation algorithm based on node importance and random walk for community detection

Science.gov (United States)

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.

Development of radio frequency interference detection algorithms for passive microwave remote sensing

Science.gov (United States)

Misra, Sidharth

Radio Frequency Interference (RFI) signals are man-made sources that are increasingly plaguing passive microwave remote sensing measurements. RFI is of insidious nature, with some signals low power enough to go undetected but large enough to impact science measurements and their results. With the launch of the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite in November 2009 and the upcoming launches of the new NASA sea-surface salinity measuring Aquarius mission in June 2011 and soil-moisture measuring Soil Moisture Active Passive (SMAP) mission around 2015, active steps are being taken to detect and mitigate RFI at L-band. An RFI detection algorithm was designed for the Aquarius mission. The algorithm performance was analyzed using kurtosis based RFI ground-truth. The algorithm has been developed with several adjustable location dependant parameters to control the detection statistics (false-alarm rate and probability of detection). The kurtosis statistical detection algorithm has been compared with the Aquarius pulse detection method. The comparative study determines the feasibility of the kurtosis detector for the SMAP radiometer, as a primary RFI detection algorithm in terms of detectability and data bandwidth. The kurtosis algorithm has superior detection capabilities for low duty-cycle radar like pulses, which are more prevalent according to analysis of field campaign data. Most RFI algorithms developed have generally been optimized for performance with individual pulsed-sinusoidal RFI sources. A new RFI detection model is developed that takes into account multiple RFI sources within an antenna footprint. The performance of the kurtosis detection algorithm under such central-limit conditions is evaluated. The SMOS mission has a unique hardware system, and conventional RFI detection techniques cannot be applied. Instead, an RFI detection algorithm for SMOS is developed and applied in the angular domain. This algorithm compares

Multi-User Identification-Based Eye-Tracking Algorithm Using Position Estimation

Directory of Open Access Journals (Sweden)

Suk-Ju Kang

2016-12-01

Full Text Available This paper proposes a new multi-user eye-tracking algorithm using position estimation. Conventional eye-tracking algorithms are typically suitable only for a single user, and thereby cannot be used for a multi-user system. Even though they can be used to track the eyes of multiple users, their detection accuracy is low and they cannot identify multiple users individually. The proposed algorithm solves these problems and enhances the detection accuracy. Specifically, the proposed algorithm adopts a classifier to detect faces for the red, green, and blue (RGB and depth images. Then, it calculates features based on the histogram of the oriented gradient for the detected facial region to identify multiple users, and selects the template that best matches the users from a pre-determined face database. Finally, the proposed algorithm extracts the final eye positions based on anatomical proportions. Simulation results show that the proposed algorithm improved the average F1 score by up to 0.490, compared with benchmark algorithms.

A New Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Complex Networks

Directory of Open Access Journals (Sweden)

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.

A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults.

Science.gov (United States)

Sun, Rui; Cheng, Qi; Wang, Guanyu; Ochieng, Washington Yotto

2017-09-29

The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs' flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF) estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.

A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults

Directory of Open Access Journals (Sweden)

Rui Sun

2017-09-01

Full Text Available The use of Unmanned Aerial Vehicles (UAVs has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs’ flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.

Testing a polarimetric cloud imager aboard research vessel Polarstern: comparison of color-based and polarimetric cloud detection algorithms.

Science.gov (United States)

Barta, András; Horváth, Gábor; Horváth, Ákos; Egri, Ádám; Blahó, Miklós; Barta, Pál; Bumke, Karl; Macke, Andreas

2015-02-10

Cloud cover estimation is an important part of routine meteorological observations. Cloudiness measurements are used in climate model evaluation, nowcasting solar radiation, parameterizing the fluctuations of sea surface insolation, and building energy transfer models of the atmosphere. Currently, the most widespread ground-based method to measure cloudiness is based on analyzing the unpolarized intensity and color distribution of the sky obtained by digital cameras. As a new approach, we propose that cloud detection can be aided by the additional use of skylight polarization measured by 180° field-of-view imaging polarimetry. In the fall of 2010, we tested such a novel polarimetric cloud detector aboard the research vessel Polarstern during expedition ANT-XXVII/1. One of our goals was to test the durability of the measurement hardware under the extreme conditions of a trans-Atlantic cruise. Here, we describe the instrument and compare the results of several different cloud detection algorithms, some conventional and some newly developed. We also discuss the weaknesses of our design and its possible improvements. The comparison with cloud detection algorithms developed for traditional nonpolarimetric full-sky imagers allowed us to evaluate the added value of polarimetric quantities. We found that (1) neural-network-based algorithms perform the best among the investigated schemes and (2) global information (the mean and variance of intensity), nonoptical information (e.g., sun-view geometry), and polarimetric information (e.g., the degree of polarization) improve the accuracy of cloud detection, albeit slightly.

Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications

Science.gov (United States)

Zhu, Zhe

2017-08-01

The free and open access to all archived Landsat images in 2008 has completely changed the way of using Landsat data. Many novel change detection algorithms based on Landsat time series have been developed We present a comprehensive review of four important aspects of change detection studies based on Landsat time series, including frequencies, preprocessing, algorithms, and applications. We observed the trend that the more recent the study, the higher the frequency of Landsat time series used. We reviewed a series of image preprocessing steps, including atmospheric correction, cloud and cloud shadow detection, and composite/fusion/metrics techniques. We divided all change detection algorithms into six categories, including thresholding, differencing, segmentation, trajectory classification, statistical boundary, and regression. Within each category, six major characteristics of different algorithms, such as frequency, change index, univariate/multivariate, online/offline, abrupt/gradual change, and sub-pixel/pixel/spatial were analyzed. Moreover, some of the widely-used change detection algorithms were also discussed. Finally, we reviewed different change detection applications by dividing these applications into two categories, change target and change agent detection.

Behavioral features recognition and oestrus detection based on fast approximate clustering algorithm in dairy cows

Science.gov (United States)

Tian, Fuyang; Cao, Dong; Dong, Xiaoning; Zhao, Xinqiang; Li, Fade; Wang, Zhonghua

2017-06-01

Behavioral features recognition was an important effect to detect oestrus and sickness in dairy herds and there is a need for heat detection aid. The detection method was based on the measure of the individual behavioural activity, standing time, and temperature of dairy using vibrational sensor and temperature sensor in this paper. The data of behavioural activity index, standing time, lying time and walking time were sent to computer by lower power consumption wireless communication system. The fast approximate K-means algorithm (FAKM) was proposed to deal the data of the sensor for behavioral features recognition. As a result of technical progress in monitoring cows using computers, automatic oestrus detection has become possible.

Development of an algorithm for heartbeats detection and classification in Holter records based on temporal and morphological features

International Nuclear Information System (INIS)

García, A; Romano, H; Laciar, E; Correa, R

2011-01-01

In this work a detection and classification algorithm for heartbeats analysis in Holter records was developed. First, a QRS complexes detector was implemented and their temporal and morphological characteristics were extracted. A vector was built with these features; this vector is the input of the classification module, based on discriminant analysis. The beats were classified in three groups: Premature Ventricular Contraction beat (PVC), Atrial Premature Contraction beat (APC) and Normal Beat (NB). These beat categories represent the most important groups of commercial Holter systems. The developed algorithms were evaluated in 76 ECG records of two validated open-access databases 'arrhythmias MIT BIH database' and M IT BIH supraventricular arrhythmias database . A total of 166343 beats were detected and analyzed, where the QRS detection algorithm provides a sensitivity of 99.69 % and a positive predictive value of 99.84 %. The classification stage gives sensitivities of 97.17% for NB, 97.67% for PCV and 92.78% for APC.

Algorithm and assessment work of active fire detection based on FengYun-3C/VIRR

Science.gov (United States)

Lin, Z.; Chen, F.

2017-12-01

The wildfire is one of the most destructive and uncontrollable disasters and causes huge environmental, ecological, social effects. To better serve scientific research and practical fire management, an algorithm and corresponding validation work of active fire detection based on FengYun-3C/VIRR data, which is an optical sensor onboard the Chinese polar-orbiting meteorological sun-synchronous satellite, is hereby introduced. While the main structure heritages the `contextual algorithm', some new concepts including `infrared channel slope' are introduced for better adaptions to different situations. The validation work contains three parts: 1) comparing with the current FengYun-3C fire product GFR; 2) comparing with MODIS fire products; 3) comparing with Landsat series data. Study areas are selected from different places all over the world from 2014 to 2016. The results showed great improvement on GFR files on accuracy of both positioning and detection rate. In most study areas, the results match well with MODIS products and Landsat series data (with over 85% match degree) despite the differences in imaging time. However, detection rates and match degrees in Africa and South-east Asia are not satisfied (around 70%), where the occurrences of numerous small fire events and corresponding smokes may strongly affect the results of the algorithm. This is our future research direction and one of the main improvements requires achieving.

Information dynamics algorithm for detecting communities in networks

Science.gov (United States)

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.

A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms.

Science.gov (United States)

Emrani, Zahra; Bateni, Soroosh; Rabbani, Hossein

2017-01-01

Real-time image processing is used in a wide variety of applications like those in medical care and industrial processes. This technique in medical care has the ability to display important patient information graphi graphically, which can supplement and help the treatment process. Medical decisions made based on real-time images are more accurate and reliable. According to the recent researches, graphic processing unit (GPU) programming is a useful method for improving the speed and quality of medical image processing and is one of the ways of real-time image processing. Edge detection is an early stage in most of the image processing methods for the extraction of features and object segments from a raw image. The Canny method, Sobel and Prewitt filters, and the Roberts' Cross technique are some examples of edge detection algorithms that are widely used in image processing and machine vision. In this work, these algorithms are implemented using the Compute Unified Device Architecture (CUDA), Open Source Computer Vision (OpenCV), and Matrix Laboratory (MATLAB) platforms. An existing parallel method for Canny approach has been modified further to run in a fully parallel manner. This has been achieved by replacing the breadth- first search procedure with a parallel method. These algorithms have been compared by testing them on a database of optical coherence tomography images. The comparison of results shows that the proposed implementation of the Canny method on GPU using the CUDA platform improves the speed of execution by 2-100× compared to the central processing unit-based implementation using the OpenCV and MATLAB platforms.

A Turn-Projected State-Based Conflict Resolution Algorithm

Science.gov (United States)

Butler, Ricky W.; Lewis, Timothy A.

2013-01-01

State-based conflict detection and resolution (CD&R) algorithms detect conflicts and resolve them on the basis on current state information without the use of additional intent information from aircraft flight plans. Therefore, the prediction of the trajectory of aircraft is based solely upon the position and velocity vectors of the traffic aircraft. Most CD&R algorithms project the traffic state using only the current state vectors. However, the past state vectors can be used to make a better prediction of the future trajectory of the traffic aircraft. This paper explores the idea of using past state vectors to detect traffic turns and resolve conflicts caused by these turns using a non-linear projection of the traffic state. A new algorithm based on this idea is presented and validated using a fast-time simulator developed for this study.

A robust human face detection algorithm

Science.gov (United States)

Raviteja, Thaluru; Karanam, Srikrishna; Yeduguru, Dinesh Reddy V.

2012-01-01

Human face detection plays a vital role in many applications like video surveillance, managing a face image database, human computer interface among others. This paper proposes a robust algorithm for face detection in still color images that works well even in a crowded environment. The algorithm uses conjunction of skin color histogram, morphological processing and geometrical analysis for detecting human faces. To reinforce the accuracy of face detection, we further identify mouth and eye regions to establish the presence/absence of face in a particular region of interest.

Improved target detection algorithm using Fukunaga-Koontz transform and distance classifier correlation filter

Science.gov (United States)

Bal, A.; Alam, M. S.; Aslan, M. S.

2006-05-01

Often sensor ego-motion or fast target movement causes the target to temporarily go out of the field-of-view leading to reappearing target detection problem in target tracking applications. Since the target goes out of the current frame and reenters at a later frame, the reentering location and variations in rotation, scale, and other 3D orientations of the target are not known thus complicating the detection algorithm has been developed using Fukunaga-Koontz Transform (FKT) and distance classifier correlation filter (DCCF). The detection algorithm uses target and background information, extracted from training samples, to detect possible candidate target images. The detected candidate target images are then introduced into the second algorithm, DCCF, called clutter rejection module, to determine the target coordinates are detected and tracking algorithm is initiated. The performance of the proposed FKT-DCCF based target detection algorithm has been tested using real-world forward looking infrared (FLIR) video sequences.

A CDMA multiuser detection algorithm on the basis of belief propagation

International Nuclear Information System (INIS)

Kabashima, Yoshiyuki

2003-01-01

An iterative algorithm for the multiuser detection problem that arises in code division multiple access (CDMA) systems is developed on the basis of Pearl's belief propagation (BP). We show that the BP-based algorithm exhibits nearly optimal performance in a practical time scale by utilizing the central limit theorem and self-averaging property appropriately, whereas direct application of BP to the detection problem is computationally difficult and far from practical. We further present close relationships of the proposed algorithm to the Thouless-Anderson-Palmer approach and replica analysis known in spin-glass research

MUSIC algorithms for rebar detection

International Nuclear Information System (INIS)

Solimene, Raffaele; Leone, Giovanni; Dell’Aversano, Angela

2013-01-01

The MUSIC (MUltiple SIgnal Classification) algorithm is employed to detect and localize an unknown number of scattering objects which are small in size as compared to the wavelength. The ensemble of objects to be detected consists of both strong and weak scatterers. This represents a scattering environment challenging for detection purposes as strong scatterers tend to mask the weak ones. Consequently, the detection of more weakly scattering objects is not always guaranteed and can be completely impaired when the noise corrupting data is of a relatively high level. To overcome this drawback, here a new technique is proposed, starting from the idea of applying a two-stage MUSIC algorithm. In the first stage strong scatterers are detected. Then, information concerning their number and location is employed in the second stage focusing only on the weak scatterers. The role of an adequate scattering model is emphasized to improve drastically detection performance in realistic scenarios. (paper)

A Comprehensive Training Data Set for the Development of Satellite-Based Volcanic Ash Detection Algorithms

Science.gov (United States)

Schmidl, Marius

2017-04-01

We present a comprehensive training data set covering a large range of atmospheric conditions, including disperse volcanic ash and desert dust layers. These data sets contain all information required for the development of volcanic ash detection algorithms based on artificial neural networks, urgently needed since volcanic ash in the airspace is a major concern of aviation safety authorities. Selected parts of the data are used to train the volcanic ash detection algorithm VADUGS. They contain atmospheric and surface-related quantities as well as the corresponding simulated satellite data for the channels in the infrared spectral range of the SEVIRI instrument on board MSG-2. To get realistic results, ECMWF, IASI-based, and GEOS-Chem data are used to calculate all parameters describing the environment, whereas the software package libRadtran is used to perform radiative transfer simulations returning the brightness temperatures for each atmospheric state. As optical properties are a prerequisite for radiative simulations accounting for aerosol layers, the development also included the computation of optical properties for a set of different aerosol types from different sources. A description of the developed software and the used methods is given, besides an overview of the resulting data sets.

Fault detection and isolation in GPS receiver autonomous integrity monitoring based on chaos particle swarm optimization-particle filter algorithm

Science.gov (United States)

Wang, Ershen; Jia, Chaoying; Tong, Gang; Qu, Pingping; Lan, Xiaoyu; Pang, Tao

2018-03-01

The receiver autonomous integrity monitoring (RAIM) is one of the most important parts in an avionic navigation system. Two problems need to be addressed to improve this system, namely, the degeneracy phenomenon and lack of samples for the standard particle filter (PF). However, the number of samples cannot adequately express the real distribution of the probability density function (i.e., sample impoverishment). This study presents a GPS receiver autonomous integrity monitoring (RAIM) method based on a chaos particle swarm optimization particle filter (CPSO-PF) algorithm with a log likelihood ratio. The chaos sequence generates a set of chaotic variables, which are mapped to the interval of optimization variables to improve particle quality. This chaos perturbation overcomes the potential for the search to become trapped in a local optimum in the particle swarm optimization (PSO) algorithm. Test statistics are configured based on a likelihood ratio, and satellite fault detection is then conducted by checking the consistency between the state estimate of the main PF and those of the auxiliary PFs. Based on GPS data, the experimental results demonstrate that the proposed algorithm can effectively detect and isolate satellite faults under conditions of non-Gaussian measurement noise. Moreover, the performance of the proposed novel method is better than that of RAIM based on the PF or PSO-PF algorithm.

A Self-embedding Robust Digital Watermarking Algorithm with Blind Detection

Directory of Open Access Journals (Sweden)

Gong Yunfeng

2014-08-01

Full Text Available In order to achieve the perfectly blind detection of robustness watermarking algorithm, a novel self-embedding robust digital watermarking algorithm with blind detection is proposed in this paper. Firstly the original image is divided to not overlap image blocks and then decomposable coefficients are obtained by lifting-based wavelet transform in every image blocks. Secondly the low-frequency coefficients of block images are selected and then approximately represented as a product of a base matrix and a coefficient matrix using NMF. Then the feature vector represent original image is obtained by quantizing coefficient matrix, and finally the adaptive quantization of the robustness watermark is embedded in the low-frequency coefficients of LWT. Experimental results show that the scheme is robust against common signal processing attacks, meanwhile perfect blind detection is achieve.

Plagiarism Detection Based on SCAM Algorithm

DEFF Research Database (Denmark)

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....

Comparison of low-contrast detectability between two CT reconstruction algorithms using voxel-based 3D printed textured phantoms.

Science.gov (United States)

Solomon, Justin; Ba, Alexandre; Bochud, François; Samei, Ehsan

2016-12-01

To use novel voxel-based 3D printed textured phantoms in order to compare low-contrast detectability between two reconstruction algorithms, FBP (filtered-backprojection) and SAFIRE (sinogram affirmed iterative reconstruction) and determine what impact background texture (i.e., anatomical noise) has on estimating the dose reduction potential of SAFIRE. Liver volumes were segmented from 23 abdominal CT cases. The volumes were characterized in terms of texture features from gray-level co-occurrence and run-length matrices. Using a 3D clustered lumpy background (CLB) model, a fitting technique based on a genetic optimization algorithm was used to find CLB textures that were reflective of the liver textures, accounting for CT system factors of spatial blurring and noise. With the modeled background texture as a guide, four cylindrical phantoms (Textures A-C and uniform, 165 mm in diameter, and 30 mm height) were designed, each containing 20 low-contrast spherical signals (6 mm diameter at nominal contrast levels of ∼3.2, 5.2, 7.2, 10, and 14 HU with four repeats per signal). The phantoms were voxelized and input into a commercial multimaterial 3D printer (Object Connex 350), with custom software for voxel-based printing (using principles of digital dithering). Images of the textured phantoms and a corresponding uniform phantom were acquired at six radiation dose levels (SOMATOM Flash, Siemens Healthcare) and observer model detection performance (detectability index of a multislice channelized Hotelling observer) was estimated for each condition (5 contrasts × 6 doses × 2 reconstructions × 4 backgrounds = 240 total conditions). A multivariate generalized regression analysis was performed (linear terms, no interactions, random error term, log link function) to assess whether dose, reconstruction algorithm, signal contrast, and background type have statistically significant effects on detectability. Also, fitted curves of detectability (averaged across contrast levels

Artifact removal algorithms for stroke detection using a multistatic MIST beamforming algorithm.

Science.gov (United States)

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.

Radiation anomaly detection algorithms for field-acquired gamma energy spectra

Science.gov (United States)

Mukhopadhyay, Sanjoy; Maurer, Richard; Wolff, Ron; Guss, Paul; Mitchell, Stephen

2015-08-01

The Remote Sensing Laboratory (RSL) is developing a tactical, networked radiation detection system that will be agile, reconfigurable, and capable of rapid threat assessment with high degree of fidelity and certainty. Our design is driven by the needs of users such as law enforcement personnel who must make decisions by evaluating threat signatures in urban settings. The most efficient tool available to identify the nature of the threat object is real-time gamma spectroscopic analysis, as it is fast and has a very low probability of producing false positive alarm conditions. Urban radiological searches are inherently challenged by the rapid and large spatial variation of background gamma radiation, the presence of benign radioactive materials in terms of the normally occurring radioactive materials (NORM), and shielded and/or masked threat sources. Multiple spectral anomaly detection algorithms have been developed by national laboratories and commercial vendors. For example, the Gamma Detector Response and Analysis Software (GADRAS) a one-dimensional deterministic radiation transport software capable of calculating gamma ray spectra using physics-based detector response functions was developed at Sandia National Laboratories. The nuisance-rejection spectral comparison ratio anomaly detection algorithm (or NSCRAD), developed at Pacific Northwest National Laboratory, uses spectral comparison ratios to detect deviation from benign medical and NORM radiation source and can work in spite of strong presence of NORM and or medical sources. RSL has developed its own wavelet-based gamma energy spectral anomaly detection algorithm called WAVRAD. Test results and relative merits of these different algorithms will be discussed and demonstrated.

A robust background regression based score estimation algorithm for hyperspectral anomaly detection

Science.gov (United States)

Zhao, Rui; Du, Bo; Zhang, Liangpei; Zhang, Lefei

2016-12-01

Anomaly detection has become a hot topic in the hyperspectral image analysis and processing fields in recent years. The most important issue for hyperspectral anomaly detection is the background estimation and suppression. Unreasonable or non-robust background estimation usually leads to unsatisfactory anomaly detection results. Furthermore, the inherent nonlinearity of hyperspectral images may cover up the intrinsic data structure in the anomaly detection. In order to implement robust background estimation, as well as to explore the intrinsic data structure of the hyperspectral image, we propose a robust background regression based score estimation algorithm (RBRSE) for hyperspectral anomaly detection. The Robust Background Regression (RBR) is actually a label assignment procedure which segments the hyperspectral data into a robust background dataset and a potential anomaly dataset with an intersection boundary. In the RBR, a kernel expansion technique, which explores the nonlinear structure of the hyperspectral data in a reproducing kernel Hilbert space, is utilized to formulate the data as a density feature representation. A minimum squared loss relationship is constructed between the data density feature and the corresponding assigned labels of the hyperspectral data, to formulate the foundation of the regression. Furthermore, a manifold regularization term which explores the manifold smoothness of the hyperspectral data, and a maximization term of the robust background average density, which suppresses the bias caused by the potential anomalies, are jointly appended in the RBR procedure. After this, a paired-dataset based k-nn score estimation method is undertaken on the robust background and potential anomaly datasets, to implement the detection output. The experimental results show that RBRSE achieves superior ROC curves, AUC values, and background-anomaly separation than some of the other state-of-the-art anomaly detection methods, and is easy to implement

MMSE-based algorithm for joint signal detection, channel and noise variance estimation for OFDM systems

CERN Document Server

Savaux, Vincent

2014-01-01

This book presents an algorithm for the detection of an orthogonal frequency division multiplexing (OFDM) signal in a cognitive radio context by means of a joint and iterative channel and noise estimation technique. Based on the minimum mean square criterion, it performs an accurate detection of a user in a frequency band, by achieving a quasi-optimal channel and noise variance estimation if the signal is present, and by estimating the noise level in the band if the signal is absent. Organized into three chapters, the first chapter provides the background against which the system model is pr

Semi-supervised spectral algorithms for community detection in complex networks based on equivalence of clustering methods

Science.gov (United States)

Ma, Xiaoke; Wang, Bingbo; Yu, Liang

2018-01-01

Community detection is fundamental for revealing the structure-functionality relationship in complex networks, which involves two issues-the quantitative function for community as well as algorithms to discover communities. Despite significant research on either of them, few attempt has been made to establish the connection between the two issues. To attack this problem, a generalized quantification function is proposed for community in weighted networks, which provides a framework that unifies several well-known measures. Then, we prove that the trace optimization of the proposed measure is equivalent with the objective functions of algorithms such as nonnegative matrix factorization, kernel K-means as well as spectral clustering. It serves as the theoretical foundation for designing algorithms for community detection. On the second issue, a semi-supervised spectral clustering algorithm is developed by exploring the equivalence relation via combining the nonnegative matrix factorization and spectral clustering. Different from the traditional semi-supervised algorithms, the partial supervision is integrated into the objective of the spectral algorithm. Finally, through extensive experiments on both artificial and real world networks, we demonstrate that the proposed method improves the accuracy of the traditional spectral algorithms in community detection.

A Time-Domain Structural Damage Detection Method Based on Improved Multiparticle Swarm Coevolution Optimization Algorithm

Directory of Open Access Journals (Sweden)

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.

