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...
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.
Improvement and implementation for Canny edge detection algorithm
Yang, Tao; Qiu, Yue-hong
2015-07-01
Edge detection is necessary for image segmentation and pattern recognition. In this paper, an improved Canny edge detection approach is proposed due to the defect of traditional algorithm. A modified bilateral filter with a compensation function based on pixel intensity similarity judgment was used to smooth image instead of Gaussian filter, which could preserve edge feature and remove noise effectively. In order to solve the problems of sensitivity to the noise in gradient calculating, the algorithm used 4 directions gradient templates. Finally, Otsu algorithm adaptively obtain the dual-threshold. All of the algorithm simulated with OpenCV 2.4.0 library in the environments of vs2010, and through the experimental analysis, the improved algorithm has been proved to detect edge details more effectively and with more adaptability.
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
Linear feature detection algorithm for astronomical surveys - I. Algorithm description
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.
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.
Vela, Adan Ernesto
2011-12-01
From 2010 to 2030, the number of instrument flight rules aircraft operations handled by Federal Aviation Administration en route traffic centers is predicted to increase from approximately 39 million flights to 64 million flights. The projected growth in air transportation demand is likely to result in traffic levels that exceed the abilities of the unaided air traffic controller in managing, separating, and providing services to aircraft. Consequently, the Federal Aviation Administration, and other air navigation service providers around the world, are making several efforts to improve the capacity and throughput of existing airspaces. Ultimately, the stated goal of the Federal Aviation Administration is to triple the available capacity of the National Airspace System by 2025. In an effort to satisfy air traffic demand through the increase of airspace capacity, air navigation service providers are considering the inclusion of advisory conflict-detection and resolution systems. In a human-in-the-loop framework, advisory conflict-detection and resolution decision-support tools identify potential conflicts and propose resolution commands for the air traffic controller to verify and issue to aircraft. A number of researchers and air navigation service providers hypothesize that the inclusion of combined conflict-detection and resolution tools into air traffic control systems will reduce or transform controller workload and enable the required increases in airspace capacity. In an effort to understand the potential workload implications of introducing advisory conflict-detection and resolution tools, this thesis provides a detailed study of the conflict event process and the implementation of conflict-detection and resolution algorithms. Specifically, the research presented here examines a metric of controller taskload: how many resolution commands an air traffic controller issues under the guidance of a conflict-detection and resolution decision-support tool. The goal
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
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.
Cascade Boosting-Based Object Detection from High-Level Description to Hardware Implementation
Directory of Open Access Journals (Sweden)
K. Khattab
2009-01-01
Full Text Available Object detection forms the first step of a larger setup for a wide variety of computer vision applications. The focus of this paper is the implementation of a real-time embedded object detection system while relying on high-level description language such as SystemC. Boosting-based object detection algorithms are considered as the fastest accurate object detection algorithms today. However, the implementation of a real time solution for such algorithms is still a challenge. A new parallel implementation, which exploits the parallelism and the pipelining in these algorithms, is proposed. We show that using a SystemC description model paired with a mainstream automatic synthesis tool can lead to an efficient embedded implementation. We also display some of the tradeoffs and considerations, for this implementation to be effective. This implementation proves capable of achieving 42 fps for 320×240 images as well as bringing regularity in time consuming.
Rahman, Nurul Hidayah Ab; Abdullah, Nurul Azma; Hamid, Isredza Rahmi A.; Wen, Chuah Chai; Jelani, Mohamad Shafiqur Rahman Mohd
2017-10-01
Closed-Circuit TV (CCTV) system is one of the technologies in surveillance field to solve the problem of detection and monitoring by providing extra features such as email alert or motion detection. However, detecting and alerting the admin on CCTV system may complicate due to the complexity to integrate the main program with an external Application Programming Interface (API). In this study, pixel processing algorithm is applied due to its efficiency and SMS alert is added as an alternative solution for users who opted out email alert system or have no Internet connection. A CCTV system with SMS alert (CMDSA) was developed using evolutionary prototyping methodology. The system interface was implemented using Microsoft Visual Studio while the backend components, which are database and coding, were implemented on SQLite database and C# programming language, respectively. The main modules of CMDSA are motion detection, capturing and saving video, image processing and Short Message Service (SMS) alert functions. Subsequently, the system is able to reduce the processing time making the detection process become faster, reduce the space and memory used to run the program and alerting the system admin instantly.
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.
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.
FPGA Implementation of a Frame Synchronization Algorithm for Powerline Communications
Directory of Open Access Journals (Sweden)
S. Tsakiris
2009-09-01
Full Text Available This paper presents an FPGA implementation of a pilot–based time synchronization scheme employing orthogonal frequency division multiplexing for powerline communication channels. The functionality of the algorithm is analyzed and tested over a real powerline residential network. For this purpose, an appropriate transmitter circuit, implemented by an FPGA, and suitable coupling circuits are constructed. The system has been developed using VHDL language on Nallatech XtremeDSP development kits. The communication system operates in the baseband up to 30 MHz. Measurements of the algorithm's good performance in terms of the number of detected frames and timing offset error are taken and compared to simulations of existing algorithms.
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...
ASPeak: an abundance sensitive peak detection algorithm for RIP-Seq.
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.
Photon Counting Using Edge-Detection Algorithm
Gin, Jonathan W.; Nguyen, Danh H.; Farr, William H.
2010-01-01
New applications such as high-datarate, photon-starved, free-space optical communications require photon counting at flux rates into gigaphoton-per-second regimes coupled with subnanosecond timing accuracy. Current single-photon detectors that are capable of handling such operating conditions are designed in an array format and produce output pulses that span multiple sample times. In order to discern one pulse from another and not to overcount the number of incoming photons, a detection algorithm must be applied to the sampled detector output pulses. As flux rates increase, the ability to implement such a detection algorithm becomes difficult within a digital processor that may reside within a field-programmable gate array (FPGA). Systems have been developed and implemented to both characterize gigahertz bandwidth single-photon detectors, as well as process photon count signals at rates into gigaphotons per second in order to implement communications links at SCPPM (serial concatenated pulse position modulation) encoded data rates exceeding 100 megabits per second with efficiencies greater than two bits per detected photon. A hardware edge-detection algorithm and corresponding signal combining and deserialization hardware were developed to meet these requirements at sample rates up to 10 GHz. The photon discriminator deserializer hardware board accepts four inputs, which allows for the ability to take inputs from a quadphoton counting detector, to support requirements for optical tracking with a reduced number of hardware components. The four inputs are hardware leading-edge detected independently. After leading-edge detection, the resultant samples are ORed together prior to deserialization. The deserialization is performed to reduce the rate at which data is passed to a digital signal processor, perhaps residing within an FPGA. The hardware implements four separate analog inputs that are connected through RF connectors. Each analog input is fed to a high-speed 1
Regression algorithm for emotion detection
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...
Real-time implementation of logo detection on open source BeagleBoard
George, M.; Kehtarnavaz, N.; Estevez, L.
2011-03-01
This paper presents the real-time implementation of our previously developed logo detection and tracking algorithm on the open source BeagleBoard mobile platform. This platform has an OMAP processor that incorporates an ARM Cortex processor. The algorithm combines Scale Invariant Feature Transform (SIFT) with k-means clustering, online color calibration and moment invariants to robustly detect and track logos in video. Various optimization steps that are carried out to allow the real-time execution of the algorithm on BeagleBoard are discussed. The results obtained are compared to the PC real-time implementation results.
Global alignment algorithms implementations | Fatumo ...
African Journals Online (AJOL)
In this paper, we implemented the two routes for sequence comparison, that is; the dotplot and Needleman-wunsch algorithm for global sequence alignment. Our algorithms were implemented in python programming language and were tested on Linux platform 1.60GHz, 512 MB of RAM SUSE 9.2 and 10.1 versions.
ALGORITHM OF PLACEMENT OF VIDEO SURVEILLANCE CAMERAS AND ITS SOFTWARE IMPLEMENTATION
Directory of Open Access Journals (Sweden)
Loktev Alexey Alexeevich
2012-10-01
Full Text Available Comprehensive distributed safety, control, and monitoring systems applied by companies and organizations of different ownership structure play a substantial role in the present-day society. Video surveillance elements that ensure image processing and decision making in automated or automatic modes are the essential components of new systems. This paper covers the modeling of video surveillance systems installed in buildings, and the algorithm, or pattern, of video camera placement with due account for nearly all characteristics of buildings, detection and recognition facilities, and cameras themselves. This algorithm will be subsequently implemented as a user application. The project contemplates a comprehensive approach to the automatic placement of cameras that take account of their mutual positioning and compatibility of tasks. The project objective is to develop the principal elements of the algorithm of recognition of a moving object to be detected by several cameras. The image obtained by different cameras will be processed. Parameters of motion are to be identified to develop a table of possible options of routes. The implementation of the recognition algorithm represents an independent research project to be covered by a different article. This project consists in the assessment of the degree of complexity of an algorithm of camera placement designated for identification of cases of inaccurate algorithm implementation, as well as in the formulation of supplementary requirements and input data by means of intercrossing sectors covered by neighbouring cameras. The project also contemplates identification of potential problems in the course of development of a physical security and monitoring system at the stage of the project design development and testing. The camera placement algorithm has been implemented as a software application that has already been pilot tested on buildings and inside premises that have irregular dimensions. The
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%.
Quantum computation with classical light: Implementation of the Deutsch–Jozsa algorithm
International Nuclear Information System (INIS)
Perez-Garcia, Benjamin; McLaren, Melanie; Goyal, Sandeep K.; Hernandez-Aranda, Raul I.; Forbes, Andrew; Konrad, Thomas
2016-01-01
Highlights: • An implementation of the Deutsch–Jozsa algorithm using classical optics is proposed. • Constant and certain balanced functions can be encoded and distinguished efficiently. • The encoding and the detection process does not require to access single path qubits. • While the scheme might be scalable in principle, it might not be in practice. • We suggest a generalisation of the Deutsch–Jozsa algorithm and its implementation. - Abstract: We propose an optical implementation of the Deutsch–Jozsa Algorithm using classical light in a binary decision-tree scheme. Our approach uses a ring cavity and linear optical devices in order to efficiently query the oracle functional values. In addition, we take advantage of the intrinsic Fourier transforming properties of a lens to read out whether the function given by the oracle is balanced or constant.
Quantum computation with classical light: Implementation of the Deutsch–Jozsa algorithm
Energy Technology Data Exchange (ETDEWEB)
Perez-Garcia, Benjamin [Photonics and Mathematical Optics Group, Tecnológico de Monterrey, Monterrey 64849 (Mexico); University of the Witwatersrand, Private Bag 3, Johannesburg 2050 (South Africa); School of Chemistry and Physics, University of KwaZulu-Natal, Private Bag X54001, Durban 4000 (South Africa); McLaren, Melanie [University of the Witwatersrand, Private Bag 3, Johannesburg 2050 (South Africa); Goyal, Sandeep K. [School of Chemistry and Physics, University of KwaZulu-Natal, Private Bag X54001, Durban 4000 (South Africa); Institute of Quantum Science and Technology, University of Calgary, Alberta T2N 1N4 (Canada); Hernandez-Aranda, Raul I. [Photonics and Mathematical Optics Group, Tecnológico de Monterrey, Monterrey 64849 (Mexico); Forbes, Andrew [University of the Witwatersrand, Private Bag 3, Johannesburg 2050 (South Africa); Konrad, Thomas, E-mail: konradt@ukzn.ac.za [School of Chemistry and Physics, University of KwaZulu-Natal, Private Bag X54001, Durban 4000 (South Africa); National Institute of Theoretical Physics, Durban Node, Private Bag X54001, Durban 4000 (South Africa)
2016-05-20
Highlights: • An implementation of the Deutsch–Jozsa algorithm using classical optics is proposed. • Constant and certain balanced functions can be encoded and distinguished efficiently. • The encoding and the detection process does not require to access single path qubits. • While the scheme might be scalable in principle, it might not be in practice. • We suggest a generalisation of the Deutsch–Jozsa algorithm and its implementation. - Abstract: We propose an optical implementation of the Deutsch–Jozsa Algorithm using classical light in a binary decision-tree scheme. Our approach uses a ring cavity and linear optical devices in order to efficiently query the oracle functional values. In addition, we take advantage of the intrinsic Fourier transforming properties of a lens to read out whether the function given by the oracle is balanced or constant.
Implementation of an Algorithm for Prosthetic Joint Infection: Deviations and Problems.
Mühlhofer, Heinrich M L; Kanz, Karl-Georg; Pohlig, Florian; Lenze, Ulrich; Lenze, Florian; Toepfer, Andreas; von Eisenhart-Rothe, Ruediger; Schauwecker, Johannes
The outcome of revision surgery in arthroplasty is based on a precise diagnosis. In addition, the treatment varies based on whether the prosthetic failure is caused by aseptic or septic loosening. Algorithms can help to identify periprosthetic joint infections (PJI) and standardize diagnostic steps, however, algorithms tend to oversimplify the treatment of complex cases. We conducted a process analysis during the implementation of a PJI algorithm to determine problems and deviations associated with the implementation of this algorithm. Fifty patients who were treated after implementing a standardized algorithm were monitored retrospectively. Their treatment plans and diagnostic cascades were analyzed for deviations from the implemented algorithm. Each diagnostic procedure was recorded, compared with the algorithm, and evaluated statistically. We detected 52 deviations while treating 50 patients. In 25 cases, no discrepancy was observed. Synovial fluid aspiration was not performed in 31.8% of patients (95% confidence interval [CI], 18.1%-45.6%), while white blood cell counts (WBCs) and neutrophil differentiation were assessed in 54.5% of patients (95% CI, 39.8%-69.3%). We also observed that the prolonged incubation of cultures was not requested in 13.6% of patients (95% CI, 3.5%-23.8%). In seven of 13 cases (63.6%; 95% CI, 35.2%-92.1%), arthroscopic biopsy was performed; 6 arthroscopies were performed in discordance with the algorithm (12%; 95% CI, 3%-21%). Self-critical analysis of diagnostic processes and monitoring of deviations using algorithms are important and could increase the quality of treatment by revealing recurring faults.
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)
Implementation of several mathematical algorithms to breast tissue density classification
Quintana, C.; Redondo, M.; Tirao, G.
2014-02-01
The accuracy of mammographic abnormality detection methods is strongly dependent on breast tissue characteristics, where a dense breast tissue can hide lesions causing cancer to be detected at later stages. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. This paper presents the implementation and the performance of different mathematical algorithms designed to standardize the categorization of mammographic images, according to the American College of Radiology classifications. These mathematical techniques are based on intrinsic properties calculations and on comparison with an ideal homogeneous image (joint entropy, mutual information, normalized cross correlation and index Q) as categorization parameters. The algorithms evaluation was performed on 100 cases of the mammographic data sets provided by the Ministerio de Salud de la Provincia de Córdoba, Argentina—Programa de Prevención del Cáncer de Mama (Department of Public Health, Córdoba, Argentina, Breast Cancer Prevention Program). The obtained breast classifications were compared with the expert medical diagnostics, showing a good performance. The implemented algorithms revealed a high potentiality to classify breasts into tissue density categories.
Elementary functions algorithms and implementation
Muller, Jean-Michel
2016-01-01
This textbook presents the concepts and tools necessary to understand, build, and implement algorithms for computing elementary functions (e.g., logarithms, exponentials, and the trigonometric functions). Both hardware- and software-oriented algorithms are included, along with issues related to accurate floating-point implementation. This third edition has been updated and expanded to incorporate the most recent advances in the field, new elementary function algorithms, and function software. After a preliminary chapter that briefly introduces some fundamental concepts of computer arithmetic, such as floating-point arithmetic and redundant number systems, the text is divided into three main parts. Part I considers the computation of elementary functions using algorithms based on polynomial or rational approximations and using table-based methods; the final chapter in this section deals with basic principles of multiple-precision arithmetic. Part II is devoted to a presentation of “shift-and-add” algorithm...
Algorithms for the detection of chewing behavior in dietary monitoring applications
Schmalz, Mark S.; Helal, Abdelsalam; Mendez-Vasquez, Andres
2009-08-01
The detection of food consumption is key to the implementation of successful behavior modification in support of dietary monitoring and therapy, for example, during the course of controlling obesity, diabetes, or cardiovascular disease. Since the vast majority of humans consume food via mastication (chewing), we have designed an algorithm that automatically detects chewing behaviors in surveillance video of a person eating. Our algorithm first detects the mouth region, then computes the spatiotemporal frequency spectrum of a small perioral region (including the mouth). Spectral data are analyzed to determine the presence of periodic motion that characterizes chewing. A classifier is then applied to discriminate different types of chewing behaviors. Our algorithm was tested on seven volunteers, whose behaviors included chewing with mouth open, chewing with mouth closed, talking, static face presentation (control case), and moving face presentation. Early test results show that the chewing behaviors induce a temporal frequency peak at 0.5Hz to 2.5Hz, which is readily detected using a distance-based classifier. Computational cost is analyzed for implementation on embedded processing nodes, for example, in a healthcare sensor network. Complexity analysis emphasizes the relationship between the work and space estimates of the algorithm, and its estimated error. It is shown that chewing detection is possible within a computationally efficient, accurate, and subject-independent framework.
Implementation of several mathematical algorithms to breast tissue density classification
International Nuclear Information System (INIS)
Quintana, C.; Redondo, M.; Tirao, G.
2014-01-01
The accuracy of mammographic abnormality detection methods is strongly dependent on breast tissue characteristics, where a dense breast tissue can hide lesions causing cancer to be detected at later stages. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. This paper presents the implementation and the performance of different mathematical algorithms designed to standardize the categorization of mammographic images, according to the American College of Radiology classifications. These mathematical techniques are based on intrinsic properties calculations and on comparison with an ideal homogeneous image (joint entropy, mutual information, normalized cross correlation and index Q) as categorization parameters. The algorithms evaluation was performed on 100 cases of the mammographic data sets provided by the Ministerio de Salud de la Provincia de Córdoba, Argentina—Programa de Prevención del Cáncer de Mama (Department of Public Health, Córdoba, Argentina, Breast Cancer Prevention Program). The obtained breast classifications were compared with the expert medical diagnostics, showing a good performance. The implemented algorithms revealed a high potentiality to classify breasts into tissue density categories. - Highlights: • Breast density classification can be obtained by suitable mathematical algorithms. • Mathematical processing help radiologists to obtain the BI-RADS classification. • The entropy and joint entropy show high performance for density classification
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.
THE APPROACHING TRAIN DETECTION ALGORITHM
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...
Design and Implementation of Video Shot Detection on Field Programmable Gate Arrays
Directory of Open Access Journals (Sweden)
Jharna Majumdar
2012-09-01
Full Text Available Video has become an interactive medium of communication in everyday life. The sheer volume of video makes it extremely difficult to browse through and find the required data. Hence extraction of key frames from the video which represents the abstract of the entire video becomes necessary. The aim of the video shot detection is to find the position of the shot boundaries, so that key frames can be selected from each shot for subsequent processing such as video summarization, indexing etc. For most of the surveillance applications like video summery, face recognition etc., the hardware (real time implementation of these algorithms becomes necessary. Here in this paper we present the architecture for simultaneous accessing of consecutive frames, which are then used for the implementation of various Video Shot Detection algorithms. We also present the real time implementation of three video shot detection algorithms using the above mentioned architecture on FPGA (Field Programmable Gate Arrays.
Real-time DSP implementation for MRF-based video motion detection.
Dumontier, C; Luthon, F; Charras, J P
1999-01-01
This paper describes the real time implementation of a simple and robust motion detection algorithm based on Markov random field (MRF) modeling, MRF-based algorithms often require a significant amount of computations. The intrinsic parallel property of MRF modeling has led most of implementations toward parallel machines and neural networks, but none of these approaches offers an efficient solution for real-world (i.e., industrial) applications. Here, an alternative implementation for the problem at hand is presented yielding a complete, efficient and autonomous real-time system for motion detection. This system is based on a hybrid architecture, associating pipeline modules with one asynchronous module to perform the whole process, from video acquisition to moving object masks visualization. A board prototype is presented and a processing rate of 15 images/s is achieved, showing the validity of the approach.
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.
An implementation of the Heaviside algorithm
International Nuclear Information System (INIS)
Dimovski, I.H.; Spiridonova, M.N.
2011-01-01
The so-called Heaviside algorithm based on the operational calculus approach is intended for solving initial value problems for linear ordinary differential equations with constant coefficients. We use it in the framework of Mikusinski's operational calculus. A description and implementation of the Heaviside algorithm using a computer algebra system are considered. Special attention is paid to the features making this implementation efficient. Illustrative examples are included
Test of TEDA, Tsunami Early Detection Algorithm
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
Prefiltering Model for Homology Detection Algorithms on GPU.
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.
Efficient parallel implementation of active appearance model fitting algorithm on GPU.
Wang, Jinwei; Ma, Xirong; Zhu, Yuanping; Sun, Jizhou
2014-01-01
The active appearance model (AAM) is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs) that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA) on the Nvidia's GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures.
Adaptive Filtering Algorithms and Practical Implementation
Diniz, Paulo S R
2013-01-01
In the fourth edition of Adaptive Filtering: Algorithms and Practical Implementation, author Paulo S.R. Diniz presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner. The main classes of adaptive filtering algorithms are presented in a unified framework, using clear notations that facilitate actual implementation. The main algorithms are described in tables, which are detailed enough to allow the reader to verify the covered concepts. Many examples address problems drawn from actual applications. New material to this edition includes: Analytical and simulation examples in Chapters 4, 5, 6 and 10 Appendix E, which summarizes the analysis of set-membership algorithm Updated problems and references Providing a concise background on adaptive filtering, this book covers the family of LMS, affine projection, RLS and data-selective set-membership algorithms as well as nonlinear, sub-band, blind, IIR adaptive filtering, and more. Several problems are...
A Cavity QED Implementation of Deutsch-Jozsa Algorithm
Guerra, E. S.
2004-01-01
The Deutsch-Jozsa algorithm is a generalization of the Deutsch algorithm which was the first algorithm written. We present schemes to implement the Deutsch algorithm and the Deutsch-Jozsa algorithm via cavity QED.
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.
VLSI implementation of MIMO detection for 802.11n using a novel adaptive tree search algorithm
International Nuclear Information System (INIS)
Yao Heng; Jian Haifang; Zhou Liguo; Shi Yin
2013-01-01
A 4×4 64-QAM multiple-input multiple-output (MIMO) detector is presented for the application of an IEEE 802.11n wireless local area network. The detectoris the implementation of a novel adaptive tree search(ATS) algorithm, and multiple ATS cores need to be instantiated to achieve the wideband requirement in the 802.11n standard. Both the ATS algorithm and the architectural considerations are explained. The latency of the detector is 0.75 μs, and the detector has a gate count of 848 k with a total of 19 parallel ATS cores. Each ATS core runs at 67 MHz. Measurement results show that compared with the floating-point ATS algorithm, the fixed-point implementation achieves a loss of 0.9 dB at a BER of 10 −3 . (semiconductor integrated circuits)
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.
Categorizing Variations of Student-Implemented Sorting Algorithms
Taherkhani, Ahmad; Korhonen, Ari; Malmi, Lauri
2012-01-01
In this study, we examined freshmen students' sorting algorithm implementations in data structures and algorithms' course in two phases: at the beginning of the course before the students received any instruction on sorting algorithms, and after taking a lecture on sorting algorithms. The analysis revealed that many students have insufficient…
On Implementing a Homogeneous Interior-Point Algorithm for Nonsymmetric Conic Optimization
DEFF Research Database (Denmark)
Skajaa, Anders; Jørgensen, John Bagterp; Hansen, Per Christian
Based on earlier work by Nesterov, an implementation of a homogeneous infeasible-start interior-point algorithm for solving nonsymmetric conic optimization problems is presented. Starting each iteration from (the vicinity of) the central path, the method computes (nearly) primal-dual symmetric...... approximate tangent directions followed by a purely primal centering procedure to locate the next central primal-dual point. Features of the algorithm include that it makes use only of the primal barrier function, that it is able to detect infeasibilities in the problem and that no phase-I method is needed...