An Automated Energy Detection Algorithm Based on Kurtosis-Histogram Excision

Science.gov (United States)

2018-01-01

10 kHz, 100 kHz, 1 MHz 100 MHz–1 GHz 1 100 kHz 3. Statistical Processing 3.1 Statistical Analysis Statistical analysis is the mathematical ...quantitative terms. In commercial prognostics and diagnostic vibrational monitoring applications , statistical techniques that are mainly used for alarm...applying statistical processing techniques to the energy detection scenario of signals in the RF spectrum domain. The algorithm was developed after

A hybrid neural network – world cup optimization algorithm for melanoma detection

Directory of Open Access Journals (Sweden)

Razmjooy Navid

2018-03-01

Full Text Available One of the most dangerous cancers in humans is Melanoma. However, early detection of melanoma can help us to cure it completely. This paper presents a new efficient method to detect malignancy in melanoma via images. At first, the extra scales are eliminated by using edge detection and smoothing. Afterwards, the proposed method can be utilized to segment the cancer images. Finally, the extra information is eliminated by morphological operations and used to focus on the area which melanoma boundary potentially exists. To do this, World Cup Optimization algorithm is utilized to optimize an MLP neural Networks (ANN. World Cup Optimization algorithm is a new meta-heuristic algorithm which is recently presented and has a good performance in some optimization problems. WCO is a derivative-free, Meta-Heuristic algorithm, mimicking the world’s FIFA competitions. World cup Optimization algorithm is a global search algorithm while gradient-based back propagation method is local search. In this proposed algorithm, multi-layer perceptron network (MLP employs the problem’s constraints and WCO algorithm attempts to minimize the root mean square error. Experimental results show that the proposed method can develop the performance of the standard MLP algorithm significantly.

Implementing a Parallel Image Edge Detection Algorithm Based on the Otsu-Canny Operator on the Hadoop Platform.

Science.gov (United States)

Cao, Jianfang; Chen, Lichao; Wang, Min; Tian, Yun

2018-01-01

The Canny operator is widely used to detect edges in images. However, as the size of the image dataset increases, the edge detection performance of the Canny operator decreases and its runtime becomes excessive. To improve the runtime and edge detection performance of the Canny operator, in this paper, we propose a parallel design and implementation for an Otsu-optimized Canny operator using a MapReduce parallel programming model that runs on the Hadoop platform. The Otsu algorithm is used to optimize the Canny operator's dual threshold and improve the edge detection performance, while the MapReduce parallel programming model facilitates parallel processing for the Canny operator to solve the processing speed and communication cost problems that occur when the Canny edge detection algorithm is applied to big data. For the experiments, we constructed datasets of different scales from the Pascal VOC2012 image database. The proposed parallel Otsu-Canny edge detection algorithm performs better than other traditional edge detection algorithms. The parallel approach reduced the running time by approximately 67.2% on a Hadoop cluster architecture consisting of 5 nodes with a dataset of 60,000 images. Overall, our approach system speeds up the system by approximately 3.4 times when processing large-scale datasets, which demonstrates the obvious superiority of our method. The proposed algorithm in this study demonstrates both better edge detection performance and improved time performance.

Optimal Detection Range of RFID Tag for RFID-based Positioning System Using the k-NN Algorithm

Directory of Open Access Journals (Sweden)

Joon Heo

2009-06-01

Full Text Available Positioning technology to track a moving object is an important and essential component of ubiquitous computing environments and applications. An RFID-based positioning system using the k-nearest neighbor (k-NN algorithm can determine the position of a moving reader from observed reference data. In this study, the optimal detection range of an RFID-based positioning system was determined on the principle that tag spacing can be derived from the detection range. It was assumed that reference tags without signal strength information are regularly distributed in 1-, 2- and 3-dimensional spaces. The optimal detection range was determined, through analytical and numerical approaches, to be 125% of the tag-spacing distance in 1-dimensional space. Through numerical approaches, the range was 134% in 2-dimensional space, 143% in 3-dimensional space.

An Idle-State Detection Algorithm for SSVEP-Based Brain-Computer Interfaces Using a Maximum Evoked Response Spatial Filter.

Science.gov (United States)

Zhang, Dan; Huang, Bisheng; Wu, Wei; Li, Siliang

2015-11-01

Although accurate recognition of the idle state is essential for the application of brain-computer interfaces (BCIs) in real-world situations, it remains a challenging task due to the variability of the idle state. In this study, a novel algorithm was proposed for the idle state detection in a steady-state visual evoked potential (SSVEP)-based BCI. The proposed algorithm aims to solve the idle state detection problem by constructing a better model of the control states. For feature extraction, a maximum evoked response (MER) spatial filter was developed to extract neurophysiologically plausible SSVEP responses, by finding the combination of multi-channel electroencephalogram (EEG) signals that maximized the evoked responses while suppressing the unrelated background EEGs. The extracted SSVEP responses at the frequencies of both the attended and the unattended stimuli were then used to form feature vectors and a series of binary classifiers for recognition of each control state and the idle state were constructed. EEG data from nine subjects in a three-target SSVEP BCI experiment with a variety of idle state conditions were used to evaluate the proposed algorithm. Compared to the most popular canonical correlation analysis-based algorithm and the conventional power spectrum-based algorithm, the proposed algorithm outperformed them by achieving an offline control state classification accuracy of 88.0 ± 11.1% and idle state false positive rates (FPRs) ranging from 7.4 ± 5.6% to 14.2 ± 10.1%, depending on the specific idle state conditions. Moreover, the online simulation reported BCI performance close to practical use: 22.0 ± 2.9 out of the 24 control commands were correctly recognized and the FPRs achieved as low as approximately 0.5 event/min in the idle state conditions with eye open and 0.05 event/min in the idle state condition with eye closed. These results demonstrate the potential of the proposed algorithm for implementing practical SSVEP BCI systems.

A New MANET Wormhole Detection Algorithm Based on Traversal Time and Hop Count Analysis

Directory of Open Access Journals (Sweden)

Göran Pulkkis

2011-11-01

Full Text Available As demand increases for ubiquitous network facilities, infrastructure-less and self-configuring systems like Mobile Ad hoc Networks (MANET are gaining popularity. MANET routing security however, is one of the most significant challenges to wide scale adoption, with wormhole attacks being an especially severe MANET routing threat. This is because wormholes are able to disrupt a major component of network traffic, while concomitantly being extremely difficult to detect. This paper introduces a new wormhole detection paradigm based upon Traversal Time and Hop Count Analysis (TTHCA, which in comparison to existing algorithms, consistently affords superior detection performance, allied with low false positive rates for all wormhole variants. Simulation results confirm that the TTHCA model exhibits robust wormhole route detection in various network scenarios, while incurring only a small network overhead. This feature makes TTHCA an attractive choice for MANET environments which generally comprise devices, such as wireless sensors, which possess a limited processing capability.

A Distributed Algorithm for the Cluster-Based Outlier Detection Using Unsupervised Extreme Learning Machines

Directory of Open Access Journals (Sweden)

Xite Wang

2017-01-01

Full Text Available Outlier detection is an important data mining task, whose target is to find the abnormal or atypical objects from a given dataset. The techniques for detecting outliers have a lot of applications, such as credit card fraud detection and environment monitoring. Our previous work proposed the Cluster-Based (CB outlier and gave a centralized method using unsupervised extreme learning machines to compute CB outliers. In this paper, we propose a new distributed algorithm for the CB outlier detection (DACB. On the master node, we collect a small number of points from the slave nodes to obtain a threshold. On each slave node, we design a new filtering method that can use the threshold to efficiently speed up the computation. Furthermore, we also propose a ranking method to optimize the order of cluster scanning. At last, the effectiveness and efficiency of the proposed approaches are verified through a plenty of simulation experiments.

Performance improvement of multi-class detection using greedy algorithm for Viola-Jones cascade selection

Science.gov (United States)

Tereshin, Alexander A.; Usilin, Sergey A.; Arlazarov, Vladimir V.

2018-04-01

This paper aims to study the problem of multi-class object detection in video stream with Viola-Jones cascades. An adaptive algorithm for selecting Viola-Jones cascade based on greedy choice strategy in solution of the N-armed bandit problem is proposed. The efficiency of the algorithm on the problem of detection and recognition of the bank card logos in the video stream is shown. The proposed algorithm can be effectively used in documents localization and identification, recognition of road scene elements, localization and tracking of the lengthy objects , and for solving other problems of rigid object detection in a heterogeneous data flows. The computational efficiency of the algorithm makes it possible to use it both on personal computers and on mobile devices based on processors with low power consumption.

A configurable tracking algorithm to detect cosmic muon tracks for the CMS-RPC based technical trigger

CERN Document Server

Rajan, R T; Loddo, F; Maggi, M; Ranieri, A; Abbrescia, M; Guida, R; Iaselli, G; Nuzzo, S; Pugliese, G; Roselli, G; Trentadue, R; Tupputi, b, S; Benussi, L; Bertani, M; Bianco, S; Fabbri, F; Cavallo, N; Cimmino, e, A; Lomidze, D; Noli, P; Paolucci, P; Piccolo, D; Polese, G; Sciacca, C; Baesso, g, P; Belli, G; Necchi, M; Ratti, S P; Pagano, D; Vitulo, P; Viviani, C; Dimitrov, A; Litov, L; Pavlov, B; Petkov, P; Genchev, V; Iaydjiev, P; Bunkowski, K; Kierzkowski, K; Konecki, M; Kudla, I; Pietrusinski, M; Pozniak, K

2009-01-01

In the CERN CMS experiment at LHC Collider special trigger signals called Technical Triggers will be used for the purpose of test and calibration. The Resistive Plate Chambers (RPC) based Technical Trigger system is a part of the CMS muon trigger system and is designed to detect cosmic muon tracks. It is based on two boards, namely RBC (RPC Balcony Collector) and TTU (Technical Trigger Unit). The proposed tracking algorithm (TA) written in VHDL and implemented in the TTU board detects single or multiple cosmic muon tracks at every bunch crossing along with their track lengths and corresponding chamber coordinates. The TA implementation in VHDL and its preliminary simulation results are presented.

Induced Voltages Ratio-Based Algorithm for Fault Detection, and Faulted Phase and Winding Identification of a Three-Winding Power Transformer

Directory of Open Access Journals (Sweden)

Byung Eun Lee

2014-09-01

Full Text Available This paper proposes an algorithm for fault detection, faulted phase and winding identification of a three-winding power transformer based on the induced voltages in the electrical power system. The ratio of the induced voltages of the primary-secondary, primary-tertiary and secondary-tertiary windings is the same as the corresponding turns ratio during normal operating conditions, magnetic inrush, and over-excitation. It differs from the turns ratio during an internal fault. For a single phase and a three-phase power transformer with wye-connected windings, the induced voltages of each pair of windings are estimated. For a three-phase power transformer with delta-connected windings, the induced voltage differences are estimated to use the line currents, because the delta winding currents are practically unavailable. Six detectors are suggested for fault detection. An additional three detectors and a rule for faulted phase and winding identification are presented as well. The proposed algorithm can not only detect an internal fault, but also identify the faulted phase and winding of a three-winding power transformer. The various test results with Electromagnetic Transients Program (EMTP-generated data show that the proposed algorithm successfully discriminates internal faults from normal operating conditions including magnetic inrush and over-excitation. This paper concludes by implementing the algorithm into a prototype relay based on a digital signal processor.

Deviation from Trajectory Detection in Vision based Robotic Navigation using SURF and Subsequent Restoration by Dynamic Auto Correction Algorithm

Directory of Open Access Journals (Sweden)

Ray Debraj

2015-01-01

Full Text Available Speeded Up Robust Feature (SURF is used to position a robot with respect to an environment and aid in vision-based robotic navigation. During the course of navigation irregularities in the terrain, especially in an outdoor environment may deviate a robot from the track. Another reason for deviation can be unequal speed of the left and right robot wheels. Hence it is essential to detect such deviations and perform corrective operations to bring the robot back to the track. In this paper we propose a novel algorithm that uses image matching using SURF to detect deviation of a robot from the trajectory and subsequent restoration by corrective operations. This algorithm is executed in parallel to positioning and navigation algorithms by distributing tasks among different CPU cores using Open Multi-Processing (OpenMP API.

Development and testing of incident detection algorithms. Vol. 2, research methodology and detailed results.

Science.gov (United States)

1976-04-01

The development and testing of incident detection algorithms was based on Los Angeles and Minneapolis freeway surveillance data. Algorithms considered were based on times series and pattern recognition techniques. Attention was given to the effects o...

ICPD-a new peak detection algorithm for LC/MS.

Science.gov (United States)

Zhang, Jianqiu; Haskins, William

2010-12-01

The identification and quantification of proteins using label-free Liquid Chromatography/Mass Spectrometry (LC/MS) play crucial roles in biological and biomedical research. Increasing evidence has shown that biomarkers are often low abundance proteins. However, LC/MS systems are subject to considerable noise and sample variability, whose statistical characteristics are still elusive, making computational identification of low abundance proteins extremely challenging. As a result, the inability of identifying low abundance proteins in a proteomic study is the main bottleneck in protein biomarker discovery. In this paper, we propose a new peak detection method called Information Combining Peak Detection (ICPD ) for high resolution LC/MS. In LC/MS, peptides elute during a certain time period and as a result, peptide isotope patterns are registered in multiple MS scans. The key feature of the new algorithm is that the observed isotope patterns registered in multiple scans are combined together for estimating the likelihood of the peptide existence. An isotope pattern matching score based on the likelihood probability is provided and utilized for peak detection. The performance of the new algorithm is evaluated based on protein standards with 48 known proteins. The evaluation shows better peak detection accuracy for low abundance proteins than other LC/MS peak detection methods.

Object Detection and Tracking using Modified Diamond Search Block Matching Motion Estimation Algorithm

Directory of Open Access Journals (Sweden)

Apurva Samdurkar

2018-06-01

Full Text Available Object tracking is one of the main fields within computer vision. Amongst various methods/ approaches for object detection and tracking, the background subtraction approach makes the detection of object easier. To the detected object, apply the proposed block matching algorithm for generating the motion vectors. The existing diamond search (DS and cross diamond search algorithms (CDS are studied and experiments are carried out on various standard video data sets and user defined data sets. Based on the study and analysis of these two existing algorithms a modified diamond search pattern (MDS algorithm is proposed using small diamond shape search pattern in initial step and large diamond shape (LDS in further steps for motion estimation. The initial search pattern consists of five points in small diamond shape pattern and gradually grows into a large diamond shape pattern, based on the point with minimum cost function. The algorithm ends with the small shape pattern at last. The proposed MDS algorithm finds the smaller motion vectors and fewer searching points than the existing DS and CDS algorithms. Further, object detection is carried out by using background subtraction approach and finally, MDS motion estimation algorithm is used for tracking the object in color video sequences. The experiments are carried out by using different video data sets containing a single object. The results are evaluated and compared by using the evaluation parameters like average searching points per frame and average computational time per frame. The experimental results show that the MDS performs better than DS and CDS on average search point and average computation time.

Nearest Neighbour Corner Points Matching Detection Algorithm

Directory of Open Access Journals (Sweden)

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.

Evaluation of Face Detection Algorithms for the Bank Client Identity Verification

Directory of Open Access Journals (Sweden)

Szczodrak Maciej

2017-06-01

Full Text Available Results of investigation of face detection algorithms efficiency in the banking client visual verification system are presented. The video recordings were made in real conditions met in three bank operating outlets employing a miniature industrial USB camera. The aim of the experiments was to check the practical usability of the face detection method in the biometric bank client verification system. The main assumption was to provide a simplified as much as possible user interaction with the application. Applied algorithms for face detection are described and achieved results of face detection in the real bank environment conditions are presented. Practical limitations of the application based on encountered problems are discussed.

MODIS 250m burned area mapping based on an algorithm using change point detection and Markov random fields.

Science.gov (United States)

Mota, Bernardo; Pereira, Jose; Campagnolo, Manuel; Killick, Rebeca

2013-04-01

Area burned in tropical savannas of Brazil was mapped using MODIS-AQUA daily 250m resolution imagery by adapting one of the European Space Agency fire_CCI project burned area algorithms, based on change point detection and Markov random fields. The study area covers 1,44 Mkm2 and was performed with data from 2005. The daily 1000 m image quality layer was used for cloud and cloud shadow screening. The algorithm addresses each pixel as a time series and detects changes in the statistical properties of NIR reflectance values, to identify potential burning dates. The first step of the algorithm is robust filtering, to exclude outlier observations, followed by application of the Pruned Exact Linear Time (PELT) change point detection technique. Near-infrared (NIR) spectral reflectance changes between time segments, and post change NIR reflectance values are combined into a fire likelihood score. Change points corresponding to an increase in reflectance are dismissed as potential burn events, as are those occurring outside of a pre-defined fire season. In the last step of the algorithm, monthly burned area probability maps and detection date maps are converted to dichotomous (burned-unburned maps) using Markov random fields, which take into account both spatial and temporal relations in the potential burned area maps. A preliminary assessment of our results is performed by comparison with data from the MODIS 1km active fires and the 500m burned area products, taking into account differences in spatial resolution between the two sensors.

Validation of an automated seizure detection algorithm for term neonates

Science.gov (United States)

Mathieson, Sean R.; Stevenson, Nathan J.; Low, Evonne; Marnane, William P.; Rennie, Janet M.; Temko, Andrey; Lightbody, Gordon; Boylan, Geraldine B.

2016-01-01

Objective The objective of this study was to validate the performance of a seizure detection algorithm (SDA) developed by our group, on previously unseen, prolonged, unedited EEG recordings from 70 babies from 2 centres. Methods EEGs of 70 babies (35 seizure, 35 non-seizure) were annotated for seizures by experts as the gold standard. The SDA was tested on the EEGs at a range of sensitivity settings. Annotations from the expert and SDA were compared using event and epoch based metrics. The effect of seizure duration on SDA performance was also analysed. Results Between sensitivity settings of 0.5 and 0.3, the algorithm achieved seizure detection rates of 52.6–75.0%, with false detection (FD) rates of 0.04–0.36 FD/h for event based analysis, which was deemed to be acceptable in a clinical environment. Time based comparison of expert and SDA annotations using Cohen’s Kappa Index revealed a best performing SDA threshold of 0.4 (Kappa 0.630). The SDA showed improved detection performance with longer seizures. Conclusion The SDA achieved promising performance and warrants further testing in a live clinical evaluation. Significance The SDA has the potential to improve seizure detection and provide a robust tool for comparing treatment regimens. PMID:26055336

An improved reconstruction algorithm based on multi-user detection for uplink grant-free NOMA

Directory of Open Access Journals (Sweden)

Hou Chengyan

2017-01-01

Full Text Available For the traditional orthogonal matching pursuit(OMP algorithm in multi-user detection(MUD for uplink grant-free NOMA, here is a poor BER performance, so in this paper we propose an temporal-correlation orthogonal matching pursuit algorithm(TOMP to realize muli-user detection. The core idea of the TOMP is to use the time correlation of the active user sets to achieve user activity and data detection in a number of continuous time slots. We use the estimated active user set in the current time slot as a priori information to estimate the active user sets for the next slot. By maintaining the active user set Tˆl of size K(K is the number of users, but modified in each iteration. Specifically, active user set is believed to be reliable in one iteration but shown error in another iteration, can be added to the set path delay Tˆl or removed from it. Theoretical analysis of the improved algorithm provide a guarantee that the multi-user can be successfully detected with a high probability. The simulation results show that the proposed scheme can achieve better bit error rate (BER performance in the uplink grant-free NOMA system.

Zero-crossing detection algorithm for arrays of optical spatial filtering velocimetry sensors

DEFF Research Database (Denmark)

Jakobsen, Michael Linde; Pedersen, Finn; Hanson, Steen Grüner

2008-01-01

This paper presents a zero-crossing detection algorithm for arrays of compact low-cost optical sensors based on spatial filtering for measuring fluctuations in angular velocity of rotating solid structures. The algorithm is applicable for signals with moderate signal-to-noise ratios, and delivers...... repeating the same measurement error for each revolution of the target, and to gain high performance measurement of angular velocity. The traditional zero-crossing detection is extended by 1) inserting an appropriate band-pass filter before the zero-crossing detection, 2) measuring time periods between zero...

Detection of Illegitimate Emails using Boosting Algorithm

DEFF Research Database (Denmark)

Nizamani, Sarwat; Memon, Nasrullah; Wiil, Uffe Kock

2011-01-01

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......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...

Automatic Detection and Quantification of WBCs and RBCs Using Iterative Structured Circle Detection Algorithm

Directory of Open Access Journals (Sweden)

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.

A novel through-wall respiration detection algorithm using UWB radar.

Science.gov (United States)

Li, Xin; Qiao, Dengyu; Li, Ye; Dai, Huhe

2013-01-01

Through-wall respiration detection using Ultra-wideband (UWB) impulse radar can be applied to the post-disaster rescue, e.g., searching living persons trapped in ruined buildings after an earthquake. Since strong interference signals always exist in the real-life scenarios, such as static clutter, noise, etc., while the respiratory signal is very weak, the signal to noise and clutter ratio (SNCR) is quite low. Therefore, through-wall respiration detection using UWB impulse radar under low SNCR is a challenging work in the research field of searching survivors after disaster. In this paper, an improved UWB respiratory signal model is built up based on an even power of cosine function for the first time. This model is used to reveal the harmonic structure of respiratory signal, based on which a novel high-performance respiration detection algorithm is proposed. This novel algorithm is assessed by experimental verification and simulation and shows about a 1.5dB improvement of SNR and SNCR.

Advanced defect detection algorithm using clustering in ultrasonic NDE

Science.gov (United States)

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.

Algorithmic detectability threshold of the stochastic block model

Science.gov (United States)

Kawamoto, Tatsuro

2018-03-01

The assumption that the values of model parameters are known or correctly learned, i.e., the Nishimori condition, is one of the requirements for the detectability analysis of the stochastic block model in statistical inference. In practice, however, there is no example demonstrating that we can know the model parameters beforehand, and there is no guarantee that the model parameters can be learned accurately. In this study, we consider the expectation-maximization (EM) algorithm with belief propagation (BP) and derive its algorithmic detectability threshold. Our analysis is not restricted to the community structure but includes general modular structures. Because the algorithm cannot always learn the planted model parameters correctly, the algorithmic detectability threshold is qualitatively different from the one with the Nishimori condition.

A Plane Target Detection Algorithm in Remote Sensing Images based on Deep Learning Network Technology

Science.gov (United States)

Shuxin, Li; Zhilong, Zhang; Biao, Li

2018-01-01

Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.

A Depth Map Generation Algorithm Based on Saliency Detection for 2D to 3D Conversion

Science.gov (United States)

Yang, Yizhong; Hu, Xionglou; Wu, Nengju; Wang, Pengfei; Xu, Dong; Rong, Shen

2017-09-01

In recent years, 3D movies attract people's attention more and more because of their immersive stereoscopic experience. However, 3D movies is still insufficient, so estimating depth information for 2D to 3D conversion from a video is more and more important. In this paper, we present a novel algorithm to estimate depth information from a video via scene classification algorithm. In order to obtain perceptually reliable depth information for viewers, the algorithm classifies them into three categories: landscape type, close-up type, linear perspective type firstly. Then we employ a specific algorithm to divide the landscape type image into many blocks, and assign depth value by similar relative height cue with the image. As to the close-up type image, a saliency-based method is adopted to enhance the foreground in the image and the method combine it with the global depth gradient to generate final depth map. By vanishing line detection, the calculated vanishing point which is regarded as the farthest point to the viewer is assigned with deepest depth value. According to the distance between the other points and the vanishing point, the entire image is assigned with corresponding depth value. Finally, depth image-based rendering is employed to generate stereoscopic virtual views after bilateral filter. Experiments show that the proposed algorithm can achieve realistic 3D effects and yield satisfactory results, while the perception scores of anaglyph images lie between 6.8 and 7.8.

STREAMFINDER I: A New Algorithm for detecting Stellar Streams

Science.gov (United States)

Malhan, Khyati; Ibata, Rodrigo A.

2018-04-01

We have designed a powerful new algorithm to detect stellar streams in an automated and systematic way. The algorithm, which we call the STREAMFINDER, is well suited for finding dynamically cold and thin stream structures that may lie along any simple or complex orbits in Galactic stellar surveys containing any combination of positional and kinematic information. In the present contribution we introduce the algorithm, lay out the ideas behind it, explain the methodology adopted to detect streams and detail its workings by running it on a suite of simulations of mock Galactic survey data of similar quality to that expected from the ESA/Gaia mission. We show that our algorithm is able to detect even ultra-faint stream features lying well below previous detection limits. Tests show that our algorithm will be able to detect distant halo stream structures >10° long containing as few as ˜15 members (ΣG ˜ 33.6 mag arcsec-2) in the Gaia dataset.

Analysis of Correlation between an Accelerometer-Based Algorithm for Detecting Parkinsonian Gait and UPDRS Subscales

Directory of Open Access Journals (Sweden)

Alejandro Rodríguez-Molinero

2017-09-01

Full Text Available BackgroundOur group earlier developed a small monitoring device, which uses accelerometer measurements to accurately detect motor fluctuations in patients with Parkinson’s (On and Off state based on an algorithm that characterizes gait through the frequency content of strides. To further validate the algorithm, we studied the correlation of its outputs with the motor section of the Unified Parkinson’s Disease Rating Scale part-III (UPDRS-III.MethodSeventy-five patients suffering from Parkinson’s disease were asked to walk both in the Off and the On state while wearing the inertial sensor on the waist. Additionally, all patients were administered the motor section of the UPDRS in both motor phases. Tests were conducted at the patient’s home. Convergence between the algorithm and the scale was evaluated by using the Spearman’s correlation coefficient.ResultsCorrelation with the UPDRS-III was moderate (rho −0.56; p < 0.001. Correlation between the algorithm outputs and the gait item in the UPDRS-III was good (rho −0.73; p < 0.001. The factorial analysis of the UPDRS-III has repeatedly shown that several of its items can be clustered under the so-called Factor 1: “axial function, balance, and gait.” The correlation between the algorithm outputs and this factor of the UPDRS-III was −0.67 (p < 0.01.ConclusionThe correlation achieved by the algorithm with the UPDRS-III scale suggests that this algorithm might be a useful tool for monitoring patients with Parkinson’s disease and motor fluctuations.

Implementation in an FPGA circuit of Edge detection algorithm based on the Discrete Wavelet Transforms

Science.gov (United States)

Bouganssa, Issam; Sbihi, Mohamed; Zaim, Mounia

2017-07-01

The 2D Discrete Wavelet Transform (DWT) is a computationally intensive task that is usually implemented on specific architectures in many imaging systems in real time. In this paper, a high throughput edge or contour detection algorithm is proposed based on the discrete wavelet transform. A technique for applying the filters on the three directions (Horizontal, Vertical and Diagonal) of the image is used to present the maximum of the existing contours. The proposed architectures were designed in VHDL and mapped to a Xilinx Sparten6 FPGA. The results of the synthesis show that the proposed architecture has a low area cost and can operate up to 100 MHz, which can perform 2D wavelet analysis for a sequence of images while maintaining the flexibility of the system to support an adaptive algorithm.

A Contextual Fire Detection Algorithm for Simulated HJ-1B Imagery

Directory of Open Access Journals (Sweden)

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.

A new peak detection algorithm for MALDI mass spectrometry data based on a modified Asymmetric Pseudo-Voigt model.

Science.gov (United States)

Wijetunge, Chalini D; Saeed, Isaam; Boughton, Berin A; Roessner, Ute; Halgamuge, Saman K

2015-01-01

Mass Spectrometry (MS) is a ubiquitous analytical tool in biological research and is used to measure the mass-to-charge ratio of bio-molecules. Peak detection is the essential first step in MS data analysis. Precise estimation of peak parameters such as peak summit location and peak area are critical to identify underlying bio-molecules and to estimate their abundances accurately. We propose a new method to detect and quantify peaks in mass spectra. It uses dual-tree complex wavelet transformation along with Stein's unbiased risk estimator for spectra smoothing. Then, a new method, based on the modified Asymmetric Pseudo-Voigt (mAPV) model and hierarchical particle swarm optimization, is used for peak parameter estimation. Using simulated data, we demonstrated the benefit of using the mAPV model over Gaussian, Lorentz and Bi-Gaussian functions for MS peak modelling. The proposed mAPV model achieved the best fitting accuracy for asymmetric peaks, with lower percentage errors in peak summit location estimation, which were 0.17% to 4.46% less than that of the other models. It also outperformed the other models in peak area estimation, delivering lower percentage errors, which were about 0.7% less than its closest competitor - the Bi-Gaussian model. In addition, using data generated from a MALDI-TOF computer model, we showed that the proposed overall algorithm outperformed the existing methods mainly in terms of sensitivity. It achieved a sensitivity of 85%, compared to 77% and 71% of the two benchmark algorithms, continuous wavelet transformation based method and Cromwell respectively. The proposed algorithm is particularly useful for peak detection and parameter estimation in MS data with overlapping peak distributions and asymmetric peaks. The algorithm is implemented using MATLAB and the source code is freely available at http://mapv.sourceforge.net.

An Early Fire Detection Algorithm Using IP Cameras

Directory of Open Access Journals (Sweden)

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.

A wavelet transform algorithm for peak detection and application to powder x-ray diffraction data.

Science.gov (United States)

Gregoire, John M; Dale, Darren; van Dover, R Bruce

2011-01-01

Peak detection is ubiquitous in the analysis of spectral data. While many noise-filtering algorithms and peak identification algorithms have been developed, recent work [P. Du, W. Kibbe, and S. Lin, Bioinformatics 22, 2059 (2006); A. Wee, D. Grayden, Y. Zhu, K. Petkovic-Duran, and D. Smith, Electrophoresis 29, 4215 (2008)] has demonstrated that both of these tasks are efficiently performed through analysis of the wavelet transform of the data. In this paper, we present a wavelet-based peak detection algorithm with user-defined parameters that can be readily applied to the application of any spectral data. Particular attention is given to the algorithm's resolution of overlapping peaks. The algorithm is implemented for the analysis of powder diffraction data, and successful detection of Bragg peaks is demonstrated for both low signal-to-noise data from theta-theta diffraction of nanoparticles and combinatorial x-ray diffraction data from a composition spread thin film. These datasets have different types of background signals which are effectively removed in the wavelet-based method, and the results demonstrate that the algorithm provides a robust method for automated peak detection.

A new algorithmic approach for fingers detection and identification

Science.gov (United States)

Mubashar Khan, Arslan; Umar, Waqas; Choudhary, Taimoor; Hussain, Fawad; Haroon Yousaf, Muhammad

2013-03-01

Gesture recognition is concerned with the goal of interpreting human gestures through mathematical algorithms. Gestures can originate from any bodily motion or state but commonly originate from the face or hand. Hand gesture detection in a real time environment, where the time and memory are important issues, is a critical operation. Hand gesture recognition largely depends on the accurate detection of the fingers. This paper presents a new algorithmic approach to detect and identify fingers of human hand. The proposed algorithm does not depend upon the prior knowledge of the scene. It detects the active fingers and Metacarpophalangeal (MCP) of the inactive fingers from an already detected hand. Dynamic thresholding technique and connected component labeling scheme are employed for background elimination and hand detection respectively. Algorithm proposed a new approach for finger identification in real time environment keeping the memory and time constraint as low as possible.

[A quick algorithm of dynamic spectrum photoelectric pulse wave detection based on LabVIEW].

Science.gov (United States)

Lin, Ling; Li, Na; Li, Gang

2010-02-01

Dynamic spectrum (DS) detection is attractive among the numerous noninvasive blood component detection methods because of the elimination of the main interference of the individual discrepancy and measure conditions. DS is a kind of spectrum extracted from the photoelectric pulse wave and closely relative to the artery blood. It can be used in a noninvasive blood component concentration examination. The key issues in DS detection are high detection precision and high operation speed. The precision of measure can be advanced by making use of over-sampling and lock-in amplifying on the pick-up of photoelectric pulse wave in DS detection. In the present paper, the theory expression formula of the over-sampling and lock-in amplifying method was deduced firstly. Then in order to overcome the problems of great data and excessive operation brought on by this technology, a quick algorithm based on LabVIEW and a method of using external C code applied in the pick-up of photoelectric pulse wave were presented. Experimental verification was conducted in the environment of LabVIEW. The results show that by the method pres ented, the speed of operation was promoted rapidly and the data memory was reduced largely.

Real time algorithms for sharp wave ripple detection.

Science.gov (United States)

Sethi, Ankit; Kemere, Caleb

2014-01-01

Neural activity during sharp wave ripples (SWR), short bursts of co-ordinated oscillatory activity in the CA1 region of the rodent hippocampus, is implicated in a variety of memory functions from consolidation to recall. Detection of these events in an algorithmic framework, has thus far relied on simple thresholding techniques with heuristically derived parameters. This study is an investigation into testing and improving the current methods for detection of SWR events in neural recordings. We propose and profile methods to reduce latency in ripple detection. Proposed algorithms are tested on simulated ripple data. The findings show that simple realtime algorithms can improve upon existing power thresholding methods and can detect ripple activity with latencies in the range of 10-20 ms.

Shockwave-Based Automated Vehicle Longitudinal Control Algorithm for Nonrecurrent Congestion Mitigation

Directory of Open Access Journals (Sweden)

Liuhui Zhao

2017-01-01

Full Text Available A shockwave-based speed harmonization algorithm for the longitudinal movement of automated vehicles is presented in this paper. In the advent of Connected/Automated Vehicle (C/AV environment, the proposed algorithm can be applied to capture instantaneous shockwaves constructed from vehicular speed profiles shared by individual equipped vehicles. With a continuous wavelet transform (CWT method, the algorithm detects abnormal speed drops in real-time and optimizes speed to prevent the shockwave propagating to the upstream traffic. A traffic simulation model is calibrated to evaluate the applicability and efficiency of the proposed algorithm. Based on 100% C/AV market penetration, the simulation results show that the CWT-based algorithm accurately detects abnormal speed drops. With the improved accuracy of abnormal speed drop detection, the simulation results also demonstrate that the congestion can be mitigated by reducing travel time and delay up to approximately 9% and 18%, respectively. It is also found that the shockwave caused by nonrecurrent congestion is quickly dissipated even with low market penetration.

Induction machine bearing faults detection based on a multi-dimensional MUSIC algorithm and maximum likelihood estimation.

Science.gov (United States)

Elbouchikhi, Elhoussin; Choqueuse, Vincent; Benbouzid, Mohamed

2016-07-01

Condition monitoring of electric drives is of paramount importance since it contributes to enhance the system reliability and availability. Moreover, the knowledge about the fault mode behavior is extremely important in order to improve system protection and fault-tolerant control. Fault detection and diagnosis in squirrel cage induction machines based on motor current signature analysis (MCSA) has been widely investigated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. This paper focuses on the application of MCSA for the detection of abnormal mechanical conditions that may lead to induction machines failure. In fact, this paper is devoted to the detection of single-point defects in bearings based on parametric spectral estimation. A multi-dimensional MUSIC (MD MUSIC) algorithm has been developed for bearing faults detection based on bearing faults characteristic frequencies. This method has been used to estimate the fundamental frequency and the fault related frequency. Then, an amplitude estimator of the fault characteristic frequencies has been proposed and fault indicator has been derived for fault severity measurement. The proposed bearing faults detection approach is assessed using simulated stator currents data, issued from a coupled electromagnetic circuits approach for air-gap eccentricity emulating bearing faults. Then, experimental data are used for validation purposes. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Comparison of machine learning algorithms for detecting coral reef

Directory of Open Access Journals (Sweden)

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.

Algorithms for the detection of chewing behavior in dietary monitoring applications

Science.gov (United States)

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.

Arctic lead detection using a waveform mixture algorithm from CryoSat-2 data

Science.gov (United States)

Lee, Sanggyun; Kim, Hyun-cheol; Im, Jungho

2018-05-01

We propose a waveform mixture algorithm to detect leads from CryoSat-2 data, which is novel and different from the existing threshold-based lead detection methods. The waveform mixture algorithm adopts the concept of spectral mixture analysis, which is widely used in the field of hyperspectral image analysis. This lead detection method was evaluated with high-resolution (250 m) MODIS images and showed comparable and promising performance in detecting leads when compared to the previous methods. The robustness of the proposed approach also lies in the fact that it does not require the rescaling of parameters (i.e., stack standard deviation, stack skewness, stack kurtosis, pulse peakiness, and backscatter σ0), as it directly uses L1B waveform data, unlike the existing threshold-based methods. Monthly lead fraction maps were produced by the waveform mixture algorithm, which shows interannual variability of recent sea ice cover during 2011-2016, excluding the summer season (i.e., June to September). We also compared the lead fraction maps to other lead fraction maps generated from previously published data sets, resulting in similar spatiotemporal patterns.

New algorithm to detect modules in a fault tree for a PSA

International Nuclear Information System (INIS)

Jung, Woo Sik

2015-01-01

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

New algorithm to detect modules in a fault tree for a PSA

Energy Technology Data Exchange (ETDEWEB)

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.

Bladed wheels damage detection through Non-Harmonic Fourier Analysis improved algorithm

Science.gov (United States)

Neri, P.

2017-05-01

Recent papers introduced the Non-Harmonic Fourier Analysis for bladed wheels damage detection. This technique showed its potential in estimating the frequency of sinusoidal signals even when the acquisition time is short with respect to the vibration period, provided that some hypothesis are fulfilled. Anyway, previously proposed algorithms showed severe limitations in cracks detection at their early stage. The present paper proposes an improved algorithm which allows to detect a blade vibration frequency shift due to a crack whose size is really small compared to the blade width. Such a technique could be implemented for condition-based maintenance, allowing to use non-contact methods for vibration measurements. A stator-fixed laser sensor could monitor all the blades as they pass in front of the spot, giving precious information about the wheel health. This configuration determines an acquisition time for each blade which become shorter as the machine rotational speed increases. In this situation, traditional Discrete Fourier Transform analysis results in poor frequency resolution, being not suitable for small frequency shift detection. Non-Harmonic Fourier Analysis instead showed high reliability in vibration frequency estimation even with data samples collected in a short time range. A description of the improved algorithm is provided in the paper, along with a comparison with the previous one. Finally, a validation of the method is presented, based on finite element simulations results.

Extended seizure detection algorithm for intracranial EEG recordings

DEFF Research Database (Denmark)

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...... and non-ictal iEEG. We compare our results to a method published by Shoeb in 2004. While the original method on sEEG was optimal with the use of only four subbands in the wavelet analysis, we found that better seizure detection could be made if all subbands were used for iEEG. Results: When using...

QRS detection based ECG quality assessment

International Nuclear Information System (INIS)

Hayn, Dieter; Jammerbund, Bernhard; Schreier, Günter

2012-01-01

Although immediate feedback concerning ECG signal quality during recording is useful, up to now not much literature describing quality measures is available. We have implemented and evaluated four ECG quality measures. Empty lead criterion (A), spike detection criterion (B) and lead crossing point criterion (C) were calculated from basic signal properties. Measure D quantified the robustness of QRS detection when applied to the signal. An advanced Matlab-based algorithm combining all four measures and a simplified algorithm for Android platforms, excluding measure D, were developed. Both algorithms were evaluated by taking part in the Computing in Cardiology Challenge 2011. Each measure's accuracy and computing time was evaluated separately. During the challenge, the advanced algorithm correctly classified 93.3% of the ECGs in the training-set and 91.6 % in the test-set. Scores for the simplified algorithm were 0.834 in event 2 and 0.873 in event 3. Computing time for measure D was almost five times higher than for other measures. Required accuracy levels depend on the application and are related to computing time. While our simplified algorithm may be accurate for real-time feedback during ECG self-recordings, QRS detection based measures can further increase the performance if sufficient computing power is available. (paper)

A rule-based electronic phenotyping algorithm for detecting clinically relevant cardiovascular disease cases.

Science.gov (United States)

Esteban, Santiago; Rodríguez Tablado, Manuel; Ricci, Ricardo Ignacio; Terrasa, Sergio; Kopitowski, Karin

2017-07-14

The implementation of electronic medical records (EMR) is becoming increasingly common. Error and data loss reduction, patient-care efficiency increase, decision-making assistance and facilitation of event surveillance, are some of the many processes that EMRs help improve. In addition, they show a lot of promise in terms of data collection to facilitate observational epidemiological studies and their use for this purpose has increased significantly over the recent years. Even though the quantity and availability of the data are clearly improved thanks to EMRs, still, the problem of the quality of the data remains. This is especially important when attempting to determine if an event has actually occurred or not. We sought to assess the sensitivity, specificity, and agreement level of a codes-based algorithm for the detection of clinically relevant cardiovascular (CaVD) and cerebrovascular (CeVD) disease cases, using data from EMRs. Three family physicians from the research group selected clinically relevant CaVD and CeVD terms from the international classification of primary care, Second Edition (ICPC-2), the ICD 10 version 2015 and SNOMED-CT 2015 Edition. These terms included both signs, symptoms, diagnoses and procedures associated with CaVD and CeVD. Terms not related to symptoms, signs, diagnoses or procedures of CaVD or CeVD and also those describing incidental findings without clinical relevance were excluded. The algorithm yielded a positive result if the patient had at least one of the selected terms in their medical records, as long as it was not recorded as an error. Else, if no terms were found, the patient was classified as negative. This algorithm was applied to a randomly selected sample of the active patients within the hospital's HMO by 1/1/2005 that were 40-79 years old, had at least one year of seniority in the HMO and at least one clinical encounter. Thus, patients were classified into four groups: (1) Negative patients (2) Patients with Ca

A matched-filter algorithm to detect amperometric spikes resulting from quantal secretion.

Science.gov (United States)

Balaji Ramachandran, Supriya; Gillis, Kevin D

2018-01-01

Electrochemical microelectrodes located immediately adjacent to the cell surface can detect spikes of amperometric current during exocytosis as the transmitter released from a single vesicle is oxidized on the electrode surface. Automated techniques to detect spikes are needed in order to quantify the spike rate as a measure of the rate of exocytosis. We have developed a Matched Filter (MF) detection algorithm that scans the data set with a library of prototype spike templates while performing a least-squares fit to determine the amplitude and standard error. The ratio of the fit amplitude to the standard error constitutes a criterion score that is assigned for each time point and for each template. A spike is detected when the criterion score exceeds a threshold and the highest-scoring template and the time of peak score is identified. The search for the next spike commences only after the score falls below a second, lower threshold to reduce false positives. The approach was extended to detect spikes with double-exponential decays with the sum of two templates. Receiver Operating Characteristic plots (ROCs) demonstrate that the algorithm detects >95% of manually identified spikes with a false-positive rate of ∼2%. ROCs demonstrate that the MF algorithm performs better than algorithms that detect spikes based on a derivative-threshold approach. The MF approach performs well and leads into approaches to identify spike parameters. Copyright © 2017 Elsevier B.V. All rights reserved.

Algorithms for boundary detection in radiographic images

International Nuclear Information System (INIS)

Gonzaga, Adilson; Franca, Celso Aparecido de

1996-01-01

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

High Precision Edge Detection Algorithm for Mechanical Parts

Science.gov (United States)

Duan, Zhenyun; Wang, Ning; Fu, Jingshun; Zhao, Wenhui; Duan, Boqiang; Zhao, Jungui

2018-04-01

High precision and high efficiency measurement is becoming an imperative requirement for a lot of mechanical parts. So in this study, a subpixel-level edge detection algorithm based on the Gaussian integral model is proposed. For this purpose, the step edge normal section line Gaussian integral model of the backlight image is constructed, combined with the point spread function and the single step model. Then gray value of discrete points on the normal section line of pixel edge is calculated by surface interpolation, and the coordinate as well as gray information affected by noise is fitted in accordance with the Gaussian integral model. Therefore, a precise location of a subpixel edge was determined by searching the mean point. Finally, a gear tooth was measured by M&M3525 gear measurement center to verify the proposed algorithm. The theoretical analysis and experimental results show that the local edge fluctuation is reduced effectively by the proposed method in comparison with the existing subpixel edge detection algorithms. The subpixel edge location accuracy and computation speed are improved. And the maximum error of gear tooth profile total deviation is 1.9 μm compared with measurement result with gear measurement center. It indicates that the method has high reliability to meet the requirement of high precision measurement.

A non-linear algorithm for current signal filtering and peak detection in SiPM

International Nuclear Information System (INIS)

Putignano, M; Intermite, A; Welsch, C P

2012-01-01

Read-out of Silicon Photomultipliers is commonly achieved by means of charge integration, a method particularly susceptible to after-pulsing noise and not efficient for low level light signals. Current signal monitoring, characterized by easier electronic implementation and intrinsically faster than charge integration, is also more suitable for low level light signals and can potentially result in much decreased after-pulsing noise effects. However, its use is to date limited by the need of developing a suitable read-out algorithm for signal analysis and filtering able to achieve current peak detection and measurement with the needed precision and accuracy. In this paper we present an original algorithm, based on a piecewise linear-fitting approach, to filter the noise of the current signal and hence efficiently identifying and measuring current peaks. The proposed algorithm is then compared with the optimal linear filtering algorithm for time-encoded peak detection, based on a moving average routine, and assessed in terms of accuracy, precision, and peak detection efficiency, demonstrating improvements of 1÷2 orders of magnitude in all these quality factors.

ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection

Directory of Open Access Journals (Sweden)

SARACOGLU, O. G.

2016-08-01

Full Text Available This paper presents a modeling approach based on the use of fuzzy reasoning mechanism to define a measured data set obtained from an optical sensing circuit. For this purpose, we implemented a simple but effective an in vitro optical sensor to measure glucose content of an aqueous solution. Measured data contain analog voltages representing the absorbance values of three wavelengths measured from an RGB LED in different glucose concentrations. To achieve a desired model performance, the parameters of the fuzzy models are optimized by using the artificial bee colony (ABC algorithm. The modeling results presented in this paper indicate that the fuzzy model optimized by the algorithm provide a successful modeling performance having the minimum mean squared error (MSE of 0.0013 which are in clearly good agreement with the measurements.

Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection.

Science.gov (United States)

Hu, Weiming; Gao, Jun; Wang, Yanguo; Wu, Ou; Maybank, Stephen

2014-01-01

Current network intrusion detection systems lack adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two online Adaboost-based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online Gaussian mixture models (GMMs) are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines. The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process that uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO, and SVM-based algorithm effectively combines the local detection models into the global model in each node; the global model in a node can handle the intrusion types that are found in other nodes, without sharing the samples of these intrusion types.

Nonlinear observer based fault detection and isolation for a momentum wheel

DEFF Research Database (Denmark)

Jensen, Hans-Christian Becker; Wisniewski, Rafal

2001-01-01

This article realizes nonlinear Fault Detection and Isolation for a momentum wheel. The Fault Detection and Isolation is based on a Failure Mode and Effect Analysis, which states which faults might occur and can be detected. The algorithms presented in this paper are based on a geometric approach...... toachieve nonlinear Fault Detection and Isolation. The proposed algorithms are tested in a simulation study and the pros and cons of the algorithm are discussed....

Improved Genetic Algorithm Optimization for Forward Vehicle Detection Problems

Directory of Open Access Journals (Sweden)

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.

Arctic lead detection using a waveform mixture algorithm from CryoSat-2 data

Directory of Open Access Journals (Sweden)

S. Lee

2018-05-01

Full Text Available We propose a waveform mixture algorithm to detect leads from CryoSat-2 data, which is novel and different from the existing threshold-based lead detection methods. The waveform mixture algorithm adopts the concept of spectral mixture analysis, which is widely used in the field of hyperspectral image analysis. This lead detection method was evaluated with high-resolution (250 m MODIS images and showed comparable and promising performance in detecting leads when compared to the previous methods. The robustness of the proposed approach also lies in the fact that it does not require the rescaling of parameters (i.e., stack standard deviation, stack skewness, stack kurtosis, pulse peakiness, and backscatter σ0, as it directly uses L1B waveform data, unlike the existing threshold-based methods. Monthly lead fraction maps were produced by the waveform mixture algorithm, which shows interannual variability of recent sea ice cover during 2011–2016, excluding the summer season (i.e., June to September. We also compared the lead fraction maps to other lead fraction maps generated from previously published data sets, resulting in similar spatiotemporal patterns.

A New Method of Cloud Detection Based on Cascaded AdaBoost

International Nuclear Information System (INIS)

Ma, C; Chen, F; Liu, J; Duan, J

2014-01-01

Cloud detection of remote sensing image is a critical step in the processing of the remote sensing images. How to quickly, accurately and effectively detect cloud on remote sensing images, is still a challenging issue in this area. In order to avoid disadvantages of the current algorithms, the cascaded AdaBoost classifier algorithm is successfully applied to the cloud detection. A new algorithm combined cascaded AdaBoost classifier and multi-features, is proposed in this paper. First, multi-features based on the color, texture and spectral features are extracted from the remote sensing image. Second, the automatic cloud detection model is obtained based on the cascaded AdaBoost algorithm. In this paper, the results show that the new algorithm can determine cloud detection model and threshold values adaptively for different resolution remote sensing training data. The accuracy of cloud detection is improved. So it is a new effective algorithm for the cloud detection of remote sensing images

A similarity based agglomerative clustering algorithm in networks

Science.gov (United States)

Liu, Zhiyuan; Wang, Xiujuan; Ma, Yinghong

2018-04-01

The detection of clusters is benefit for understanding the organizations and functions of networks. Clusters, or communities, are usually groups of nodes densely interconnected but sparsely linked with any other clusters. To identify communities, an efficient and effective community agglomerative algorithm based on node similarity is proposed. The proposed method initially calculates similarities between each pair of nodes, and form pre-partitions according to the principle that each node is in the same community as its most similar neighbor. After that, check each partition whether it satisfies community criterion. For the pre-partitions who do not satisfy, incorporate them with others that having the biggest attraction until there are no changes. To measure the attraction ability of a partition, we propose an attraction index that based on the linked node's importance in networks. Therefore, our proposed method can better exploit the nodes' properties and network's structure. To test the performance of our algorithm, both synthetic and empirical networks ranging in different scales are tested. Simulation results show that the proposed algorithm can obtain superior clustering results compared with six other widely used community detection algorithms.