Vision-based vehicle detection and tracking algorithm design
Hwang, Junyeon; Huh, Kunsoo; Lee, Donghwi
2009-12-01
The vision-based vehicle detection in front of an ego-vehicle is regarded as promising for driver assistance as well as for autonomous vehicle guidance. The feasibility of vehicle detection in a passenger car requires accurate and robust sensing performance. A multivehicle detection system based on stereo vision has been developed for better accuracy and robustness. This system utilizes morphological filter, feature detector, template matching, and epipolar constraint techniques in order to detect the corresponding pairs of vehicles. After the initial detection, the system executes the tracking algorithm for the vehicles. The proposed system can detect front vehicles such as the leading vehicle and side-lane vehicles. The position parameters of the vehicles located in front are obtained based on the detection information. The proposed vehicle detection system is implemented on a passenger car, and its performance is verified experimentally.
Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU
Directory of Open Access Journals (Sweden)
Jinwei Wang
2014-01-01
Full Text Available The active appearance model (AAM is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA on the Nvidia’s GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures.
Pietrzyk, Mariusz W.; Rannou, Didier; Brennan, Patrick C.
2012-02-01
This pilot study examines the effect of a novel decision support system in medical image interpretation. This system is based on combining image spatial frequency properties and eye-tracking data in order to recognize over and under calling errors. Thus, before it can be implemented as a detection aided schema, training is required during which SVMbased algorithm learns to recognize FP from all reported outcomes, and, FN from all unreported prolonged dwelled regions. Eight radiologists inspected 50 PA chest radiographs with the specific task of identifying lung nodules. Twentyfive cases contained CT proven subtle malignant lesions (5-20mm), but prevalence was not known by the subjects, who took part in two sequential reading sessions, the second, without and with support system feedback. MCMR ROC DBM and JAFROC analyses were conducted and demonstrated significantly higher scores following feedback with p values of 0.04, and 0.03 respectively, highlighting significant improvements in radiology performance once feedback was used. This positive effect on radiologists' performance might have important implications for future CAD-system development.
FPGA-based implementation for steganalysis: a JPEG-compatibility algorithm
Gutierrez-Fernandez, E.; Portela-García, M.; Lopez-Ongil, C.; Garcia-Valderas, M.
2013-05-01
Steganalysis is a process to detect hidden data in cover documents, like digital images, videos, audio files, etc. This is the inverse process of steganography, which is the used method to hide secret messages. The widely use of computers and network technologies make digital files very easy-to-use means for storing secret data or transmitting secret messages through the Internet. Depending on the cover medium used to embed the data, there are different steganalysis methods. In case of images, many of the steganalysis and steganographic methods are focused on JPEG image formats, since JPEG is one of the most common formats. One of the main important handicaps of steganalysis methods is the processing speed, since it is usually necessary to process huge amount of data or it can be necessary to process the on-going internet traffic in real-time. In this paper, a JPEG steganalysis system is implemented in an FPGA in order to speed-up the detection process with respect to software-based implementations and to increase the throughput. In particular, the implemented method is the JPEG-compatibility detection algorithm that is based on the fact that when a JPEG image is modified, the resulting image is incompatible with the JPEG compression process.
Object-Oriented Implementation of Adaptive Mesh Refinement Algorithms
Directory of Open Access Journals (Sweden)
William Y. Crutchfield
1993-01-01
Full Text Available We describe C++ classes that simplify development of adaptive mesh refinement (AMR algorithms. The classes divide into two groups, generic classes that are broadly useful in adaptive algorithms, and application-specific classes that are the basis for our AMR algorithm. We employ two languages, with C++ responsible for the high-level data structures, and Fortran responsible for low-level numerics. The C++ implementation is as fast as the original Fortran implementation. Use of inheritance has allowed us to extend the original AMR algorithm to other problems with greatly reduced development time.
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...
A New Method of Histogram Computation for Efficient Implementation of the HOG Algorithm
Directory of Open Access Journals (Sweden)
Mariana-Eugenia Ilas
2018-03-01
Full Text Available In this paper we introduce a new histogram computation method to be used within the histogram of oriented gradients (HOG algorithm. The new method replaces the arctangent with the slope computation and the classical magnitude allocation based on interpolation with a simpler algorithm. The new method allows a more efficient implementation of HOG in general, and particularly in field-programmable gate arrays (FPGAs, by considerably reducing the area (thus increasing the level of parallelism, while maintaining very close classification accuracy compared to the original algorithm. Thus, the new method is attractive for many applications, including car detection and classification.
Maximum entropy algorithm and its implementation for the neutral beam profile measurement
Energy Technology Data Exchange (ETDEWEB)
Lee, Seung Wook; Cho, Gyu Seong [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of); Cho, Yong Sub [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)
1998-12-31
A tomography algorithm to maximize the entropy of image using Lagrangian multiplier technique and conjugate gradient method has been designed for the measurement of 2D spatial distribution of intense neutral beams of KSTAR NBI (Korea Superconducting Tokamak Advanced Research Neutral Beam Injector), which is now being designed. A possible detection system was assumed and a numerical simulation has been implemented to test the reconstruction quality of given beam profiles. This algorithm has the good applicability for sparse projection data and thus, can be used for the neutral beam tomography. 8 refs., 3 figs. (Author)
Maximum entropy algorithm and its implementation for the neutral beam profile measurement
Energy Technology Data Exchange (ETDEWEB)
Lee, Seung Wook; Cho, Gyu Seong [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of); Cho, Yong Sub [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)
1997-12-31
A tomography algorithm to maximize the entropy of image using Lagrangian multiplier technique and conjugate gradient method has been designed for the measurement of 2D spatial distribution of intense neutral beams of KSTAR NBI (Korea Superconducting Tokamak Advanced Research Neutral Beam Injector), which is now being designed. A possible detection system was assumed and a numerical simulation has been implemented to test the reconstruction quality of given beam profiles. This algorithm has the good applicability for sparse projection data and thus, can be used for the neutral beam tomography. 8 refs., 3 figs. (Author)
Multiple Lookup Table-Based AES Encryption Algorithm Implementation
Gong, Jin; Liu, Wenyi; Zhang, Huixin
Anew AES (Advanced Encryption Standard) encryption algorithm implementation was proposed in this paper. It is based on five lookup tables, which are generated from S-box(the substitution table in AES). The obvious advantages are reducing the code-size, improving the implementation efficiency, and helping new learners to understand the AES encryption algorithm and GF(28) multiplication which are necessary to correctly implement AES[1]. This method can be applied on processors with word length 32 or above, FPGA and others. And correspondingly we can implement it by VHDL, Verilog, VB and other languages.
Efficient Implementation Algorithms for Homogenized Energy Models
National Research Council Canada - National Science Library
Braun, Thomas R; Smith, Ralph C
2005-01-01
... for real-time control implementation. In this paper, we develop algorithms employing lookup tables which permit the high speed implementation of formulations which incorporate relaxation mechanisms and electromechanical coupling...
Efficient Implementation of Nested-Loop Multimedia Algorithms
Directory of Open Access Journals (Sweden)
Kittitornkun Surin
2001-01-01
Full Text Available A novel dependence graph representation called the multiple-order dependence graph for nested-loop formulated multimedia signal processing algorithms is proposed. It allows a concise representation of an entire family of dependence graphs. This powerful representation facilitates the development of innovative implementation approach for nested-loop formulated multimedia algorithms such as motion estimation, matrix-matrix product, 2D linear transform, and others. In particular, algebraic linear mapping (assignment and scheduling methodology can be applied to implement such algorithms on an array of simple-processing elements. The feasibility of this new approach is demonstrated in three major target architectures: application-specific integrated circuit (ASIC, field programmable gate array (FPGA, and a programmable clustered VLIW processor.
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.
A very fast implementation of 2D iterative reconstruction algorithms
DEFF Research Database (Denmark)
Toft, Peter Aundal; Jensen, Peter James
1996-01-01
that iterative reconstruction algorithms can be implemented and run almost as fast as direct reconstruction algorithms. The method has been implemented in a software package that is available for free, providing reconstruction algorithms using ART, EM, and the Least Squares Conjugate Gradient Method...
Implementation of an algorithm for cylindrical object identification using range data
Bozeman, Sylvia T.; Martin, Benjamin J.
1989-01-01
One of the problems in 3-D object identification and localization is addressed. In robotic and navigation applications the vision system must be able to distinguish cylindrical or spherical objects as well as those of other geometric shapes. An algorithm was developed to identify cylindrical objects in an image when range data is used. The algorithm incorporates the Hough transform for line detection using edge points which emerge from a Sobel mask. Slices of the data are examined to locate arcs of circles using the normal equations of an over-determined linear system. Current efforts are devoted to testing the computer implementation of the algorithm. Refinements are expected to continue in order to accommodate cylinders in various positions. A technique is sought which is robust in the presence of noise and partial occlusions.
Comparison of tracking algorithms implemented in OpenCV
Directory of Open Access Journals (Sweden)
Janku Peter
2016-01-01
Full Text Available Computer vision is very progressive and modern part of computer science. From scientific point of view, theoretical aspects of computer vision algorithms prevail in many papers and publications. The underlying theory is really important, but on the other hand, the final implementation of an algorithm significantly affects its performance and robustness. For this reason, this paper tries to compare real implementation of tracking algorithms (one part of computer vision problem, which can be found in the very popular library OpenCV. Moreover, the possibilities of optimizations 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.
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
Evaluation of novel algorithm embedded in a wearable sEMG device for seizure detection
DEFF Research Database (Denmark)
Conradsen, Isa; Beniczky, Sandor; Wolf, Peter
2012-01-01
We implemented a modified version of a previously published algorithm for detection of generalized tonic-clonic seizures into a prototype wireless surface electromyography (sEMG) recording device. The method was modified to require minimum computational load, and two parameters were trained...... on prior sEMG data recorded with the device. Along with the normal sEMG recording, the device is able to set an alarm whenever the implemented algorithm detects a seizure. These alarms are annotated in the data file along with the signal. The device was tested at the Epilepsy Monitoring Unit (EMU......) at the Danish Epilepsy Center. Five patients were included in the study and two of them had generalized tonic-clonic seizures. All patients were monitored for 2–5 days. A double-blind study was made on the five patients. The overall result showed that the device detected four of seven seizures and had a false...
A new approach to optic disc detection in human retinal images using the firefly algorithm.
Rahebi, Javad; Hardalaç, Fırat
2016-03-01
There are various methods and algorithms to detect the optic discs in retinal images. In recent years, much attention has been given to the utilization of the intelligent algorithms. In this paper, we present a new automated method of optic disc detection in human retinal images using the firefly algorithm. The firefly intelligent algorithm is an emerging intelligent algorithm that was inspired by the social behavior of fireflies. The population in this algorithm includes the fireflies, each of which has a specific rate of lighting or fitness. In this method, the insects are compared two by two, and the less attractive insects can be observed to move toward the more attractive insects. Finally, one of the insects is selected as the most attractive, and this insect presents the optimum response to the problem in question. Here, we used the light intensity of the pixels of the retinal image pixels instead of firefly lightings. The movement of these insects due to local fluctuations produces different light intensity values in the images. Because the optic disc is the brightest area in the retinal images, all of the insects move toward brightest area and thus specify the location of the optic disc in the image. The results of implementation show that proposed algorithm could acquire an accuracy rate of 100 % in DRIVE dataset, 95 % in STARE dataset, and 94.38 % in DiaRetDB1 dataset. The results of implementation reveal high capability and accuracy of proposed algorithm in the detection of the optic disc from retinal images. Also, recorded required time for the detection of the optic disc in these images is 2.13 s for DRIVE dataset, 2.81 s for STARE dataset, and 3.52 s for DiaRetDB1 dataset accordingly. These time values are average value.
A Linked List-Based Algorithm for Blob Detection on Embedded Vision-Based Sensors
Directory of Open Access Journals (Sweden)
Ricardo Acevedo-Avila
2016-05-01
Full Text Available Blob detection is a common task in vision-based applications. Most existing algorithms are aimed at execution on general purpose computers; while very few can be adapted to the computing restrictions present in embedded platforms. This paper focuses on the design of an algorithm capable of real-time blob detection that minimizes system memory consumption. The proposed algorithm detects objects in one image scan; it is based on a linked-list data structure tree used to label blobs depending on their shape and node information. An example application showing the results of a blob detection co-processor has been built on a low-powered field programmable gate array hardware as a step towards developing a smart video surveillance system. The detection method is intended for general purpose application. As such, several test cases focused on character recognition are also examined. The results obtained present a fair trade-off between accuracy and memory requirements; and prove the validity of the proposed approach for real-time implementation on resource-constrained computing platforms.
A Linked List-Based Algorithm for Blob Detection on Embedded Vision-Based Sensors.
Acevedo-Avila, Ricardo; Gonzalez-Mendoza, Miguel; Garcia-Garcia, Andres
2016-05-28
Blob detection is a common task in vision-based applications. Most existing algorithms are aimed at execution on general purpose computers; while very few can be adapted to the computing restrictions present in embedded platforms. This paper focuses on the design of an algorithm capable of real-time blob detection that minimizes system memory consumption. The proposed algorithm detects objects in one image scan; it is based on a linked-list data structure tree used to label blobs depending on their shape and node information. An example application showing the results of a blob detection co-processor has been built on a low-powered field programmable gate array hardware as a step towards developing a smart video surveillance system. The detection method is intended for general purpose application. As such, several test cases focused on character recognition are also examined. The results obtained present a fair trade-off between accuracy and memory requirements; and prove the validity of the proposed approach for real-time implementation on resource-constrained computing platforms.
Detection of algorithmic trading
Bogoev, Dimitar; Karam, Arzé
2017-10-01
We develop a new approach to reflect the behavior of algorithmic traders. Specifically, we provide an analytical and tractable way to infer patterns of quote volatility and price momentum consistent with different types of strategies employed by algorithmic traders, and we propose two ratios to quantify these patterns. Quote volatility ratio is based on the rate of oscillation of the best ask and best bid quotes over an extremely short period of time; whereas price momentum ratio is based on identifying patterns of rapid upward or downward movement in prices. The two ratios are evaluated across several asset classes. We further run a two-stage Artificial Neural Network experiment on the quote volatility ratio; the first stage is used to detect the quote volatility patterns resulting from algorithmic activity, while the second is used to validate the quality of signal detection provided by our measure.
AES ALGORITHM IMPLEMENTATION IN PROGRAMMING LANGUAGES
Directory of Open Access Journals (Sweden)
Luminiţa DEFTA
2010-12-01
Full Text Available Information encryption represents the usage of an algorithm to convert an unknown message into an encrypted one. It is used to protect the data against unauthorized access. Protected data can be stored on a media device or can be transmitted through the network. In this paper we describe a concrete implementation of the AES algorithm in the Java programming language (available from Java Development Kit 6 libraries and C (using the OpenSSL library. AES (Advanced Encryption Standard is an asymmetric key encryption algorithm formally adopted by the U.S. government and was elected after a long process of standardization.
Robust and unobtrusive algorithm based on position independence for step detection
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.
A wavelet transform algorithm for peak detection and application to powder x-ray diffraction data.
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.
An analytic parton shower. Algorithms, implementation and validation
Energy Technology Data Exchange (ETDEWEB)
Schmidt, Sebastian
2012-06-15
The realistic simulation of particle collisions is an indispensable tool to interpret the data measured at high-energy colliders, for example the now running Large Hadron Collider at CERN. These collisions at these colliders are usually simulated in the form of exclusive events. This thesis focuses on the perturbative QCD part involved in the simulation of these events, particularly parton showers and the consistent combination of parton showers and matrix elements. We present an existing parton shower algorithm for emissions off final state partons along with some major improvements. Moreover, we present a new parton shower algorithm for emissions off incoming partons. The aim of these particular algorithms, called analytic parton shower algorithms, is to be able to calculate the probabilities for branchings and for whole events after the event has been generated. This allows a reweighting procedure to be applied after the events have been simulated. We show a detailed description of the algorithms, their implementation and the interfaces to the event generator WHIZARD. Moreover we discuss the implementation of a MLM-type matching procedure and an interface to the shower and hadronization routines from PYTHIA. Finally, we compare several predictions by our implementation to experimental measurements at LEP, Tevatron and LHC, as well as to predictions obtained using PYTHIA. (orig.)
An analytic parton shower. Algorithms, implementation and validation
International Nuclear Information System (INIS)
Schmidt, Sebastian
2012-06-01
The realistic simulation of particle collisions is an indispensable tool to interpret the data measured at high-energy colliders, for example the now running Large Hadron Collider at CERN. These collisions at these colliders are usually simulated in the form of exclusive events. This thesis focuses on the perturbative QCD part involved in the simulation of these events, particularly parton showers and the consistent combination of parton showers and matrix elements. We present an existing parton shower algorithm for emissions off final state partons along with some major improvements. Moreover, we present a new parton shower algorithm for emissions off incoming partons. The aim of these particular algorithms, called analytic parton shower algorithms, is to be able to calculate the probabilities for branchings and for whole events after the event has been generated. This allows a reweighting procedure to be applied after the events have been simulated. We show a detailed description of the algorithms, their implementation and the interfaces to the event generator WHIZARD. Moreover we discuss the implementation of a MLM-type matching procedure and an interface to the shower and hadronization routines from PYTHIA. Finally, we compare several predictions by our implementation to experimental measurements at LEP, Tevatron and LHC, as well as to predictions obtained using PYTHIA. (orig.)
A robust human face detection algorithm
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.
A Finite State Machine Approach to Algorithmic Lateral Inhibition for Real-Time Motion Detection †
Directory of Open Access Journals (Sweden)
María T. López
2018-05-01
Full Text Available Many researchers have explored the relationship between recurrent neural networks and finite state machines. Finite state machines constitute the best-characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. The neurally-inspired lateral inhibition method, and its application to motion detection tasks, have been successfully implemented in recent years. In this paper, control knowledge of the algorithmic lateral inhibition (ALI method is described and applied by means of finite state machines, in which the state space is constituted from the set of distinguishable cases of accumulated charge in a local memory. The article describes an ALI implementation for a motion detection task. For the implementation, we have chosen to use one of the members of the 16-nm Kintex UltraScale+ family of Xilinx FPGAs. FPGAs provide the necessary accuracy, resolution, and precision to run neural algorithms alongside current sensor technologies. The results offered in this paper demonstrate that this implementation provides accurate object tracking performance on several datasets, obtaining a high F-score value (0.86 for the most complex sequence used. Moreover, it outperforms implementations of a complete ALI algorithm and a simplified version of the ALI algorithm—named “accumulative computation”—which was run about ten years ago, now reaching real-time processing times that were simply not achievable at that time for ALI.
Prototype Implementation of Two Efficient Low-Complexity Digital Predistortion Algorithms
Directory of Open Access Journals (Sweden)
Timo I. Laakso
2008-01-01
Full Text Available Predistortion (PD lineariser for microwave power amplifiers (PAs is an important topic of research. With larger and larger bandwidth as it appears today in modern WiMax standards as well as in multichannel base stations for 3GPP standards, the relatively simple nonlinear effect of a PA becomes a complex memory-including function, severely distorting the output signal. In this contribution, two digital PD algorithms are investigated for the linearisation of microwave PAs in mobile communications. The first one is an efficient and low-complexity algorithm based on a memoryless model, called the simplicial canonical piecewise linear (SCPWL function that describes the static nonlinear characteristic of the PA. The second algorithm is more general, approximating the pre-inverse filter of a nonlinear PA iteratively using a Volterra model. The first simpler algorithm is suitable for compensation of amplitude compression and amplitude-to-phase conversion, for example, in mobile units with relatively small bandwidths. The second algorithm can be used to linearise PAs operating with larger bandwidths, thus exhibiting memory effects, for example, in multichannel base stations. A measurement testbed which includes a transmitter-receiver chain with a microwave PA is built for testing and prototyping of the proposed PD algorithms. In the testing phase, the PD algorithms are implemented using MATLAB (floating-point representation and tested in record-and-playback mode. The iterative PD algorithm is then implemented on a Field Programmable Gate Array (FPGA using fixed-point representation. The FPGA implementation allows the pre-inverse filter to be tested in a real-time mode. Measurement results show excellent linearisation capabilities of both the proposed algorithms in terms of adjacent channel power suppression. It is also shown that the fixed-point FPGA implementation of the iterative algorithm performs as well as the floating-point implementation.
Lamberti, A; Vanlanduit, S; De Pauw, B; Berghmans, F
2014-03-24
Fiber Bragg Gratings (FBGs) can be used as sensors for strain, temperature and pressure measurements. For this purpose, the ability to determine the Bragg peak wavelength with adequate wavelength resolution and accuracy is essential. However, conventional peak detection techniques, such as the maximum detection algorithm, can yield inaccurate and imprecise results, especially when the Signal to Noise Ratio (SNR) and the wavelength resolution are poor. Other techniques, such as the cross-correlation demodulation algorithm are more precise and accurate but require a considerable higher computational effort. To overcome these problems, we developed a novel fast phase correlation (FPC) peak detection algorithm, which computes the wavelength shift in the reflected spectrum of a FBG sensor. This paper analyzes the performance of the FPC algorithm for different values of the SNR and wavelength resolution. Using simulations and experiments, we compared the FPC with the maximum detection and cross-correlation algorithms. The FPC method demonstrated a detection precision and accuracy comparable with those of cross-correlation demodulation and considerably higher than those obtained with the maximum detection technique. Additionally, FPC showed to be about 50 times faster than the cross-correlation. It is therefore a promising tool for future implementation in real-time systems or in embedded hardware intended for FBG sensor interrogation.
EV Charging Algorithm Implementation with User Price Preference
Energy Technology Data Exchange (ETDEWEB)
Wang, Bin; Hu, Boyang; Qiu, Charlie; Chu, Peter; Gadh, Rajit
2015-02-17
in this paper, we propose and implement a smart Electric Vehicle (EV) charging algorithm to control the EV charging infrastructures according to users’ price preferences. EVSE (Electric Vehicle Supply Equipment), equipped with bidirectional communication devices and smart meters, can be remotely monitored by the proposed charging algorithm applied to EV control center and mobile app. On the server side, ARIMA model is utilized to fit historical charging load data and perform day-ahead prediction. A pricing strategy with energy bidding policy is proposed and implemented to generate a charging price list to be broadcasted to EV users through mobile app. On the user side, EV drivers can submit their price preferences and daily travel schedules to negotiate with Control Center to consume the expected energy and minimize charging cost simultaneously. The proposed algorithm is tested and validated through the experimental implementations in UCLA parking lots.
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.
FPGA Implementation of Computer Vision Algorithm
Zhou, Zhonghua
2014-01-01
Computer vision algorithms, which play an significant role in vision processing, is widely applied in many aspects such as geology survey, traffic management and medical care, etc.. Most of the situations require the process to be real-timed, in other words, as fast as possible. Field Programmable Gate Arrays (FPGAs) have a advantage of parallelism fabric in programming, comparing to the serial communications of CPUs, which makes FPGA a perfect platform for implementing vision algorithms. The...
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)
Super-Encryption Implementation Using Monoalphabetic Algorithm and XOR Algorithm for Data Security
Rachmawati, Dian; Andri Budiman, Mohammad; Aulia, Indra
2018-03-01
The exchange of data that occurs offline and online is very vulnerable to the threat of data theft. In general, cryptography is a science and art to maintain data secrecy. An encryption is a cryptography algorithm in which data is transformed into cipher text, which is something that is unreadable and meaningless so it cannot be read or understood by other parties. In super-encryption, two or more encryption algorithms are combined to make it more secure. In this work, Monoalphabetic algorithm and XOR algorithm are combined to form a super- encryption. Monoalphabetic algorithm works by changing a particular letter into a new letter based on existing keywords while the XOR algorithm works by using logic operation XOR Since Monoalphabetic algorithm is a classical cryptographic algorithm and XOR algorithm is a modern cryptographic algorithm, this scheme is expected to be both easy-to-implement and more secure. The combination of the two algorithms is capable of securing the data and restoring it back to its original form (plaintext), so the data integrity is still ensured.