ECG Based Heart Arrhythmia Detection Using Wavelet Coherence and Bat Algorithm

Science.gov (United States)

Kora, Padmavathi; Sri Rama Krishna, K.

2016-12-01

Atrial fibrillation (AF) is a type of heart abnormality, during the AF electrical discharges in the atrium are rapid, results in abnormal heart beat. The morphology of ECG changes due to the abnormalities in the heart. This paper consists of three major steps for the detection of heart diseases: signal pre-processing, feature extraction and classification. Feature extraction is the key process in detecting the heart abnormality. Most of the ECG detection systems depend on the time domain features for cardiac signal classification. In this paper we proposed a wavelet coherence (WTC) technique for ECG signal analysis. The WTC calculates the similarity between two waveforms in frequency domain. Parameters extracted from WTC function is used as the features of the ECG signal. These features are optimized using Bat algorithm. The Levenberg Marquardt neural network classifier is used to classify the optimized features. The performance of the classifier can be improved with the optimized features.

Automatic metal parts inspection: Use of thermographic images and anomaly detection algorithms

Science.gov (United States)

Benmoussat, M. S.; Guillaume, M.; Caulier, Y.; Spinnler, K.

2013-11-01

A fully-automatic approach based on the use of induction thermography and detection algorithms is proposed to inspect industrial metallic parts containing different surface and sub-surface anomalies such as open cracks, open and closed notches with different sizes and depths. A practical experimental setup is developed, where lock-in and pulsed thermography (LT and PT, respectively) techniques are used to establish a dataset of thermal images for three different mockups. Data cubes are constructed by stacking up the temporal sequence of thermogram images. After the reduction of the data space dimension by means of denoising and dimensionality reduction methods; anomaly detection algorithms are applied on the reduced data cubes. The dimensions of the reduced data spaces are automatically calculated with arbitrary criterion. The results show that, when reduced data cubes are used, the anomaly detection algorithms originally developed for hyperspectral data, the well-known Reed and Xiaoli Yu detector (RX) and the regularized adaptive RX (RARX), give good detection performances for both surface and sub-surface defects in a non-supervised way.

Structure and performance of a real-time algorithm to detect tsunami or tsunami-like alert conditions based on sea-level records analysis

Directory of Open Access Journals (Sweden)

L. Bressan

2011-05-01

Full Text Available The goal of this paper is to present an original real-time algorithm devised for detection of tsunami or tsunami-like waves we call TEDA (Tsunami Early Detection Algorithm, and to introduce a methodology to evaluate its performance. TEDA works on the sea level records of a single station and implements two distinct modules running concurrently: one to assess the presence of tsunami waves ("tsunami detection" and the other to identify high-amplitude long waves ("secure detection". Both detection methods are based on continuously updated time functions depending on a number of parameters that can be varied according to the application. In order to select the most adequate parameter setting for a given station, a methodology to evaluate TEDA performance has been devised, that is based on a number of indicators and that is simple to use. In this paper an example of TEDA application is given by using data from a tide gauge located at the Adak Island in Alaska, USA, that resulted in being quite suitable since it recorded several tsunamis in the last years using the sampling rate of 1 min.

Optimization of an NLEO-based algorithm for automated detection of spontaneous activity transients in early preterm EEG

International Nuclear Information System (INIS)

Palmu, Kirsi; Vanhatalo, Sampsa; Stevenson, Nathan; Wikström, Sverre; Hellström-Westas, Lena; Palva, J Matias

2010-01-01

We propose here a simple algorithm for automated detection of spontaneous activity transients (SATs) in early preterm electroencephalography (EEG). The parameters of the algorithm were optimized by supervised learning using a gold standard created from visual classification data obtained from three human raters. The generalization performance of the algorithm was estimated by leave-one-out cross-validation. The mean sensitivity of the optimized algorithm was 97% (range 91–100%) and specificity 95% (76–100%). The optimized algorithm makes it possible to systematically study brain state fluctuations of preterm infants. (note)

Algorithms for detection of objects in image sequences captured from an airborne imaging system

Science.gov (United States)

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.

High Precision Edge Detection Algorithm for Mechanical Parts

Directory of Open Access Journals (Sweden)

Duan Zhenyun

2018-04-01

Full Text Available High precision and high efficiency measurement is becoming an imperative requirement for a lot of mechanical parts. So in this study, a subpixel-level edge detection algorithm based on the Gaussian integral model is proposed. For this purpose, the step edge normal section line Gaussian integral model of the backlight image is constructed, combined with the point spread function and the single step model. Then gray value of discrete points on the normal section line of pixel edge is calculated by surface interpolation, and the coordinate as well as gray information affected by noise is fitted in accordance with the Gaussian integral model. Therefore, a precise location of a subpixel edge was determined by searching the mean point. Finally, a gear tooth was measured by M&M3525 gear measurement center to verify the proposed algorithm. The theoretical analysis and experimental results show that the local edge fluctuation is reduced effectively by the proposed method in comparison with the existing subpixel edge detection algorithms. The subpixel edge location accuracy and computation speed are improved. And the maximum error of gear tooth profile total deviation is 1.9 μm compared with measurement result with gear measurement center. It indicates that the method has high reliability to meet the requirement of high precision measurement.

Algorithms and data structures for automated change detection and classification of sidescan sonar imagery

Science.gov (United States)

Gendron, Marlin Lee

During Mine Warfare (MIW) operations, MIW analysts perform change detection by visually comparing historical sidescan sonar imagery (SSI) collected by a sidescan sonar with recently collected SSI in an attempt to identify objects (which might be explosive mines) placed at sea since the last time the area was surveyed. This dissertation presents a data structure and three algorithms, developed by the author, that are part of an automated change detection and classification (ACDC) system. MIW analysts at the Naval Oceanographic Office, to reduce the amount of time to perform change detection, are currently using ACDC. The dissertation introductory chapter gives background information on change detection, ACDC, and describes how SSI is produced from raw sonar data. Chapter 2 presents the author's Geospatial Bitmap (GB) data structure, which is capable of storing information geographically and is utilized by the three algorithms. This chapter shows that a GB data structure used in a polygon-smoothing algorithm ran between 1.3--48.4x faster than a sparse matrix data structure. Chapter 3 describes the GB clustering algorithm, which is the author's repeatable, order-independent method for clustering. Results from tests performed in this chapter show that the time to cluster a set of points is not affected by the distribution or the order of the points. In Chapter 4, the author presents his real-time computer-aided detection (CAD) algorithm that automatically detects mine-like objects on the seafloor in SSI. The author ran his GB-based CAD algorithm on real SSI data, and results of these tests indicate that his real-time CAD algorithm performs comparably to or better than other non-real-time CAD algorithms. The author presents his computer-aided search (CAS) algorithm in Chapter 5. CAS helps MIW analysts locate mine-like features that are geospatially close to previously detected features. A comparison between the CAS and a great circle distance algorithm shows that the

Cloud detection algorithm comparison and validation for operational Landsat data products

Science.gov (United States)

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

A false-alarm aware methodology to develop robust and efficient multi-scale infrared small target detection algorithm

Science.gov (United States)

Moradi, Saed; Moallem, Payman; Sabahi, Mohamad Farzan

2018-03-01

False alarm rate and detection rate are still two contradictory metrics for infrared small target detection in an infrared search and track system (IRST), despite the development of new detection algorithms. In certain circumstances, not detecting true targets is more tolerable than detecting false items as true targets. Hence, considering background clutter and detector noise as the sources of the false alarm in an IRST system, in this paper, a false alarm aware methodology is presented to reduce false alarm rate while the detection rate remains undegraded. To this end, advantages and disadvantages of each detection algorithm are investigated and the sources of the false alarms are determined. Two target detection algorithms having independent false alarm sources are chosen in a way that the disadvantages of the one algorithm can be compensated by the advantages of the other one. In this work, multi-scale average absolute gray difference (AAGD) and Laplacian of point spread function (LoPSF) are utilized as the cornerstones of the desired algorithm of the proposed methodology. After presenting a conceptual model for the desired algorithm, it is implemented through the most straightforward mechanism. The desired algorithm effectively suppresses background clutter and eliminates detector noise. Also, since the input images are processed through just four different scales, the desired algorithm has good capability for real-time implementation. Simulation results in term of signal to clutter ratio and background suppression factor on real and simulated images prove the effectiveness and the performance of the proposed methodology. Since the desired algorithm was developed based on independent false alarm sources, our proposed methodology is expandable to any pair of detection algorithms which have different false alarm sources.

Multivariate algorithms for initiating event detection and identification in nuclear power plants

International Nuclear Information System (INIS)

Wu, Shun-Chi; Chen, Kuang-You; Lin, Ting-Han; Chou, Hwai-Pwu

2018-01-01

Highlights: •Multivariate algorithms for NPP initiating event detection and identification. •Recordings from multiple sensors are simultaneously considered for detection. •Both spatial and temporal information is used for event identification. •Untrained event isolation avoids falsely relating an untrained event. •Efficacy of the algorithms is verified with data from the Maanshan NPP simulator. -- Abstract: To prevent escalation of an initiating event into a severe accident, promptly detecting its occurrence and precisely identifying its type are essential. In this study, several multivariate algorithms for initiating event detection and identification are proposed to help maintain safe operations of nuclear power plants (NPPs). By monitoring changes in the NPP sensing variables, an event is detected when the preset thresholds are exceeded. Unlike existing approaches, recordings from sensors of the same type are simultaneously considered for detection, and no subjective reasoning is involved in setting these thresholds. To facilitate efficient event identification, a spatiotemporal feature extractor is proposed. The extracted features consist of the temporal traits used by existing techniques and the spatial signature of an event. Through an F-score-based feature ranking, only those that are most discriminant in classifying the events under consideration will be retained for identification. Moreover, an untrained event isolation scheme is introduced to avoid relating an untrained event to those in the event dataset so that improper recovery actions can be prevented. Results from experiments containing data of 12 event classes and a total of 125 events generated using a Taiwan’s Maanshan NPP simulator are provided to illustrate the efficacy of the proposed algorithms.

Dynamic multiple thresholding breast boundary detection algorithm for mammograms

International Nuclear Information System (INIS)

Wu, Yi-Ta; Zhou Chuan; Chan, Heang-Ping; Paramagul, Chintana; Hadjiiski, Lubomir M.; Daly, Caroline Plowden; Douglas, Julie A.; Zhang Yiheng; Sahiner, Berkman; Shi Jiazheng; Wei Jun

2010-01-01

Purpose: Automated detection of breast boundary is one of the fundamental steps for computer-aided analysis of mammograms. In this study, the authors developed a new dynamic multiple thresholding based breast boundary (MTBB) detection method for digitized mammograms. Methods: A large data set of 716 screen-film mammograms (442 CC view and 274 MLO view) obtained from consecutive cases of an Institutional Review Board approved project were used. An experienced breast radiologist manually traced the breast boundary on each digitized image using a graphical interface to provide a reference standard. The initial breast boundary (MTBB-Initial) was obtained by dynamically adapting the threshold to the gray level range in local regions of the breast periphery. The initial breast boundary was then refined by using gradient information from horizontal and vertical Sobel filtering to obtain the final breast boundary (MTBB-Final). The accuracy of the breast boundary detection algorithm was evaluated by comparison with the reference standard using three performance metrics: The Hausdorff distance (HDist), the average minimum Euclidean distance (AMinDist), and the area overlap measure (AOM). Results: In comparison with the authors' previously developed gradient-based breast boundary (GBB) algorithm, it was found that 68%, 85%, and 94% of images had HDist errors less than 6 pixels (4.8 mm) for GBB, MTBB-Initial, and MTBB-Final, respectively. 89%, 90%, and 96% of images had AMinDist errors less than 1.5 pixels (1.2 mm) for GBB, MTBB-Initial, and MTBB-Final, respectively. 96%, 98%, and 99% of images had AOM values larger than 0.9 for GBB, MTBB-Initial, and MTBB-Final, respectively. The improvement by the MTBB-Final method was statistically significant for all the evaluation measures by the Wilcoxon signed rank test (p<0.0001). Conclusions: The MTBB approach that combined dynamic multiple thresholding and gradient information provided better performance than the breast boundary

Novel Rock Detection Intelligence for Space Exploration Based on Non-Symbolic Algorithms and Concepts

Science.gov (United States)

Yildirim, Sule; Beachell, Ronald L.; Veflingstad, Henning

2007-01-01

Future space exploration can utilize artificial intelligence as an integral part of next generation space rover technology to make the rovers more autonomous in performing mission objectives. The main advantage of the increased autonomy through a higher degree of intelligence is that it allows for greater utilization of rover resources by reducing the frequency of time consuming communications between rover and earth. In this paper, we propose a space exploration application of our research on a non-symbolic algorithm and concepts model. This model is based on one of the most recent approaches of cognitive science and artificial intelligence research, a parallel distributed processing approach. We use the Mars rovers. Sprit and Opportunity, as a starting point for proposing what rovers in the future could do if the presented model of non-symbolic algorithms and concepts is embedded in a future space rover. The chosen space exploration application for this paper, novel rock detection, is only one of many potential space exploration applications which can be optimized (through reduction of the frequency of rover-earth communications. collection and transmission of only data that is distinctive/novel) through the use of artificial intelligence technology compared to existing approaches.

Home Camera-Based Fall Detection System for the Elderly.

Science.gov (United States)

de Miguel, Koldo; Brunete, Alberto; Hernando, Miguel; Gambao, Ernesto

2017-12-09

Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms (background subtraction, Kalman filtering and optical flow) as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%.

Through-Wall Multiple Targets Vital Signs Tracking Based on VMD Algorithm

Directory of Open Access Journals (Sweden)

Jiaming Yan

2016-08-01

Full Text Available Targets located at the same distance are easily neglected in most through-wall multiple targets detecting applications which use the single-input single-output (SISO ultra-wideband (UWB radar system. In this paper, a novel multiple targets vital signs tracking algorithm for through-wall detection using SISO UWB radar has been proposed. Taking advantage of the high-resolution decomposition of the Variational Mode Decomposition (VMD based algorithm, the respiration signals of different targets can be decomposed into different sub-signals, and then, we can track the time-varying respiration signals accurately when human targets located in the same distance. Intensive evaluation has been conducted to show the effectiveness of our scheme with a 0.15 m thick concrete brick wall. Constant, piecewise-constant and time-varying vital signs could be separated and tracked successfully with the proposed VMD based algorithm for two targets, even up to three targets. For the multiple targets’ vital signs tracking issues like urban search and rescue missions, our algorithm has superior capability in most detection applications.

Research on data auto-analysis algorithms in the explosive detection system

International Nuclear Information System (INIS)

Wang Haidong; Li Yuanjing; Yang Yigang; Li Tiezhu; Chen Boxian; Cheng Jianping

2006-01-01

This paper mainly describe some auto-analysis algorithms in explosive detection system with TNA method. These include the auto-calibration algorithm when disturbed by other factors, MCA auto-calibration algorithm with calibrated spectrum, the auto-fitting and integral of hydrogen and nitrogen elements data. With these numerical algorithms, the authors can automatically and precisely analysis the gamma-spectra and ultimately achieve the explosive auto-detection. (authors)

Leukemia and colon tumor detection based on microarray data classification using momentum backpropagation and genetic algorithm as a feature selection method

Science.gov (United States)

Wisesty, Untari N.; Warastri, Riris S.; Puspitasari, Shinta Y.

2018-03-01

Cancer is one of the major causes of mordibility and mortality problems in the worldwide. Therefore, the need of a system that can analyze and identify a person suffering from a cancer by using microarray data derived from the patient’s Deoxyribonucleic Acid (DNA). But on microarray data has thousands of attributes, thus making the challenges in data processing. This is often referred to as the curse of dimensionality. Therefore, in this study built a system capable of detecting a patient whether contracted cancer or not. The algorithm used is Genetic Algorithm as feature selection and Momentum Backpropagation Neural Network as a classification method, with data used from the Kent Ridge Bio-medical Dataset. Based on system testing that has been done, the system can detect Leukemia and Colon Tumor with best accuracy equal to 98.33% for colon tumor data and 100% for leukimia data. Genetic Algorithm as feature selection algorithm can improve system accuracy, which is from 64.52% to 98.33% for colon tumor data and 65.28% to 100% for leukemia data, and the use of momentum parameters can accelerate the convergence of the system in the training process of Neural Network.

Statistical Outlier Detection for Jury Based Grading Systems

DEFF Research Database (Denmark)

Thompson, Mary Kathryn; Clemmensen, Line Katrine Harder; Rosas, Harvey

2013-01-01

This paper presents an algorithm that was developed to identify statistical outliers from the scores of grading jury members in a large project-based first year design course. The background and requirements for the outlier detection system are presented. The outlier detection algorithm...... and the follow-up procedures for score validation and appeals are described in detail. Finally, the impact of various elements of the outlier detection algorithm, their interactions, and the sensitivity of their numerical values are investigated. It is shown that the difference in the mean score produced...... by a grading jury before and after a suspected outlier is removed from the mean is the single most effective criterion for identifying potential outliers but that all of the criteria included in the algorithm have an effect on the outlier detection process....

An FPGA-Based People Detection System

Directory of Open Access Journals (Sweden)

James J. Clark

2005-05-01

Full Text Available This paper presents an FPGA-based system for detecting people from video. The system is designed to use JPEG-compressed frames from a network camera. Unlike previous approaches that use techniques such as background subtraction and motion detection, we use a machine-learning-based approach to train an accurate detector. We address the hardware design challenges involved in implementing such a detector, along with JPEG decompression, on an FPGA. We also present an algorithm that efficiently combines JPEG decompression with the detection process. This algorithm carries out the inverse DCT step of JPEG decompression only partially. Therefore, it is computationally more efficient and simpler to implement, and it takes up less space on the chip than the full inverse DCT algorithm. The system is demonstrated on an automated video surveillance application and the performance of both hardware and software implementations is analyzed. The results show that the system can detect people accurately at a rate of about 2.5 frames per second on a Virtex-II 2V1000 using a MicroBlaze processor running at 75 MHz, communicating with dedicated hardware over FSL links.

A novel time-domain signal processing algorithm for real time ventricular fibrillation detection

International Nuclear Information System (INIS)

Monte, G E; Scarone, N C; Liscovsky, P O; Rotter, P

2011-01-01

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.

A novel time-domain signal processing algorithm for real time ventricular fibrillation detection

Science.gov (United States)

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.

Single electron based binary multipliers with overflow detection ...

African Journals Online (AJOL)

electron based device. Multipliers with overflow detection based on serial and parallel prefix computation algorithm are elaborately discussed analytically and designed. The overflow detection circuits works in parallel with a simplified multiplier to ...

An efficient and cost effective FPGA based implementation of the Viola-Jones face detection algorithm

Directory of Open Access Journals (Sweden)

Peter Irgens

2017-04-01

Full Text Available We present an field programmable gate arrays (FPGA based implementation of the popular Viola-Jones face detection algorithm, which is an essential building block in many applications such as video surveillance and tracking. Our implementation is a complete system level hardware design described in a hardware description language and validated on the affordable DE2-115 evaluation board. Our primary objective is to study the achievable performance with a low-end FPGA chip based implementation. In addition, we release to the public domain the entire project. We hope that this will enable other researchers to easily replicate and compare their results to ours and that it will encourage and facilitate further research and educational ideas in the areas of image processing, computer vision, and advanced digital design and FPGA prototyping.

A computer-aided detection (CAD) system with a 3D algorithm for small acute intracranial hemorrhage

Science.gov (United States)

Wang, Ximing; Fernandez, James; Deshpande, Ruchi; Lee, Joon K.; Chan, Tao; Liu, Brent

2012-02-01

Acute Intracranial hemorrhage (AIH) requires urgent diagnosis in the emergency setting to mitigate eventual sequelae. However, experienced radiologists may not always be available to make a timely diagnosis. This is especially true for small AIH, defined as lesion smaller than 10 mm in size. A computer-aided detection (CAD) system for the detection of small AIH would facilitate timely diagnosis. A previously developed 2D algorithm shows high false positive rates in the evaluation based on LAC/USC cases, due to the limitation of setting up correct coordinate system for the knowledge-based classification system. To achieve a higher sensitivity and specificity, a new 3D algorithm is developed. The algorithm utilizes a top-hat transformation and dynamic threshold map to detect small AIH lesions. Several key structures of brain are detected and are used to set up a 3D anatomical coordinate system. A rule-based classification of the lesion detected is applied based on the anatomical coordinate system. For convenient evaluation in clinical environment, the CAD module is integrated with a stand-alone system. The CAD is evaluated by small AIH cases and matched normal collected in LAC/USC. The result of 3D CAD and the previous 2D CAD has been compared.

Image denoising based on noise detection

Science.gov (United States)

Jiang, Yuanxiang; Yuan, Rui; Sun, Yuqiu; Tian, Jinwen

2018-03-01

Because of the noise points in the images, any operation of denoising would change the original information of non-noise pixel. A noise detection algorithm based on fractional calculus was proposed to denoise in this paper. Convolution of the image was made to gain direction gradient masks firstly. Then, the mean gray was calculated to obtain the gradient detection maps. Logical product was made to acquire noise position image next. Comparisons in the visual effect and evaluation parameters after processing, the results of experiment showed that the denoising algorithms based on noise were better than that of traditional methods in both subjective and objective aspects.

Algorithms for Speeding up Distance-Based Outlier Detection

Data.gov (United States)

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...

Shot Boundary Detection in Soccer Video using Twin-comparison Algorithm and Dominant Color Region

Directory of Open Access Journals (Sweden)

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.

Microcontroller-based real-time QRS detection.

Science.gov (United States)

Sun, Y; Suppappola, S; Wrublewski, T A

1992-01-01

The authors describe the design of a system for real-time detection of QRS complexes in the electrocardiogram based on a single-chip microcontroller (Motorola 68HC811). A systematic analysis of the instrumentation requirements for QRS detection and of the various design techniques is also given. Detection algorithms using different nonlinear transforms for the enhancement of QRS complexes are evaluated by using the ECG database of the American Heart Association. The results show that the nonlinear transform involving multiplication of three adjacent, sign-consistent differences in the time domain gives a good performance and a quick response. When implemented with an appropriate sampling rate, this algorithm is also capable of rejecting pacemaker spikes. The eight-bit single-chip microcontroller provides sufficient throughput and shows a satisfactory performance. Implementation of multiple detection algorithms in the same system improves flexibility and reliability. The low chip count in the design also favors maintainability and cost-effectiveness.

Intrusion-Aware Alert Validation Algorithm for Cooperative Distributed Intrusion Detection Schemes of Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Young-Jae Song

2009-07-01

Full Text Available Existing anomaly and intrusion detection schemes of wireless sensor networks have mainly focused on the detection of intrusions. Once the intrusion is detected, an alerts or claims will be generated. However, any unidentified malicious nodes in the network could send faulty anomaly and intrusion claims about the legitimate nodes to the other nodes. Verifying the validity of such claims is a critical and challenging issue that is not considered in the existing cooperative-based distributed anomaly and intrusion detection schemes of wireless sensor networks. In this paper, we propose a validation algorithm that addresses this problem. This algorithm utilizes the concept of intrusion-aware reliability that helps to provide adequate reliability at a modest communication cost. In this paper, we also provide a security resiliency analysis of the proposed intrusion-aware alert validation algorithm.

Home Camera-Based Fall Detection System for the Elderly

Directory of Open Access Journals (Sweden)

Koldo de Miguel

2017-12-01

Full Text Available Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms (background subtraction, Kalman filtering and optical flow as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%.

Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

Science.gov (United States)

Ganapathy, S.; Yogesh, P.; Kannan, A.

2012-01-01

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set. PMID:23056036

A game theoretic algorithm to detect overlapping community structure in networks

Science.gov (United States)

Zhou, Xu; Zhao, Xiaohui; Liu, Yanheng; Sun, Geng

2018-04-01

Community detection can be used as an important technique for product and personalized service recommendation. A game theory based approach to detect overlapping community structure is introduced in this paper. The process of the community formation is converted into a game, when all agents (nodes) cannot improve their own utility, the game process will be terminated. The utility function is composed of a gain and a loss function and we present a new gain function in this paper. In addition, different from choosing action randomly among join, quit and switch for each agent to get new label, two new strategies for each agent to update its label are designed during the game, and the strategies are also evaluated and compared for each agent in order to find its best result. The overlapping community structure is naturally presented when the stop criterion is satisfied. The experimental results demonstrate that the proposed algorithm outperforms other similar algorithms for detecting overlapping communities in networks.

An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network.

Science.gov (United States)

Sun, Xuemei; Yan, Bo; Zhang, Xinzhong; Rong, Chuitian

2015-01-01

Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish-Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection.

An effective detection algorithm for region duplication forgery in digital images

Science.gov (United States)

Yavuz, Fatih; Bal, Abdullah; Cukur, Huseyin

2016-04-01

Powerful image editing tools are very common and easy to use these days. This situation may cause some forgeries by adding or removing some information on the digital images. In order to detect these types of forgeries such as region duplication, we present an effective algorithm based on fixed-size block computation and discrete wavelet transform (DWT). In this approach, the original image is divided into fixed-size blocks, and then wavelet transform is applied for dimension reduction. Each block is processed by Fourier Transform and represented by circle regions. Four features are extracted from each block. Finally, the feature vectors are lexicographically sorted, and duplicated image blocks are detected according to comparison metric results. The experimental results show that the proposed algorithm presents computational efficiency due to fixed-size circle block architecture.

An adaptive algorithm for the detection of microcalcifications in simulated low-dose mammography

Science.gov (United States)

Treiber, O.; Wanninger, F.; Führ, H.; Panzer, W.; Regulla, D.; Winkler, G.

2003-02-01

This paper uses the task of microcalcification detection as a benchmark problem to assess the potential for dose reduction in x-ray mammography. We present the results of a newly developed algorithm for detection of microcalcifications as a case study for a typical commercial film-screen system (Kodak Min-R 2000/2190). The first part of the paper deals with the simulation of dose reduction for film-screen mammography based on a physical model of the imaging process. Use of a more sensitive film-screen system is expected to result in additional smoothing of the image. We introduce two different models of that behaviour, called moderate and strong smoothing. We then present an adaptive, model-based microcalcification detection algorithm. Comparing detection results with ground-truth images obtained under the supervision of an expert radiologist allows us to establish the soundness of the detection algorithm. We measure the performance on the dose-reduced images in order to assess the loss of information due to dose reduction. It turns out that the smoothing behaviour has a strong influence on detection rates. For moderate smoothing, a dose reduction by 25% has no serious influence on the detection results, whereas a dose reduction by 50% already entails a marked deterioration of the performance. Strong smoothing generally leads to an unacceptable loss of image quality. The test results emphasize the impact of the more sensitive film-screen system and its characteristics on the problem of assessing the potential for dose reduction in film-screen mammography. The general approach presented in the paper can be adapted to fully digital mammography.

An adaptive algorithm for the detection of microcalcifications in simulated low-dose mammography

International Nuclear Information System (INIS)

Treiber, O; Wanninger, F; Fuehr, H; Panzer, W; Regulla, D; Winkler, G

2003-01-01

This paper uses the task of microcalcification detection as a benchmark problem to assess the potential for dose reduction in x-ray mammography. We present the results of a newly developed algorithm for detection of microcalcifications as a case study for a typical commercial film-screen system (Kodak Min-R 2000/2190). The first part of the paper deals with the simulation of dose reduction for film-screen mammography based on a physical model of the imaging process. Use of a more sensitive film-screen system is expected to result in additional smoothing of the image. We introduce two different models of that behaviour, called moderate and strong smoothing. We then present an adaptive, model-based microcalcification detection algorithm. Comparing detection results with ground-truth images obtained under the supervision of an expert radiologist allows us to establish the soundness of the detection algorithm. We measure the performance on the dose-reduced images in order to assess the loss of information due to dose reduction. It turns out that the smoothing behaviour has a strong influence on detection rates. For moderate smoothing, a dose reduction by 25% has no serious influence on the detection results, whereas a dose reduction by 50% already entails a marked deterioration of the performance. Strong smoothing generally leads to an unacceptable loss of image quality. The test results emphasize the impact of the more sensitive film-screen system and its characteristics on the problem of assessing the potential for dose reduction in film-screen mammography. The general approach presented in the paper can be adapted to fully digital mammography

Potential fire detection based on Kalman-driven change detection

CSIR Research Space (South Africa)

Van Den Bergh, F

2009-07-01

Full Text Available A new active fire event detection algorithm for data collected with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor, based on the extended Kalman filter, is introduced. Instead of using the observed temperatures of the spatial...

A simplified Suomi NPP VIIRS dust detection algorithm

Science.gov (United States)

Yang, Yikun; Sun, Lin; Zhu, Jinshan; Wei, Jing; Su, Qinghua; Sun, Wenxiao; Liu, Fangwei; Shu, Meiyan

2017-11-01

Due to the complex characteristics of dust and sparse ground-based monitoring stations, dust monitoring is facing severe challenges, especially in dust storm-prone areas. Aim at constructing a high-precision dust storm detection model, a pixel database, consisted of dusts over a variety of typical feature types such as cloud, vegetation, Gobi and ice/snow, was constructed, and their distributions of reflectance and Brightness Temperatures (BT) were analysed, based on which, a new Simplified Dust Detection Algorithm (SDDA) for the Suomi National Polar-Orbiting Partnership Visible infrared Imaging Radiometer (NPP VIIRS) is proposed. NPP VIIRS images covering the northern China and Mongolian regions, where features serious dust storms, were selected to perform the dust detection experiments. The monitoring results were compared with the true colour composite images, and results showed that most of the dust areas can be accurately detected, except for fragmented thin dusts over bright surfaces. The dust ground-based measurements obtained from the Meteorological Information Comprehensive Analysis and Process System (MICAPS) and the Ozone Monitoring Instrument Aerosol Index (OMI AI) products were selected for comparison purposes. Results showed that the dust monitoring results agreed well in the spatial distribution with OMI AI dust products and the MICAPS ground-measured data with an average high accuracy of 83.10%. The SDDA is relatively robust and can realize automatic monitoring for dust storms.

Ensemble method: Community detection based on game theory

Science.gov (United States)

Zhang, Xia; Xia, Zhengyou; Xu, Shengwu; Wang, J. D.

2014-08-01

Timely and cost-effective analytics over social network has emerged as a key ingredient for success in many businesses and government endeavors. Community detection is an active research area of relevance to analyze online social network. The problem of selecting a particular community detection algorithm is crucial if the aim is to unveil the community structure of a network. The choice of a given methodology could affect the outcome of the experiments because different algorithms have different advantages and depend on tuning specific parameters. In this paper, we propose a community division model based on the notion of game theory, which can combine advantages of previous algorithms effectively to get a better community classification result. By making experiments on some standard dataset, it verifies that our community detection model based on game theory is valid and better.

Phenotyping for patient safety: algorithm development for electronic health record based automated adverse event and medical error detection in neonatal intensive care.

Science.gov (United States)

Li, Qi; Melton, Kristin; Lingren, Todd; Kirkendall, Eric S; Hall, Eric; Zhai, Haijun; Ni, Yizhao; Kaiser, Megan; Stoutenborough, Laura; Solti, Imre

2014-01-01

Although electronic health records (EHRs) have the potential to provide a foundation for quality and safety algorithms, few studies have measured their impact on automated adverse event (AE) and medical error (ME) detection within the neonatal intensive care unit (NICU) environment. This paper presents two phenotyping AE and ME detection algorithms (ie, IV infiltrations, narcotic medication oversedation and dosing errors) and describes manual annotation of airway management and medication/fluid AEs from NICU EHRs. From 753 NICU patient EHRs from 2011, we developed two automatic AE/ME detection algorithms, and manually annotated 11 classes of AEs in 3263 clinical notes. Performance of the automatic AE/ME detection algorithms was compared to trigger tool and voluntary incident reporting results. AEs in clinical notes were double annotated and consensus achieved under neonatologist supervision. Sensitivity, positive predictive value (PPV), and specificity are reported. Twelve severe IV infiltrates were detected. The algorithm identified one more infiltrate than the trigger tool and eight more than incident reporting. One narcotic oversedation was detected demonstrating 100% agreement with the trigger tool. Additionally, 17 narcotic medication MEs were detected, an increase of 16 cases over voluntary incident reporting. Automated AE/ME detection algorithms provide higher sensitivity and PPV than currently used trigger tools or voluntary incident-reporting systems, including identification of potential dosing and frequency errors that current methods are unequipped to detect. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

An Alternative to Chaid Segmentation Algorithm Based on Entropy.

Directory of Open Access Journals (Sweden)

María Purificación Galindo Villardón

2010-07-01

Full Text Available The CHAID (Chi-Squared Automatic Interaction Detection treebased segmentation technique has been found to be an effective approach for obtaining meaningful segments that are predictive of a K-category (nominal or ordinal criterion variable. CHAID was designed to detect, in an automatic way, the  nteraction between several categorical or ordinal predictors in explaining a categorical response, but, this may not be true when Simpson’s paradox is present. This is due to the fact that CHAID is a forward selection algorithm based on the marginal counts. In this paper we propose a backwards elimination algorithm that starts with the full set of predictors (or full tree and eliminates predictors progressively. The elimination procedure is based on Conditional Independence contrasts using the concept of entropy. The proposed procedure is compared to CHAID.

Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm.

Science.gov (United States)

Zhou, Hui; Ji, Ning; Samuel, Oluwarotimi Williams; Cao, Yafei; Zhao, Zheyi; Chen, Shixiong; Li, Guanglin

2016-10-01

Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO) and heel strike (HS) gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses.

Algorithm for Fast and Efficient Detection and Reaction to Angle Instability Conditions Using Phasor Measurement Unit Data

Directory of Open Access Journals (Sweden)

Igor Ivanković

2018-03-01

Full Text Available In wide area monitoring, protection, and control (WAMPAC systems, angle stability of transmission network is monitored using data from phasor measurement units (PMU placed on transmission lines. Based on this PMU data stream advanced algorithm for out-of-step condition detection and early warning issuing is developed. The algorithm based on theoretical background described in this paper is backed up by the data and results from corresponding simulations done in Matlab environment. Presented results aim to provide the insights of the potential benefits, such as fast and efficient detection and reaction to angle instability, this algorithm can have on the improvement of the power system protection. Accordingly, suggestion is given how the developed algorithm can be implemented in protection segments of the WAMPAC systems in the transmission system operator control centers.

Comparative study of adaptive-noise-cancellation algorithms for intrusion detection systems

International Nuclear Information System (INIS)

Claassen, J.P.; Patterson, M.M.

1981-01-01

Some intrusion detection systems are susceptible to nonstationary noise resulting in frequent nuisance alarms and poor detection when the noise is present. Adaptive inverse filtering for single channel systems and adaptive noise cancellation for two channel systems have both demonstrated good potential in removing correlated noise components prior detection. For such noise susceptible systems the suitability of a noise reduction algorithm must be established in a trade-off study weighing algorithm complexity against performance. The performance characteristics of several distinct classes of algorithms are established through comparative computer studies using real signals. The relative merits of the different algorithms are discussed in the light of the nature of intruder and noise signals

Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi-Objective Evolutionary Algorithms

Directory of Open Access Journals (Sweden)

Min-Yin Liu

2017-05-01

Full Text Available Sleep spindles are brief bursts of brain activity in the sigma frequency range (11–16 Hz measured by electroencephalography (EEG mostly during non-rapid eye movement (NREM stage 2 sleep. These oscillations are of great biological and clinical interests because they potentially play an important role in identifying and characterizing the processes of various neurological disorders. Conventionally, sleep spindles are identified by expert sleep clinicians via visual inspection of EEG signals. The process is laborious and the results are inconsistent among different experts. To resolve the problem, numerous computerized methods have been developed to automate the process of sleep spindle identification. Still, the performance of these automated sleep spindle detection methods varies inconsistently from study to study. There are two reasons: (1 the lack of common benchmark databases, and (2 the lack of commonly accepted evaluation metrics. In this study, we focus on tackling the second problem by proposing to evaluate the performance of a spindle detector in a multi-objective optimization context and hypothesize that using the resultant Pareto fronts for deriving evaluation metrics will improve automatic sleep spindle detection. We use a popular multi-objective evolutionary algorithm (MOEA, the Strength Pareto Evolutionary Algorithm (SPEA2, to optimize six existing frequency-based sleep spindle detection algorithms. They include three Fourier, one continuous wavelet transform (CWT, and two Hilbert-Huang transform (HHT based algorithms. We also explore three hybrid approaches. Trained and tested on open-access DREAMS and MASS databases, two new hybrid methods of combining Fourier with HHT algorithms show significant performance improvement with F1-scores of 0.726–0.737.

Frequency hopping signal detection based on wavelet decomposition and Hilbert-Huang transform

Science.gov (United States)

Zheng, Yang; Chen, Xihao; Zhu, Rui

2017-07-01

Frequency hopping (FH) signal is widely adopted by military communications as a kind of low probability interception signal. Therefore, it is very important to research the FH signal detection algorithm. The existing detection algorithm of FH signals based on the time-frequency analysis cannot satisfy the time and frequency resolution requirement at the same time due to the influence of window function. In order to solve this problem, an algorithm based on wavelet decomposition and Hilbert-Huang transform (HHT) was proposed. The proposed algorithm removes the noise of the received signals by wavelet decomposition and detects the FH signals by Hilbert-Huang transform. Simulation results show the proposed algorithm takes into account both the time resolution and the frequency resolution. Correspondingly, the accuracy of FH signals detection can be improved.

Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers

Directory of Open Access Journals (Sweden)

Dawei Li

2017-01-01

Full Text Available This study develops a tree augmented naive Bayesian (TAN classifier based incident detection algorithm. Compared with the Bayesian networks based detection algorithms developed in the previous studies, this algorithm has less dependency on experts’ knowledge. The structure of TAN classifier for incident detection is learned from data. The discretization of continuous attributes is processed using an entropy-based method automatically. A simulation dataset on the section of the Ayer Rajah Expressway (AYE in Singapore is used to demonstrate the development of proposed algorithm, including wavelet denoising, normalization, entropy-based discretization, and structure learning. The performance of TAN based algorithm is evaluated compared with the previous developed Bayesian network (BN based and multilayer feed forward (MLF neural networks based algorithms with the same AYE data. The experiment results show that the TAN based algorithms perform better than the BN classifiers and have a similar performance to the MLF based algorithm. However, TAN based algorithm would have wider vista of applications because the theory of TAN classifiers is much less complicated than MLF. It should be found from the experiment that the TAN classifier based algorithm has a significant superiority over the speed of model training and calibration compared with MLF.

A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set and its Application in Anomaly Detection

Directory of Open Access Journals (Sweden)

Wen Chen

2011-08-01

Full Text Available A negative selection algorithm based on the hierarchical clustering of self set HC-RNSA is introduced in this paper. Several strategies are applied to improve the algorithm performance. First, the self data set is replaced by the self cluster centers to compare with the detector candidates in each cluster level. As the number of self clusters is much less than the self set size, the detector generation efficiency is improved. Second, during the detector generation process, the detector candidates are restricted to the lower coverage space to reduce detector redundancy. In the article, the problem that the distances between antigens coverage to a constant value in the high dimensional space is analyzed, accordingly the Principle Component Analysis (PCA method is used to reduce the data dimension, and the fractional distance function is employed to enhance the distinctiveness between the self and non-self antigens. The detector generation procedure is terminated when the expected non-self coverage is reached. The theory analysis and experimental results demonstrate that the detection rate of HC-RNSA is higher than that of the traditional negative selection algorithms while the false alarm rate and time cost are reduced.

A Low-Complexity Joint Synchronization and Detection Algorithm for Single-Band DS-CDMA UWB Communications

Directory of Open Access Journals (Sweden)

Christensen Lars PB

2005-01-01

Full Text Available The problem of asynchronous direct-sequence code-division multiple-access (DS-CDMA detection over the ultra-wideband (UWB multipath channel is considered. A joint synchronization, channel-estimation, and multiuser detection scheme based on the adaptive linear minimum mean square error (LMMSE receiver is presented and evaluated. Further, a novel nonrecursive least-squares algorithm capable of reducing the complexity of the adaptation in the receiver while preserving the advantages of the recursive least-squares (RLS algorithm is presented.

The derivation of distributed termination detection algorithms from garbage collection schemes

NARCIS (Netherlands)

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

Improved OAM-Based Radar Targets Detection Using Uniform Concentric Circular Arrays

Directory of Open Access Journals (Sweden)

Mingtuan Lin

2016-01-01

Full Text Available Without any relative moves or beam scanning, the novel Orbital-Angular-Momentum- (OAM- based radar targets detection technique using uniform concentric circular arrays (UCCAs shows the azimuthal estimation ability, which provides new perspective for radar system design. However, the main estimation method, that is, Fast Fourier Transform (FFT, under this scheme suffers from low resolution. As a solution, this paper rebuilds the OAM-based radar targets detection model and introduces the multiple signal classification (MUSIC algorithm to improve the resolution for detecting targets within the main lobes. The spatial smoothing technique is proposed to tackle the coherent problem brought by the proposed model. Analytical study and simulation demonstrate the superresolution estimation capacity the MUSIC algorithm can achieve for detecting targets within the main lobes. The performance of the MUSIC algorithm to detect targets not illuminated by the main lobes is further evaluated. Despite the fact that MUSIC algorithm loses the resolution advantage under this case, its estimation is more robust than that of the FFT method. Overall, the proposed MUSIC algorithm for the OAM-based radar system demonstrates the superresolution ability for detecting targets within the main lobes and good robustness for targets out of the main lobes.

Robust and accurate detection algorithm for multimode polymer optical FBG sensor system

DEFF Research Database (Denmark)

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 and accur...

Robustness and precision of an automatic marker detection algorithm for online prostate daily targeting using a standard V-EPID.

Science.gov (United States)

Aubin, S; Beaulieu, L; Pouliot, S; Pouliot, J; Roy, R; Girouard, L M; Martel-Brisson, N; Vigneault, E; Laverdière, J

2003-07-01

An algorithm for the daily localization of the prostate using implanted markers and a standard video-based electronic portal imaging device (V-EPID) has been tested. Prior to planning, three gold markers were implanted in the prostate of seven patients. The clinical images were acquired with a BeamViewPlus 2.1 V-EPID for each field during the normal course radiotherapy treatment and are used off-line to determine the ability of the automatic marker detection algorithm to adequately and consistently detect the markers. Clinical images were obtained with various dose levels from ranging 2.5 to 75 MU. The algorithm is based on marker attenuation characterization in the portal image and spatial distribution. A total of 1182 clinical images were taken. The results show an average efficiency of 93% for the markers detected individually and 85% for the group of markers. This algorithm accomplishes the detection and validation in 0.20-0.40 s. When the center of mass of the group of implanted markers is used, then all displacements can be corrected to within 1.0 mm in 84% of the cases and within 1.5 mm in 97% of cases. The standard video-based EPID tested provides excellent marker detection capability even with low dose levels. The V-EPID can be used successfully with radiopaque markers and the automatic detection algorithm to track and correct the daily setup deviations due to organ motions.

Multicriteria Similarity-Based Anomaly Detection Using Pareto Depth Analysis.

Science.gov (United States)

Hsiao, Ko-Jen; Xu, Kevin S; Calder, Jeff; Hero, Alfred O

2016-06-01

We consider the problem of identifying patterns in a data set that exhibits anomalous behavior, often referred to as anomaly detection. Similarity-based anomaly detection algorithms detect abnormally large amounts of similarity or dissimilarity, e.g., as measured by the nearest neighbor Euclidean distances between a test sample and the training samples. In many application domains, there may not exist a single dissimilarity measure that captures all possible anomalous patterns. In such cases, multiple dissimilarity measures can be defined, including nonmetric measures, and one can test for anomalies by scalarizing using a nonnegative linear combination of them. If the relative importance of the different dissimilarity measures are not known in advance, as in many anomaly detection applications, the anomaly detection algorithm may need to be executed multiple times with different choices of weights in the linear combination. In this paper, we propose a method for similarity-based anomaly detection using a novel multicriteria dissimilarity measure, the Pareto depth. The proposed Pareto depth analysis (PDA) anomaly detection algorithm uses the concept of Pareto optimality to detect anomalies under multiple criteria without having to run an algorithm multiple times with different choices of weights. The proposed PDA approach is provably better than using linear combinations of the criteria, and shows superior performance on experiments with synthetic and real data sets.

A low complexity VBLAST OFDM detection algorithm for wireless LAN systems

NARCIS (Netherlands)

Wu, Y.; Lei, Zhongding; Sun, Sumei

2004-01-01

A low complexity detection algorithm for VBLAST OFDM system is presented. Using the fact that the correlation among neighboring subcarriers is high for wireless LAN channels, this algorithm significantly reduces the complexity of VBLAST OFDM detection. The performance degradation of the proposed

Photon Counting Using Edge-Detection Algorithm

Science.gov (United States)

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

Random Valued Impulse Noise Removal Using Region Based Detection Approach

Directory of Open Access Journals (Sweden)

S. Banerjee

2017-12-01

Full Text Available Removal of random valued noisy pixel is extremely challenging when the noise density is above 50%. The existing filters are generally not capable of eliminating such noise when density is above 70%. In this paper a region wise density based detection algorithm for random valued impulse noise has been proposed. On the basis of the intensity values, the pixels of a particular window are sorted and then stored into four regions. The higher density based region is considered for stepwise detection of noisy pixels. As a result of this detection scheme a maximum of 75% of noisy pixels can be detected. For this purpose this paper proposes a unique noise removal algorithm. It was experimentally proved that the proposed algorithm not only performs exceptionally when it comes to visual qualitative judgment of standard images but also this filter combination outsmarts the existing algorithm in terms of MSE, PSNR and SSIM comparison even up to 70% noise density level.

An OMIC biomarker detection algorithm TriVote and its application in methylomic biomarker detection.

Science.gov (United States)

Xu, Cheng; Liu, Jiamei; Yang, Weifeng; Shu, Yayun; Wei, Zhipeng; Zheng, Weiwei; Feng, Xin; Zhou, Fengfeng

2018-04-01

Transcriptomic and methylomic patterns represent two major OMIC data sources impacted by both inheritable genetic information and environmental factors, and have been widely used as disease diagnosis and prognosis biomarkers. Modern transcriptomic and methylomic profiling technologies detect the status of tens of thousands or even millions of probing residues in the human genome, and introduce a major computational challenge for the existing feature selection algorithms. This study proposes a three-step feature selection algorithm, TriVote, to detect a subset of transcriptomic or methylomic residues with highly accurate binary classification performance. TriVote outperforms both filter and wrapper feature selection algorithms with both higher classification accuracy and smaller feature number on 17 transcriptomes and two methylomes. Biological functions of the methylome biomarkers detected by TriVote were discussed for their disease associations. An easy-to-use Python package is also released to facilitate the further applications.

The Automated Assessment of Postural Stability: Balance Detection Algorithm.

Science.gov (United States)

Napoli, Alessandro; Glass, Stephen M; Tucker, Carole; Obeid, Iyad

2017-12-01

Impaired balance is a common indicator of mild traumatic brain injury, concussion and musculoskeletal injury. Given the clinical relevance of such injuries, especially in military settings, it is paramount to develop more accurate and reliable on-field evaluation tools. This work presents the design and implementation of the automated assessment of postural stability (AAPS) system, for on-field evaluations following concussion. The AAPS is a computer system, based on inexpensive off-the-shelf components and custom software, that aims to automatically and reliably evaluate balance deficits, by replicating a known on-field clinical test, namely, the Balance Error Scoring System (BESS). The AAPS main innovation is its balance error detection algorithm that has been designed to acquire data from a Microsoft Kinect ® sensor and convert them into clinically-relevant BESS scores, using the same detection criteria defined by the original BESS test. In order to assess the AAPS balance evaluation capability, a total of 15 healthy subjects (7 male, 8 female) were required to perform the BESS test, while simultaneously being tracked by a Kinect 2.0 sensor and a professional-grade motion capture system (Qualisys AB, Gothenburg, Sweden). High definition videos with BESS trials were scored off-line by three experienced observers for reference scores. AAPS performance was assessed by comparing the AAPS automated scores to those derived by three experienced observers. Our results show that the AAPS error detection algorithm presented here can accurately and precisely detect balance deficits with performance levels that are comparable to those of experienced medical personnel. Specifically, agreement levels between the AAPS algorithm and the human average BESS scores ranging between 87.9% (single-leg on foam) and 99.8% (double-leg on firm ground) were detected. Moreover, statistically significant differences in balance scores were not detected by an ANOVA test with alpha equal to 0

Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

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%.

CONEDEP: COnvolutional Neural network based Earthquake DEtection and Phase Picking

Science.gov (United States)

Zhou, Y.; Huang, Y.; Yue, H.; Zhou, S.; An, S.; Yun, N.

2017-12-01

We developed an automatic local earthquake detection and phase picking algorithm based on Fully Convolutional Neural network (FCN). The FCN algorithm detects and segments certain features (phases) in 3 component seismograms to realize efficient picking. We use STA/LTA algorithm and template matching algorithm to construct the training set from seismograms recorded 1 month before and after the Wenchuan earthquake. Precise P and S phases are identified and labeled to construct the training set. Noise data are produced by combining back-ground noise and artificial synthetic noise to form the equivalent scale of noise set as the signal set. Training is performed on GPUs to achieve efficient convergence. Our algorithm has significantly improved performance in terms of the detection rate and precision in comparison with STA/LTA and template matching algorithms.