ROAD DETECTION BY NEURAL AND GENETIC ALGORITHM IN URBAN ENVIRONMENT
Directory of Open Access Journals (Sweden)
A. Barsi
2012-07-01
Full Text Available In the urban object detection challenge organized by the ISPRS WG III/4 high geometric and radiometric resolution aerial images about Vaihingen/Stuttgart, Germany are distributed. The acquired data set contains optical false color, near infrared images and airborne laserscanning data. The presented research focused exclusively on the optical image, so the elevation information was ignored. The road detection procedure has been built up of two main phases: a segmentation done by neural networks and a compilation made by genetic algorithms. The applied neural networks were support vector machines with radial basis kernel function and self-organizing maps with hexagonal network topology and Euclidean distance function for neighborhood management. The neural techniques have been compared by hyperbox classifier, known from the statistical image classification practice. The compilation of the segmentation is realized by a novel application of the common genetic algorithm and by differential evolution technique. The genes were implemented to detect the road elements by evaluating a special binary fitness function. The results have proven that the evolutional technique can automatically find major road segments.
Automatic detection of ECG electrode misplacement: a tale of two algorithms
International Nuclear Information System (INIS)
Xia, Henian; Garcia, Gabriel A; Zhao, Xiaopeng
2012-01-01
Artifacts in an electrocardiogram (ECG) due to electrode misplacement can lead to wrong diagnoses. Various computer methods have been developed for automatic detection of electrode misplacement. Here we reviewed and compared the performance of two algorithms with the highest accuracies on several databases from PhysioNet. These algorithms were implemented into four models. For clean ECG records with clearly distinguishable waves, the best model produced excellent accuracies (> = 98.4%) for all misplacements except the LA/LL interchange (87.4%). However, the accuracies were significantly lower for records with noise and arrhythmias. Moreover, when the algorithms were tested on a database that was independent from the training database, the accuracies may be poor. For the worst scenario, the best accuracies for different types of misplacements ranged from 36.1% to 78.4%. A large number of ECGs of various qualities and pathological conditions are collected every day. To improve the quality of health care, the results of this paper call for more robust and accurate algorithms for automatic detection of electrode misplacement, which should be developed and tested using a database of extensive ECG records. (paper)
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 ...
Local Community Detection Algorithm Based on Minimal Cluster
Directory of Open Access Journals (Sweden)
Yong Zhou
2016-01-01
Full Text Available In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.
Hatzaki, Maria; Flocas, Elena A.; Simmonds, Ian; Kouroutzoglou, John; Keay, Kevin; Rudeva, Irina
2013-04-01
Migratory cyclones and anticyclones mainly account for the short-term weather variations in extra-tropical regions. By contrast to cyclones that have drawn major scientific attention due to their direct link to active weather and precipitation, climatological studies on anticyclones are limited, even though they also are associated with extreme weather phenomena and play an important role in global and regional climate. This is especially true for the Mediterranean, a region particularly vulnerable to climate change, and the little research which has been done is essentially confined to the manual analysis of synoptic charts. For the construction of a comprehensive climatology of migratory anticyclonic systems in the Mediterranean using an objective methodology, the Melbourne University automatic tracking algorithm is applied, based to the ERA-Interim reanalysis mean sea level pressure database. The algorithm's reliability in accurately capturing the weather patterns and synoptic climatology of the transient activity has been widely proven. This algorithm has been extensively applied for cyclone studies worldwide and it has been also successfully applied for the Mediterranean, though its use for anticyclone tracking is limited to the Southern Hemisphere. In this study the performance of the tracking algorithm under different data resolutions and different choices of parameter settings in the scheme is examined. Our focus is on the appropriate modification of the algorithm in order to efficiently capture the individual characteristics of the anticyclonic tracks in the Mediterranean, a closed basin with complex topography. We show that the number of the detected anticyclonic centers and the resulting tracks largely depend upon the data resolution and the search radius. We also find that different scale anticyclones and secondary centers that lie within larger anticyclone structures can be adequately represented; this is important, since the extensions of major
An efficient algorithm for the detection of exposed and hidden wormhole attack
International Nuclear Information System (INIS)
Khan, Z.A.; Rehman, S.U.; Islam, M.H.
2016-01-01
MANETs (Mobile Ad Hoc Networks) are slowly integrating into our everyday lives, their most prominent uses are visible in the disaster and war struck areas where physical infrastructure is almost impossible or very hard to build. MANETs like other networks are facing the threat of malicious users and their activities. A number of attacks have been identified but the most severe of them is the wormhole attack which has the ability to succeed even in case of encrypted traffic and secure networks. Once wormhole is launched successfully, the severity increases by the fact that attackers can launch other attacks too. This paper presents a comprehensive algorithm for the detection of exposed as well as hidden wormhole attack while keeping the detection rate to maximum and at the same reducing false alarms. The algorithm does not require any extra hardware, time synchronization or any special type of nodes. The architecture consists of the combination of Routing Table, RTT (Round Trip Time) and RSSI (Received Signal Strength Indicator) for comprehensive detection of wormhole attack. The proposed technique is robust, light weight, has low resource requirements and provides real-time detection against the wormhole attack. Simulation results show that the algorithm is able to provide a higher detection rate, packet delivery ratio, negligible false alarms and is also better in terms of Ease of Implementation, Detection Accuracy/ Speed and processing overhead. (author)
International Nuclear Information System (INIS)
Plimpton, Steven J.; Hendrickson, Bruce; Burns, Shawn P.; McLendon, William III; Rauchwerger, Lawrence
2005-01-01
The method of discrete ordinates is commonly used to solve the Boltzmann transport equation. The solution in each ordinate direction is most efficiently computed by sweeping the radiation flux across the computational grid. For unstructured grids this poses many challenges, particularly when implemented on distributed-memory parallel machines where the grid geometry is spread across processors. We present several algorithms relevant to this approach: (a) an asynchronous message-passing algorithm that performs sweeps simultaneously in multiple ordinate directions, (b) a simple geometric heuristic to prioritize the computational tasks that a processor works on, (c) a partitioning algorithm that creates columnar-style decompositions for unstructured grids, and (d) an algorithm for detecting and eliminating cycles that sometimes exist in unstructured grids and can prevent sweeps from successfully completing. Algorithms (a) and (d) are fully parallel; algorithms (b) and (c) can be used in conjunction with (a) to achieve higher parallel efficiencies. We describe our message-passing implementations of these algorithms within a radiation transport package. Performance and scalability results are given for unstructured grids with up to 3 million elements (500 million unknowns) running on thousands of processors of Sandia National Laboratories' Intel Tflops machine and DEC-Alpha CPlant cluster
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.
Implementation Issues of Adaptive Energy Detection in Heterogeneous Wireless Networks
Sobron, Iker; Eizmendi, Iñaki; Martins, Wallace A.; Diniz, Paulo S. R.; Ordiales, Juan Luis; Velez, Manuel
2017-01-01
Spectrum sensing (SS) enables the coexistence of non-coordinated heterogeneous wireless systems operating in the same band. Due to its computational simplicity, energy detection (ED) technique has been widespread employed in SS applications; nonetheless, the conventional ED may be unreliable under environmental impairments, justifying the use of ED-based variants. Assessing ED algorithms from theoretical and simulation viewpoints relies on several assumptions and simplifications which, eventually, lead to conclusions that do not necessarily meet the requirements imposed by real propagation environments. This work addresses those problems by dealing with practical implementation issues of adaptive least mean square (LMS)-based ED algorithms. The paper proposes a new adaptive ED algorithm that uses a variable step-size guaranteeing the LMS convergence in time-varying environments. Several implementation guidelines are provided and, additionally, an empirical assessment and validation with a software defined radio-based hardware is carried out. Experimental results show good performance in terms of probabilities of detection (Pd>0.9) and false alarm (Pf∼0.05) in a range of low signal-to-noise ratios around [-4,1] dB, in both single-node and cooperative modes. The proposed sensing methodology enables a seamless monitoring of the radio electromagnetic spectrum in order to provide band occupancy information for an efficient usage among several wireless communications systems. PMID:28441751
A GPU-paralleled implementation of an enhanced face recognition algorithm
Chen, Hao; Liu, Xiyang; Shao, Shuai; Zan, Jiguo
2013-03-01
Face recognition algorithm based on compressed sensing and sparse representation is hotly argued in these years. The scheme of this algorithm increases recognition rate as well as anti-noise capability. However, the computational cost is expensive and has become a main restricting factor for real world applications. In this paper, we introduce a GPU-accelerated hybrid variant of face recognition algorithm named parallel face recognition algorithm (pFRA). We describe here how to carry out parallel optimization design to take full advantage of many-core structure of a GPU. The pFRA is tested and compared with several other implementations under different data sample size. Finally, Our pFRA, implemented with NVIDIA GPU and Computer Unified Device Architecture (CUDA) programming model, achieves a significant speedup over the traditional CPU implementations.
Bladed wheels damage detection through Non-Harmonic Fourier Analysis improved algorithm
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.
Seizure detection algorithms based on EMG signals
DEFF Research Database (Denmark)
Conradsen, Isa
Background: the currently used non-invasive seizure detection methods are not reliable. Muscle fibers are directly connected to the nerves, whereby electric signals are generated during activity. Therefore, an alarm system on electromyography (EMG) signals is a theoretical possibility. Objective...... on the amplitude of the signal. The other algorithm was based on information of the signal in the frequency domain, and it focused on synchronisation of the electrical activity in a single muscle during the seizure. Results: The amplitude-based algorithm reliably detected seizures in 2 of the patients, while...... the frequency-based algorithm was efficient for detecting the seizures in the third patient. Conclusion: Our results suggest that EMG signals could be used to develop an automatic seizuredetection system. However, different patients might require different types of algorithms /approaches....
Parallel implementation of DNA sequences matching algorithms using PWM on GPU architecture.
Sharma, Rahul; Gupta, Nitin; Narang, Vipin; Mittal, Ankush
2011-01-01
Positional Weight Matrices (PWMs) are widely used in representation and detection of Transcription Factor Of Binding Sites (TFBSs) on DNA. We implement online PWM search algorithm over parallel architecture. A large PWM data can be processed on Graphic Processing Unit (GPU) systems in parallel which can help in matching sequences at a faster rate. Our method employs extensive usage of highly multithreaded architecture and shared memory of multi-cored GPU. An efficient use of shared memory is required to optimise parallel reduction in CUDA. Our optimised method has a speedup of 230-280x over linear implementation on GPU named GeForce GTX 280.
Analysis of the Chirplet Transform-Based Algorithm for Radar Detection of Accelerated Targets
Galushko, V. G.; Vavriv, D. M.
2017-06-01
Purpose: Efficiency analysis of an optimal algorithm of chirp signal processing based on the chirplet transform as applied to detection of radar targets in uniformly accelerated motion. Design/methodology/approach: Standard methods of the optimal filtration theory are used to investigate the ambiguity function of chirp signals. Findings: An analytical expression has been derived for the ambiguity function of chirp signals that is analyzed with respect to detection of radar targets moving at a constant acceleration. Sidelobe level and characteristic width of the ambiguity function with respect to the coordinates frequency and rate of its change have been estimated. The gain in the signal-to-noise ratio has been assessed that is provided by the algorithm under consideration as compared with application of the standard Fourier transform to detection of chirp signals against a “white” noise background. It is shown that already with a comparatively small (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.
AdaBoost-based algorithm for network intrusion detection.
Hu, Weiming; Hu, Wei; Maybank, Steve
2008-04-01
Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. It is an indispensable part of the information security system. Due to the variety of network behaviors and the rapid development of attack fashions, it is necessary to develop fast machine-learning-based intrusion detection algorithms with high detection rates and low false-alarm rates. In this correspondence, we propose an intrusion detection algorithm based on the AdaBoost algorithm. In the algorithm, decision stumps are used as weak classifiers. The decision rules are provided for both categorical and continuous features. By combining the weak classifiers for continuous features and the weak classifiers for categorical features into a strong classifier, the relations between these two different types of features are handled naturally, without any forced conversions between continuous and categorical features. Adaptable initial weights and a simple strategy for avoiding overfitting are adopted to improve the performance of the algorithm. Experimental results show that our algorithm has low computational complexity and error rates, as compared with algorithms of higher computational complexity, as tested on the benchmark sample data.
Directory of Open Access Journals (Sweden)
Azman Hamzah
2013-09-01
Full Text Available Computer vision systems have found wide application in foods processing industry to perform quality evaluation. The systems enable to replace human inspectors for the evaluation of a variety of quality attributes. This paper describes the implementation of the Fast Fourier Transform and Kalman filtering algorithms to detect the glutinous rice flour slurry (GRFS gelatinization in an enzymatic „dodol. processing. The onset of the GRFS gelatinization is critical in determining the quality of an enzymatic „dodol.. Combinations of these two algorithms were able to detect the gelatinization of the GRFS. The result shows that the gelatinization of the GRFS was at the time range of 11.75 minutes to 14.75 minutes for 24 batches of processing. This paper will highlight the capability of computer vision using our proposed algorithms in monitoring and controlling of an enzymatic „dodol. processing via image processing technology.
Directory of Open Access Journals (Sweden)
Azman Hamzah
2007-11-01
Full Text Available Computer vision systems have found wide application in foods processing industry to perform the quality evaluation. The systems enable to replace human inspectors for the evaluation of a variety of quality attributes. This paper describes the implementation of the Fast Fourier Transform and Kalman filtering algorithms to detect the glutinous rice flour slurry (GRFS gelatinization in an enzymatic ‘dodol’ processing. The onset of the GRFS gelatinization is critical in determining the quality of an enzymatic ‘dodol’. Combinations of these two algorithms were able to detect the gelatinization of the GRFS. The result shows that the gelatinization of the GRFS was at the time range of 11.75 minutes to 15.33 minutes for 20 batches of processing. This paper will highlight the capability of computer vision using our proposed algorithms in monitoring and controlling of an enzymatic ‘dodol’ processing via image processing technology.
Implementation of fuzzy logic control algorithm in embedded ...
African Journals Online (AJOL)
Fuzzy logic control algorithm solves problems that are difficult to address with traditional control techniques. This paper describes an implementation of fuzzy logic control algorithm using inexpensive hardware as well as how to use fuzzy logic to tackle a specific control problem without any special software tools. As a case ...
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 Plagiarism Detection Algorithm based on Extended Winnowing
Directory of Open Access Journals (Sweden)
Duan Xuliang
2017-01-01
Full Text Available Plagiarism is a common problem faced by academia and education. Mature commercial plagiarism detection system has the advantages of comprehensive and high accuracy, but the expensive detection costs make it unsuitable for real-time, lightweight application environment such as the student assignments plagiarism detection. This paper introduces the method of extending classic Winnowing plagiarism detection algorithm, expands the algorithm in functionality. The extended algorithm can retain the text location and length information in original document while extracting the fingerprints of a document, so that the locating and marking for plagiarism text fragment are much easier to achieve. The experimental results and several years of running practice show that the expansion of the algorithm has little effect on its performance, normal hardware configuration of PC will be able to meet small and medium-sized applications requirements. Based on the characteristics of lightweight, high efficiency, reliability and flexibility of Winnowing, the extended algorithm further enhances the adaptability and extends the application areas.
A Weighted Spatial-Spectral Kernel RX Algorithm and Efficient Implementation on GPUs
Directory of Open Access Journals (Sweden)
Chunhui Zhao
2017-02-01
Full Text Available The kernel RX (KRX detector proposed by Kwon and Nasrabadi exploits a kernel function to obtain a better detection performance. However, it still has two limits that can be improved. On the one hand, reasonable integration of spatial-spectral information can be used to further improve its detection accuracy. On the other hand, parallel computing can be used to reduce the processing time in available KRX detectors. Accordingly, this paper presents a novel weighted spatial-spectral kernel RX (WSSKRX detector and its parallel implementation on graphics processing units (GPUs. The WSSKRX utilizes the spatial neighborhood resources to reconstruct the testing pixels by introducing a spectral factor and a spatial window, thereby effectively reducing the interference of background noise. Then, the kernel function is redesigned as a mapping trick in a KRX detector to implement the anomaly detection. In addition, a powerful architecture based on the GPU technique is designed to accelerate WSSKRX. To substantiate the performance of the proposed algorithm, both synthetic and real data are conducted for experiments.
Study of hardware implementations of fast tracking algorithms
International Nuclear Information System (INIS)
Song, Z.; Huang, G.; Wang, D.; Lentdecker, G. De; Dong, J.; Léonard, A.; Robert, F.; Yang, Y.
2017-01-01
Real-time track reconstruction at high event rates is a major challenge for future experiments in high energy physics. To perform pattern-recognition and track fitting, artificial retina or Hough transformation methods have been introduced in the field which have to be implemented in FPGA firmware. In this note we report on a case study of a possible FPGA hardware implementation approach of the retina algorithm based on a Floating-Point core. Detailed measurements with this algorithm are investigated. Retina performance and capabilities of the FPGA are discussed along with perspectives for further optimization and applications.
A real time QRS detection using delay-coordinate mapping for the microcontroller implementation.
Lee, Jeong-Whan; Kim, Kyeong-Seop; Lee, Bongsoo; Lee, Byungchae; Lee, Myoung-Ho
2002-01-01
In this article, we propose a new algorithm using the characteristics of reconstructed phase portraits by delay-coordinate mapping utilizing lag rotundity for a real-time detection of QRS complexes in ECG signals. In reconstructing phase portrait the mapping parameters, time delay, and mapping dimension play important roles in shaping of portraits drawn in a new dimensional space. Experimentally, the optimal mapping time delay for detection of QRS complexes turned out to be 20 ms. To explore the meaning of this time delay and the proper mapping dimension, we applied a fill factor, mutual information, and autocorrelation function algorithm that were generally used to analyze the chaotic characteristics of sampled signals. From these results, we could find the fact that the performance of our proposed algorithms relied mainly on the geometrical property such as an area of the reconstructed phase portrait. For the real application, we applied our algorithm for designing a small cardiac event recorder. This system was to record patients' ECG and R-R intervals for 1 h to investigate HRV characteristics of the patients who had vasovagal syncope symptom and for the evaluation, we implemented our algorithm in C language and applied to MIT/BIH arrhythmia database of 48 subjects. Our proposed algorithm achieved a 99.58% detection rate of QRS complexes.
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.
Algorithms for detection of objects in image sequences captured from an airborne imaging system
Kasturi, Rangachar; Camps, Octavia; Tang, Yuan-Liang; Devadiga, Sadashiva; Gandhi, Tarak
1995-01-01
This research was initiated as a part of the effort at the NASA Ames Research Center to design a computer vision based system that can enhance the safety of navigation by aiding the pilots in detecting various obstacles on the runway during critical section of the flight such as a landing maneuver. The primary goal is the development of algorithms for detection of moving objects from a sequence of images obtained from an on-board video camera. Image regions corresponding to the independently moving objects are segmented from the background by applying constraint filtering on the optical flow computed from the initial few frames of the sequence. These detected regions are tracked over subsequent frames using a model based tracking algorithm. Position and velocity of the moving objects in the world coordinate is estimated using an extended Kalman filter. The algorithms are tested using the NASA line image sequence with six static trucks and a simulated moving truck and experimental results are described. Various limitations of the currently implemented version of the above algorithm are identified and possible solutions to build a practical working system are investigated.
Acoustic change detection algorithm using an FM radio
Goldman, Geoffrey H.; Wolfe, Owen
2012-06-01
The U.S. Army is interested in developing low-cost, low-power, non-line-of-sight sensors for monitoring human activity. One modality that is often overlooked is active acoustics using sources of opportunity such as speech or music. Active acoustics can be used to detect human activity by generating acoustic images of an area at different times, then testing for changes among the imagery. A change detection algorithm was developed to detect physical changes in a building, such as a door changing positions or a large box being moved using acoustics sources of opportunity. The algorithm is based on cross correlating the acoustic signal measured from two microphones. The performance of the algorithm was shown using data generated with a hand-held FM radio as a sound source and two microphones. The algorithm could detect a door being opened in a hallway.
A Formally Verified Conflict Detection Algorithm for Polynomial Trajectories
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.
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...
Implementation of a partitioned algorithm for simulation of large CSI problems
Alvin, Kenneth F.; Park, K. C.
1991-01-01
The implementation of a partitioned numerical algorithm for determining the dynamic response of coupled structure/controller/estimator finite-dimensional systems is reviewed. The partitioned approach leads to a set of coupled first and second-order linear differential equations which are numerically integrated with extrapolation and implicit step methods. The present software implementation, ACSIS, utilizes parallel processing techniques at various levels to optimize performance on a shared-memory concurrent/vector processing system. A general procedure for the design of controller and filter gains is also implemented, which utilizes the vibration characteristics of the structure to be solved. Also presented are: example problems; a user's guide to the software; the procedures and algorithm scripts; a stability analysis for the algorithm; and the source code for the parallel implementation.
Rapid Change Detection Algorithm for Disaster Management
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.
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.
Algorithmic detectability threshold of the stochastic block model
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.
Alphus D. Wilson
2012-01-01
Novel mobile electronic-nose (e-nose) devices and algorithms capable of real-time detection of industrial and municipal pollutants, released from point-sources, recently have been developed by scientists worldwide that are useful for monitoring specific environmental-pollutant levels for enforcement and implementation of effective pollution-abatement programs. E-nose...
International Nuclear Information System (INIS)
Marghany, Maged
2014-01-01
Highlights: • An oil platform located 70 km from the coast of Louisiana sank on Thursday. • Oil spill has backscatter values of −25 dB in RADARSAT-2 SAR. • Oil spill is portrayed in SCNB mode by shallower incidence angle. • Ideal detection of oil spills in SAR images requires moderate wind speeds. • Genetic algorithm is excellent tool for automatic detection of oil spill in RADARSAT-2 SAR data. - Abstract: In this work, a genetic algorithm is applied for the automatic detection of oil spills. The procedure is implemented using sequences from RADARSAT-2 SAR ScanSAR Narrow single-beam data acquired in the Gulf of Mexico. The study demonstrates that the implementation of crossover allows for the generation of an accurate oil spill pattern. This conclusion is confirmed by the receiver-operating characteristic (ROC) curve. The ROC curve indicates that the existence of oil slick footprints can be identified using the area between the ROC curve and the no-discrimination line of 90%, which is greater than that of other surrounding environmental features. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills, and the ScanSAR Narrow single-beam mode serves as an excellent sensor for oil spill detection and survey
Directory of Open Access Journals (Sweden)
Fabián Santos
2017-01-01
Full Text Available The Andean Amazon is an endangered biodiversity hot spot but its forest dynamics are less studied than those of the Amazon lowland and forests from middle or high latitudes. This is because its landscape variability, complex topography and cloudy conditions constitute a challenging environment for any remote-sensing assessment. Breakpoint detection with Landsat time-series data is an established robust approach for monitoring forest dynamics around the globe but has not been properly evaluated for implementation in the Andean Amazon. We analyzed breakpoint detection-generated forest dynamics in order to determine its limitations when applied to three different study areas located along an altitude gradient in the Andean Amazon in Ecuador. Using all available Landsat imagery for the period 1997–2016, we evaluated different pre-processing approaches, noise reduction techniques, and breakpoint detection algorithms. These procedures were integrated into a complex function called the processing chain generator. Calibration was not straightforward since it required us to define values for 24 parameters. To solve this problem, we implemented a novel approach using genetic algorithms. We calibrated the processing chain generator by applying a stratified training sampling and a reference dataset based on high resolution imagery. After the best calibration solution was found and the processing chain generator executed, we assessed accuracy and found that data gaps, inaccurate co-registration, radiometric variability in sensor calibration, unmasked cloud, and shadows can drastically affect the results, compromising the application of breakpoint detection in mountainous areas of the Andean Amazon. Moreover, since breakpoint detection analysis of landscape variability in the Andean Amazon requires a unique calibration of algorithms, the time required to optimize analysis could complicate its proper implementation and undermine its application for large
IMPLEMENTATION OF A REAL-TIME STACKING ALGORITHM IN A PHOTOGRAMMETRIC DIGITAL CAMERA FOR UAVS
Directory of Open Access Journals (Sweden)
A. Audi
2017-08-01
Full Text Available In the recent years, unmanned aerial vehicles (UAVs have become an interesting tool in aerial photography and photogrammetry activities. In this context, some applications (like cloudy sky surveys, narrow-spectral imagery and night-vision imagery need a longexposure time where one of the main problems is the motion blur caused by the erratic camera movements during image acquisition. This paper describes an automatic real-time stacking algorithm which produces a high photogrammetric quality final composite image with an equivalent long-exposure time using several images acquired with short-exposure times. Our method is inspired by feature-based image registration technique. The algorithm is implemented on the light-weight IGN camera, which has an IMU sensor and a SoC/FPGA. To obtain the correct parameters for the resampling of images, the presented method accurately estimates the geometrical relation between the first and the Nth image, taking into account the internal parameters and the distortion of the camera. Features are detected in the first image by the FAST detector, than homologous points on other images are obtained by template matching aided by the IMU sensors. The SoC/FPGA in the camera is used to speed up time-consuming parts of the algorithm such as features detection and images resampling in order to achieve a real-time performance as we want to write only the resulting final image to save bandwidth on the storage device. The paper includes a detailed description of the implemented algorithm, resource usage summary, resulting processing time, resulting images, as well as block diagrams of the described architecture. The resulting stacked image obtained on real surveys doesn’t seem visually impaired. Timing results demonstrate that our algorithm can be used in real-time since its processing time is less than the writing time of an image in the storage device. An interesting by-product of this algorithm is the 3D rotation
STREAMFINDER I: A New Algorithm for detecting Stellar Streams
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.
Implementations of PI-line based FBP and BPF algorithms on GPGPU
Energy Technology Data Exchange (ETDEWEB)
Shen, Le [Tsinghua Univ., Beijing (China). Dept. of Engineering Physics; Xing, Yuxiang [Tsinghua Univ., Beijing (China). Dept. of Engineering Physics; Ministry of Education, Beijing (China). Key Lab. of Particle and Radiation Imaging
2011-07-01
Exact reconstruction is under the spotlight in cone beam CT. Katsevich put forward the first exact inversion formula for helical cone beam CT, which belongs to FBP type. Also, Pan Xiaochuan's group proposed another PI-line based exact reconstruction algorithm of BPF type. These two exact reconstruction algorithms and their derivative forms have been widely studied. In this paper, we present a different way of selecting PI-line segments appropriate for both Katsevich's FBP and Pan Xiaochuan's BPF algorithms. As 3D reconstruction contributes to massive computations and takes long time, people have made efforts to speed up the algorithms with the help of multi-core CPUs and GPGPU (General Purpose Graphics Processing Unit). In this paper, we also presents implementations for these two algorithms on GPGPU using an innovative way of selecting PI-line segments. Acceleration techniques and implementations are addressed in detail. The methods are tested on the Shepp-Logan phantom. Compared with our CPU's implementations, the accelerated algorithms on GPGPU are tens to hundreds times faster. (orig.)
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.
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 new algorithmic approach for fingers detection and identification
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.
Scharm, Martin; Wolkenhauer, Olaf; Waltemath, Dagmar
2016-02-15
Repositories support the reuse of models and ensure transparency about results in publications linked to those models. With thousands of models available in repositories, such as the BioModels database or the Physiome Model Repository, a framework to track the differences between models and their versions is essential to compare and combine models. Difference detection not only allows users to study the history of models but also helps in the detection of errors and inconsistencies. Existing repositories lack algorithms to track a model's development over time. Focusing on SBML and CellML, we present an algorithm to accurately detect and describe differences between coexisting versions of a model with respect to (i) the models' encoding, (ii) the structure of biological networks and (iii) mathematical expressions. This algorithm is implemented in a comprehensive and open source library called BiVeS. BiVeS helps to identify and characterize changes in computational models and thereby contributes to the documentation of a model's history. Our work facilitates the reuse and extension of existing models and supports collaborative modelling. Finally, it contributes to better reproducibility of modelling results and to the challenge of model provenance. The workflow described in this article is implemented in BiVeS. BiVeS is freely available as source code and binary from sems.uni-rostock.de. The web interface BudHat demonstrates the capabilities of BiVeS at budhat.sems.uni-rostock.de. © The Author 2015. Published by Oxford University Press.
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.
Real time algorithms for sharp wave ripple detection.
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.
Improving Polyp Detection Algorithms for CT Colonography: Pareto Front Approach.
Huang, Adam; Li, Jiang; Summers, Ronald M; Petrick, Nicholas; Hara, Amy K
2010-03-21
We investigated a Pareto front approach to improving polyp detection algorithms for CT colonography (CTC). A dataset of 56 CTC colon surfaces with 87 proven positive detections of 53 polyps sized 4 to 60 mm was used to evaluate the performance of a one-step and a two-step curvature-based region growing algorithm. The algorithmic performance was statistically evaluated and compared based on the Pareto optimal solutions from 20 experiments by evolutionary algorithms. The false positive rate was lower (pPareto optimization process can effectively help in fine-tuning and redesigning polyp detection algorithms.
Hard Ware Implementation of Diamond Search Algorithm for Motion Estimation and Object Tracking
International Nuclear Information System (INIS)
Hashimaa, S.M.; Mahmoud, I.I.; Elazm, A.A.
2009-01-01
Object tracking is very important task in computer vision. Fast search algorithms emerged as important search technique to achieve real time tracking results. To enhance the performance of these algorithms, we advocate the hardware implementation of such algorithms. Diamond search block matching motion estimation has been proposed recently to reduce the complexity of motion estimation. In this paper we selected the diamond search algorithm (DS) for implementation using FPGA. This is due to its fundamental role in all fast search patterns. The proposed architecture is simulated and synthesized using Xilinix and modelsim soft wares. The results agree with the algorithm implementation in Matlab environment.
Ripple FPN reduced algorithm based on temporal high-pass filter and hardware implementation
Li, Yiyang; Li, Shuo; Zhang, Zhipeng; Jin, Weiqi; Wu, Lei; Jin, Minglei
2016-11-01
Cooled infrared detector arrays always suffer from undesired Ripple Fixed-Pattern Noise (FPN) when observe the scene of sky. The Ripple Fixed-Pattern Noise seriously affect the imaging quality of thermal imager, especially for small target detection and tracking. It is hard to eliminate the FPN by the Calibration based techniques and the current scene-based nonuniformity algorithms. In this paper, we present a modified space low-pass and temporal high-pass nonuniformity correction algorithm using adaptive time domain threshold (THP&GM). The threshold is designed to significantly reduce ghosting artifacts. We test the algorithm on real infrared in comparison to several previously published methods. This algorithm not only can effectively correct common FPN such as Stripe, but also has obviously advantage compared with the current methods in terms of detail protection and convergence speed, especially for Ripple FPN correction. Furthermore, we display our architecture with a prototype built on a Xilinx Virtex-5 XC5VLX50T field-programmable gate array (FPGA). The hardware implementation of the algorithm based on FPGA has two advantages: (1) low resources consumption, and (2) small hardware delay (less than 20 lines). The hardware has been successfully applied in actual system.
An Algorithm for Pedestrian Detection in Multispectral Image Sequences
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.
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.
Parallel GPU implementation of iterative PCA algorithms.
Andrecut, M
2009-11-01
Principal component analysis (PCA) is a key statistical technique for multivariate data analysis. For large data sets, the common approach to PCA computation is based on the standard NIPALS-PCA algorithm, which unfortunately suffers from loss of orthogonality, and therefore its applicability is usually limited to the estimation of the first few components. Here we present an algorithm based on Gram-Schmidt orthogonalization (called GS-PCA), which eliminates this shortcoming of NIPALS-PCA. Also, we discuss the GPU (Graphics Processing Unit) parallel implementation of both NIPALS-PCA and GS-PCA algorithms. The numerical results show that the GPU parallel optimized versions, based on CUBLAS (NVIDIA), are substantially faster (up to 12 times) than the CPU optimized versions based on CBLAS (GNU Scientific Library).
A pipelined FPGA implementation of an encryption algorithm based on genetic algorithm
Thirer, Nonel
2013-05-01
With the evolution of digital data storage and exchange, it is essential to protect the confidential information from every unauthorized access. High performance encryption algorithms were developed and implemented by software and hardware. Also many methods to attack the cipher text were developed. In the last years, the genetic algorithm has gained much interest in cryptanalysis of cipher texts and also in encryption ciphers. This paper analyses the possibility to use the genetic algorithm as a multiple key sequence generator for an AES (Advanced Encryption Standard) cryptographic system, and also to use a three stages pipeline (with four main blocks: Input data, AES Core, Key generator, Output data) to provide a fast encryption and storage/transmission of a large amount of data.
Implementation of Period-Finding Algorithm by Means of Simulating Quantum Fourier Transform
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Zohreh Moghareh Abed
2010-01-01
Full Text Available In this paper, we introduce quantum fourier transform as a key ingredient for many useful algorithms. These algorithms make a solution for problems which is considered to be intractable problems on a classical computer. Quantum Fourier transform is propounded as a key for quantum phase estimation algorithm. In this paper our aim is the implementation of period-finding algorithm.Quantum computer solves this problem, exponentially faster than classical one. Quantum phase estimation algorithm is the key for the period-finding problem .Therefore, by means of simulating quantum Fourier transform, we are able to implement the period-finding algorithm. In this paper, the simulation of quantum Fourier transform is carried out by Matlab software.
A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms.
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.
On a new implementation of the Lanczos algorithm
International Nuclear Information System (INIS)
Caurier, E.; Zuker, A.P.; Poves, A.
1991-01-01
The new implementation proposed is based on a block labelling scheme described in detail. Time reversal, f-projection, sum rule pivots and strength functions are discussed by the aid of the new implementation of the Lanczos algorithm. Energetics and magnetic dipole behaviour of 48 Ti are studied as examples illustrating the applications of the method. (G.P.) 9 refs.; 4 figs.; 1 tab
Directory of Open Access Journals (Sweden)
Axel Newe
2016-03-01
Full Text Available According to the World Health Organization, one of the criteria for the standardized assessment of case causality in adverse drug reactions is the temporal relationship between the intake of a drug and the occurrence of a reaction or a laboratory test abnormality. This article presents and describes an algorithm for the detection of a reasonable temporal correlation between the administration of a drug and the alteration of a laboratory value course. The algorithm is designed to process normalized lab values and is therefore universally applicable. It has a sensitivity of 0.932 for the detection of lab value courses that show changes in temporal correlation with the administration of a drug and it has a specificity of 0.967 for the detection of lab value courses that show no changes. Therefore, the algorithm is appropriate to screen the data of electronic health records and to support human experts in revealing adverse drug reactions. A reference implementation in Python programming language is available.
Implementation of software-based sensor linearization algorithms on low-cost microcontrollers.
Erdem, Hamit
2010-10-01
Nonlinear sensors and microcontrollers are used in many embedded system designs. As the input-output characteristic of most sensors is nonlinear in nature, obtaining data from a nonlinear sensor by using an integer microcontroller has always been a design challenge. This paper discusses the implementation of six software-based sensor linearization algorithms for low-cost microcontrollers. The comparative study of the linearization algorithms is performed by using a nonlinear optical distance-measuring sensor. The performance of the algorithms is examined with respect to memory space usage, linearization accuracy and algorithm execution time. The implementation and comparison results can be used for selection of a linearization algorithm based on the sensor transfer function, expected linearization accuracy and microcontroller capacity. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.
Rommens, Nicole; Geertsema, Evelien; Jansen Holleboom, Lisanne; Cox, Fieke; Visser, Gerhard
2018-05-11
User safety and the quality of diagnostics on the epilepsy monitoring unit (EMU) depend on reaction to seizures. Online seizure detection might improve this. While good sensitivity and specificity is reported, the added value above staff response is unclear. We ascertained the added value of two electroencephalograph (EEG) seizure detection algorithms in terms of additional detected seizures or faster detection time. EEG-video seizure recordings of people admitted to an EMU over one year were included, with a maximum of two seizures per subject. All recordings were retrospectively analyzed using Encevis EpiScan and BESA Epilepsy. Detection sensitivity and latency of the algorithms were compared to staff responses. False positive rates were estimated on 30 uninterrupted recordings (roughly 24 h per subject) of consecutive subjects admitted to the EMU. EEG-video recordings used included 188 seizures. The response rate of staff was 67%, of Encevis 67%, and of BESA Epilepsy 65%. Of the 62 seizures missed by staff, 66% were recognized by Encevis and 39% by BESA Epilepsy. The median latency was 31 s (staff), 10 s (Encevis), and 14 s (BESA Epilepsy). After correcting for walking time from the observation room to the subject, both algorithms detected faster than staff in 65% of detected seizures. The full recordings included 617 h of EEG. Encevis had a median false positive rate of 4.9 per 24 h and BESA Epilepsy of 2.1 per 24 h. EEG-video seizure detection algorithms may improve reaction to seizures by improving the total number of seizures detected and the speed of detection. The false positive rate is feasible for use in a clinical situation. Implementation of these algorithms might result in faster diagnostic testing and better observation during seizures. Copyright © 2018. Published by Elsevier Inc.
Improved implementation algorithms of the two-dimensional nonseparable linear canonical transform.
Ding, Jian-Jiun; Pei, Soo-Chang; Liu, Chun-Lin
2012-08-01
The two-dimensional nonseparable linear canonical transform (2D NSLCT), which is a generalization of the fractional Fourier transform and the linear canonical transform, is useful for analyzing optical systems. However, since the 2D NSLCT has 16 parameters and is very complicated, it is a great challenge to implement it in an efficient way. In this paper, we improved the previous work and propose an efficient way to implement the 2D NSLCT. The proposed algorithm can minimize the numerical error arising from interpolation operations and requires fewer chirp multiplications. The simulation results show that, compared with the existing algorithm, the proposed algorithms can implement the 2D NSLCT more accurately and the required computation time is also less.
Texture orientation-based algorithm for detecting infrared maritime targets.
Wang, Bin; Dong, Lili; Zhao, Ming; Wu, Houde; Xu, Wenhai
2015-05-20
Infrared maritime target detection is a key technology for maritime target searching systems. However, in infrared maritime images (IMIs) taken under complicated sea conditions, background clutters, such as ocean waves, clouds or sea fog, usually have high intensity that can easily overwhelm the brightness of real targets, which is difficult for traditional target detection algorithms to deal with. To mitigate this problem, this paper proposes a novel target detection algorithm based on texture orientation. This algorithm first extracts suspected targets by analyzing the intersubband correlation between horizontal and vertical wavelet subbands of the original IMI on the first scale. Then the self-adaptive wavelet threshold denoising and local singularity analysis of the original IMI is combined to remove false alarms further. Experiments show that compared with traditional algorithms, this algorithm can suppress background clutter much better and realize better single-frame detection for infrared maritime targets. Besides, in order to guarantee accurate target extraction further, the pipeline-filtering algorithm is adopted to eliminate residual false alarms. The high practical value and applicability of this proposed strategy is backed strongly by experimental data acquired under different environmental conditions.
The implement of Talmud property allocation algorithm based on graphic point-segment way
Cen, Haifeng
2017-04-01
Under the guidance of the Talmud allocation scheme's theory, the paper analyzes the algorithm implemented process via the perspective of graphic point-segment way, and designs the point-segment way's Talmud property allocation algorithm. Then it uses Java language to implement the core of allocation algorithm, by using Android programming to build a visual interface.
Design and Implementation of Numerical Linear Algebra Algorithms on Fixed Point DSPs
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Gene Frantz
2007-01-01
Full Text Available Numerical linear algebra algorithms use the inherent elegance of matrix formulations and are usually implemented using C/C++ floating point representation. The system implementation is faced with practical constraints because these algorithms usually need to run in real time on fixed point digital signal processors (DSPs to reduce total hardware costs. Converting the simulation model to fixed point arithmetic and then porting it to a target DSP device is a difficult and time-consuming process. In this paper, we analyze the conversion process. We transformed selected linear algebra algorithms from floating point to fixed point arithmetic, and compared real-time requirements and performance between the fixed point DSP and floating point DSP algorithm implementations. We also introduce an advanced code optimization and an implementation by DSP-specific, fixed point C code generation. By using the techniques described in the paper, speed can be increased by a factor of up to 10 compared to floating point emulation on fixed point hardware.
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.
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
Sun, Lihui; Wang, Yongzhong; He, Yongqiang
2007-01-01
The detection for motive small target in infrared image sequence has become a hot topic nowadays. Background suppress algorithm based on minim gradient median filter and temporal recursion target detection algorithm are introduced. On the basis of contents previously mentioned, a four stages pipeline structure infrared small target detection process system, which aims at characters of algorithm complexity, large amounts of data to process, high frame frequency and exigent real-time character in this kind of application, is designed and implemented. The logical structure of the system was introduced and the function and signals flows are programmed. The system is composed of two FPGA chips and two DSP chips of TI. According to the function of each part, the system is divided into image preprocess stage, target detection stage, track relation stage and image output stage. The experiment of running algorithms on the system presented in this paper proved that the system could meet acquisition and process of 50Hz 240x320 digital image and the system could real time detect small target with a signal-noise ratio more than 3 reliably. The system achieves the characters of large amount of memory, high real-time processing, excellent extension and favorable interactive interface.
Detection algorithm of infrared small target based on improved SUSAN operator
Liu, Xingmiao; Wang, Shicheng; Zhao, Jing
2010-10-01
The methods of detecting small moving targets in infrared image sequences that contain moving nuisance objects and background noise is analyzed in this paper. A novel infrared small target detection algorithm based on improved SUSAN operator is put forward. The algorithm selects double templates for the infrared small target detection: one size is greater than the small target point size and another size is equal to the small target point size. First, the algorithm uses the big template to calculate the USAN of each pixel in the image and detect the small target, the edge of the image and isolated noise pixels; Then the algorithm uses the another template to calculate the USAN of pixels detected in the first step and improves the principles of SUSAN algorithm based on the characteristics of the small target so that the algorithm can only detect small targets and don't sensitive to the edge pixels of the image and isolated noise pixels. So the interference of the edge of the image and isolate noise points are removed and the candidate target points can be identified; At last, the target is detected by utilizing the continuity and consistency of target movement. The experimental results indicate that the improved SUSAN detection algorithm can quickly and effectively detect the infrared small targets.
A simulation study comparing aberration detection algorithms for syndromic surveillance
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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.
Autonomous intelligent vehicles theory, algorithms, and implementation
Cheng, Hong
2011-01-01
Here is the latest on intelligent vehicles, covering object and obstacle detection and recognition and vehicle motion control. Includes a navigation approach using global views; introduces algorithms for lateral and longitudinal motion control and more.
Aguiar, Derek; Halldórsson, Bjarni V.; Morrow, Eric M.; Istrail, Sorin
2012-01-01
Motivation: The understanding of the genetic determinants of complex disease is undergoing a paradigm shift. Genetic heterogeneity of rare mutations with deleterious effects is more commonly being viewed as a major component of disease. Autism is an excellent example where research is active in identifying matches between the phenotypic and genomic heterogeneities. A considerable portion of autism appears to be correlated with copy number variation, which is not directly probed by single nucleotide polymorphism (SNP) array or sequencing technologies. Identifying the genetic heterogeneity of small deletions remains a major unresolved computational problem partly due to the inability of algorithms to detect them. Results: In this article, we present an algorithmic framework, which we term DELISHUS, that implements three exact algorithms for inferring regions of hemizygosity containing genomic deletions of all sizes and frequencies in SNP genotype data. We implement an efficient backtracking algorithm—that processes a 1 billion entry genome-wide association study SNP matrix in a few minutes—to compute all inherited deletions in a dataset. We further extend our model to give an efficient algorithm for detecting de novo deletions. Finally, given a set of called deletions, we also give a polynomial time algorithm for computing the critical regions of recurrent deletions. DELISHUS achieves significantly lower false-positive rates and higher power than previously published algorithms partly because it considers all individuals in the sample simultaneously. DELISHUS may be applied to SNP array or sequencing data to identify the deletion spectrum for family-based association studies. Availability: DELISHUS is available at http://www.brown.edu/Research/Istrail_Lab/. Contact: Eric_Morrow@brown.edu and Sorin_Istrail@brown.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22689755
Implementing Deutsch-Jozsa algorithm using light shifts and atomic ensembles
International Nuclear Information System (INIS)
Dasgupta, Shubhrangshu; Biswas, Asoka; Agarwal, G.S.
2005-01-01
We present an optical scheme to implement the Deutsch-Jozsa algorithm using ac Stark shifts. The scheme uses an atomic ensemble consisting of four-level atoms interacting dispersively with a field. This leads to a Hamiltonian in the atom-field basis which is quite suitable for quantum computation. We show how one can implement the algorithm by performing proper one- and two-qubit operations. We emphasize that in our model the decoherence is expected to be minimal due to our usage of atomic ground states and freely propagating photon
Algorithm for detecting violations of traffic rules based on computer vision approaches
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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.
Community detection algorithm evaluation with ground-truth data
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.
Research and Implementation of the Practical Texture Synthesis Algorithms
Institute of Scientific and Technical Information of China (English)
孙家广; 周毅
1991-01-01
How to generate pictures real and esthetic objects is an important subject of computer graphics.The techniques of mapping textures onto the surfaces of an object in the 3D space are efficient approaches for the purpose.We developed and implemented algorithms for generating objects with appearances stone,wood grain,ice lattice,brick,doors and windows on Apollo workstations.All the algorithms have been incorporated into the 3D grometry modelling system (GEMS) developed by the CAD Center of Tsinghua University.This paper emphasizes the wood grain and the ice lattice algorithms.
Low Rank Approximation Algorithms, Implementation, Applications
Markovsky, Ivan
2012-01-01
Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequently in many different fields. Low Rank Approximation: Algorithms, Implementation, Applications is a comprehensive exposition of the theory, algorithms, and applications of structured low-rank approximation. Local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. A major part of the text is devoted to application of the theory. Applications described include: system and control theory: approximate realization, model reduction, output error, and errors-in-variables identification; signal processing: harmonic retrieval, sum-of-damped exponentials, finite impulse response modeling, and array processing; machine learning: multidimensional scaling and recommender system; computer vision: algebraic curve fitting and fundamental matrix estimation; bioinformatics for microarray data analysis; chemometrics for multivariate calibration; ...
A simple fall detection algorithm for Powered Two Wheelers
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...
Directory of Open Access Journals (Sweden)
E. Biffi
2010-01-01
Full Text Available Neurons cultured in vitro on MicroElectrode Array (MEA devices connect to each other, forming a network. To study electrophysiological activity and long term plasticity effects, long period recording and spike sorter methods are needed. Therefore, on-line and real time analysis, optimization of memory use and data transmission rate improvement become necessary. We developed an algorithm for amplitude-threshold spikes detection, whose performances were verified with (a statistical analysis on both simulated and real signal and (b Big O Notation. Moreover, we developed a PCA-hierarchical classifier, evaluated on simulated and real signal. Finally we proposed a spike detection hardware design on FPGA, whose feasibility was verified in terms of CLBs number, memory occupation and temporal requirements; once realized, it will be able to execute on-line detection and real time waveform analysis, reducing data storage problems.
An implementation of Kovacic's algorithm for solving ordinary differential equations in FORMAC
International Nuclear Information System (INIS)
Zharkov, A.Yu.
1987-01-01
An implementation of Kovacic's algorithm for finding Liouvillian solutions of the differential equations y'' + a(x)y' + b(x)y = 0 with rational coefficients a(x) and b(x) in the Computer Algebra System FORMAC is described. The algorithm description is presented in such a way that one can easily implement it in a suitable Computer Algebra System
Rizvi, Syed S.; Shah, Dipali; Riasat, Aasia
The Time Wrap algorithm [3] offers a run time recovery mechanism that deals with the causality errors. These run time recovery mechanisms consists of rollback, anti-message, and Global Virtual Time (GVT) techniques. For rollback, there is a need to compute GVT which is used in discrete-event simulation to reclaim the memory, commit the output, detect the termination, and handle the errors. However, the computation of GVT requires dealing with transient message problem and the simultaneous reporting problem. These problems can be dealt in an efficient manner by the Samadi's algorithm [8] which works fine in the presence of causality errors. However, the performance of both Time Wrap and Samadi's algorithms depends on the latency involve in GVT computation. Both algorithms give poor latency for large simulation systems especially in the presence of causality errors. To improve the latency and reduce the processor ideal time, we implement tree and butterflies barriers with the optimistic algorithm. Our analysis shows that the use of synchronous barriers such as tree and butterfly with the optimistic algorithm not only minimizes the GVT latency but also minimizes the processor idle time.