Detecting an atomic clock frequency anomaly using an adaptive Kalman filter algorithm

Science.gov (United States)

Song, Huijie; Dong, Shaowu; Wu, Wenjun; Jiang, Meng; Wang, Weixiong

2018-06-01

The abnormal frequencies of an atomic clock mainly include frequency jump and frequency drift jump. Atomic clock frequency anomaly detection is a key technique in time-keeping. The Kalman filter algorithm, as a linear optimal algorithm, has been widely used in real-time detection for abnormal frequency. In order to obtain an optimal state estimation, the observation model and dynamic model of the Kalman filter algorithm should satisfy Gaussian white noise conditions. The detection performance is degraded if anomalies affect the observation model or dynamic model. The idea of the adaptive Kalman filter algorithm, applied to clock frequency anomaly detection, uses the residuals given by the prediction for building ‘an adaptive factor’ the prediction state covariance matrix is real-time corrected by the adaptive factor. The results show that the model error is reduced and the detection performance is improved. The effectiveness of the algorithm is verified by the frequency jump simulation, the frequency drift jump simulation and the measured data of the atomic clock by using the chi-square test.

Simulation-Based Evaluation of the Performances of an Algorithm for Detecting Abnormal Disease-Related Features in Cattle Mortality Records.

Science.gov (United States)

Perrin, Jean-Baptiste; Durand, Benoît; Gay, Emilie; Ducrot, Christian; Hendrikx, Pascal; Calavas, Didier; Hénaux, Viviane

2015-01-01

We performed a simulation study to evaluate the performances of an anomaly detection algorithm considered in the frame of an automated surveillance system of cattle mortality. The method consisted in a combination of temporal regression and spatial cluster detection which allows identifying, for a given week, clusters of spatial units showing an excess of deaths in comparison with their own historical fluctuations. First, we simulated 1,000 outbreaks of a disease causing extra deaths in the French cattle population (about 200,000 herds and 20 million cattle) according to a model mimicking the spreading patterns of an infectious disease and injected these disease-related extra deaths in an authentic mortality dataset, spanning from January 2005 to January 2010. Second, we applied our algorithm on each of the 1,000 semi-synthetic datasets to identify clusters of spatial units showing an excess of deaths considering their own historical fluctuations. Third, we verified if the clusters identified by the algorithm did contain simulated extra deaths in order to evaluate the ability of the algorithm to identify unusual mortality clusters caused by an outbreak. Among the 1,000 simulations, the median duration of simulated outbreaks was 8 weeks, with a median number of 5,627 simulated deaths and 441 infected herds. Within the 12-week trial period, 73% of the simulated outbreaks were detected, with a median timeliness of 1 week, and a mean of 1.4 weeks. The proportion of outbreak weeks flagged by an alarm was 61% (i.e. sensitivity) whereas one in three alarms was a true alarm (i.e. positive predictive value). The performances of the detection algorithm were evaluated for alternative combination of epidemiologic parameters. The results of our study confirmed that in certain conditions automated algorithms could help identifying abnormal cattle mortality increases possibly related to unidentified health events.

An improved algorithm for automatic detection of saccades in eye movement data and for calculating saccade parameters.

Science.gov (United States)

Behrens, F; Mackeben, M; Schröder-Preikschat, W

2010-08-01

This analysis of time series of eye movements is a saccade-detection algorithm that is based on an earlier algorithm. It achieves substantial improvements by using an adaptive-threshold model instead of fixed thresholds and using the eye-movement acceleration signal. This has four advantages: (1) Adaptive thresholds are calculated automatically from the preceding acceleration data for detecting the beginning of a saccade, and thresholds are modified during the saccade. (2) The monotonicity of the position signal during the saccade, together with the acceleration with respect to the thresholds, is used to reliably determine the end of the saccade. (3) This allows differentiation between saccades following the main-sequence and non-main-sequence saccades. (4) Artifacts of various kinds can be detected and eliminated. The algorithm is demonstrated by applying it to human eye movement data (obtained by EOG) recorded during driving a car. A second demonstration of the algorithm detects microsleep episodes in eye movement data.

Costs and consequences of automated algorithms versus manual grading for the detection of referable diabetic retinopathy.

Science.gov (United States)

Scotland, G S; McNamee, P; Fleming, A D; Goatman, K A; Philip, S; Prescott, G J; Sharp, P F; Williams, G J; Wykes, W; Leese, G P; Olson, J A

2010-06-01

To assess the cost-effectiveness of an improved automated grading algorithm for diabetic retinopathy against a previously described algorithm, and in comparison with manual grading. Efficacy of the alternative algorithms was assessed using a reference graded set of images from three screening centres in Scotland (1253 cases with observable/referable retinopathy and 6333 individuals with mild or no retinopathy). Screening outcomes and grading and diagnosis costs were modelled for a cohort of 180 000 people, with prevalence of referable retinopathy at 4%. Algorithm (b), which combines image quality assessment with detection algorithms for microaneurysms (MA), blot haemorrhages and exudates, was compared with a simpler algorithm (a) (using image quality assessment and MA/dot haemorrhage (DH) detection), and the current practice of manual grading. Compared with algorithm (a), algorithm (b) would identify an additional 113 cases of referable retinopathy for an incremental cost of pound 68 per additional case. Compared with manual grading, automated grading would be expected to identify between 54 and 123 fewer referable cases, for a grading cost saving between pound 3834 and pound 1727 per case missed. Extrapolation modelling over a 20-year time horizon suggests manual grading would cost between pound 25,676 and pound 267,115 per additional quality adjusted life year gained. Algorithm (b) is more cost-effective than the algorithm based on quality assessment and MA/DH detection. With respect to the value of introducing automated detection systems into screening programmes, automated grading operates within the recommended national standards in Scotland and is likely to be considered a cost-effective alternative to manual disease/no disease grading.

Detection of the arcuate fasciculus in congenital amusia depends on the tractography algorithm

Directory of Open Access Journals (Sweden)

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.

Detection of the arcuate fasciculus in congenital amusia depends on the tractography algorithm.

Science.gov (United States)

Chen, Joyce L; Kumar, Sukhbinder; Williamson, Victoria J; Scholz, Jan; Griffiths, Timothy D; Stewart, Lauren

2015-01-01

The advent of diffusion magnetic resonance imaging (MRI) allows researchers to virtually dissect white matter fiber pathways in the brain in vivo. This, for example, allows us to characterize and quantify how fiber 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 fiber 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-fiber 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.

Improved algorithm for quantum separability and entanglement detection

International Nuclear Information System (INIS)

Ioannou, L.M.; Ekert, A.K.; Travaglione, B.C.; Cheung, D.

2004-01-01

Determining whether a quantum state is separable or entangled is a problem of fundamental importance in quantum information science. It has recently been shown that this problem is NP-hard, suggesting that an efficient, general solution does not exist. There is a highly inefficient 'basic algorithm' for solving the quantum separability problem which follows from the definition of a separable state. By exploiting specific properties of the set of separable states, we introduce a classical algorithm that solves the problem significantly faster than the 'basic algorithm', allowing a feasible separability test where none previously existed, e.g., in 3x3-dimensional systems. Our algorithm also provides a unique tool in the experimental detection of entanglement

Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings.

Science.gov (United States)

Baldassano, Steven N; Brinkmann, Benjamin H; Ung, Hoameng; Blevins, Tyler; Conrad, Erin C; Leyde, Kent; Cook, Mark J; Khambhati, Ankit N; Wagenaar, Joost B; Worrell, Gregory A; Litt, Brian

2017-06-01

There exist significant clinical and basic research needs for accurate, automated seizure detection algorithms. These algorithms have translational potential in responsive neurostimulation devices and in automatic parsing of continuous intracranial electroencephalography data. An important barrier to developing accurate, validated algorithms for seizure detection is limited access to high-quality, expertly annotated seizure data from prolonged recordings. To overcome this, we hosted a kaggle.com competition to crowdsource the development of seizure detection algorithms using intracranial electroencephalography from canines and humans with epilepsy. The top three performing algorithms from the contest were then validated on out-of-sample patient data including standard clinical data and continuous ambulatory human data obtained over several years using the implantable NeuroVista seizure advisory system. Two hundred teams of data scientists from all over the world participated in the kaggle.com competition. The top performing teams submitted highly accurate algorithms with consistent performance in the out-of-sample validation study. The performance of these seizure detection algorithms, achieved using freely available code and data, sets a new reproducible benchmark for personalized seizure detection. We have also shared a 'plug and play' pipeline to allow other researchers to easily use these algorithms on their own datasets. The success of this competition demonstrates how sharing code and high quality data results in the creation of powerful translational tools with significant potential to impact patient care. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Multiple-Features-Based Semisupervised Clustering DDoS Detection Method

Directory of Open Access Journals (Sweden)

Yonghao Gu

2017-01-01

Full Text Available DDoS attack stream from different agent host converged at victim host will become very large, which will lead to system halt or network congestion. Therefore, it is necessary to propose an effective method to detect the DDoS attack behavior from the massive data stream. In order to solve the problem that large numbers of labeled data are not provided in supervised learning method, and the relatively low detection accuracy and convergence speed of unsupervised k-means algorithm, this paper presents a semisupervised clustering detection method using multiple features. In this detection method, we firstly select three features according to the characteristics of DDoS attacks to form detection feature vector. Then, Multiple-Features-Based Constrained-K-Means (MF-CKM algorithm is proposed based on semisupervised clustering. Finally, using MIT Laboratory Scenario (DDoS 1.0 data set, we verify that the proposed method can improve the convergence speed and accuracy of the algorithm under the condition of using a small amount of labeled data sets.

Rapid Change Detection Algorithm for Disaster Management

Science.gov (United States)

Michel, U.; Thunig, H.; Ehlers, M.; Reinartz, P.

2012-07-01

This paper focuses on change detection applications in areas where catastrophic events took place which resulted in rapid destruction especially of manmade objects. Standard methods for automated change detection prove not to be sufficient; therefore a new method was developed and tested. The presented method allows a fast detection and visualization of change in areas of crisis or catastrophes. While often new methods of remote sensing are developed without user oriented aspects, organizations and authorities are not able to use these methods because of absence of remote sensing know how. Therefore a semi-automated procedure was developed. Within a transferable framework, the developed algorithm can be implemented for a set of remote sensing data among different investigation areas. Several case studies are the base for the retrieved results. Within a coarse dividing into statistical parts and the segmentation in meaningful objects, the framework is able to deal with different types of change. By means of an elaborated Temporal Change Index (TCI) only panchromatic datasets are used to extract areas which are destroyed, areas which were not affected and in addition areas where rebuilding has already started.

Trojan detection model based on network behavior analysis

International Nuclear Information System (INIS)

Liu Junrong; Liu Baoxu; Wang Wenjin

2012-01-01

Based on the analysis of existing Trojan detection technology, this paper presents a Trojan detection model based on network behavior analysis. First of all, we abstract description of the Trojan network behavior, then according to certain rules to establish the characteristic behavior library, and then use the support vector machine algorithm to determine whether a Trojan invasion. Finally, through the intrusion detection experiments, shows that this model can effectively detect Trojans. (authors)

A new approach to voltage sag detection based on wavelet transform

Energy Technology Data Exchange (ETDEWEB)

Gencer, Oezguer; Oeztuerk, Semra; Erfidan, Tarik [Kocaeli University, Faculty of Engineering, Department of Electrical Engineering, Veziroglu Kampuesue, Eski Goelcuek Yolu, Kocaeli (Turkey)

2010-02-15

In this work, a new voltage sag detection method based on wavelet transform is developed. Voltage sag detection algorithms, so far have proved their efficiency and computational ability. Using several windowing techniques take long computational times for disturbance detection. Also researchers have been working on separating voltage sags from other voltage disturbances for the last decade. Due to increasing power quality standards new high performance disturbance detection algorithms are necessary to obtain high power quality standards. For this purpose, the wavelet technique is used for detecting voltage sag duration and magnitude. The developed voltage sag detection algorithm is implemented with high speed microcontroller. Test results show that, the new approach provides very accurate and satisfactory voltage sag detection. (author)

A Robust Motion Artifact Detection Algorithm for Accurate Detection of Heart Rates From Photoplethysmographic Signals Using Time-Frequency Spectral Features.

Science.gov (United States)

Dao, Duy; Salehizadeh, S M A; Noh, Yeonsik; Chong, Jo Woon; Cho, Chae Ho; McManus, Dave; Darling, Chad E; Mendelson, Yitzhak; Chon, Ki H

2017-09-01

Motion and noise artifacts (MNAs) impose limits on the usability of the photoplethysmogram (PPG), particularly in the context of ambulatory monitoring. MNAs can distort PPG, causing erroneous estimation of physiological parameters such as heart rate (HR) and arterial oxygen saturation (SpO2). In this study, we present a novel approach, "TifMA," based on using the time-frequency spectrum of PPG to first detect the MNA-corrupted data and next discard the nonusable part of the corrupted data. The term "nonusable" refers to segments of PPG data from which the HR signal cannot be recovered accurately. Two sequential classification procedures were included in the TifMA algorithm. The first classifier distinguishes between MNA-corrupted and MNA-free PPG data. Once a segment of data is deemed MNA-corrupted, the next classifier determines whether the HR can be recovered from the corrupted segment or not. A support vector machine (SVM) classifier was used to build a decision boundary for the first classification task using data segments from a training dataset. Features from time-frequency spectra of PPG were extracted to build the detection model. Five datasets were considered for evaluating TifMA performance: (1) and (2) were laboratory-controlled PPG recordings from forehead and finger pulse oximeter sensors with subjects making random movements, (3) and (4) were actual patient PPG recordings from UMass Memorial Medical Center with random free movements and (5) was a laboratory-controlled PPG recording dataset measured at the forehead while the subjects ran on a treadmill. The first dataset was used to analyze the noise sensitivity of the algorithm. Datasets 2-4 were used to evaluate the MNA detection phase of the algorithm. The results from the first phase of the algorithm (MNA detection) were compared to results from three existing MNA detection algorithms: the Hjorth, kurtosis-Shannon entropy, and time-domain variability-SVM approaches. This last is an approach

Inversion Algorithms and PS Detection in SAR Tomography, Case Study of Bucharest City

Directory of Open Access Journals (Sweden)

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 regression-based differential expression detection algorithm for microarray studies with ultra-low sample size.

Directory of Open Access Journals (Sweden)

Daniel Vasiliu

Full Text Available Global gene expression analysis using microarrays and, more recently, RNA-seq, has allowed investigators to understand biological processes at a system level. However, the identification of differentially expressed genes in experiments with small sample size, high dimensionality, and high variance remains challenging, limiting the usability of these tens of thousands of publicly available, and possibly many more unpublished, gene expression datasets. We propose a novel variable selection algorithm for ultra-low-n microarray studies using generalized linear model-based variable selection with a penalized binomial regression algorithm called penalized Euclidean distance (PED. Our method uses PED to build a classifier on the experimental data to rank genes by importance. In place of cross-validation, which is required by most similar methods but not reliable for experiments with small sample size, we use a simulation-based approach to additively build a list of differentially expressed genes from the rank-ordered list. Our simulation-based approach maintains a low false discovery rate while maximizing the number of differentially expressed genes identified, a feature critical for downstream pathway analysis. We apply our method to microarray data from an experiment perturbing the Notch signaling pathway in Xenopus laevis embryos. This dataset was chosen because it showed very little differential expression according to limma, a powerful and widely-used method for microarray analysis. Our method was able to detect a significant number of differentially expressed genes in this dataset and suggest future directions for investigation. Our method is easily adaptable for analysis of data from RNA-seq and other global expression experiments with low sample size and high dimensionality.

Detecting structural breaks in time series via genetic algorithms

DEFF Research Database (Denmark)

Doerr, Benjamin; Fischer, Paul; Hilbert, Astrid

2016-01-01

of the time series under consideration is available. Therefore, a black-box optimization approach is our method of choice for detecting structural breaks. We describe a genetic algorithm framework which easily adapts to a large number of statistical settings. To evaluate the usefulness of different crossover...... 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...... operator alone. Moreover, we present a specific fitness function which exploits the sparse structure of the break points and which can be evaluated particularly efficiently. The experiments on artificial and real-world time series show that the resulting algorithm detects break points with high precision...

Vision based speed breaker detection for autonomous vehicle

Science.gov (United States)

C. S., Arvind; Mishra, Ritesh; Vishal, Kumar; Gundimeda, Venugopal

2018-04-01

In this paper, we are presenting a robust and real-time, vision-based approach to detect speed breaker in urban environments for autonomous vehicle. Our method is designed to detect the speed breaker using visual inputs obtained from a camera mounted on top of a vehicle. The method performs inverse perspective mapping to generate top view of the road and segment out region of interest based on difference of Gaussian and median filter images. Furthermore, the algorithm performs RANSAC line fitting to identify the possible speed breaker candidate region. This initial guessed region via RANSAC, is validated using support vector machine. Our algorithm can detect different categories of speed breakers on cement, asphalt and interlock roads at various conditions and have achieved a recall of 0.98.

Swarm, genetic and evolutionary programming algorithms applied to multiuser detection

Directory of Open Access Journals (Sweden)

Paul Jean Etienne Jeszensky

2005-02-01

Full Text Available In this paper, the particles swarm optimization technique, recently published in the literature, and applied to Direct Sequence/Code Division Multiple Access systems (DS/CDMA with multiuser detection (MuD is analyzed, evaluated and compared. The Swarm algorithm efficiency when applied to the DS-CDMA multiuser detection (Swarm-MuD is compared through the tradeoff performance versus computational complexity, being the complexity expressed in terms of the number of necessary operations in order to reach the performance obtained through the optimum detector or the Maximum Likelihood detector (ML. The comparison is accomplished among the genetic algorithm, evolutionary programming with cloning and Swarm algorithm under the same simulation basis. Additionally, it is proposed an heuristics-MuD complexity analysis through the number of computational operations. Finally, an analysis is carried out for the input parameters of the Swarm algorithm in the attempt to find the optimum parameters (or almost-optimum for the algorithm applied to the MuD problem.

Fast iterative censoring CFAR algorithm for ship detection from SAR images

Science.gov (United States)

Gu, Dandan; Yue, Hui; Zhang, Yuan; Gao, Pengcheng

2017-11-01

Ship detection is one of the essential techniques for ship recognition from synthetic aperture radar (SAR) images. This paper presents a fast iterative detection procedure to eliminate the influence of target returns on the estimation of local sea clutter distributions for constant false alarm rate (CFAR) detectors. A fast block detector is first employed to extract potential target sub-images; and then, an iterative censoring CFAR algorithm is used to detect ship candidates from each target blocks adaptively and efficiently, where parallel detection is available, and statistical parameters of G0 distribution fitting local sea clutter well can be quickly estimated based on an integral image operator. Experimental results of TerraSAR-X images demonstrate the effectiveness of the proposed technique.

Detection of Cheating by Decimation Algorithm

Science.gov (United States)

Yamanaka, Shogo; Ohzeki, Masayuki; Decelle, Aurélien

2015-02-01

We expand the item response theory to study the case of "cheating students" for a set of exams, trying to detect them by applying a greedy algorithm of inference. This extended model is closely related to the Boltzmann machine learning. In this paper we aim to infer the correct biases and interactions of our model by considering a relatively small number of sets of training data. Nevertheless, the greedy algorithm that we employed in the present study exhibits good performance with a few number of training data. The key point is the sparseness of the interactions in our problem in the context of the Boltzmann machine learning: the existence of cheating students is expected to be very rare (possibly even in real world). We compare a standard approach to infer the sparse interactions in the Boltzmann machine learning to our greedy algorithm and we find the latter to be superior in several aspects.

AN ALGORITHM TO DETECT THE RETINAL REGION OF INTEREST

Directory of Open Access Journals (Sweden)

E. Şehirli

2017-11-01

Full Text Available Retina is one of the important layers of the eyes, which includes sensitive cells to colour and light and nerve fibers. Retina can be displayed by using some medical devices such as fundus camera, ophthalmoscope. Hence, some lesions like microaneurysm, haemorrhage, exudate with many diseases of the eye can be detected by looking at the images taken by devices. In computer vision and biomedical areas, studies to detect lesions of the eyes automatically have been done for a long time. In order to make automated detections, the concept of ROI may be utilized. ROI which stands for region of interest generally serves the purpose of focusing on particular targets. The main concentration of this paper is the algorithm to automatically detect retinal region of interest belonging to different retinal images on a software application. The algorithm consists of three stages such as pre-processing stage, detecting ROI on processed images and overlapping between input image and obtained ROI of the image.

An Algorithm to Detect the Retinal Region of Interest

Science.gov (United States)

Şehirli, E.; Turan, M. K.; Demiral, E.

2017-11-01

Retina is one of the important layers of the eyes, which includes sensitive cells to colour and light and nerve fibers. Retina can be displayed by using some medical devices such as fundus camera, ophthalmoscope. Hence, some lesions like microaneurysm, haemorrhage, exudate with many diseases of the eye can be detected by looking at the images taken by devices. In computer vision and biomedical areas, studies to detect lesions of the eyes automatically have been done for a long time. In order to make automated detections, the concept of ROI may be utilized. ROI which stands for region of interest generally serves the purpose of focusing on particular targets. The main concentration of this paper is the algorithm to automatically detect retinal region of interest belonging to different retinal images on a software application. The algorithm consists of three stages such as pre-processing stage, detecting ROI on processed images and overlapping between input image and obtained ROI of the image.

NASA airborne radar wind shear detection algorithm and the detection of wet microbursts in the vicinity of Orlando, Florida

Science.gov (United States)

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.

Case-Based Multi-Sensor Intrusion Detection

Science.gov (United States)

Schwartz, Daniel G.; Long, Jidong

2009-08-01

Multi-sensor intrusion detection systems (IDSs) combine the alerts raised by individual IDSs and possibly other kinds of devices such as firewalls and antivirus software. A critical issue in building a multi-sensor IDS is alert-correlation, i.e., determining which alerts are caused by the same attack. This paper explores a novel approach to alert correlation using case-based reasoning (CBR). Each case in the CBR system's library contains a pattern of alerts raised by some known attack type, together with the identity of the attack. Then during run time, the alert streams gleaned from the sensors are compared with the patterns in the cases, and a match indicates that the attack described by that case has occurred. For this purpose the design of a fast and accurate matching algorithm is imperative. Two such algorithms were explored: (i) the well-known Hungarian algorithm, and (ii) an order-preserving matching of our own device. Tests were conducted using the DARPA Grand Challenge Problem attack simulator. These showed that the both matching algorithms are effective in detecting attacks; but the Hungarian algorithm is inefficient; whereas the order-preserving one is very efficient, in fact runs in linear time.

Combined Dust Detection Algorithm by Using MODIS Infrared Channels over East Asia

Science.gov (United States)

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.

Ion trace detection algorithm to extract pure ion chromatograms to improve untargeted peak detection quality for liquid chromatography/time-of-flight mass spectrometry-based metabolomics data.

Science.gov (United States)

Wang, San-Yuan; Kuo, Ching-Hua; Tseng, Yufeng J

2015-03-03

Able to detect known and unknown metabolites, untargeted metabolomics has shown great potential in identifying novel biomarkers. However, elucidating all possible liquid chromatography/time-of-flight mass spectrometry (LC/TOF-MS) ion signals in a complex biological sample remains challenging since many ions are not the products of metabolites. Methods of reducing ions not related to metabolites or simply directly detecting metabolite related (pure) ions are important. In this work, we describe PITracer, a novel algorithm that accurately detects the pure ions of a LC/TOF-MS profile to extract pure ion chromatograms and detect chromatographic peaks. PITracer estimates the relative mass difference tolerance of ions and calibrates the mass over charge (m/z) values for peak detection algorithms with an additional option to further mass correction with respect to a user-specified metabolite. PITracer was evaluated using two data sets containing 373 human metabolite standards, including 5 saturated standards considered to be split peaks resultant from huge m/z fluctuation, and 12 urine samples spiked with 50 forensic drugs of varying concentrations. Analysis of these data sets show that PITracer correctly outperformed existing state-of-art algorithm and extracted the pure ion chromatograms of the 5 saturated standards without generating split peaks and detected the forensic drugs with high recall, precision, and F-score and small mass error.