Improved QRD-M Detection Algorithm for Generalized Spatial Modulation Scheme
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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.
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)
Comparison of public peak detection algorithms for MALDI mass spectrometry data analysis.
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.
Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral
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Wenhui Li
2014-01-01
Full Text Available Vision-based multivehicle detection plays an important role in Forward Collision Warning Systems (FCWS and Blind Spot Detection Systems (BSDS. The performance of these systems depends on the real-time capability, accuracy, and robustness of vehicle detection methods. To improve the accuracy of vehicle detection algorithm, we propose a multifeature fusion vehicle detection algorithm based on Choquet integral. This algorithm divides the vehicle detection problem into two phases: feature similarity measure and multifeature fusion. In the feature similarity measure phase, we first propose a taillight-based vehicle detection method, and then vehicle taillight feature similarity measure is defined. Second, combining with the definition of Choquet integral, the vehicle symmetry similarity measure and the HOG + AdaBoost feature similarity measure are defined. Finally, these three features are fused together by Choquet integral. Being evaluated on public test collections and our own test images, the experimental results show that our method has achieved effective and robust multivehicle detection in complicated environments. Our method can not only improve the detection rate but also reduce the false alarm rate, which meets the engineering requirements of Advanced Driving Assistance Systems (ADAS.
An algorithm, implementation and execution ontology design pattern
Lawrynowicz, A.; Esteves, D.; Panov, P.; Soru, T.; Dzeroski, S.; Vanschoren, J.
2016-01-01
This paper describes an ontology design pattern for modeling algorithms, their implementations and executions. This pattern is derived from the research results on data mining/machine learning ontologies, but is more generic. We argue that the proposed pattern will foster the development of
A Modularity Degree Based Heuristic Community Detection Algorithm
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Dongming Chen
2014-01-01
Full Text Available A community in a complex network can be seen as a subgroup of nodes that are densely connected. Discovery of community structures is a basic problem of research and can be used in various areas, such as biology, computer science, and sociology. Existing community detection methods usually try to expand or collapse the nodes partitions in order to optimize a given quality function. These optimization function based methods share the same drawback of inefficiency. Here we propose a heuristic algorithm (MDBH algorithm based on network structure which employs modularity degree as a measure function. Experiments on both synthetic benchmarks and real-world networks show that our algorithm gives competitive accuracy with previous modularity optimization methods, even though it has less computational complexity. Furthermore, due to the use of modularity degree, our algorithm naturally improves the resolution limit in community detection.
AlgoRun: a Docker-based packaging system for platform-agnostic implemented algorithms.
Hosny, Abdelrahman; Vera-Licona, Paola; Laubenbacher, Reinhard; Favre, Thibauld
2016-08-01
There is a growing need in bioinformatics for easy-to-use software implementations of algorithms that are usable across platforms. At the same time, reproducibility of computational results is critical and often a challenge due to source code changes over time and dependencies. The approach introduced in this paper addresses both of these needs with AlgoRun, a dedicated packaging system for implemented algorithms, using Docker technology. Implemented algorithms, packaged with AlgoRun, can be executed through a user-friendly interface directly from a web browser or via a standardized RESTful web API to allow easy integration into more complex workflows. The packaged algorithm includes the entire software execution environment, thereby eliminating the common problem of software dependencies and the irreproducibility of computations over time. AlgoRun-packaged algorithms can be published on http://algorun.org, a centralized searchable directory to find existing AlgoRun-packaged algorithms. AlgoRun is available at http://algorun.org and the source code under GPL license is available at https://github.com/algorun laubenbacher@uchc.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
The selection and implementation of hidden line algorithms
International Nuclear Information System (INIS)
Schneider, A.
1983-06-01
One of the most challenging problems in the field of computer graphics is the elimination of hidden lines in images of nontransparent bodies. In the real world the nontransparent material hinders the light ray coming from hidden regions to the observer. In the computer based image formation process there is no automatic visibility regulation of this kind. So many lines are created which result in a poor quality of the spacial representation. Therefore a three-dimensional representation on the screen is only meaningfull if the hidden lines are eliminated. For this process many algorithms have been developed in the past. A common feature of these codes is the large amount of computer time needed. In the first generation of algorithms, which are commonly used today, the bodies are modeled by plane polygons. More recently, however, also algorithms are in use, which are able to treat curved surfaces without discretisation by plane surfaces. In this paper the first group of algorithms is reviewed, and the most important codes are described. The experience obtained during the implementation of two algorithms is presented. (orig.) [de
A Space Object Detection Algorithm using Fourier Domain Likelihood Ratio Test
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.
Caliko: An Inverse Kinematics Software Library Implementation of the FABRIK Algorithm
Lansley, Alastair; Vamplew, Peter; Smith, Philip; Foale, Cameron
2016-01-01
The Caliko library is an implementation of the FABRIK (Forward And Backward Reaching Inverse Kinematics) algorithm written in Java. The inverse kinematics (IK) algorithm is implemented in both 2D and 3D, and incorporates a variety of joint constraints as well as the ability to connect multiple IK chains together in a hierarchy. The library allows for the simple creation and solving of multiple IK chains as well as visualisation of these solutions. It is licensed under the MIT software license...
Implementing Modifed Burg Algorithms in Multivariate Subset Autoregressive Modeling
Directory of Open Access Journals (Sweden)
A. Alexandre Trindade
2003-02-01
Full Text Available The large number of parameters in subset vector autoregressive models often leads one to procure fast, simple, and efficient alternatives or precursors to maximum likelihood estimation. We present the solution of the multivariate subset Yule-Walker equations as one such alternative. In recent work, Brockwell, Dahlhaus, and Trindade (2002, show that the Yule-Walker estimators can actually be obtained as a special case of a general recursive Burg-type algorithm. We illustrate the structure of this Algorithm, and discuss its implementation in a high-level programming language. Applications of the Algorithm in univariate and bivariate modeling are showcased in examples. Univariate and bivariate versions of the Algorithm written in Fortran 90 are included in the appendix, and their use illustrated.
IMPLEMENTATION OF OBJECT TRACKING ALGORITHMS ON THE BASIS OF CUDA TECHNOLOGY
Directory of Open Access Journals (Sweden)
B. A. Zalesky
2014-01-01
Full Text Available A fast version of correlation algorithm to track objects on video-sequences made by a nonstabilized camcorder is presented. The algorithm is based on comparison of local correlations of the object image and regions of video-frames. The algorithm is implemented in programming technology CUDA. Application of CUDA allowed to attain real time execution of the algorithm. To improve its precision and stability, a robust version of the Kalman filter has been incorporated into the flowchart. Tests showed applicability of the algorithm to practical object tracking.
General purpose graphic processing unit implementation of adaptive pulse compression algorithms
Cai, Jingxiao; Zhang, Yan
2017-07-01
This study introduces a practical approach to implement real-time signal processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is needed and investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar.
Error tolerance in an NMR implementation of Grover's fixed-point quantum search algorithm
International Nuclear Information System (INIS)
Xiao Li; Jones, Jonathan A.
2005-01-01
We describe an implementation of Grover's fixed-point quantum search algorithm on a nuclear magnetic resonance quantum computer, searching for either one or two matching items in an unsorted database of four items. In this algorithm the target state (an equally weighted superposition of the matching states) is a fixed point of the recursive search operator, so that the algorithm always moves towards the desired state. The effects of systematic errors in the implementation are briefly explored
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)
A Region Tracking-Based Vehicle Detection Algorithm in Nighttime Traffic Scenes
Directory of Open Access Journals (Sweden)
Jianqiang Wang
2013-12-01
Full Text Available The preceding vehicles detection technique in nighttime traffic scenes is an important part of the advanced driver assistance system (ADAS. This paper proposes a region tracking-based vehicle detection algorithm via the image processing technique. First, the brightness of the taillights during nighttime is used as the typical feature, and we use the existing global detection algorithm to detect and pair the taillights. When the vehicle is detected, a time series analysis model is introduced to predict vehicle positions and the possible region (PR of the vehicle in the next frame. Then, the vehicle is only detected in the PR. This could reduce the detection time and avoid the false pairing between the bright spots in the PR and the bright spots out of the PR. Additionally, we present a thresholds updating method to make the thresholds adaptive. Finally, experimental studies are provided to demonstrate the application and substantiate the superiority of the proposed algorithm. The results show that the proposed algorithm can simultaneously reduce both the false negative detection rate and the false positive detection rate.
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.
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.
A robust background regression based score estimation algorithm for hyperspectral anomaly detection
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
Implementation and analysis of a Navier-Stokes algorithm on parallel computers
Fatoohi, Raad A.; Grosch, Chester E.
1988-01-01
The results of the implementation of a Navier-Stokes algorithm on three parallel/vector computers are presented. The object of this research is to determine how well, or poorly, a single numerical algorithm would map onto three different architectures. The algorithm is a compact difference scheme for the solution of the incompressible, two-dimensional, time-dependent Navier-Stokes equations. The computers were chosen so as to encompass a variety of architectures. They are the following: the MPP, an SIMD machine with 16K bit serial processors; Flex/32, an MIMD machine with 20 processors; and Cray/2. The implementation of the algorithm is discussed in relation to these architectures and measures of the performance on each machine are given. The basic comparison is among SIMD instruction parallelism on the MPP, MIMD process parallelism on the Flex/32, and vectorization of a serial code on the Cray/2. Simple performance models are used to describe the performance. These models highlight the bottlenecks and limiting factors for this algorithm on these architectures. Finally, conclusions are presented.
Kyrkou, Christos; Theocharides, Theocharis
2016-07-01
Object detection is a major step in several computer vision applications and a requirement for most smart camera systems. Recent advances in hardware acceleration for real-time object detection feature extensive use of reconfigurable hardware [field programmable gate arrays (FPGAs)], and relevant research has produced quite fascinating results, in both the accuracy of the detection algorithms as well as the performance in terms of frames per second (fps) for use in embedded smart camera systems. Detecting objects in images, however, is a daunting task and often involves hardware-inefficient steps, both in terms of the datapath design and in terms of input/output and memory access patterns. We present how a visual-feature-directed search cascade composed of motion detection, depth computation, and edge detection, can have a significant impact in reducing the data that needs to be examined by the classification engine for the presence of an object of interest. Experimental results on a Spartan 6 FPGA platform for face detection indicate data search reduction of up to 95%, which results in the system being able to process up to 50 1024×768 pixels images per second with a significantly reduced number of false positives.
Hybrid sparse blind deconvolution: an implementation of SOOT algorithm to real data
Pakmanesh, Parvaneh; Goudarzi, Alireza; Kourki, Meisam
2018-06-01
Getting information of seismic data depends on deconvolution as an important processing step; it provides the reflectivity series by signal compression. This compression can be obtained by removing the wavelet effects on the traces. The recently blind deconvolution has provided reliable performance for sparse signal recovery. In this study, two deconvolution methods have been implemented to the seismic data; the convolution of these methods provides a robust spiking deconvolution approach. This hybrid deconvolution is applied using the sparse deconvolution (MM algorithm) and the Smoothed-One-Over-Two algorithm (SOOT) in a chain. The MM algorithm is based on the minimization of the cost function defined by standards l1 and l2. After applying the two algorithms to the seismic data, the SOOT algorithm provided well-compressed data with a higher resolution than the MM algorithm. The SOOT algorithm requires initial values to be applied for real data, such as the wavelet coefficients and reflectivity series that can be achieved through the MM algorithm. The computational cost of the hybrid method is high, and it is necessary to be implemented on post-stack or pre-stack seismic data of complex structure regions.
Modified SURF Algorithm Implementation on FPGA For Real-Time Object Tracking
Directory of Open Access Journals (Sweden)
Tomyslav Sledevič
2013-05-01
Full Text Available The paper describes the FPGA-based implementation of the modified speeded-up robust features (SURF algorithm. FPGA was selected for parallel process implementation using VHDL to ensure features extraction in real-time. A sliding 84×84 size window was used to store integral pixels and accelerate Hessian determinant calculation, orientation assignment and descriptor estimation. The local extreme searching was used to find point of interest in 8 scales. The simplified descriptor and orientation vector were calculated in parallel in 6 scales. The algorithm was investigated by tracking marker and drawing a plane or cube. All parts of algorithm worked on 25 MHz clock. The video stream was generated using 60 fps and 640×480 pixel camera.Article in Lithuanian
Information dynamics algorithm for detecting communities in networks
Massaro, Emanuele; Bagnoli, Franco; Guazzini, Andrea; Lió, Pietro
2012-11-01
The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network-inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method [4] by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark and on computer generated networks with known community structure. Our approach has three important features: the capacity of detecting overlapping communities, the capability of identifying communities from an individual point of view and the fine tuning the community detectability with respect to prior knowledge of the data. Finally we discuss how to use a Shannon entropy measure for parameter estimation in complex networks.
Conflict Detection Algorithm to Minimize Locking for MPI-IO Atomicity
Sehrish, Saba; Wang, Jun; Thakur, Rajeev
Many scientific applications require high-performance concurrent I/O accesses to a file by multiple processes. Those applications rely indirectly on atomic I/O capabilities in order to perform updates to structured datasets, such as those stored in HDF5 format files. Current support for atomicity in MPI-IO is provided by locking around the operations, imposing lock overhead in all situations, even though in many cases these operations are non-overlapping in the file. We propose to isolate non-overlapping accesses from overlapping ones in independent I/O cases, allowing the non-overlapping ones to proceed without imposing lock overhead. To enable this, we have implemented an efficient conflict detection algorithm in MPI-IO using MPI file views and datatypes. We show that our conflict detection scheme incurs minimal overhead on I/O operations, making it an effective mechanism for avoiding locks when they are not needed.
FPGA-Based Implementation of Lithuanian Isolated Word Recognition Algorithm
Directory of Open Access Journals (Sweden)
Tomyslav Sledevič
2013-05-01
Full Text Available The paper describes the FPGA-based implementation of Lithuanian isolated word recognition algorithm. FPGA is selected for parallel process implementation using VHDL to ensure fast signal processing at low rate clock signal. Cepstrum analysis was applied to features extraction in voice. The dynamic time warping algorithm was used to compare the vectors of cepstrum coefficients. A library of 100 words features was created and stored in the internal FPGA BRAM memory. Experimental testing with speaker dependent records demonstrated the recognition rate of 94%. The recognition rate of 58% was achieved for speaker-independent records. Calculation of cepstrum coefficients lasted for 8.52 ms at 50 MHz clock, while 100 DTWs took 66.56 ms at 25 MHz clock.Article in Lithuanian
The Automated Assessment of Postural Stability: Balance Detection Algorithm.
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
Comparison Of Hybrid Sorting Algorithms Implemented On Different Parallel Hardware Platforms
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Dominik Zurek
2013-01-01
Full Text Available Sorting is a common problem in computer science. There are lot of well-known sorting algorithms created for sequential execution on a single processor. Recently, hardware platforms enable to create wide parallel algorithms. We have standard processors consist of multiple cores and hardware accelerators like GPU. The graphic cards with their parallel architecture give new possibility to speed up many algorithms. In this paper we describe results of implementation of a few different sorting algorithms on GPU cards and multicore processors. Then hybrid algorithm will be presented which consists of parts executed on both platforms, standard CPU and GPU.
Lining seam elimination algorithm and surface crack detection in concrete tunnel lining
Qu, Zhong; Bai, Ling; An, Shi-Quan; Ju, Fang-Rong; Liu, Ling
2016-11-01
Due to the particularity of the surface of concrete tunnel lining and the diversity of detection environments such as uneven illumination, smudges, localized rock falls, water leakage, and the inherent seams of the lining structure, existing crack detection algorithms cannot detect real cracks accurately. This paper proposed an algorithm that combines lining seam elimination with the improved percolation detection algorithm based on grid cell analysis for surface crack detection in concrete tunnel lining. First, check the characteristics of pixels within the overlapping grid to remove the background noise and generate the percolation seed map (PSM). Second, cracks are detected based on the PSM by the accelerated percolation algorithm so that the fracture unit areas can be scanned and connected. Finally, the real surface cracks in concrete tunnel lining can be obtained by removing the lining seam and performing percolation denoising. Experimental results show that the proposed algorithm can accurately, quickly, and effectively detect the real surface cracks. Furthermore, it can fill the gap in the existing concrete tunnel lining surface crack detection by removing the lining seam.
A New Lightweight Watchdog-Based Algorithm for Detecting Sybil Nodes in Mobile WSNs
Directory of Open Access Journals (Sweden)
Rezvan Almas Shehni
2017-12-01
Full Text Available Wide-spread deployment of Wireless Sensor Networks (WSN necessitates special attention to security issues, amongst which Sybil attacks are the most important ones. As a core to Sybil attacks, malicious nodes try to disrupt network operations by creating several fabricated IDs. Due to energy consumption concerns in WSNs, devising detection algorithms which release the sensor nodes from high computational and communicational loads are of great importance. In this paper, a new computationally lightweight watchdog-based algorithm is proposed for detecting Sybil IDs in mobile WSNs. The proposed algorithm employs watchdog nodes for collecting detection information and a designated watchdog node for detection information processing and the final Sybil list generation. Benefiting from a newly devised co-presence state diagram and adequate detection rules, the new algorithm features low extra communication overhead, as well as a satisfactory compromise between two otherwise contradictory detection measures of performance, True Detection Rate (TDR and False Detection Rate (FDR. Extensive simulation results illustrate the merits of the new algorithm compared to a couple of recent watchdog-based Sybil detection algorithms.
Lashkin, S. V.; Kozelkov, A. S.; Yalozo, A. V.; Gerasimov, V. Yu.; Zelensky, D. K.
2017-12-01
This paper describes the details of the parallel implementation of the SIMPLE algorithm for numerical solution of the Navier-Stokes system of equations on arbitrary unstructured grids. The iteration schemes for the serial and parallel versions of the SIMPLE algorithm are implemented. In the description of the parallel implementation, special attention is paid to computational data exchange among processors under the condition of the grid model decomposition using fictitious cells. We discuss the specific features for the storage of distributed matrices and implementation of vector-matrix operations in parallel mode. It is shown that the proposed way of matrix storage reduces the number of interprocessor exchanges. A series of numerical experiments illustrates the effect of the multigrid SLAE solver tuning on the general efficiency of the algorithm; the tuning involves the types of the cycles used (V, W, and F), the number of iterations of a smoothing operator, and the number of cells for coarsening. Two ways (direct and indirect) of efficiency evaluation for parallelization of the numerical algorithm are demonstrated. The paper presents the results of solving some internal and external flow problems with the evaluation of parallelization efficiency by two algorithms. It is shown that the proposed parallel implementation enables efficient computations for the problems on a thousand processors. Based on the results obtained, some general recommendations are made for the optimal tuning of the multigrid solver, as well as for selecting the optimal number of cells per processor.
Modification of MSDR algorithm and ITS implementation on graph clustering
Prastiwi, D.; Sugeng, K. A.; Siswantining, T.
2017-07-01
Maximum Standard Deviation Reduction (MSDR) is a graph clustering algorithm to minimize the distance variation within a cluster. In this paper we propose a modified MSDR by replacing one technical step in MSDR which uses polynomial regression, with a new and simpler step. This leads to our new algorithm called Modified MSDR (MMSDR). We implement the new algorithm to separate a domestic flight network of an Indonesian airline into two large clusters. Further analysis allows us to discover a weak link in the network, which should be improved by adding more flights.
Directory of Open Access Journals (Sweden)
L. S. Sindhuja
2016-01-01
Full Text Available Security of Mobile Wireless Sensor Networks is a vital challenge as the sensor nodes are deployed in unattended environment and they are prone to various attacks. One among them is the node replication attack. In this, the physically insecure nodes are acquired by the adversary to clone them by having the same identity of the captured node, and the adversary deploys an unpredictable number of replicas throughout the network. Hence replica node detection is an important challenge in Mobile Wireless Sensor Networks. Various replica node detection techniques have been proposed to detect these replica nodes. These methods incur control overheads and the detection accuracy is low when the replica is selected as a witness node. This paper proposes to solve these issues by enhancing the Single Hop Detection (SHD method using the Clonal Selection algorithm to detect the clones by selecting the appropriate witness nodes. The advantages of the proposed method include (i increase in the detection ratio, (ii decrease in the control overhead, and (iii increase in throughput. The performance of the proposed work is measured using detection ratio, false detection ratio, packet delivery ratio, average delay, control overheads, and throughput. The implementation is done using ns-2 to exhibit the actuality of the proposed work.
Implementations of back propagation algorithm in ecosystems applications
Ali, Khalda F.; Sulaiman, Riza; Elamir, Amir Mohamed
2015-05-01
Artificial Neural Networks (ANNs) have been applied to an increasing number of real world problems of considerable complexity. Their most important advantage is in solving problems which are too complex for conventional technologies, that do not have an algorithmic solutions or their algorithmic Solutions is too complex to be found. In general, because of their abstraction from the biological brain, ANNs are developed from concept that evolved in the late twentieth century neuro-physiological experiments on the cells of the human brain to overcome the perceived inadequacies with conventional ecological data analysis methods. ANNs have gained increasing attention in ecosystems applications, because of ANN's capacity to detect patterns in data through non-linear relationships, this characteristic confers them a superior predictive ability. In this research, ANNs is applied in an ecological system analysis. The neural networks use the well known Back Propagation (BP) Algorithm with the Delta Rule for adaptation of the system. The Back Propagation (BP) training Algorithm is an effective analytical method for adaptation of the ecosystems applications, the main reason because of their capacity to detect patterns in data through non-linear relationships. This characteristic confers them a superior predicting ability. The BP algorithm uses supervised learning, which means that we provide the algorithm with examples of the inputs and outputs we want the network to compute, and then the error is calculated. The idea of the back propagation algorithm is to reduce this error, until the ANNs learns the training data. The training begins with random weights, and the goal is to adjust them so that the error will be minimal. This research evaluated the use of artificial neural networks (ANNs) techniques in an ecological system analysis and modeling. The experimental results from this research demonstrate that an artificial neural network system can be trained to act as an expert
A high performance hardware implementation image encryption with AES algorithm
Farmani, Ali; Jafari, Mohamad; Miremadi, Seyed Sohrab
2011-06-01
This paper describes implementation of a high-speed encryption algorithm with high throughput for encrypting the image. Therefore, we select a highly secured symmetric key encryption algorithm AES(Advanced Encryption Standard), in order to increase the speed and throughput using pipeline technique in four stages, control unit based on logic gates, optimal design of multiplier blocks in mixcolumn phase and simultaneous production keys and rounds. Such procedure makes AES suitable for fast image encryption. Implementation of a 128-bit AES on FPGA of Altra company has been done and the results are as follow: throughput, 6 Gbps in 471MHz. The time of encrypting in tested image with 32*32 size is 1.15ms.
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.
Pipeline Implementation of Polyphase PSO for Adaptive Beamforming Algorithm
Directory of Open Access Journals (Sweden)
Shaobing Huang
2017-01-01
Full Text Available Adaptive beamforming is a powerful technique for anti-interference, where searching and tracking optimal solutions are a great challenge. In this paper, a partial Particle Swarm Optimization (PSO algorithm is proposed to track the optimal solution of an adaptive beamformer due to its great global searching character. Also, due to its naturally parallel searching capabilities, a novel Field Programmable Gate Arrays (FPGA pipeline architecture using polyphase filter bank structure is designed. In order to perform computations with large dynamic range and high precision, the proposed implementation algorithm uses an efficient user-defined floating-point arithmetic. In addition, a polyphase architecture is proposed to achieve full pipeline implementation. In the case of PSO with large population, the polyphase architecture can significantly save hardware resources while achieving high performance. Finally, the simulation results are presented by cosimulation with ModelSim and SIMULINK.
A new edge detection algorithm based on Canny idea
Feng, Yingke; Zhang, Jinmin; Wang, Siming
2017-10-01
The traditional Canny algorithm has poor self-adaptability threshold, and it is more sensitive to noise. In order to overcome these drawbacks, this paper proposed a new edge detection method based on Canny algorithm. Firstly, the media filtering and filtering based on the method of Euclidean distance are adopted to process it; secondly using the Frei-chen algorithm to calculate gradient amplitude; finally, using the Otsu algorithm to calculate partial gradient amplitude operation to get images of thresholds value, then find the average of all thresholds that had been calculated, half of the average is high threshold value, and the half of the high threshold value is low threshold value. Experiment results show that this new method can effectively suppress noise disturbance, keep the edge information, and also improve the edge detection accuracy.
A parallel algorithm for transient solid dynamics simulations with contact detection
International Nuclear Information System (INIS)
Attaway, S.; Hendrickson, B.; Plimpton, S.; Gardner, D.; Vaughan, C.; Heinstein, M.; Peery, J.
1996-01-01
Solid dynamics simulations with Lagrangian finite elements are used to model a wide variety of problems, such as the calculation of impact damage to shipping containers for nuclear waste and the analysis of vehicular crashes. Using parallel computers for these simulations has been hindered by the difficulty of searching efficiently for material surface contacts in parallel. A new parallel algorithm for calculation of arbitrary material contacts in finite element simulations has been developed and implemented in the PRONTO3D transient solid dynamics code. This paper will explore some of the issues involved in developing efficient, portable, parallel finite element models for nonlinear transient solid dynamics simulations. The contact-detection problem poses interesting challenges for efficient implementation of a solid dynamics simulation on a parallel computer. The finite element mesh is typically partitioned so that each processor owns a localized region of the finite element mesh. This mesh partitioning is optimal for the finite element portion of the calculation since each processor must communicate only with the few connected neighboring processors that share boundaries with the decomposed mesh. However, contacts can occur between surfaces that may be owned by any two arbitrary processors. Hence, a global search across all processors is required at every time step to search for these contacts. Load-imbalance can become a problem since the finite element decomposition divides the volumetric mesh evenly across processors but typically leaves the surface elements unevenly distributed. In practice, these complications have been limiting factors in the performance and scalability of transient solid dynamics on massively parallel computers. In this paper the authors present a new parallel algorithm for contact detection that overcomes many of these limitations
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.
International Nuclear Information System (INIS)
Crawford, Kevan C.; Sandquist, Gary M.
1990-01-01
The emphasis of this work is the development and implementation of an automatic control philosophy which uses the classical operational philosophies as a foundation. Three control algorithms were derived based on various simplifying assumptions. Two of the algorithms were tested in computer simulations. After realizing the insensitivity of the system to the simplifications, the most reduced form of the algorithms was implemented on the computer control system at the University of Utah (UNEL). Since the operational philosophies have a higher priority than automatic control, they determine when automatic control may be utilized. Unlike the operational philosophies, automatic control is not concerned with component failures. The object of this philosophy is the movement of absorber rods to produce a requested power. When the current power level is compared to the requested power level, an error may be detected which will require the movement of a control rod to correct the error. The automatic control philosophy adds another dimension to the classical operational philosophies. Using this philosophy, normal operator interactions with the computer would be limited only to run parameters such as power, period, and run time. This eliminates subjective judgements, objective judgements under pressure, and distractions to the operator and insures the reactor will be operated in a safe and controlled manner as well as providing reproducible operations
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
Caliko: An Inverse Kinematics Software Library Implementation of the FABRIK Algorithm
Directory of Open Access Journals (Sweden)
Alastair Lansley
2016-09-01
Full Text Available The Caliko library is an implementation of the FABRIK (Forward And Backward Reaching Inverse Kinematics algorithm written in Java. The inverse kinematics (IK algorithm is implemented in both 2D and 3D, and incorporates a variety of joint constraints as well as the ability to connect multiple IK chains together in a hierarchy. The library allows for the simple creation and solving of multiple IK chains as well as visualisation of these solutions. It is licensed under the MIT software license and the source code is freely available for use and modification at: https://github.com/feduni/caliko
A novel line segment detection algorithm based on graph search
Zhao, Hong-dan; Liu, Guo-ying; Song, Xu
2018-02-01
To overcome the problem of extracting line segment from an image, a method of line segment detection was proposed based on the graph search algorithm. After obtaining the edge detection result of the image, the candidate straight line segments are obtained in four directions. For the candidate straight line segments, their adjacency relationships are depicted by a graph model, based on which the depth-first search algorithm is employed to determine how many adjacent line segments need to be merged. Finally we use the least squares method to fit the detected straight lines. The comparative experimental results verify that the proposed algorithm has achieved better results than the line segment detector (LSD).
Adaptive algorithm of magnetic heading detection
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.
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
A fast implementation of the incremental backprojection algorithms for parallel beam geometries
International Nuclear Information System (INIS)
Chen, C.M.; Wang, C.Y.; Cho, Z.H.
1996-01-01
Filtered-backprojection algorithms are the most widely used approaches for reconstruction of computed tomographic (CT) images, such as X-ray CT and positron emission tomographic (PET) images. The Incremental backprojection algorithm is a fast backprojection approach based on restructuring the Shepp and Logan algorithm. By exploiting interdependency (position and values) of adjacent pixels, the Incremental algorithm requires only O(N) and O(N 2 ) multiplications in contrast to O(N 2 ) and O(N 3 ) multiplications for the Shepp and Logan algorithm in two-dimensional (2-D) and three-dimensional (3-D) backprojections, respectively, for each view, where N is the size of the image in each dimension. In addition, it may reduce the number of additions for each pixel computation. The improvement achieved by the Incremental algorithm in practice was not, however, as significant as expected. One of the main reasons is due to inevitably visiting pixels outside the beam in the searching flow scheme originally developed for the Incremental algorithm. To optimize implementation of the Incremental algorithm, an efficient scheme, namely, coded searching flow scheme, is proposed in this paper to minimize the overhead caused by searching for all pixels in a beam. The key idea of this scheme is to encode the searching flow for all pixels inside each beam. While backprojecting, all pixels may be visited without any overhead due to using the coded searching flow as the a priori information. The proposed coded searching flow scheme has been implemented on a Sun Sparc 10 and a Sun Sparc 20 workstations. The implementation results show that the proposed scheme is 1.45--2.0 times faster than the original searching flow scheme for most cases tested
The derivation of distributed termination detection algorithms from garbage collection schemes
Tel, G.; Mattern, F.
1990-01-01
It is shown that the termination detection problem for distributed computations can be modelled as an instance of the garbage collection problem. Consequently, algorithms for the termination detection problem are obtained by applying transformations to garbage collection algorithms. The
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...
Memory Efficient VLSI Implementation of Real-Time Motion Detection System Using FPGA Platform
Directory of Open Access Journals (Sweden)
Sanjay Singh
2017-06-01
Full Text Available Motion detection is the heart of a potentially complex automated video surveillance system, intended to be used as a standalone system. Therefore, in addition to being accurate and robust, a successful motion detection technique must also be economical in the use of computational resources on selected FPGA development platform. This is because many other complex algorithms of an automated video surveillance system also run on the same platform. Keeping this key requirement as main focus, a memory efficient VLSI architecture for real-time motion detection and its implementation on FPGA platform is presented in this paper. This is accomplished by proposing a new memory efficient motion detection scheme and designing its VLSI architecture. The complete real-time motion detection system using the proposed memory efficient architecture along with proper input/output interfaces is implemented on Xilinx ML510 (Virtex-5 FX130T FPGA development platform and is capable of operating at 154.55 MHz clock frequency. Memory requirement of the proposed architecture is reduced by 41% compared to the standard clustering based motion detection architecture. The new memory efficient system robustly and automatically detects motion in real-world scenarios (both for the static backgrounds and the pseudo-stationary backgrounds in real-time for standard PAL (720 × 576 size color video.
Performances of the New Real Time Tsunami Detection Algorithm applied to tide gauges data
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 low complexity VBLAST OFDM detection algorithm for wireless LAN systems
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
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.
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...
An OMIC biomarker detection algorithm TriVote and its application in methylomic biomarker detection.
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.
Hardware realization of an SVM algorithm implemented in FPGAs
Wiśniewski, Remigiusz; Bazydło, Grzegorz; Szcześniak, Paweł
2017-08-01
The paper proposes a technique of hardware realization of a space vector modulation (SVM) of state function switching in matrix converter (MC), oriented on the implementation in a single field programmable gate array (FPGA). In MC the SVM method is based on the instantaneous space-vector representation of input currents and output voltages. The traditional computation algorithms usually involve digital signal processors (DSPs) which consumes the large number of power transistors (18 transistors and 18 independent PWM outputs) and "non-standard positions of control pulses" during the switching sequence. Recently, hardware implementations become popular since computed operations may be executed much faster and efficient due to nature of the digital devices (especially concurrency). In the paper, we propose a hardware algorithm of SVM computation. In opposite to the existing techniques, the presented solution applies COordinate Rotation DIgital Computer (CORDIC) method to solve the trigonometric operations. Furthermore, adequate arithmetic modules (that is, sub-devices) used for intermediate calculations, such as code converters or proper sectors selectors (for output voltages and input current) are presented in detail. The proposed technique has been implemented as a design described with the use of Verilog hardware description language. The preliminary results of logic implementation oriented on the Xilinx FPGA (particularly, low-cost device from Artix-7 family from Xilinx was used) are also presented.
Artifact removal algorithms for stroke detection using a multistatic MIST beamforming algorithm.
Ricci, E; Di Domenico, S; Cianca, E; Rossi, T
2015-01-01
Microwave imaging (MWI) has been recently proved as a promising imaging modality for low-complexity, low-cost and fast brain imaging tools, which could play a fundamental role to efficiently manage emergencies related to stroke and hemorrhages. This paper focuses on the UWB radar imaging approach and in particular on the processing algorithms of the backscattered signals. Assuming the use of the multistatic version of the MIST (Microwave Imaging Space-Time) beamforming algorithm, developed by Hagness et al. for the early detection of breast cancer, the paper proposes and compares two artifact removal algorithms. Artifacts removal is an essential step of any UWB radar imaging system and currently considered artifact removal algorithms have been shown not to be effective in the specific scenario of brain imaging. First of all, the paper proposes modifications of a known artifact removal algorithm. These modifications are shown to be effective to achieve good localization accuracy and lower false positives. However, the main contribution is the proposal of an artifact removal algorithm based on statistical methods, which allows to achieve even better performance but with much lower computational complexity.
Comparison of spike-sorting algorithms for future hardware implementation.
Gibson, Sarah; Judy, Jack W; Markovic, Dejan
2008-01-01
Applications such as brain-machine interfaces require hardware spike sorting in order to (1) obtain single-unit activity and (2) perform data reduction for wireless transmission of data. Such systems must be low-power, low-area, high-accuracy, automatic, and able to operate in real time. Several detection and feature extraction algorithms for spike sorting are described briefly and evaluated in terms of accuracy versus computational complexity. The nonlinear energy operator method is chosen as the optimal spike detection algorithm, being most robust over noise and relatively simple. The discrete derivatives method [1] is chosen as the optimal feature extraction method, maintaining high accuracy across SNRs with a complexity orders of magnitude less than that of traditional methods such as PCA.
Directory of Open Access Journals (Sweden)
O. Ahmed
2011-01-01
Full Text Available Packet classification plays a crucial role for a number of network services such as policy-based routing, firewalls, and traffic billing, to name a few. However, classification can be a bottleneck in the above-mentioned applications if not implemented properly and efficiently. In this paper, we propose PCIU, a novel classification algorithm, which improves upon previously published work. PCIU provides lower preprocessing time, lower memory consumption, ease of incremental rule update, and reasonable classification time compared to state-of-the-art algorithms. The proposed algorithm was evaluated and compared to RFC and HiCut using several benchmarks. Results obtained indicate that PCIU outperforms these algorithms in terms of speed, memory usage, incremental update capability, and preprocessing time. The algorithm, furthermore, was improved and made more accessible for a variety of applications through implementation in hardware. Two such implementations are detailed and discussed in this paper. The results indicate that a hardware/software codesign approach results in a slower, but easier to optimize and improve within time constraints, PCIU solution. A hardware accelerator based on an ESL approach using Handel-C, on the other hand, resulted in a 31x speed-up over a pure software implementation running on a state of the art Xeon processor.
Implementation of an evolutionary algorithm in planning investment in a power distribution system
Directory of Open Access Journals (Sweden)
Carlos Andrés García Montoya
2011-06-01
Full Text Available The definition of an investment plan to implement in a distribution power system, is a task that constantly faced by utilities. This work presents a methodology for determining the investment plan for a distribution power system under a shortterm, using as a criterion for evaluating investment projects, associated costs and customers benefit from its implementation. Given the number of projects carried out annually on the system, the definition of an investment plan requires the use of computational tools to evaluate, a set of possibilities, the one that best suits the needs of the present system and better results. That is why in the job, implementing a multi objective evolutionary algorithm SPEA (Strength Pareto Evolutionary Algorithm, which, based on the principles of Pareto optimality, it deliver to the planning expert, the best solutions found in the optimization process. The performance of the algorithm is tested using a set of projects to determine the best among the possible plans. We analyze also the effect of operators on the performance of evolutionary algorithm and results.
Detecting an atomic clock frequency anomaly using an adaptive Kalman filter algorithm
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.
On distribution reduction and algorithm implementation in inconsistent ordered information systems.
Zhang, Yanqin
2014-01-01
As one part of our work in ordered information systems, distribution reduction is studied in inconsistent ordered information systems (OISs). Some important properties on distribution reduction are studied and discussed. The dominance matrix is restated for reduction acquisition in dominance relations based information systems. Matrix algorithm for distribution reduction acquisition is stepped. And program is implemented by the algorithm. The approach provides an effective tool for the theoretical research and the applications for ordered information systems in practices. For more detailed and valid illustrations, cases are employed to explain and verify the algorithm and the program which shows the effectiveness of the algorithm in complicated information systems.
Implementation of Real-Time Feedback Flow Control Algorithms on a Canonical Testbed
Tian, Ye; Song, Qi; Cattafesta, Louis
2005-01-01
This report summarizes the activities on "Implementation of Real-Time Feedback Flow Control Algorithms on a Canonical Testbed." The work summarized consists primarily of two parts. The first part summarizes our previous work and the extensions to adaptive ID and control algorithms. The second part concentrates on the validation of adaptive algorithms by applying them to a vibration beam test bed. Extensions to flow control problems are discussed.
A street rubbish detection algorithm based on Sift and RCNN
Yu, XiPeng; Chen, Zhong; Zhang, Shuo; Zhang, Ting
2018-02-01
This paper presents a street rubbish detection algorithm based on image registration with Sift feature and RCNN. Firstly, obtain the rubbish region proposal on the real-time street image and set up the CNN convolution neural network trained by the rubbish samples set consists of rubbish and non-rubbish images; Secondly, for every clean street image, obtain the Sift feature and do image registration with the real-time street image to obtain the differential image, the differential image filters a lot of background information, obtain the rubbish region proposal rect where the rubbish may appear on the differential image by the selective search algorithm. Then, the CNN model is used to detect the image pixel data in each of the region proposal on the real-time street image. According to the output vector of the CNN, it is judged whether the rubbish is in the region proposal or not. If it is rubbish, the region proposal on the real-time street image is marked. This algorithm avoids the large number of false detection caused by the detection on the whole image because the CNN is used to identify the image only in the region proposal on the real-time street image that may appear rubbish. Different from the traditional object detection algorithm based on the region proposal, the region proposal is obtained on the differential image not whole real-time street image, and the number of the invalid region proposal is greatly reduced. The algorithm has the high mean average precision (mAP).
Implementation of Genetic Algorithm in Control Structure of Induction Motor A.C. Drive
Directory of Open Access Journals (Sweden)
BRANDSTETTER, P.
2014-11-01
Full Text Available Modern concepts of control systems with digital signal processors allow the implementation of time-consuming control algorithms in real-time, for example soft computing methods. The paper deals with the design and technical implementation of a genetic algorithm for setting proportional and integral gain of the speed controller of the A.C. drive with the vector-controlled induction motor. Important simulations and experimental measurements have been realized that confirm the correctness of the proposed speed controller tuned by the genetic algorithm and the quality speed response of the A.C. drive with changing parameters and disturbance variables, such as changes in load torque.
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.
International Nuclear Information System (INIS)
Bankier, A.; Herold, C.J.; Fleischmann, D.; Janata-Schwatczek, K.
1998-01-01
Purpose: Debate about the potential implementation of Spiral-CT in diagnostic algorithms of pulmonary embolism are often focussed on sensitivity and specificity in the context of comparative methodologic studies. We intend to investigate whether additional factors might influence this debate. Results: The factors availability, acceptance, patient-outcome, and cost-effectiveness-studies do have substantial influence on the implementation of Spiral-CT in the diagnostic algorithms of pulmonary embolism. Incorporation of these factors into the discussion might lead to more flexible and more patient-oriented algorithms for the diagnosis of pulmonary embolism. Conclusion: Availability of equipment, acceptance among clinicians, patient-out-come, and cost-effectiveness evaluations should be implemented into the debate about potential implementation of Spiral-CT in routine diagnostic imaging algorithms of pulmonary embolism. (orig./AJ) [de
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.
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...
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
An Implementable First-Order Primal-Dual Algorithm for Structured Convex Optimization
Directory of Open Access Journals (Sweden)
Feng Ma
2014-01-01
Full Text Available Many application problems of practical interest can be posed as structured convex optimization models. In this paper, we study a new first-order primaldual algorithm. The method can be easily implementable, provided that the resolvent operators of the component objective functions are simple to evaluate. We show that the proposed method can be interpreted as a proximal point algorithm with a customized metric proximal parameter. Convergence property is established under the analytic contraction framework. Finally, we verify the efficiency of the algorithm by solving the stable principal component pursuit problem.
FPGA implementation of image dehazing algorithm for real time applications
Kumar, Rahul; Kaushik, Brajesh Kumar; Balasubramanian, R.
2017-09-01
Weather degradation such as haze, fog, mist, etc. severely reduces the effective range of visual surveillance. This degradation is a spatially varying phenomena, which makes this problem non trivial. Dehazing is an essential preprocessing stage in applications such as long range imaging, border security, intelligent transportation system, etc. However, these applications require low latency of the preprocessing block. In this work, single image dark channel prior algorithm is modified and implemented for fast processing with comparable visual quality of the restored image/video. Although conventional single image dark channel prior algorithm is computationally expensive, it yields impressive results. Moreover, a two stage image dehazing architecture is introduced, wherein, dark channel and airlight are estimated in the first stage. Whereas, transmission map and intensity restoration are computed in the next stages. The algorithm is implemented using Xilinx Vivado software and validated by using Xilinx zc702 development board, which contains an Artix7 equivalent Field Programmable Gate Array (FPGA) and ARM Cortex A9 dual core processor. Additionally, high definition multimedia interface (HDMI) has been incorporated for video feed and display purposes. The results show that the dehazing algorithm attains 29 frames per second for the image resolution of 1920x1080 which is suitable of real time applications. The design utilizes 9 18K_BRAM, 97 DSP_48, 6508 FFs and 8159 LUTs.
Detecting microsatellites within genomes: significant variation among algorithms
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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.
Smith, D. E.; Felizardo, C.; Minson, S. E.; Boese, M.; Langbein, J. O.; Guillemot, C.; Murray, J. R.
2015-12-01
The earthquake early warning (EEW) systems in California and elsewhere can greatly benefit from algorithms that generate estimates of finite-fault parameters. These estimates could significantly improve real-time shaking calculations and yield important information for immediate disaster response. Minson et al. (2015) determined that combining FinDer's seismic-based algorithm (Böse et al., 2012) with BEFORES' geodetic-based algorithm (Minson et al., 2014) yields a more robust and informative joint solution than using either algorithm alone. FinDer examines the distribution of peak ground accelerations from seismic stations and determines the best finite-fault extent and strike from template matching. BEFORES employs a Bayesian framework to search for the best slip inversion over all possible fault geometries in terms of strike and dip. Using FinDer and BEFORES together generates estimates of finite-fault extent, strike, dip, preferred slip, and magnitude. To yield the quickest, most flexible, and open-source version of the joint algorithm, we translated BEFORES and FinDer from Matlab into C++. We are now developing a C++ Application Protocol Interface for these two algorithms to be connected to the seismic and geodetic data flowing from the EEW system. The interface that is being developed will also enable communication between the two algorithms to generate the joint solution of finite-fault parameters. Once this interface is developed and implemented, the next step will be to run test seismic and geodetic data through the system via the Earthworm module, Tank Player. This will allow us to examine algorithm performance on simulated data and past real events.
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.
Detecting fire in video stream using statistical analysis
Directory of Open Access Journals (Sweden)
Koplík Karel
2017-01-01
Full Text Available The real time fire detection in video stream is one of the most interesting problems in computer vision. In fact, in most cases it would be nice to have fire detection algorithm implemented in usual industrial cameras and/or to have possibility to replace standard industrial cameras with one implementing the fire detection algorithm. In this paper, we present new algorithm for detecting fire in video. The algorithm is based on tracking suspicious regions in time with statistical analysis of their trajectory. False alarms are minimized by combining multiple detection criteria: pixel brightness, trajectories of suspicious regions for evaluating characteristic fire flickering and persistence of alarm state in sequence of frames. The resulting implementation is fast and therefore can run on wide range of affordable hardware.
Implementation Aspects of a Flexible Frequency Spectrum Usage Algorithm for Cognitive OFDM Systems
DEFF Research Database (Denmark)
Sacchi, Claudio; Tonelli, Oscar; Cattoni, Andrea Fabio
2011-01-01
time on a shared spectrum chunk, emphasizes the role of resource allocation as a critical system design issue. This work is aimed at analyzing the practical issues related to the Software Defined Radio (SDR)-based implementation of a dynamic spectrum allocation algorithm, designed for OFDM...... on a Xilinx ML506 development board is performed. The main novelty proposed in this paper consists in the SDR-based implementation of a computationally-sustainable resource allocation algorithm for FSU on low-cost commercial FPGA platforms. The proposed implementation is competitive with respect to other ones...... on a Virtex 5 FPGA. Experimental results will illustrate that the selected core functionalities are effectively implementable with around 3% or less of the total FPGA computing resources....
Computationally efficient algorithms for statistical image processing : implementation in R
Langovoy, M.; Wittich, O.
2010-01-01
In the series of our earlier papers on the subject, we proposed a novel statistical hypothesis testing method for detection of objects in noisy images. The method uses results from percolation theory and random graph theory. We developed algorithms that allowed to detect objects of unknown shapes in
An implementation of a data-transmission pipelining algorithm on Imote2 platforms
Li, Xu; Dorvash, Siavash; Cheng, Liang; Pakzad, Shamim
2011-04-01
Over the past several years, wireless network systems and sensing technologies have been developed significantly. This has resulted in the broad application of wireless sensor networks (WSNs) in many engineering fields and in particular structural health monitoring (SHM). The movement of traditional SHM toward the new generation of SHM, which utilizes WSNs, relies on the advantages of this new approach such as relatively low costs, ease of implementation and the capability of onboard data processing and management. In the particular case of long span bridge monitoring, a WSN should be capable of transmitting commands and measurement data over long network geometry in a reliable manner. While using single-hop data transmission in such geometry requires a long radio range and consequently a high level of power supply, multi-hop communication may offer an effective and reliable way for data transmissions across the network. Using a multi-hop communication protocol, the network relays data from a remote node to the base station via intermediary nodes. We have proposed a data-transmission pipelining algorithm to enable an effective use of the available bandwidth and minimize the energy consumption and the delay performance by the multi-hop communication protocol. This paper focuses on the implementation aspect of the pipelining algorithm on Imote2 platforms for SHM applications, describes its interaction with underlying routing protocols, and presents the solutions to various implementation issues of the proposed pipelining algorithm. Finally, the performance of the algorithm is evaluated based on the results of an experimental implementation.
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...
VIRTEX-5 Fpga Implementation of Advanced Encryption Standard Algorithm
Rais, Muhammad H.; Qasim, Syed M.