Novel algorithm for simultaneous component detection and pseudo-molecular ion characterization in liquid chromatography–mass spectrometry

Energy Technology Data Exchange (ETDEWEB)

Zhang, Yufeng; Wang, Xiaoan; Wo, Siukwan [School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong (China); Ho, Hingman; Han, Quanbin [School of Chinese Medicine, Hong Kong Baptist University, 7 Baptist University Road, Kowloon Tong, Hong Kong (China); Fan, Xiaohui [College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 (China); Zuo, Zhong, E-mail: joanzuo@cuhk.edu.hk [School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong (China)

2015-01-01

Highlights: • Novel stepwise component detection algorithm (SCDA) for LC–MS datasets. • New isotopic distribution and adduct-ion models for mass spectra. • Automatic component classification based on adduct-ion and isotopic distributions. - Abstract: Resolving components and determining their pseudo-molecular ions (PMIs) are crucial steps in identifying complex herbal mixtures by liquid chromatography–mass spectrometry. To tackle such labor-intensive steps, we present here a novel algorithm for simultaneous detection of components and their PMIs. Our method consists of three steps: (1) obtaining a simplified dataset containing only mono-isotopic masses by removal of background noise and isotopic cluster ions based on the isotopic distribution model derived from all the reported natural compounds in dictionary of natural products; (2) stepwise resolving and removing all features of the highest abundant component from current simplified dataset and calculating PMI of each component according to an adduct-ion model, in which all non-fragment ions in a mass spectrum are considered as PMI plus one or several neutral species; (3) visual classification of detected components by principal component analysis (PCA) to exclude possible non-natural compounds (such as pharmaceutical excipients). This algorithm has been successfully applied to a standard mixture and three herbal extract/preparations. It indicated that our algorithm could detect components’ features as a whole and report their PMI with an accuracy of more than 98%. Furthermore, components originated from excipients/contaminants could be easily separated from those natural components in the bi-plots of PCA.

Novel algorithm for simultaneous component detection and pseudo-molecular ion characterization in liquid chromatography–mass spectrometry

International Nuclear Information System (INIS)

Zhang, Yufeng; Wang, Xiaoan; Wo, Siukwan; Ho, Hingman; Han, Quanbin; Fan, Xiaohui; Zuo, Zhong

2015-01-01

Highlights: • Novel stepwise component detection algorithm (SCDA) for LC–MS datasets. • New isotopic distribution and adduct-ion models for mass spectra. • Automatic component classification based on adduct-ion and isotopic distributions. - Abstract: Resolving components and determining their pseudo-molecular ions (PMIs) are crucial steps in identifying complex herbal mixtures by liquid chromatography–mass spectrometry. To tackle such labor-intensive steps, we present here a novel algorithm for simultaneous detection of components and their PMIs. Our method consists of three steps: (1) obtaining a simplified dataset containing only mono-isotopic masses by removal of background noise and isotopic cluster ions based on the isotopic distribution model derived from all the reported natural compounds in dictionary of natural products; (2) stepwise resolving and removing all features of the highest abundant component from current simplified dataset and calculating PMI of each component according to an adduct-ion model, in which all non-fragment ions in a mass spectrum are considered as PMI plus one or several neutral species; (3) visual classification of detected components by principal component analysis (PCA) to exclude possible non-natural compounds (such as pharmaceutical excipients). This algorithm has been successfully applied to a standard mixture and three herbal extract/preparations. It indicated that our algorithm could detect components’ features as a whole and report their PMI with an accuracy of more than 98%. Furthermore, components originated from excipients/contaminants could be easily separated from those natural components in the bi-plots of PCA

Securing mobile ad hoc networks using danger theory-based artificial immune algorithm.

Science.gov (United States)

Abdelhaq, Maha; Alsaqour, Raed; Abdelhaq, Shawkat

2015-01-01

A mobile ad hoc network (MANET) is a set of mobile, decentralized, and self-organizing nodes that are used in special cases, such as in the military. MANET properties render the environment of this network vulnerable to different types of attacks, including black hole, wormhole and flooding-based attacks. Flooding-based attacks are one of the most dangerous attacks that aim to consume all network resources and thus paralyze the functionality of the whole network. Therefore, the objective of this paper is to investigate the capability of a danger theory-based artificial immune algorithm called the mobile dendritic cell algorithm (MDCA) to detect flooding-based attacks in MANETs. The MDCA applies the dendritic cell algorithm (DCA) to secure the MANET with additional improvements. The MDCA is tested and validated using Qualnet v7.1 simulation tool. This work also introduces a new simulation module for a flooding attack called the resource consumption attack (RCA) using Qualnet v7.1. The results highlight the high efficiency of the MDCA in detecting RCAs in MANETs.

Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching.

Science.gov (United States)

Du, Pan; Kibbe, Warren A; Lin, Simon M

2006-09-01

A major problem for current peak detection algorithms is that noise in mass spectrometry (MS) spectra gives rise to a high rate of false positives. The false positive rate is especially problematic in detecting peaks with low amplitudes. Usually, various baseline correction algorithms and smoothing methods are applied before attempting peak detection. This approach is very sensitive to the amount of smoothing and aggressiveness of the baseline correction, which contribute to making peak detection results inconsistent between runs, instrumentation and analysis methods. Most peak detection algorithms simply identify peaks based on amplitude, ignoring the additional information present in the shape of the peaks in a spectrum. In our experience, 'true' peaks have characteristic shapes, and providing a shape-matching function that provides a 'goodness of fit' coefficient should provide a more robust peak identification method. Based on these observations, a continuous wavelet transform (CWT)-based peak detection algorithm has been devised that identifies peaks with different scales and amplitudes. By transforming the spectrum into wavelet space, the pattern-matching problem is simplified and in addition provides a powerful technique for identifying and separating the signal from the spike noise and colored noise. This transformation, with the additional information provided by the 2D CWT coefficients can greatly enhance the effective signal-to-noise ratio. Furthermore, with this technique no baseline removal or peak smoothing preprocessing steps are required before peak detection, and this improves the robustness of peak detection under a variety of conditions. The algorithm was evaluated with SELDI-TOF spectra with known polypeptide positions. Comparisons with two other popular algorithms were performed. The results show the CWT-based algorithm can identify both strong and weak peaks while keeping false positive rate low. The algorithm is implemented in R and will be

From Pixels to Region: A Salient Region Detection Algorithm for Location-Quantification Image

Directory of Open Access Journals (Sweden)

Mengmeng Zhang

2014-01-01

Full Text Available Image saliency detection has become increasingly important with the development of intelligent identification and machine vision technology. This process is essential for many image processing algorithms such as image retrieval, image segmentation, image recognition, and adaptive image compression. We propose a salient region detection algorithm for full-resolution images. This algorithm analyzes the randomness and correlation of image pixels and pixel-to-region saliency computation mechanism. The algorithm first obtains points with more saliency probability by using the improved smallest univalue segment assimilating nucleus operator. It then reconstructs the entire saliency region detection by taking these points as reference and combining them with image spatial color distribution, as well as regional and global contrasts. The results for subjective and objective image saliency detection show that the proposed algorithm exhibits outstanding performance in terms of technology indices such as precision and recall rates.

Integrated artificial intelligence algorithm for skin detection

Directory of Open Access Journals (Sweden)

Bush Idoko John

2018-01-01

Full Text Available The detection of skin colour has been a useful and renowned technique due to its wide range of application in both analyses based on diagnostic and human computer interactions. Various problems could be solved by simply providing an appropriate method for pixel-like skin parts. Presented in this study is a colour segmentation algorithm that works directly in RGB colour space without converting the colour space. Genfis function as used in this study formed the Sugeno fuzzy network and utilizing Fuzzy C-Mean (FCM clustering rule, clustered the data and for each cluster/class a rule is generated. Finally, corresponding output from data mapping of pseudo-polynomial is obtained from input dataset to the adaptive neuro fuzzy inference system (ANFIS.

A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data

Science.gov (United States)

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. PMID:27093601

Fast Parabola Detection Using Estimation of Distribution Algorithms

Directory of Open Access Journals (Sweden)

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.

Android malware detection based on evolutionary super-network

Science.gov (United States)

Yan, Haisheng; Peng, Lingling

2018-04-01

In the paper, an android malware detection method based on evolutionary super-network is proposed in order to improve the precision of android malware detection. Chi square statistics method is used for selecting characteristics on the basis of analyzing android authority. Boolean weighting is utilized for calculating characteristic weight. Processed characteristic vector is regarded as the system training set and test set; hyper edge alternative strategy is used for training super-network classification model, thereby classifying test set characteristic vectors, and it is compared with traditional classification algorithm. The results show that the detection method proposed in the paper is close to or better than traditional classification algorithm. The proposed method belongs to an effective Android malware detection means.

Grid impedance estimation based hybrid islanding detection method for AC microgrids

DEFF Research Database (Denmark)

Ghzaiel, Walid; Jebali-Ben Ghorbal, Manel; Slama-Belkhodja, Ilhem

2017-01-01

This paper focuses on a hybrid islanding detection algorithm for parallel-inverters-based microgrids. The proposed algorithm is implemented on the unit ensuring the control of the intelligent bypass switch connecting or disconnecting the microgrid from the utility. This method employs a grid...... to avoid interactions with other units. The selected inverter will be the one closest to the controllable distributed generation system or to a healthy grid side in case of meshed microgrid with multiple-grid connections. The detection algorithm is applied to quickly detect the resonance phenomena, so...

QRS Detection Based on Improved Adaptive Threshold

Directory of Open Access Journals (Sweden)

Xuanyu Lu

2018-01-01

Full Text Available Cardiovascular disease is the first cause of death around the world. In accomplishing quick and accurate diagnosis, automatic electrocardiogram (ECG analysis algorithm plays an important role, whose first step is QRS detection. The threshold algorithm of QRS complex detection is known for its high-speed computation and minimized memory storage. In this mobile era, threshold algorithm can be easily transported into portable, wearable, and wireless ECG systems. However, the detection rate of the threshold algorithm still calls for improvement. An improved adaptive threshold algorithm for QRS detection is reported in this paper. The main steps of this algorithm are preprocessing, peak finding, and adaptive threshold QRS detecting. The detection rate is 99.41%, the sensitivity (Se is 99.72%, and the specificity (Sp is 99.69% on the MIT-BIH Arrhythmia database. A comparison is also made with two other algorithms, to prove our superiority. The suspicious abnormal area is shown at the end of the algorithm and RR-Lorenz plot drawn for doctors and cardiologists to use as aid for diagnosis.

Nonlinear Algorithms for Channel Equalization and Map Symbol Detection.

Science.gov (United States)

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

Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms.

Science.gov (United States)

Karthick, P A; Ghosh, Diptasree Maitra; Ramakrishnan, S

2018-02-01

Surface electromyography (sEMG) based muscle fatigue research is widely preferred in sports science and occupational/rehabilitation studies due to its noninvasiveness. However, these signals are complex, multicomponent and highly nonstationary with large inter-subject variations, particularly during dynamic contractions. Hence, time-frequency based machine learning methodologies can improve the design of automated system for these signals. In this work, the analysis based on high-resolution time-frequency methods, namely, Stockwell transform (S-transform), B-distribution (BD) and extended modified B-distribution (EMBD) are proposed to differentiate the dynamic muscle nonfatigue and fatigue conditions. The nonfatigue and fatigue segments of sEMG signals recorded from the biceps brachii of 52 healthy volunteers are preprocessed and subjected to S-transform, BD and EMBD. Twelve features are extracted from each method and prominent features are selected using genetic algorithm (GA) and binary particle swarm optimization (BPSO). Five machine learning algorithms, namely, naïve Bayes, support vector machine (SVM) of polynomial and radial basis kernel, random forest and rotation forests are used for the classification. The results show that all the proposed time-frequency distributions (TFDs) are able to show the nonstationary variations of sEMG signals. Most of the features exhibit statistically significant difference in the muscle fatigue and nonfatigue conditions. The maximum number of features (66%) is reduced by GA and BPSO for EMBD and BD-TFD respectively. The combination of EMBD- polynomial kernel based SVM is found to be most accurate (91% accuracy) in classifying the conditions with the features selected using GA. The proposed methods are found to be capable of handling the nonstationary and multicomponent variations of sEMG signals recorded in dynamic fatiguing contractions. Particularly, the combination of EMBD- polynomial kernel based SVM could be used to

Assessment of delay-and-sum algorithms for damage detection in aluminium and composite plates

International Nuclear Information System (INIS)

Sharif-Khodaei, Z; Aliabadi, M H

2014-01-01

Piezoelectric sensors are increasingly being used in active structural health monitoring, due to their durability, light weight and low power consumption. In the present work damage detection and characterization methodologies based on Lamb waves have been evaluated for aircraft panels. The applicability of various proposed delay-and-sum algorithms on isotropic and composite stiffened panels have been investigated, both numerically and experimentally. A numerical model for ultrasonic wave propagation in composite laminates is proposed and compared to signals recorded from experiments. A modified delay-and-sum algorithm is then proposed for detecting impact damage in composite plates with and without a stiffener which is shown to capture and localize damage with only four transducers. (papers)

Enhancing Community Detection By Affinity-based Edge Weighting Scheme

Energy Technology Data Exchange (ETDEWEB)

Yoo, Andy [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Sanders, Geoffrey [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Henson, Van [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Vassilevski, Panayot [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2015-10-05

Community detection refers to an important graph analytics problem of finding a set of densely-connected subgraphs in a graph and has gained a great deal of interest recently. The performance of current community detection algorithms is limited by an inherent constraint of unweighted graphs that offer very little information on their internal community structures. In this paper, we propose a new scheme to address this issue that weights the edges in a given graph based on recently proposed vertex affinity. The vertex affinity quantifies the proximity between two vertices in terms of their clustering strength, and therefore, it is ideal for graph analytics applications such as community detection. We also demonstrate that the affinity-based edge weighting scheme can improve the performance of community detection algorithms significantly.

An Algorithm for Real-Time Pulse Waveform Segmentation and Artifact Detection in Photoplethysmograms.

Science.gov (United States)

Fischer, Christoph; Domer, Benno; Wibmer, Thomas; Penzel, Thomas

2017-03-01

Photoplethysmography has been used in a wide range of medical devices for measuring oxygen saturation, cardiac output, assessing autonomic function, and detecting peripheral vascular disease. Artifacts can render the photoplethysmogram (PPG) useless. Thus, algorithms capable of identifying artifacts are critically important. However, the published PPG algorithms are limited in algorithm and study design. Therefore, the authors developed a novel embedded algorithm for real-time pulse waveform (PWF) segmentation and artifact detection based on a contour analysis in the time domain. This paper provides an overview about PWF and artifact classifications, presents the developed PWF analysis, and demonstrates the implementation on a 32-bit ARM core microcontroller. The PWF analysis was validated with data records from 63 subjects acquired in a sleep laboratory, ergometry laboratory, and intensive care unit in equal parts. The output of the algorithm was compared with harmonized experts' annotations of the PPG with a total duration of 31.5 h. The algorithm achieved a beat-to-beat comparison sensitivity of 99.6%, specificity of 90.5%, precision of 98.5%, and accuracy of 98.3%. The interrater agreement expressed as Cohen's kappa coefficient was 0.927 and as F-measure was 0.990. In conclusion, the PWF analysis seems to be a suitable method for PPG signal quality determination, real-time annotation, data compression, and calculation of additional pulse wave metrics such as amplitude, duration, and rise time.

An algorithm of local earthquake detection from digital records

Directory of Open Access Journals (Sweden)

A. PROZOROV

1978-06-01

Full Text Available The problem of automatical detection of earthquake signals in seismograms
and definition of first arrivals of p and s waves is considered.
The algorithm is based on the analysis of t(A function which represents
the time of first appearence of a number of going one after another
swings of amplitudes greather than A in seismic signals. It allows to explore
such common features of seismograms of earthquakes as sudden
first p-arrivals of amplitude greater than general amplitude of noise and
after the definite interval of time before s-arrival the amplitude of which
overcomes the amplitude of p-arrival. The method was applied to
3-channel recods of Friuli aftershocks, ¿'-arrivals were defined correctly
in all cases; p-arrivals were defined in most cases using strict criteria of
detection. Any false signals were not detected. All p-arrivals were defined
using soft criteria of detection but less reliability and two false events
were obtained.

Modified automatic R-peak detection algorithm for patients with epilepsy using a portable electrocardiogram recorder.

Science.gov (United States)

Jeppesen, J; Beniczky, S; Fuglsang Frederiksen, A; Sidenius, P; Johansen, P

2017-07-01

Earlier studies have shown that short term heart rate variability (HRV) analysis of ECG seems promising for detection of epileptic seizures. A precise and accurate automatic R-peak detection algorithm is a necessity in a real-time, continuous measurement of HRV, in a portable ECG device. We used the portable CE marked ePatch® heart monitor to record the ECG of 14 patients, who were enrolled in the videoEEG long term monitoring unit for clinical workup of epilepsy. Recordings of the first 7 patients were used as training set of data for the R-peak detection algorithm and the recordings of the last 7 patients (467.6 recording hours) were used to test the performance of the algorithm. We aimed to modify an existing QRS-detection algorithm to a more precise R-peak detection algorithm to avoid the possible jitter Qand S-peaks can create in the tachogram, which causes error in short-term HRVanalysis. The proposed R-peak detection algorithm showed a high sensitivity (Se = 99.979%) and positive predictive value (P+ = 99.976%), which was comparable with a previously published QRS-detection algorithm for the ePatch® ECG device, when testing the same dataset. The novel R-peak detection algorithm designed to avoid jitter has very high sensitivity and specificity and thus is a suitable tool for a robust, fast, real-time HRV-analysis in patients with epilepsy, creating the possibility for real-time seizure detection for these patients.

A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks.

Science.gov (United States)

Ma, Tao; Wang, Fen; Cheng, Jianjun; Yu, Yang; Chen, Xiaoyun

2016-10-13

The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.

Anomaly Detection and Diagnosis Algorithms for Discrete Symbols

Data.gov (United States)

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...

Airport Traffic Conflict Detection and Resolution Algorithm Evaluation

Science.gov (United States)

Jones, Denise R.; Chartrand, Ryan C.; Wilson, Sara R.; Commo, Sean A.; Ballard, Kathryn M.; Otero, Sharon D.; Barker, Glover D.

2016-01-01

Two conflict detection and resolution (CD&R) algorithms for the terminal maneuvering area (TMA) were evaluated in a fast-time batch simulation study at the National Aeronautics and Space Administration (NASA) Langley Research Center. One CD&R algorithm, developed at NASA, was designed to enhance surface situation awareness and provide cockpit alerts of potential conflicts during runway, taxi, and low altitude air-to-air operations. The second algorithm, Enhanced Traffic Situation Awareness on the Airport Surface with Indications and Alerts (SURF IA), was designed to increase flight crew 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 aircraft-based CD&R algorithms during various runway, taxiway, and low altitude scenarios, multiple levels of CD&R system equipage, and various levels of horizontal position accuracy. Algorithm performance was assessed through various metrics including the collision rate, nuisance and missed alert rate, and alert toggling rate. The data suggests that, in general, alert toggling, nuisance and missed alerts, and unnecessary maneuvering occurred more frequently as the position accuracy was reduced. Collision avoidance was more effective when all of the aircraft were equipped with CD&R and maneuvered to avoid a collision after an alert was issued. In order to reduce the number of unwanted (nuisance) alerts when taxiing across a runway, a buffer is needed between the hold line and the alerting zone so alerts are not generated when an aircraft is behind the hold line. All of the results support RTCA horizontal position accuracy requirements for performing a CD&R function to reduce the likelihood and severity of runway incursions and collisions.

Cable Damage Detection System and Algorithms Using Time Domain Reflectometry

Energy Technology Data Exchange (ETDEWEB)

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-based

Opposition-Based Adaptive Fireworks Algorithm

Directory of Open Access Journals (Sweden)

Chibing Gong

2016-07-01

Full Text Available A fireworks algorithm (FWA is a recent swarm intelligence algorithm that is inspired by observing fireworks explosions. An adaptive fireworks algorithm (AFWA proposes additional adaptive amplitudes to improve the performance of the enhanced fireworks algorithm (EFWA. The purpose of this paper is to add opposition-based learning (OBL to AFWA with the goal of further boosting performance and achieving global optimization. Twelve benchmark functions are tested in use of an opposition-based adaptive fireworks algorithm (OAFWA. The final results conclude that OAFWA significantly outperformed EFWA and AFWA in terms of solution accuracy. Additionally, OAFWA was compared with a bat algorithm (BA, differential evolution (DE, self-adapting control parameters in differential evolution (jDE, a firefly algorithm (FA, and a standard particle swarm optimization 2011 (SPSO2011 algorithm. The research results indicate that OAFWA ranks the highest of the six algorithms for both solution accuracy and runtime cost.

Colour based fire detection method with temporal intensity variation filtration

International Nuclear Information System (INIS)

Trambitckii, K; Musalimov, V; Anding, K; Linß, G

2015-01-01

Development of video, computing technologies and computer vision gives a possibility of automatic fire detection on video information. Under that project different algorithms was implemented to find more efficient way of fire detection. In that article colour based fire detection algorithm is described. But it is not enough to use only colour information to detect fire properly. The main reason of this is that in the shooting conditions may be a lot of things having colour similar to fire. A temporary intensity variation of pixels is used to separate them from the fire. These variations are averaged over the series of several frames. This algorithm shows robust work and was realised as a computer program by using of the OpenCV library

Colour based fire detection method with temporal intensity variation filtration

Science.gov (United States)

Trambitckii, K.; Anding, K.; Musalimov, V.; Linß, G.

2015-02-01

Development of video, computing technologies and computer vision gives a possibility of automatic fire detection on video information. Under that project different algorithms was implemented to find more efficient way of fire detection. In that article colour based fire detection algorithm is described. But it is not enough to use only colour information to detect fire properly. The main reason of this is that in the shooting conditions may be a lot of things having colour similar to fire. A temporary intensity variation of pixels is used to separate them from the fire. These variations are averaged over the series of several frames. This algorithm shows robust work and was realised as a computer program by using of the OpenCV library.

Opposition-Based Adaptive Fireworks Algorithm

OpenAIRE

Chibing Gong

2016-01-01

A fireworks algorithm (FWA) is a recent swarm intelligence algorithm that is inspired by observing fireworks explosions. An adaptive fireworks algorithm (AFWA) proposes additional adaptive amplitudes to improve the performance of the enhanced fireworks algorithm (EFWA). The purpose of this paper is to add opposition-based learning (OBL) to AFWA with the goal of further boosting performance and achieving global optimization. Twelve benchmark functions are tested in use of an opposition-based a...

An Overlapping Communities Detection Algorithm via Maxing Modularity in Opportunistic Networks

Directory of Open Access Journals (Sweden)

Gao Zhi-Peng

2016-01-01

Full Text Available Community detection in opportunistic networks has been a significant and hot issue, which is used to understand characteristics of networks through analyzing structure of it. Community is used to represent a group of nodes in a network where nodes inside the community have more internal connections than external connections. However, most of the existing community detection algorithms focus on binary networks or disjoint community detection. In this paper, we propose a novel algorithm via maxing modularity of communities (MMCto find overlapping community structure in opportunistic networks. It utilizes contact history of nodes to calculate the relation intensity between nodes. It finds nodes with high relation intensity as the initial community and extend the community with nodes of higher belong degree. The algorithm achieves a rapid and efficient overlapping community detection method by maxing the modularity of community continuously. The experiments prove that MMC is effective for uncovering overlapping communities and it achieves better performance than COPRA and Conductance.

Penalty dynamic programming algorithm for dim targets detection in sensor systems.

Science.gov (United States)

Huang, Dayu; Xue, Anke; Guo, Yunfei

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, the algorithm combines traditional tracking techniques with DP-TBD and it can be applied to simultaneously detect and track maneuvering dim targets. Meanwhile, a reasonable constraint that a sensor measurement can originate from one target or clutter is proposed to minimize track separation. Thus, the algorithm can be used in the multi-target situation with unknown target numbers. The efficiency and advantages of PDP-TBD compared with two existing methods are demonstrated by several simulations.

Penalty Dynamic Programming Algorithm for Dim Targets Detection in Sensor Systems

Directory of Open Access Journals (Sweden)

Yunfei Guo

2012-04-01

Full Text Available 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, the algorithm combines traditional tracking techniques with DP-TBD and it can be applied to simultaneously detect and track maneuvering dim targets. Meanwhile, a reasonable constraint that a sensor measurement can originate from one target or clutter is proposed to minimize track separation. Thus, the algorithm can be used in the multi-target situation with unknown target numbers. The efficiency and advantages of PDP-TBD compared with two existing methods are demonstrated by several simulations.

A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

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.

Low power multi-camera system and algorithms for automated threat detection

Science.gov (United States)

Huber, David J.; Khosla, Deepak; Chen, Yang; Van Buer, Darrel J.; Martin, Kevin

2013-05-01

A key to any robust automated surveillance system is continuous, wide field-of-view sensor coverage and high accuracy target detection algorithms. Newer systems typically employ an array of multiple fixed cameras that provide individual data streams, each of which is managed by its own processor. This array can continuously capture the entire field of view, but collecting all the data and back-end detection algorithm consumes additional power and increases the size, weight, and power (SWaP) of the package. This is often unacceptable, as many potential surveillance applications have strict system SWaP requirements. This paper describes a wide field-of-view video system that employs multiple fixed cameras and exhibits low SWaP without compromising the target detection rate. We cycle through the sensors, fetch a fixed number of frames, and process them through a modified target detection algorithm. During this time, the other sensors remain powered-down, which reduces the required hardware and power consumption of the system. We show that the resulting gaps in coverage and irregular frame rate do not affect the detection accuracy of the underlying algorithms. This reduces the power of an N-camera system by up to approximately N-fold compared to the baseline normal operation. This work was applied to Phase 2 of DARPA Cognitive Technology Threat Warning System (CT2WS) program and used during field testing.