2010-06-01
In this paper, we present an implementation of Advanced Encryption Standard (AES) cryptographic algorithm using state-of-the-art Virtex-5 Field Programmable Gate Array (FPGA). The design is coded in Very High Speed Integrated Circuit Hardware Description Language (VHDL). Timing simulation is performed to verify the functionality of the designed circuit. Performance evaluation is also done in terms of throughput and area. The design implemented on Virtex-5 (XC5VLX50FFG676-3) FPGA achieves a maximum throughput of 4.34 Gbps utilizing a total of 399 slices.
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.
Detection of Cheating by Decimation Algorithm
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.
Gear Tooth Wear Detection Algorithm
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.
Implementation of trigonometric function using CORDIC algorithms
Mokhtar, A. S. N.; Ayub, M. I.; Ismail, N.; Daud, N. G. Nik
2018-02-01
In 1959, Jack E. Volder presents a brand new formula to the real-time solution of the equation raised in navigation system. This new algorithm was the most beneficial replacement of analog navigation system by the digital. The CORDIC (Coordinate Rotation Digital Computer) algorithm are used for the rapid calculation associated with elementary operates like trigonometric function, multiplication, division and logarithm function, and also various conversions such as conversion of rectangular to polar coordinate including the conversion between binary coded information. In this current time CORDIC formula have many applications in the field of communication, signal processing, 3-D graphics, and others. This paper would be presents the trigonometric function implementation by using CORDIC algorithm in rotation mode for circular coordinate system. The CORDIC technique is used in order to generating the output angle between range 0o to 90o and error analysis is concern. The result showed that the average percentage error is about 0.042% at angles between ranges 00 to 900. But the average percentage error rose up to 45% at angle 90o and above. So, this method is very accurate at the 1st quadrant. The mirror properties method is used to find out an angle at 2nd, 3rd and 4th quadrant.
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 Swarm Optimization Algorithm for Multimodal Functions and Its Application in Multicircle Detection
Directory of Open Access Journals (Sweden)
Erik Cuevas
2013-01-01
Full Text Available In engineering problems due to physical and cost constraints, the best results, obtained by a global optimization algorithm, cannot be realized always. Under such conditions, if multiple solutions (local and global are known, the implementation can be quickly switched to another solution without much interrupting the design process. This paper presents a new swarm multimodal optimization algorithm named as the collective animal behavior (CAB. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central location, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, searcher agents emulate a group of animals which interact with each other based on simple biological laws that are modeled as evolutionary operators. Numerical experiments are conducted to compare the proposed method with the state-of-the-art methods on benchmark functions. The proposed algorithm has been also applied to the engineering problem of multi-circle detection, achieving satisfactory results.
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
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.
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.
Advanced defect detection algorithm using clustering in ultrasonic NDE
Gongzhang, Rui; Gachagan, Anthony
2016-02-01
A range of materials used in industry exhibit scattering properties which limits ultrasonic NDE. Many algorithms have been proposed to enhance defect detection ability, such as the well-known Split Spectrum Processing (SSP) technique. Scattering noise usually cannot be fully removed and the remaining noise can be easily confused with real feature signals, hence becoming artefacts during the image interpretation stage. This paper presents an advanced algorithm to further reduce the influence of artefacts remaining in A-scan data after processing using a conventional defect detection algorithm. The raw A-scan data can be acquired from either traditional single transducer or phased array configurations. The proposed algorithm uses the concept of unsupervised machine learning to cluster segmental defect signals from pre-processed A-scans into different classes. The distinction and similarity between each class and the ensemble of randomly selected noise segments can be observed by applying a classification algorithm. Each class will then be labelled as `legitimate reflector' or `artefacts' based on this observation and the expected probability of defection (PoD) and probability of false alarm (PFA) determined. To facilitate data collection and validate the proposed algorithm, a 5MHz linear array transducer is used to collect A-scans from both austenitic steel and Inconel samples. Each pulse-echo A-scan is pre-processed using SSP and the subsequent application of the proposed clustering algorithm has provided an additional reduction to PFA while maintaining PoD for both samples compared with SSP results alone.
Implementation of the Grover search algorithm with Josephson charge qubits
International Nuclear Information System (INIS)
Zheng Xiaohu; Dong Ping; Xue Zhengyuan; Cao Zhuoliang
2007-01-01
A scheme of implementing the Grover search algorithm based on Josephson charge qubits has been proposed, which would be a key step to scale more complex quantum algorithms and very important for constructing a real quantum computer via Josephson charge qubits. The present scheme is simple but fairly efficient, and easily manipulated because any two-charge-qubit can be selectively and effectively coupled by a common inductance. More manipulations can be carried out before decoherence sets in. Our scheme can be realized within the current technology
Infinitely oscillating wavelets and a efficient implementation algorithm based the FFT
Directory of Open Access Journals (Sweden)
Marcela Fabio
2015-01-01
Full Text Available In this work we present the design of an orthogonal wavelet, infinitely oscillating, located in time with decay 1/|t|n and limited-band. Its appli- cation leads to the signal decomposition in waves of instantaneous, well defined frequency. We also present the implementation algorithm for the analysis and synthesis based on the Fast Fourier Transform (FFT with the same complexity as Mallat’s algorithm.
A multithreaded parallel implementation of a dynamic programming algorithm for sequence comparison.
Martins, W S; Del Cuvillo, J B; Useche, F J; Theobald, K B; Gao, G R
2001-01-01
This paper discusses the issues involved in implementing a dynamic programming algorithm for biological sequence comparison on a general-purpose parallel computing platform based on a fine-grain event-driven multithreaded program execution model. Fine-grain multithreading permits efficient parallelism exploitation in this application both by taking advantage of asynchronous point-to-point synchronizations and communication with low overheads and by effectively tolerating latency through the overlapping of computation and communication. We have implemented our scheme on EARTH, a fine-grain event-driven multithreaded execution and architecture model which has been ported to a number of parallel machines with off-the-shelf processors. Our experimental results show that the dynamic programming algorithm can be efficiently implemented on EARTH systems with high performance (e.g., speedup of 90 on 120 nodes), good programmability and reasonable cost.
Concurrent applicative implementations of nondeterministic algorithms
Energy Technology Data Exchange (ETDEWEB)
Salter, R
1983-01-01
The author introduces a methodology for utilizing concurrency in place of backtracking in the implementation of nondeterministic algorithms. This is achieved in an applicative setting through the use of the Friedman-Wise multiprogramming primitive frons, and a paradigm which views the action of nondeterministic algorithms as one of data structure construction. The element by element nondeterminism arising from a linearized search is replaced by a control structure which is oriented towards constructing sets of partial computations. This point of view is facilitated by the use of suspensions, which allow control disciplines to be embodied in the form of conceptual data structures that in reality manifest themselves only for purposes of control. He applies this methodology to the class of problems usually solved through the use of simple backtracking (e.g. 'eight queens'), and to a problem presented by Lindstrom (1979) to illustrate the use of coroutine controlled backtracking, to produce backtrack-free solutions. The solution to the latter illustrates the coroutine capability of suspended structures, but also demonstrates a need for further investigations into resolving problems of process communication in applicative languages. 14 references.
Combined Dust Detection Algorithm by Using MODIS Infrared Channels over East Asia
Park, Sang Seo; Kim, Jhoon; Lee, Jaehwa; Lee, Sukjo; Kim, Jeong Soo; Chang, Lim Seok; Ou, Steve
2014-01-01
A new dust detection algorithm is developed by combining the results of multiple dust detectionmethods using IR channels onboard the MODerate resolution Imaging Spectroradiometer (MODIS). Brightness Temperature Difference (BTD) between two wavelength channels has been used widely in previous dust detection methods. However, BTDmethods have limitations in identifying the offset values of the BTDto discriminate clear-sky areas. The current algorithm overcomes the disadvantages of previous dust detection methods by considering the Brightness Temperature Ratio (BTR) values of the dual wavelength channels with 30-day composite, the optical properties of the dust particles, the variability of surface properties, and the cloud contamination. Therefore, the current algorithm shows improvements in detecting the dust loaded region over land during daytime. Finally, the confidence index of the current dust algorithm is shown in 10 × 10 pixels of the MODIS observations. From January to June, 2006, the results of the current algorithm are within 64 to 81% of those found using the fine mode fraction (FMF) and aerosol index (AI) from the MODIS and Ozone Monitoring Instrument (OMI). The agreement between the results of the current algorithm and the OMI AI over the non-polluted land also ranges from 60 to 67% to avoid errors due to the anthropogenic aerosol. In addition, the developed algorithm shows statistically significant results at four AErosol RObotic NETwork (AERONET) sites in East Asia.
Night-Time Vehicle Detection Algorithm Based on Visual Saliency and Deep Learning
Directory of Open Access Journals (Sweden)
Yingfeng Cai
2016-01-01
Full Text Available Night vision systems get more and more attention in the field of automotive active safety field. In this area, a number of researchers have proposed far-infrared sensor based night-time vehicle detection algorithm. However, existing algorithms have low performance in some indicators such as the detection rate and processing time. To solve this problem, we propose a far-infrared image vehicle detection algorithm based on visual saliency and deep learning. Firstly, most of the nonvehicle pixels will be removed with visual saliency computation. Then, vehicle candidate will be generated by using prior information such as camera parameters and vehicle size. Finally, classifier trained with deep belief networks will be applied to verify the candidates generated in last step. The proposed algorithm is tested in around 6000 images and achieves detection rate of 92.3% and processing time of 25 Hz which is better than existing methods.
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
Jin, Minglei; Jin, Weiqi; Li, Yiyang; Li, Shuo
2015-08-01
In this paper, we propose a novel scene-based non-uniformity correction algorithm for infrared image processing-temporal high-pass non-uniformity correction algorithm based on grayscale mapping (THP and GM). The main sources of non-uniformity are: (1) detector fabrication inaccuracies; (2) non-linearity and variations in the read-out electronics and (3) optical path effects. The non-uniformity will be reduced by non-uniformity correction (NUC) algorithms. The NUC algorithms are often divided into calibration-based non-uniformity correction (CBNUC) algorithms and scene-based non-uniformity correction (SBNUC) algorithms. As non-uniformity drifts temporally, CBNUC algorithms must be repeated by inserting a uniform radiation source which SBNUC algorithms do not need into the view, so the SBNUC algorithm becomes an essential part of infrared imaging system. The SBNUC algorithms' poor robustness often leads two defects: artifacts and over-correction, meanwhile due to complicated calculation process and large storage consumption, hardware implementation of the SBNUC algorithms is difficult, especially in Field Programmable Gate Array (FPGA) platform. The THP and GM algorithm proposed in this paper can eliminate the non-uniformity without causing defects. The hardware implementation of the algorithm only based on FPGA has two advantages: (1) low resources consumption, and (2) small hardware delay: less than 20 lines, it can be transplanted to a variety of infrared detectors equipped with FPGA image processing module, it can reduce the stripe non-uniformity and the ripple non-uniformity.
Detection of honeycomb cell walls from measurement data based on Harris corner detection algorithm
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.
Decoding the Brain’s Algorithm for Categorization from its Neural Implementation
Mack, Michael L.; Preston, Alison R.; Love, Bradley C.
2013-01-01
Summary Acts of cognition can be described at different levels of analysis: what behavior should characterize the act, what algorithms and representations underlie the behavior, and how the algorithms are physically realized in neural activity [1]. Theories that bridge levels of analysis offer more complete explanations by leveraging the constraints present at each level [2–4]. Despite the great potential for theoretical advances, few studies of cognition bridge levels of analysis. For example, formal cognitive models of category decisions accurately predict human decision making [5, 6], but whether model algorithms and representations supporting category decisions are consistent with underlying neural implementation remains unknown. This uncertainty is largely due to the hurdle of forging links between theory and brain [7–9]. Here, we tackle this critical problem by using brain response to characterize the nature of mental computations that support category decisions to evaluate two dominant, and opposing, models of categorization. We found that brain states during category decisions were significantly more consistent with latent model representations from exemplar [5] rather than prototype theory [10, 11]. Representations of individual experiences, not the abstraction of experiences, are critical for category decision making. Holding models accountable for behavior and neural implementation provides a means for advancing more complete descriptions of the algorithms of cognition. PMID:24094852
High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm
Directory of Open Access Journals (Sweden)
Dieter Hendricks
2016-02-01
Full Text Available We implement a master-slave parallel genetic algorithm with a bespoke log-likelihood fitness function to identify emergent clusters within price evolutions. We use graphics processing units (GPUs to implement a parallel genetic algorithm and visualise the results using disjoint minimal spanning trees. We demonstrate that our GPU parallel genetic algorithm, implemented on a commercially available general purpose GPU, is able to recover stock clusters in sub-second speed, based on a subset of stocks in the South African market. This approach represents a pragmatic choice for low-cost, scalable parallel computing and is significantly faster than a prototype serial implementation in an optimised C-based fourth-generation programming language, although the results are not directly comparable because of compiler differences. Combined with fast online intraday correlation matrix estimation from high frequency data for cluster identification, the proposed implementation offers cost-effective, near-real-time risk assessment for financial practitioners.
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.
A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data
Goldstein, Markus; Uchida, Seiichi
2016-01-01
Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks. PMID:27093601
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.
Directory of Open Access Journals (Sweden)
Simona M.C. Porto
2012-06-01
Full Text Available The objective of this study was to investigate the applicability of the Viola-Jones algorithm for continuous detection of the feeding behaviour of dairy cows housed in an open free-stall barn. A methodology was proposed in order to train, test and validate the classifier. A lower number of positive and negative images than those used by Viola and Jones were required during the training. The testing produced the following results: hit rate of about 97.85%, missed rate of about 2.15%, and false positive rate of about 0.67%. The validation was carried out by an accuracy assessment procedure which required the time-consuming work of an operator who labelled the true position of the cows within the barn and their behaviours. The accuracy assessment revealed that among the 715 frames about 90.63% contained only true positives, whereas about 9.37% were affected by underestimation, i.e., contained also one or two false negatives. False positives occurred only in 2.93% of the analyzed frames. Though a moderate mismatch between the testing and the validation performances was registered, the results obtained revealed the adequacy of the Viola-Jones algorithm for detecting the feeding behaviour of dairy cows housed in open free-stall barns. This, in turn, opens up opportunities for an automatic analysis of cow behaviour.
Fast intersection detection algorithm for PC-based robot off-line programming
Fedrowitz, Christian H.
1994-11-01
This paper presents a method for fast and reliable collision detection in complex production cells. The algorithm is part of the PC-based robot off-line programming system of the University of Siegen (Ropsus). The method is based on a solid model which is managed by a simplified constructive solid geometry model (CSG-model). The collision detection problem is divided in two steps. In the first step the complexity of the problem is reduced in linear time. In the second step the remaining solids are tested for intersection. For this the Simplex algorithm, which is known from linear optimization, is used. It computes a point which is common to two convex polyhedra. The polyhedra intersect, if such a point exists. Regarding the simplified geometrical model of Ropsus the algorithm runs also in linear time. In conjunction with the first step a resultant collision detection algorithm is found which requires linear time in all. Moreover it computes the resultant intersection polyhedron using the dual transformation.
Development of CAD implementing the algorithm of boundary elements’ numerical analytical method
Directory of Open Access Journals (Sweden)
Yulia V. Korniyenko
2015-03-01
Full Text Available Up to recent days the algorithms for numerical-analytical boundary elements method had been implemented with programs written in MATLAB environment language. Each program had a local character, i.e. used to solve a particular problem: calculation of beam, frame, arch, etc. Constructing matrices in these programs was carried out “manually” therefore being time-consuming. The research was purposed onto a reasoned choice of programming language for new CAD development, allows to implement algorithm of numerical analytical boundary elements method and to create visualization tools for initial objects and calculation results. Research conducted shows that among wide variety of programming languages the most efficient one for CAD development, employing the numerical analytical boundary elements method algorithm, is the Java language. This language provides tools not only for development of calculating CAD part, but also to build the graphic interface for geometrical models construction and calculated results interpretation.
Directory of Open Access Journals (Sweden)
Ahmad Audi
2017-07-01
Full Text Available Images acquired with a long exposure time using a camera embedded on UAVs (Unmanned Aerial Vehicles exhibit motion blur due to the erratic movements of the UAV. The aim of the present work is to be able to acquire several images with a short exposure time and use an image processing algorithm to produce a stacked image with an equivalent long exposure time. Our method is based on the feature point image registration technique. The algorithm is implemented on the light-weight IGN (Institut national de l’information géographique camera, which has an IMU (Inertial Measurement Unit sensor and an SoC (System on Chip/FPGA (Field-Programmable Gate Array. To obtain the correct parameters for the resampling of the images, the proposed method accurately estimates the geometrical transformation between the first and the N-th images. Feature points are detected in the first image using the FAST (Features from Accelerated Segment Test detector, then homologous points on other images are obtained by template matching using an initial position benefiting greatly from the presence of the IMU sensor. The SoC/FPGA in the camera is used to speed up some parts of the algorithm in order to achieve real-time performance as our ultimate objective is to exclusively write the resulting image to save bandwidth on the storage device. The paper includes a detailed description of the implemented algorithm, resource usage summary, resulting processing time, resulting images and block diagrams of the described architecture. The resulting stacked image obtained for real surveys does not seem visually impaired. An interesting by-product of this algorithm is the 3D rotation estimated by a photogrammetric method between poses, which can be used to recalibrate in real time the gyrometers of the IMU. Timing results demonstrate that the image resampling part of this algorithm is the most demanding processing task and should also be accelerated in the FPGA in future work.
Audi, Ahmad; Pierrot-Deseilligny, Marc; Meynard, Christophe; Thom, Christian
2017-07-18
Images acquired with a long exposure time using a camera embedded on UAVs (Unmanned Aerial Vehicles) exhibit motion blur due to the erratic movements of the UAV. The aim of the present work is to be able to acquire several images with a short exposure time and use an image processing algorithm to produce a stacked image with an equivalent long exposure time. Our method is based on the feature point image registration technique. The algorithm is implemented on the light-weight IGN (Institut national de l'information géographique) camera, which has an IMU (Inertial Measurement Unit) sensor and an SoC (System on Chip)/FPGA (Field-Programmable Gate Array). To obtain the correct parameters for the resampling of the images, the proposed method accurately estimates the geometrical transformation between the first and the N -th images. Feature points are detected in the first image using the FAST (Features from Accelerated Segment Test) detector, then homologous points on other images are obtained by template matching using an initial position benefiting greatly from the presence of the IMU sensor. The SoC/FPGA in the camera is used to speed up some parts of the algorithm in order to achieve real-time performance as our ultimate objective is to exclusively write the resulting image to save bandwidth on the storage device. The paper includes a detailed description of the implemented algorithm, resource usage summary, resulting processing time, resulting images and block diagrams of the described architecture. The resulting stacked image obtained for real surveys does not seem visually impaired. An interesting by-product of this algorithm is the 3D rotation estimated by a photogrammetric method between poses, which can be used to recalibrate in real time the gyrometers of the IMU. Timing results demonstrate that the image resampling part of this algorithm is the most demanding processing task and should also be accelerated in the FPGA in future work.
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.
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.
Blind information-theoretic multiuser detection algorithms for DS-CDMA and WCDMA downlink systems.
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).
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.
International Nuclear Information System (INIS)
Spiekerman, G.
1988-09-01
A partial blockage of the cooling channels of a fuel element in a swimming pool reactor could lead to vapour generation and to burn-out. To detect such anomalies, a pattern recognition algorithm based on power spectra density (PSD) proposed by Piety was further developed and implemented on a PDP 11/23 for on-line applications. This algorithm identifies anomalies by measuring the PSD on the process signal and comparing them with a standard baseline previously formed. Up to 8 decision discriminants help to recognize spectral changes due to anomalies. In our application, to detect boiling as quickly as possible with sufficient sensitivity, Piety's algorithm was modified using overlapped Fast-Fourier-Transform-Processing and the averaging of the PSDs over a large sample of preceding instantaneous PSDs. This processing allows high sensitivity in detecting weak disturbances without reducing response time. The algorithm was tested with simulation-of-boiling experiments where nitrogen in a cooling channel of a mock-up of a fuel element was injected. Void fractions higher than 30 % in the channel can be detected. In the case of boiling, it is believed that this limit is lower because collapsing bubbles could give rise to stronger fluctuations. The algorithm was also tested with a boiling experiment where the reactor coolant flow was actually reduced. The results showed that the discriminant D5 of Piety's algorithm based on neutron noise obtained from the existing neutron chambers of the reactor control system could sensitively recognize boiling. The detection time amounts to 7-30 s depending on the strength of the disturbances. Other events, which arise during a normal reactor run like scrams, removal of isotope elements without scramming or control rod movements and which could lead to false alarms, can be distinguished from boiling. 49 refs., 104 figs., 5 tabs
Radiation anomaly detection algorithms for field-acquired gamma energy spectra
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.
Anomaly Detection and Diagnosis Algorithms for Discrete Symbols
National Aeronautics and Space Administration — We present a set of novel algorithms which we call sequenceMiner that detect and characterize anomalies in large sets of high-dimensional symbol sequences that arise...
VHDL implementation of feature-extraction algorithm for the PANDA electromagnetic calorimeter
Energy Technology Data Exchange (ETDEWEB)
Guliyev, E. [Kernfysisch Versneller Instituut, University of Groningen, Zernikelaan 25, NL-9747 AA Groningen (Netherlands); Kavatsyuk, M., E-mail: m.kavatsyuk@rug.nl [Kernfysisch Versneller Instituut, University of Groningen, Zernikelaan 25, NL-9747 AA Groningen (Netherlands); Lemmens, P.J.J.; Tambave, G.; Loehner, H. [Kernfysisch Versneller Instituut, University of Groningen, Zernikelaan 25, NL-9747 AA Groningen (Netherlands)
2012-02-01
A simple, efficient, and robust feature-extraction algorithm, developed for the digital front-end electronics of the electromagnetic calorimeter of the PANDA spectrometer at FAIR, Darmstadt, is implemented in VHDL for a commercial 16 bit 100 MHz sampling ADC. The source-code is available as an open-source project and is adaptable for other projects and sampling ADCs. Best performance with different types of signal sources can be achieved through flexible parameter selection. The on-line data-processing in FPGA enables to construct an almost dead-time free data acquisition system which is successfully evaluated as a first step towards building a complete trigger-less readout chain. Prototype setups are studied to determine the dead-time of the implemented algorithm, the rate of false triggering, timing performance, and event correlations.
VHDL implementation of feature-extraction algorithm for the PANDA electromagnetic calorimeter
International Nuclear Information System (INIS)
Guliyev, E.; Kavatsyuk, M.; Lemmens, P.J.J.; Tambave, G.; Löhner, H.
2012-01-01
A simple, efficient, and robust feature-extraction algorithm, developed for the digital front-end electronics of the electromagnetic calorimeter of the PANDA spectrometer at FAIR, Darmstadt, is implemented in VHDL for a commercial 16 bit 100 MHz sampling ADC. The source-code is available as an open-source project and is adaptable for other projects and sampling ADCs. Best performance with different types of signal sources can be achieved through flexible parameter selection. The on-line data-processing in FPGA enables to construct an almost dead-time free data acquisition system which is successfully evaluated as a first step towards building a complete trigger-less readout chain. Prototype setups are studied to determine the dead-time of the implemented algorithm, the rate of false triggering, timing performance, and event correlations.
International Nuclear Information System (INIS)
Russell, K.R.; Saxner, M.; Ahnesjoe, A.; Montelius, A.; Grusell, E.; Dahlgren, C.V.
2000-01-01
The implementation of two algorithms for calculating dose distributions for radiation therapy treatment planning of intermediate energy proton beams is described. A pencil kernel algorithm and a depth penetration algorithm have been incorporated into a commercial three-dimensional treatment planning system (Helax-TMS, Helax AB, Sweden) to allow conformal planning techniques using irregularly shaped fields, proton range modulation, range modification and dose calculation for non-coplanar beams. The pencil kernel algorithm is developed from the Fermi-Eyges formalism and Moliere multiple-scattering theory with range straggling corrections applied. The depth penetration algorithm is based on the energy loss in the continuous slowing down approximation with simple correction factors applied to the beam penumbra region and has been implemented for fast, interactive treatment planning. Modelling of the effects of air gaps and range modifying device thickness and position are implicit to both algorithms. Measured and calculated dose values are compared for a therapeutic proton beam in both homogeneous and heterogeneous phantoms of varying complexity. Both algorithms model the beam penumbra as a function of depth in a homogeneous phantom with acceptable accuracy. Results show that the pencil kernel algorithm is required for modelling the dose perturbation effects from scattering in heterogeneous media. (author)
Linear segmentation algorithm for detecting layer boundary with lidar.