A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble Learning

Directory of Open Access Journals (Sweden)

Bin Jia

2017-01-01

Full Text Available The explosive growth of network traffic and its multitype on Internet have brought new and severe challenges to DDoS attack detection. To get the higher True Negative Rate (TNR, accuracy, and precision and to guarantee the robustness, stability, and universality of detection system, in this paper, we propose a DDoS attack detection method based on hybrid heterogeneous multiclassifier ensemble learning and design a heuristic detection algorithm based on Singular Value Decomposition (SVD to construct our detection system. Experimental results show that our detection method is excellent in TNR, accuracy, and precision. Therefore, our algorithm has good detective performance for DDoS attack. Through the comparisons with Random Forest, k-Nearest Neighbor (k-NN, and Bagging comprising the component classifiers when the three algorithms are used alone by SVD and by un-SVD, it is shown that our model is superior to the state-of-the-art attack detection techniques in system generalization ability, detection stability, and overall detection performance.

Computerized detection of masses on mammograms: A comparative study of two algorithms

International Nuclear Information System (INIS)

Tiedeu, A.; Kom, G.; Kom, M.

2007-02-01

In this paper, we implement and carry out the comparison of two methods of computer-aided-detection of masses on mammograms. The two algorithms basically consist of 3 steps each: segmentation, binarization and noise suppression but using different techniques for each step. A database of 60 images was used to compare the performance of the two algorithms in terms of general detection efficiency, conservation of size and shape of detected masses. (author)

Application of a kernel-based online learning algorithm to the classification of nodule candidates in computer-aided detection of CT lung nodules

International Nuclear Information System (INIS)

Matsumoto, S.; Ohno, Y.; Takenaka, D.; Sugimura, K.; Yamagata, H.

2007-01-01

Classification of the nodule candidates in computer-aided detection (CAD) of lung nodules in CT images was addressed by constructing a nonlinear discriminant function using a kernel-based learning algorithm called the kernel recursive least-squares (KRLS) algorithm. Using the nodule candidates derived from the processing by a CAD scheme of 100 CT datasets containing 253 non-calcified nodules or 3 mm or larger as determined by the consensus of two thoracic radiologists, the following trial were carried out 100 times: by randomly selecting 50 datasets for training, a nonlinear discriminant function was obtained using the nodule candidates in the training datasets and tested with the remaining candidates; for comparison, a rule-based classification was tested in a similar manner. At the number of false positives per case of about 5, the nonlinear classification method showed an improved sensitivity of 80% (mean over the 100 trials) compared with 74% of the rule-based method. (orig.)

A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features

Directory of Open Access Journals (Sweden)

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.

Assessment of a novel mass detection algorithm in mammograms

Directory of Open Access Journals (Sweden)

Ehsan Kozegar

2013-01-01

Settings and Design: The proposed mass detector consists of two major steps. In the first step, several suspicious regions are extracted from the mammograms using an adaptive thresholding technique. In the second step, false positives originating by the previous stage are reduced by a machine learning approach. Materials and Methods: All modules of the mass detector were assessed on mini-MIAS database. In addition, the algorithm was tested on INBreast database for more validation. Results: According to FROC analysis, our mass detection algorithm outperforms other competing methods. Conclusions: We should not just insist on sensitivity in the segmentation phase because if we forgot FP rate, and our goal was just higher sensitivity, then the learning algorithm would be biased more toward false positives and the sensitivity would decrease dramatically in the false positive reduction phase. Therefore, we should consider the mass detection problem as a cost sensitive problem because misclassification costs are not the same in this type of problems.

Dynamic route guidance algorithm based algorithm based on artificial immune system

Institute of Scientific and Technical Information of China (English)

无

2007-01-01

To improve the performance of the K-shortest paths search in intelligent traffic guidance systems,this paper proposes an optimal search algorithm based on the intelligent optimization search theory and the memphor mechanism of vertebrate immune systems.This algorithm,applied to the urban traffic network model established by the node-expanding method,can expediently realize K-shortest paths search in the urban traffic guidance systems.Because of the immune memory and global parallel search ability from artificial immune systems,K shortest paths can be found without any repeat,which indicates evidently the superiority of the algorithm to the conventional ones.Not only does it perform a better parallelism,the algorithm also prevents premature phenomenon that often occurs in genetic algorithms.Thus,it is especially suitable for real-time requirement of the traffic guidance system and other engineering optimal applications.A case study verifies the efficiency and the practicability of the algorithm aforementioned.

A Multi-Scale Settlement Matching Algorithm Based on ARG

Science.gov (United States)

Yue, Han; Zhu, Xinyan; Chen, Di; Liu, Lingjia

2016-06-01

Homonymous entity matching is an important part of multi-source spatial data integration, automatic updating and change detection. Considering the low accuracy of existing matching methods in dealing with matching multi-scale settlement data, an algorithm based on Attributed Relational Graph (ARG) is proposed. The algorithm firstly divides two settlement scenes at different scales into blocks by small-scale road network and constructs local ARGs in each block. Then, ascertains candidate sets by merging procedures and obtains the optimal matching pairs by comparing the similarity of ARGs iteratively. Finally, the corresponding relations between settlements at large and small scales are identified. At the end of this article, a demonstration is presented and the results indicate that the proposed algorithm is capable of handling sophisticated cases.

Driver Distraction Using Visual-Based Sensors and Algorithms.

Science.gov (United States)

Fernández, Alberto; Usamentiaga, Rubén; Carús, Juan Luis; Casado, Rubén

2016-10-28

Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

Driver Distraction Using Visual-Based Sensors and Algorithms

Directory of Open Access Journals (Sweden)

Alberto Fernández

2016-10-01

Full Text Available Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information or even, distraction detection from specific actions (e.g., phone usage. Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

Adaboost-based algorithm for human action recognition

KAUST Repository

Zerrouki, Nabil

2017-11-28

This paper presents a computer vision-based methodology for human action recognition. First, the shape based pose features are constructed based on area ratios to identify the human silhouette in images. The proposed features are invariance to translation and scaling. Once the human body features are extracted from videos, different human actions are learned individually on the training frames of each class. Then, we apply the Adaboost algorithm for the classification process. We assessed the proposed approach using the UR Fall Detection dataset. In this study six classes of activities are considered namely: walking, standing, bending, lying, squatting, and sitting. Results demonstrate the efficiency of the proposed methodology.

Adaboost-based algorithm for human action recognition

KAUST Repository

Zerrouki, Nabil; Harrou, Fouzi; Sun, Ying; Houacine, Amrane

2017-01-01

This paper presents a computer vision-based methodology for human action recognition. First, the shape based pose features are constructed based on area ratios to identify the human silhouette in images. The proposed features are invariance to translation and scaling. Once the human body features are extracted from videos, different human actions are learned individually on the training frames of each class. Then, we apply the Adaboost algorithm for the classification process. We assessed the proposed approach using the UR Fall Detection dataset. In this study six classes of activities are considered namely: walking, standing, bending, lying, squatting, and sitting. Results demonstrate the efficiency of the proposed methodology.

Optimal Feature Space Selection in Detecting Epileptic Seizure based on Recurrent Quantification Analysis and Genetic Algorithm

Directory of Open Access Journals (Sweden)

Saleh LAshkari

2016-06-01

Full Text Available Selecting optimal features based on nature of the phenomenon and high discriminant ability is very important in the data classification problems. Since it doesn't require any assumption about stationary condition and size of the signal and the noise in Recurrent Quantification Analysis (RQA, it may be useful for epileptic seizure Detection. In this study, RQA was used to discriminate ictal EEG from the normal EEG where optimal features selected by combination of algorithm genetic and Bayesian Classifier. Recurrence plots of hundred samples in each two categories were obtained with five distance norms in this study: Euclidean, Maximum, Minimum, Normalized and Fixed Norm. In order to choose optimal threshold for each norm, ten threshold of ε was generated and then the best feature space was selected by genetic algorithm in combination with a bayesian classifier. The results shown that proposed method is capable of discriminating the ictal EEG from the normal EEG where for Minimum norm and 0.1˂ε˂1, accuracy was 100%. In addition, the sensitivity of proposed framework to the ε and the distance norm parameters was low. The optimal feature presented in this study is Trans which it was selected in most feature spaces with high accuracy.

Change detection algorithms for surveillance in visual iot: a comparative study

International Nuclear Information System (INIS)

Akram, B.A.; Zafar, A.; Akbar, A.H.; Chaudhry, A.

2018-01-01

The VIoT (Visual Internet of Things) connects virtual information world with real world objects using sensors and pervasive computing. For video surveillance in VIoT, ChD (Change Detection) is a critical component. ChD algorithms identify regions of change in multiple images of the same scene recorded at different time intervals for video surveillance. This paper presents performance comparison of histogram thresholding and classification ChD algorithms using quantitative measures for video surveillance in VIoT based on salient features of datasets. The thresholding algorithms Otsu, Kapur, Rosin and classification methods k-means, EM (Expectation Maximization) were simulated in MATLAB using diverse datasets. For performance evaluation, the quantitative measures used include OSR (Overall Success Rate), YC (Yule’s Coefficient) and JC (Jaccard’s Coefficient), execution time and memory consumption. Experimental results showed that Kapur’s algorithm performed better for both indoor and outdoor environments with illumination changes, shadowing and medium to fast moving objects. However, it reflected degraded performance for small object size with minor changes. Otsu algorithm showed better results for indoor environments with slow to medium changes and nomadic object mobility. k-means showed good results in indoor environment with small object size producing slow change, no shadowing and scarce illumination changes. (author)

Blind information-theoretic multiuser detection algorithms for DS-CDMA and WCDMA downlink systems.

Science.gov (United States)

Waheed, Khuram; Salem, Fathi M

2005-07-01

Code division multiple access (CDMA) is based on the spread-spectrum technology and is a dominant air interface for 2.5G, 3G, and future wireless networks. For the CDMA downlink, the transmitted CDMA signals from the base station (BS) propagate through a noisy multipath fading communication channel before arriving at the receiver of the user equipment/mobile station (UE/MS). Classical CDMA single-user detection (SUD) algorithms implemented in the UE/MS receiver do not provide the required performance for modern high data-rate applications. In contrast, multi-user detection (MUD) approaches require a lot of a priori information not available to the UE/MS. In this paper, three promising adaptive Riemannian contra-variant (or natural) gradient based user detection approaches, capable of handling the highly dynamic wireless environments, are proposed. The first approach, blind multiuser detection (BMUD), is the process of simultaneously estimating multiple symbol sequences associated with all the users in the downlink of a CDMA communication system using only the received wireless data and without any knowledge of the user spreading codes. This approach is applicable to CDMA systems with relatively short spreading codes but becomes impractical for systems using long spreading codes. We also propose two other adaptive approaches, namely, RAKE -blind source recovery (RAKE-BSR) and RAKE-principal component analysis (RAKE-PCA) that fuse an adaptive stage into a standard RAKE receiver. This adaptation results in robust user detection algorithms with performance exceeding the linear minimum mean squared error (LMMSE) detectors for both Direct Sequence CDMA (DS-CDMA) and wide-band CDMA (WCDMA) systems under conditions of congestion, imprecise channel estimation and unmodeled multiple access interference (MAI).

Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms

OpenAIRE

Zhang, James; Vukotic, Ilija; Gardner, Robert

2018-01-01

Anomaly detection is the practice of identifying items or events that do not conform to an expected behavior or do not correlate with other items in a dataset. It has previously been applied to areas such as intrusion detection, system health monitoring, and fraud detection in credit card transactions. In this paper, we describe a new method for detecting anomalous behavior over network performance data, gathered by perfSONAR, using two machine learning algorithms: Boosted Decision Trees (BDT...

Image Registration Algorithm Based on Parallax Constraint and Clustering Analysis

Science.gov (United States)

Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

2018-01-01

To resolve the problem of slow computation speed and low matching accuracy in image registration, a new image registration algorithm based on parallax constraint and clustering analysis is proposed. Firstly, Harris corner detection algorithm is used to extract the feature points of two images. Secondly, use Normalized Cross Correlation (NCC) function to perform the approximate matching of feature points, and the initial feature pair is obtained. Then, according to the parallax constraint condition, the initial feature pair is preprocessed by K-means clustering algorithm, which is used to remove the feature point pairs with obvious errors in the approximate matching process. Finally, adopt Random Sample Consensus (RANSAC) algorithm to optimize the feature points to obtain the final feature point matching result, and the fast and accurate image registration is realized. The experimental results show that the image registration algorithm proposed in this paper can improve the accuracy of the image matching while ensuring the real-time performance of the algorithm.

Algorithm for detection of the broken phase conductor in the radial networks

Directory of Open Access Journals (Sweden)

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.

Knowledge-Base Application to Ground Moving Target Detection

National Research Council Canada - National Science Library

Adve, R

2001-01-01

This report summarizes a multi-year in-house effort to apply knowledge-base control techniques and advanced Space-Time Adaptive Processing algorithms to improve detection performance and false alarm...

An improved algorithm of laser spot center detection in strong noise background

Science.gov (United States)

Zhang, Le; Wang, Qianqian; Cui, Xutai; Zhao, Yu; Peng, Zhong

2018-01-01

Laser spot center detection is demanded in many applications. The common algorithms for laser spot center detection such as centroid and Hough transform method have poor anti-interference ability and low detection accuracy in the condition of strong background noise. In this paper, firstly, the median filtering was used to remove the noise while preserving the edge details of the image. Secondly, the binarization of the laser facula image was carried out to extract target image from background. Then the morphological filtering was performed to eliminate the noise points inside and outside the spot. At last, the edge of pretreated facula image was extracted and the laser spot center was obtained by using the circle fitting method. In the foundation of the circle fitting algorithm, the improved algorithm added median filtering, morphological filtering and other processing methods. This method could effectively filter background noise through theoretical analysis and experimental verification, which enhanced the anti-interference ability of laser spot center detection and also improved the detection accuracy.

Archimedean copula estimation of distribution algorithm based on artificial bee colony algorithm

Institute of Scientific and Technical Information of China (English)

Haidong Xu; Mingyan Jiang; Kun Xu

2015-01-01

The artificial bee colony (ABC) algorithm is a com-petitive stochastic population-based optimization algorithm. How-ever, the ABC algorithm does not use the social information and lacks the knowledge of the problem structure, which leads to in-sufficiency in both convergent speed and searching precision. Archimedean copula estimation of distribution algorithm (ACEDA) is a relatively simple, time-economic and multivariate correlated EDA. This paper proposes a novel hybrid algorithm based on the ABC algorithm and ACEDA cal ed Archimedean copula estima-tion of distribution based on the artificial bee colony (ACABC) algorithm. The hybrid algorithm utilizes ACEDA to estimate the distribution model and then uses the information to help artificial bees to search more efficiently in the search space. Six bench-mark functions are introduced to assess the performance of the ACABC algorithm on numerical function optimization. Experimen-tal results show that the ACABC algorithm converges much faster with greater precision compared with the ABC algorithm, ACEDA and the global best (gbest)-guided ABC (GABC) algorithm in most of the experiments.

ASPeak: an abundance sensitive peak detection algorithm for RIP-Seq.

Science.gov (United States)

Kucukural, Alper; Özadam, Hakan; Singh, Guramrit; Moore, Melissa J; Cenik, Can

2013-10-01

Unlike DNA, RNA abundances can vary over several orders of magnitude. Thus, identification of RNA-protein binding sites from high-throughput sequencing data presents unique challenges. Although peak identification in ChIP-Seq data has been extensively explored, there are few bioinformatics tools tailored for peak calling on analogous datasets for RNA-binding proteins. Here we describe ASPeak (abundance sensitive peak detection algorithm), an implementation of an algorithm that we previously applied to detect peaks in exon junction complex RNA immunoprecipitation in tandem experiments. Our peak detection algorithm yields stringent and robust target sets enabling sensitive motif finding and downstream functional analyses. ASPeak is implemented in Perl as a complete pipeline that takes bedGraph files as input. ASPeak implementation is freely available at https://sourceforge.net/projects/as-peak under the GNU General Public License. ASPeak can be run on a personal computer, yet is designed to be easily parallelizable. ASPeak can also run on high performance computing clusters providing efficient speedup. The documentation and user manual can be obtained from http://master.dl.sourceforge.net/project/as-peak/manual.pdf.

Smartphone-based low light detection for bioluminescence application

Science.gov (United States)

We report a smartphone-based device and associated imaging-processing algorithm to maximize the sensitivity of standard smartphone cameras, that can detect the presence of single-digit pW of radiant flux intensity. The proposed hardware and software, called bioluminescent-based analyte quantitation ...

Object Detection and Tracking-Based Camera Calibration for Normalized Human Height Estimation

Directory of Open Access Journals (Sweden)

Jaehoon Jung

2016-01-01

Full Text Available This paper presents a normalized human height estimation algorithm using an uncalibrated camera. To estimate the normalized human height, the proposed algorithm detects a moving object and performs tracking-based automatic camera calibration. The proposed method consists of three steps: (i moving human detection and tracking, (ii automatic camera calibration, and (iii human height estimation and error correction. The proposed method automatically calibrates camera by detecting moving humans and estimates the human height using error correction. The proposed method can be applied to object-based video surveillance systems and digital forensic.

Body-Sensor-Network-Based Spasticity Detection.

Science.gov (United States)

Misgeld, Berno J E; Luken, Markus; Heitzmann, Daniel; Wolf, Sebastian I; Leonhardt, Steffen

2016-05-01

Spasticity is a common disorder of the skeletal muscle with a high incidence in industrialised countries. A quantitative measure of spasticity using body-worn sensors is important in order to assess rehabilitative motor training and to adjust the rehabilitative therapy accordingly. We present a new approach to spasticity detection using the Integrated Posture and Activity Network by Medit Aachen body sensor network (BSN). For this, a new electromyography (EMG) sensor node was developed and employed in human locomotion. Following an analysis of the clinical gait data of patients with unilateral cerebral palsy, a novel algorithm was developed based on the idea to detect coactivation of antagonistic muscle groups as observed in the exaggerated stretch reflex with associated joint rigidity. The algorithm applies a cross-correlation function to the EMG signals of two antagonistically working muscles and subsequent weighting using a Blackman window. The result is a coactivation index which is also weighted by the signal equivalent energy to exclude positive detection of inactive muscles. Our experimental study indicates good performance in the detection of coactive muscles associated with spasticity from clinical data as well as measurements from a BSN in qualitative comparison with the Modified Ashworth Scale as classified by clinical experts. Possible applications of the new algorithm include (but are not limited to) use in robotic sensorimotor therapy to reduce the effect of spasticity.

Model Based Fault Detection in a Centrifugal Pump Application

DEFF Research Database (Denmark)

Kallesøe, Carsten; Cocquempot, Vincent; Izadi-Zamanabadi, Roozbeh

2006-01-01

A model based approach for fault detection in a centrifugal pump, driven by an induction motor, is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, observer design and Analytical Redundancy Relation (ARR) design. Structural considerations...

Practical Algorithms for Subgroup Detection in Covert Networks

DEFF Research Database (Denmark)

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...

Proposed Network Intrusion Detection System ‎Based on Fuzzy c Mean Algorithm in Cloud ‎Computing Environment

Directory of Open Access Journals (Sweden)

Shawq Malik Mehibs

2017-12-01

Full Text Available Nowadays cloud computing had become is an integral part of IT industry, cloud computing provides Working environment allow a user of environmental to share data and resources over the internet. Where cloud computing its virtual grouping of resources offered over the internet, this lead to different matters related to the security and privacy in cloud computing. And therefore, create intrusion detection very important to detect outsider and insider intruders of cloud computing with high detection rate and low false positive alarm in the cloud environment. This work proposed network intrusion detection module using fuzzy c mean algorithm. The kdd99 dataset used for experiments .the proposed system characterized by a high detection rate with low false positive alarm

Evaluation of hybrids algorithms for mass detection in digitalized mammograms

International Nuclear Information System (INIS)

Cordero, Jose; Garzon Reyes, Johnson

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.

Global contrast based salient region detection

KAUST Repository

Cheng, Ming-Ming

2011-08-25

Reliable estimation of visual saliency allows appropriate processing of images without prior knowledge of their contents, and thus remains an important step in many computer vision tasks including image segmentation, object recognition, and adaptive compression. We propose a regional contrast based saliency extraction algorithm, which simultaneously evaluates global contrast differences and spatial coherence. The proposed algorithm is simple, efficient, and yields full resolution saliency maps. Our algorithm consistently outperformed existing saliency detection methods, yielding higher precision and better recall rates, when evaluated using one of the largest publicly available data sets. We also demonstrate how the extracted saliency map can be used to create high quality segmentation masks for subsequent image processing.

Global contrast based salient region detection

KAUST Repository

Cheng, Ming-Ming; Zhang, Guo-Xin; Mitra, Niloy J.; Huang, Xiaolei; Hu, Shi-Min

2011-01-01

Reliable estimation of visual saliency allows appropriate processing of images without prior knowledge of their contents, and thus remains an important step in many computer vision tasks including image segmentation, object recognition, and adaptive compression. We propose a regional contrast based saliency extraction algorithm, which simultaneously evaluates global contrast differences and spatial coherence. The proposed algorithm is simple, efficient, and yields full resolution saliency maps. Our algorithm consistently outperformed existing saliency detection methods, yielding higher precision and better recall rates, when evaluated using one of the largest publicly available data sets. We also demonstrate how the extracted saliency map can be used to create high quality segmentation masks for subsequent image processing.

Moving object detection in video satellite image based on deep learning

Science.gov (United States)

Zhang, Xueyang; Xiang, Junhua

2017-11-01

Moving object detection in video satellite image is studied. A detection algorithm based on deep learning is proposed. The small scale characteristics of remote sensing video objects are analyzed. Firstly, background subtraction algorithm of adaptive Gauss mixture model is used to generate region proposals. Then the objects in region proposals are classified via the deep convolutional neural network. Thus moving objects of interest are detected combined with prior information of sub-satellite point. The deep convolution neural network employs a 21-layer residual convolutional neural network, and trains the network parameters by transfer learning. Experimental results about video from Tiantuo-2 satellite demonstrate the effectiveness of the algorithm.

Road detection in SAR images using a tensor voting algorithm

Science.gov (United States)

Shen, Dajiang; Hu, Chun; Yang, Bing; Tian, Jinwen; Liu, Jian

2007-11-01

In this paper, the problem of the detection of road networks in Synthetic Aperture Radar (SAR) images is addressed. Most of the previous methods extract the road by detecting lines and network reconstruction. Traditional algorithms such as MRFs, GA, Level Set, used in the progress of reconstruction are iterative. The tensor voting methodology we proposed is non-iterative, and non-sensitive to initialization. Furthermore, the only free parameter is the size of the neighborhood, related to the scale. The algorithm we present is verified to be effective when it's applied to the road extraction using the real Radarsat Image.

Fast detection of the fuzzy communities based on leader-driven algorithm

Science.gov (United States)

Fang, Changjian; Mu, Dejun; Deng, Zhenghong; Hu, Jun; Yi, Chen-He

2018-03-01

In this paper, we present the leader-driven algorithm (LDA) for learning community structure in networks. The algorithm allows one to find overlapping clusters in a network, an important aspect of real networks, especially social networks. The algorithm requires no input parameters and learns the number of clusters naturally from the network. It accomplishes this using leadership centrality in a clever manner. It identifies local minima of leadership centrality as followers which belong only to one cluster, and the remaining nodes are leaders which connect clusters. In this way, the number of clusters can be learned using only the network structure. The LDA is also an extremely fast algorithm, having runtime linear in the network size. Thus, this algorithm can be used to efficiently cluster extremely large networks.

An evaluation of classification algorithms for intrusion detection ...

African Journals Online (AJOL)

An evaluation of classification algorithms for intrusion detection. ... Log in or Register to get access to full text downloads. ... Most of the available IDSs use all the 41 features in the network to evaluate and search for intrusive pattern in which ...

An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt

Directory of Open Access Journals (Sweden)

Qingming Zhan

2017-08-01

Full Text Available An adaptive spatial clustering (ASC algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation, or Voronoi diagram. It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need for prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.

A Multi-Scale Settlement Matching Algorithm Based on ARG

Directory of Open Access Journals (Sweden)

H. Yue

2016-06-01

Full Text Available Homonymous entity matching is an important part of multi-source spatial data integration, automatic updating and change detection. Considering the low accuracy of existing matching methods in dealing with matching multi-scale settlement data, an algorithm based on Attributed Relational Graph (ARG is proposed. The algorithm firstly divides two settlement scenes at different scales into blocks by small-scale road network and constructs local ARGs in each block. Then, ascertains candidate sets by merging procedures and obtains the optimal matching pairs by comparing the similarity of ARGs iteratively. Finally, the corresponding relations between settlements at large and small scales are identified. At the end of this article, a demonstration is presented and the results indicate that the proposed algorithm is capable of handling sophisticated cases.