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.
An Overlapping Communities Detection Algorithm via Maxing Modularity in Opportunistic Networks
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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.
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.
Energy Technology Data Exchange (ETDEWEB)
Archer, Daniel E. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Hornback, Donald Eric [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Johnson, Jeffrey O. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Nicholson, Andrew D. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Patton, Bruce W. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Peplow, Douglas E. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Miller, Thomas Martin [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Ayaz-Maierhafer, Birsen [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2015-01-01
This report summarizes the findings of a two year effort to systematically assess neutron and gamma backgrounds relevant to operational modeling and detection technology implementation. The first year effort focused on reviewing the origins of background sources and their impact on measured rates in operational scenarios of interest. The second year has focused on the assessment of detector and algorithm performance as they pertain to operational requirements against the various background sources and background levels.
Low power multi-camera system and algorithms for automated threat detection
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.
Multi–GPU Implementation of Machine Learning Algorithm using CUDA and OpenCL
Directory of Open Access Journals (Sweden)
Jan Masek
2016-06-01
Full Text Available Using modern Graphic Processing Units (GPUs becomes very useful for computing complex and time consuming processes. GPUs provide high–performance computation capabilities with a good price. This paper deals with a multi–GPU OpenCL and CUDA implementations of k–Nearest Neighbor (k–NN algorithm. This work compares performances of OpenCLand CUDA implementations where each of them is suitable for different number of used attributes. The proposed CUDA algorithm achieves acceleration up to 880x in comparison witha single thread CPU version. The common k-NN was modified to be faster when the lower number of k neighbors is set. The performance of algorithm was verified with two GPUs dual-core NVIDIA GeForce GTX 690 and CPU Intel Core i7 3770 with 4.1 GHz frequency. The results of speed up were measured for one GPU, two GPUs, three and four GPUs. We performed several tests with data sets containing up to 4 million elements with various number of attributes.
Comparison of multihardware parallel implementations for a phase unwrapping algorithm
Hernandez-Lopez, Francisco Javier; Rivera, Mariano; Salazar-Garibay, Adan; Legarda-Sáenz, Ricardo
2018-04-01
Phase unwrapping is an important problem in the areas of optical metrology, synthetic aperture radar (SAR) image analysis, and magnetic resonance imaging (MRI) analysis. These images are becoming larger in size and, particularly, the availability and need for processing of SAR and MRI data have increased significantly with the acquisition of remote sensing data and the popularization of magnetic resonators in clinical diagnosis. Therefore, it is important to develop faster and accurate phase unwrapping algorithms. We propose a parallel multigrid algorithm of a phase unwrapping method named accumulation of residual maps, which builds on a serial algorithm that consists of the minimization of a cost function; minimization achieved by means of a serial Gauss-Seidel kind algorithm. Our algorithm also optimizes the original cost function, but unlike the original work, our algorithm is a parallel Jacobi class with alternated minimizations. This strategy is known as the chessboard type, where red pixels can be updated in parallel at same iteration since they are independent. Similarly, black pixels can be updated in parallel in an alternating iteration. We present parallel implementations of our algorithm for different parallel multicore architecture such as CPU-multicore, Xeon Phi coprocessor, and Nvidia graphics processing unit. In all the cases, we obtain a superior performance of our parallel algorithm when compared with the original serial version. In addition, we present a detailed comparative performance of the developed parallel versions.
A Greedy Algorithm for Neighborhood Overlap-Based Community Detection
Directory of Open Access Journals (Sweden)
Natarajan Meghanathan
2016-01-01
Full Text Available The neighborhood overlap (NOVER of an edge u-v is defined as the ratio of the number of nodes who are neighbors for both u and v to that of the number of nodes who are neighbors of at least u or v. In this paper, we hypothesize that an edge u-v with a lower NOVER score bridges two or more sets of vertices, with very few edges (other than u-v connecting vertices from one set to another set. Accordingly, we propose a greedy algorithm of iteratively removing the edges of a network in the increasing order of their neighborhood overlap and calculating the modularity score of the resulting network component(s after the removal of each edge. The network component(s that have the largest cumulative modularity score are identified as the different communities of the network. We evaluate the performance of the proposed NOVER-based community detection algorithm on nine real-world network graphs and compare the performance against the multi-level aggregation-based Louvain algorithm, as well as the original and time-efficient versions of the edge betweenness-based Girvan-Newman (GN community detection algorithm.
Multiangle Implementation of Atmospheric Correction (MAIAC): 2. Aerosol Algorithm
Lyapustin, A.; Wang, Y.; Laszlo, I.; Kahn, R.; Korkin, S.; Remer, L.; Levy, R.; Reid, J. S.
2011-01-01
An aerosol component of a new multiangle implementation of atmospheric correction (MAIAC) algorithm is presented. MAIAC is a generic algorithm developed for the Moderate Resolution Imaging Spectroradiometer (MODIS), which performs aerosol retrievals and atmospheric correction over both dark vegetated surfaces and bright deserts based on a time series analysis and image-based processing. The MAIAC look-up tables explicitly include surface bidirectional reflectance. The aerosol algorithm derives the spectral regression coefficient (SRC) relating surface bidirectional reflectance in the blue (0.47 micron) and shortwave infrared (2.1 micron) bands; this quantity is prescribed in the MODIS operational Dark Target algorithm based on a parameterized formula. The MAIAC aerosol products include aerosol optical thickness and a fine-mode fraction at resolution of 1 km. This high resolution, required in many applications such as air quality, brings new information about aerosol sources and, potentially, their strength. AERONET validation shows that the MAIAC and MOD04 algorithms have similar accuracy over dark and vegetated surfaces and that MAIAC generally improves accuracy over brighter surfaces due to the SRC retrieval and explicit bidirectional reflectance factor characterization, as demonstrated for several U.S. West Coast AERONET sites. Due to its generic nature and developed angular correction, MAIAC performs aerosol retrievals over bright deserts, as demonstrated for the Solar Village Aerosol Robotic Network (AERONET) site in Saudi Arabia.
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.
A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features
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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.
Chang, Chein-I
2017-01-01
This book explores recursive architectures in designing progressive hyperspectral imaging algorithms. In particular, it makes progressive imaging algorithms recursive by introducing the concept of Kalman filtering in algorithm design so that hyperspectral imagery can be processed not only progressively sample by sample or band by band but also recursively via recursive equations. This book can be considered a companion book of author’s books, Real-Time Progressive Hyperspectral Image Processing, published by Springer in 2016. Explores recursive structures in algorithm architecture Implements algorithmic recursive architecture in conjunction with progressive sample and band processing Derives Recursive Hyperspectral Sample Processing (RHSP) techniques according to Band-Interleaved Sample/Pixel (BIS/BIP) acquisition format Develops Recursive Hyperspectral Band Processing (RHBP) techniques according to Band SeQuential (BSQ) acquisition format for hyperspectral data.
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.
Advanced Oil Spill Detection Algorithms For Satellite Based Maritime Environment Monitoring
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
Durand, Melissa A; Wang, Steven; Hooley, Regina J; Raghu, Madhavi; Philpotts, Liane E
2016-01-01
As use of digital breast tomosynthesis becomes increasingly widespread, new management challenges are inevitable because tomosynthesis may reveal suspicious lesions not visible at conventional two-dimensional (2D) full-field digital mammography. Architectural distortion is a mammographic finding associated with a high positive predictive value for malignancy. It is detected more frequently at tomosynthesis than at 2D digital mammography and may even be occult at conventional 2D imaging. Few studies have focused on tomosynthesis-detected architectural distortions to date, and optimal management of these distortions has yet to be well defined. Since implementing tomosynthesis at our institution in 2011, we have learned some practical ways to assess architectural distortion. Because distortions may be subtle, tomosynthesis localization tools plus improved visualization of adjacent landmarks are crucial elements in guiding mammographic identification of elusive distortions. These same tools can guide more focused ultrasonography (US) of the breast, which facilitates detection and permits US-guided tissue sampling. Some distortions may be sonographically occult, in which case magnetic resonance imaging may be a reasonable option, both to increase diagnostic confidence and to provide a means for image-guided biopsy. As an alternative, tomosynthesis-guided biopsy, conventional stereotactic biopsy (when possible), or tomosynthesis-guided needle localization may be used to achieve tissue diagnosis. Practical uses for tomosynthesis in evaluation of architectural distortion are highlighted, potential complications are identified, and a working algorithm for management of tomosynthesis-detected architectural distortion is proposed. (©)RSNA, 2016.
Aircraft target detection algorithm based on high resolution spaceborne SAR imagery
Zhang, Hui; Hao, Mengxi; Zhang, Cong; Su, Xiaojing
2018-03-01
In this paper, an image classification algorithm for airport area is proposed, which based on the statistical features of synthetic aperture radar (SAR) images and the spatial information of pixels. The algorithm combines Gamma mixture model and MRF. The algorithm using Gamma mixture model to obtain the initial classification result. Pixel space correlation based on the classification results are optimized by the MRF technique. Additionally, morphology methods are employed to extract airport (ROI) region where the suspected aircraft target samples are clarified to reduce the false alarm and increase the detection performance. Finally, this paper presents the plane target detection, which have been verified by simulation test.
Directory of Open Access Journals (Sweden)
ALEX RAJ S. M.
2017-09-01
Full Text Available Underwater images raise new challenges in the field of digital image processing technology in recent years because of its widespread applications. There are many tangled matters to be considered in processing of images collected from water medium due to the adverse effects imposed by the environment itself. Image segmentation is preferred as basal stage of many digital image processing techniques which distinguish multiple segments in an image and reveal the hidden crucial information required for a peculiar application. There are so many general purpose algorithms and techniques that have been developed for image segmentation. Discontinuity based segmentation are most promising approach for image segmentation, in which Canny Edge detection based segmentation is more preferred for its high level of noise immunity and ability to tackle underwater environment. Since dealing with real time underwater image segmentation algorithm, which is computationally complex enough, an efficient hardware implementation is to be considered. The FPGA based realization of the referred segmentation algorithm is presented in this paper.
Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms
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...
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.
An improved algorithm of laser spot center detection in strong noise background
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.
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.
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
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.
A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults.
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.
Ren, Feixiang; Huang, Jinsheng; Terauchi, Mutsuhiro; Jiang, Ruyi; Klette, Reinhard
A robust and efficient lane detection system is an essential component of Lane Departure Warning Systems, which are commonly used in many vision-based Driver Assistance Systems (DAS) in intelligent transportation. Various computation platforms have been proposed in the past few years for the implementation of driver assistance systems (e.g., PC, laptop, integrated chips, PlayStation, and so on). In this paper, we propose a new platform for the implementation of lane detection, which is based on a mobile phone (the iPhone). Due to physical limitations of the iPhone w.r.t. memory and computing power, a simple and efficient lane detection algorithm using a Hough transform is developed and implemented on the iPhone, as existing algorithms developed based on the PC platform are not suitable for mobile phone devices (currently). Experiments of the lane detection algorithm are made both on PC and on iPhone.
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 matched-filter algorithm to detect amperometric spikes resulting from quantal secretion.
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.
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
An Implementation and Detailed Analysis of the K-SVD Image Denoising Algorithm
Directory of Open Access Journals (Sweden)
Marc Lebrun
2012-05-01
Full Text Available K-SVD is a signal representation method which, from a set of signals, can derive a dictionary able to approximate each signal with a sparse combination of the atoms. This paper focuses on the K-SVD-based image denoising algorithm. The implementation is described in detail and its parameters are analyzed and varied to come up with a reliable implementation.
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...
A complete implementation of the conjugate gradient algorithm on a reconfigurable supercomputer
International Nuclear Information System (INIS)
Dubois, David H.; Dubois, Andrew J.; Connor, Carolyn M.; Boorman, Thomas M.; Poole, Stephen W.
2008-01-01
The conjugate gradient is a prominent iterative method for solving systems of sparse linear equations. Large-scale scientific applications often utilize a conjugate gradient solver at their computational core. In this paper we present a field programmable gate array (FPGA) based implementation of a double precision, non-preconditioned, conjugate gradient solver for fmite-element or finite-difference methods. OUf work utilizes the SRC Computers, Inc. MAPStation hardware platform along with the 'Carte' software programming environment to ease the programming workload when working with the hybrid (CPUIFPGA) environment. The implementation is designed to handle large sparse matrices of up to order N x N where N <= 116,394, with up to 7 non-zero, 64-bit elements per sparse row. This implementation utilizes an optimized sparse matrix-vector multiply operation which is critical for obtaining high performance. Direct parallel implementations of loop unrolling and loop fusion are utilized to extract performance from the various vector/matrix operations. Rather than utilize the FPGA devices as function off-load accelerators, our implementation uses the FPGAs to implement the core conjugate gradient algorithm. Measured run-time performance data is presented comparing the FPGA implementation to a software-only version showing that the FPGA can outperform processors running up to 30x the clock rate. In conclusion we take a look at the new SRC-7 system and estimate the performance of this algorithm on that architecture.
Road detection in SAR images using a tensor voting algorithm
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.
FPGA implementation of ICA algorithm for blind signal separation and adaptive noise canceling.
Kim, Chang-Min; Park, Hyung-Min; Kim, Taesu; Choi, Yoon-Kyung; Lee, Soo-Young
2003-01-01
An field programmable gate array (FPGA) implementation of independent component analysis (ICA) algorithm is reported for blind signal separation (BSS) and adaptive noise canceling (ANC) in real time. In order to provide enormous computing power for ICA-based algorithms with multipath reverberation, a special digital processor is designed and implemented in FPGA. The chip design fully utilizes modular concept and several chips may be put together for complex applications with a large number of noise sources. Experimental results with a fabricated test board are reported for ANC only, BSS only, and simultaneous ANC/BSS, which demonstrates successful speech enhancement in real environments in real time.
Purgatorio - A new implementation of the Inferno algorithm
Energy Technology Data Exchange (ETDEWEB)
Wilson, B; Sonnad, V; Sterne, P; Isaacs, W
2005-03-29
For astrophysical applications, as well as modeling laser-produced plasmas, there is a continual need for equation-of-state data over a wide domain of physical conditions. This paper presents algorithmic aspects for computing the Helmholtz free energy of plasma electrons for temperatures spanning from a few Kelvin to several KeV, and densities ranging from essentially isolated ion conditions to such large compressions that most bound orbitals become delocalized. The objective is high precision results in order to compute pressure and other thermodynamic quantities by numerical differentiation. This approach has the advantage that internal thermodynamic self-consistency is ensured, regardless of the specific physical model, but at the cost of very stringent numerical tolerances for each operation. The computational aspects we address in this paper are faced by any model that relies on input from the quantum mechanical spectrum of a spherically symmetric Hamiltonian operator. The particular physical model we employ is that of INFERNO; of a spherically averaged ion embedded in jellium. An overview of PURGATORIO, a new implementation of the INFERNO equation of state model, is presented. The new algorithm emphasizes a novel decimation scheme for automatically resolving the structure of the continuum density of states, circumventing limitations of the pseudo-R matrix algorithm previously utilized.
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.
Implementation of domain decomposition and data decomposition algorithms in RMC code
International Nuclear Information System (INIS)
Liang, J.G.; Cai, Y.; Wang, K.; She, D.
2013-01-01
The applications of Monte Carlo method in reactor physics analysis is somewhat restricted due to the excessive memory demand in solving large-scale problems. Memory demand in MC simulation is analyzed firstly, it concerns geometry data, data of nuclear cross-sections, data of particles, and data of tallies. It appears that tally data is dominant in memory cost and should be focused on in solving the memory problem. Domain decomposition and tally data decomposition algorithms are separately designed and implemented in the reactor Monte Carlo code RMC. Basically, the domain decomposition algorithm is a strategy of 'divide and rule', which means problems are divided into different sub-domains to be dealt with separately and some rules are established to make sure the whole results are correct. Tally data decomposition consists in 2 parts: data partition and data communication. Two algorithms with differential communication synchronization mechanisms are proposed. Numerical tests have been executed to evaluate performance of the new algorithms. Domain decomposition algorithm shows potentials to speed up MC simulation as a space parallel method. As for tally data decomposition algorithms, memory size is greatly reduced
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 ...
A Fast Algorithm of Generalized Radon-Fourier Transform for Weak Maneuvering Target Detection
Directory of Open Access Journals (Sweden)
Weijie Xia
2016-01-01
Full Text Available The generalized Radon-Fourier transform (GRFT has been proposed to detect radar weak maneuvering targets by realizing coherent integration via jointly searching in motion parameter space. Two main drawbacks of GRFT are the heavy computational burden and the blind speed side lobes (BSSL which will cause serious false alarms. The BSSL learning-based particle swarm optimization (BPSO has been proposed before to reduce the computational burden of GRFT and solve the BSSL problem simultaneously. However, the BPSO suffers from an apparent loss in detection performance compared with GRFT. In this paper, a fast implementation algorithm of GRFT using the BSSL learning-based modified wind-driven optimization (BMWDO is proposed. In the BMWDO, the BSSL learning procedure is also used to deal with the BSSL phenomenon. Besides, the MWDO adjusts the coefficients in WDO with Levy distribution and uniform distribution, and it outperforms PSO in a noisy environment. Compared with BPSO, the proposed method can achieve better detection performance with a similar computational cost. Several numerical experiments are also provided to demonstrate the effectiveness of the proposed method.
Nonlinear Algorithms for Channel Equalization and Map Symbol Detection.
Giridhar, K.
The transfer of information through a communication medium invariably results in various kinds of distortion to the transmitted signal. In this dissertation, a feed -forward neural network-based equalizer, and a family of maximum a posteriori (MAP) symbol detectors are proposed for signal recovery in the presence of intersymbol interference (ISI) and additive white Gaussian noise. The proposed neural network-based equalizer employs a novel bit-mapping strategy to handle multilevel data signals in an equivalent bipolar representation. It uses a training procedure to learn the channel characteristics, and at the end of training, the multilevel symbols are recovered from the corresponding inverse bit-mapping. When the channel characteristics are unknown and no training sequences are available, blind estimation of the channel (or its inverse) and simultaneous data recovery is required. Convergence properties of several existing Bussgang-type blind equalization algorithms are studied through computer simulations, and a unique gain independent approach is used to obtain a fair comparison of their rates of convergence. Although simple to implement, the slow convergence of these Bussgang-type blind equalizers make them unsuitable for many high data-rate applications. Rapidly converging blind algorithms based on the principle of MAP symbol-by -symbol detection are proposed, which adaptively estimate the channel impulse response (CIR) and simultaneously decode the received data sequence. Assuming a linear and Gaussian measurement model, the near-optimal blind MAP symbol detector (MAPSD) consists of a parallel bank of conditional Kalman channel estimators, where the conditioning is done on each possible data subsequence that can convolve with the CIR. This algorithm is also extended to the recovery of convolutionally encoded waveforms in the presence of ISI. Since the complexity of the MAPSD algorithm increases exponentially with the length of the assumed CIR, a suboptimal
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.
Leakage Detection and Estimation Algorithm for Loss Reduction in Water Piping Networks
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Kazeem B. Adedeji
2017-10-01
Full Text Available Water loss through leaking pipes constitutes a major challenge to the operational service of water utilities. In recent years, increasing concern about the financial loss and environmental pollution caused by leaking pipes has been driving the development of efficient algorithms for detecting leakage in water piping networks. Water distribution networks (WDNs are disperse in nature with numerous number of nodes and branches. Consequently, identifying the segment(s of the network and the exact leaking pipelines connected to this segment(s where higher background leakage outflow occurs is a challenging task. Background leakage concerns the outflow from small cracks or deteriorated joints. In addition, because they are diffuse flow, they are not characterised by quick pressure drop and are not detectable by measuring instruments. Consequently, they go unreported for a long period of time posing a threat to water loss volume. Most of the existing research focuses on the detection and localisation of burst type leakages which are characterised by a sudden pressure drop. In this work, an algorithm for detecting and estimating background leakage in water distribution networks is presented. The algorithm integrates a leakage model into a classical WDN hydraulic model for solving the network leakage flows. The applicability of the developed algorithm is demonstrated on two different water networks. The results of the tested networks are discussed and the solutions obtained show the benefits of the proposed algorithm. A noteworthy evidence is that the algorithm permits the detection of critical segments or pipes of the network experiencing higher leakage outflow and indicates the probable pipes of the network where pressure control can be performed. However, the possible position of pressure control elements along such critical pipes will be addressed in future work.
A review of feature detection and match algorithms for localization and mapping
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.
Breadth-First Search-Based Single-Phase Algorithms for Bridge Detection in Wireless Sensor Networks
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
High Precision Edge Detection Algorithm for Mechanical Parts
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 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.
SURF IA Conflict Detection and Resolution Algorithm Evaluation
Jones, Denise R.; Chartrand, Ryan C.; Wilson, Sara R.; Commo, Sean A.; Barker, Glover D.
2012-01-01
The Enhanced Traffic Situational Awareness on the Airport Surface with Indications and Alerts (SURF IA) algorithm was evaluated in a fast-time batch simulation study at the National Aeronautics and Space Administration (NASA) Langley Research Center. SURF IA is designed to increase flight crew situation awareness of the runway environment and facilitate an appropriate and timely response to potential conflict situations. The purpose of the study was to evaluate the performance of the SURF IA algorithm under various runway scenarios, multiple levels of conflict detection and resolution (CD&R) system equipage, and various levels of horizontal position accuracy. This paper gives an overview of the SURF IA concept, simulation study, and results. Runway incursions are a serious aviation safety hazard. As such, the FAA is committed to reducing the severity, number, and rate of runway incursions by implementing a combination of guidance, education, outreach, training, technology, infrastructure, and risk identification and mitigation initiatives [1]. Progress has been made in reducing the number of serious incursions - from a high of 67 in Fiscal Year (FY) 2000 to 6 in FY2010. However, the rate of all incursions has risen steadily over recent years - from a rate of 12.3 incursions per million operations in FY2005 to a rate of 18.9 incursions per million operations in FY2010 [1, 2]. The National Transportation Safety Board (NTSB) also considers runway incursions to be a serious aviation safety hazard, listing runway incursion prevention as one of their most wanted transportation safety improvements [3]. The NTSB recommends that immediate warning of probable collisions/incursions be given directly to flight crews in the cockpit [4].
Multispectral fluorescence image algorithms for detection of frass on mature tomatoes
A multispectral algorithm derived from hyperspectral line-scan fluorescence imaging under violet LED excitation was developed for the detection of frass contamination on mature tomatoes. The algorithm utilized the fluorescence intensities at five wavebands, 515 nm, 640 nm, 664 nm, 690 nm, and 724 nm...
Dupuy, C; Morignat, E; Dorea, F; Ducrot, C; Calavas, D; Gay, E
2015-09-01
The objective of this study was to assess the performance of several algorithms for outbreak detection based on weekly proportions of whole carcass condemnations. Data from one French slaughterhouse over the 2005-2009 period were used (177 098 slaughtered cattle, 0.97% of whole carcass condemnations). The method involved three steps: (i) preparation of an outbreak-free historical baseline over 5 years, (ii) simulation of over 100 years of baseline time series with injection of artificial outbreak signals with several shapes, durations and magnitudes, and (iii) assessment of the performance (sensitivity, specificity, outbreak detection precocity) of several algorithms to detect these artificial outbreak signals. The algorithms tested included the Shewart p chart, confidence interval of the negative binomial model, the exponentially weighted moving average (EWMA); and cumulative sum (CUSUM). The highest sensitivity was obtained using a negative binomial algorithm and the highest specificity with CUSUM or EWMA. EWMA sensitivity was too low to select this algorithm for efficient outbreak detection. CUSUM's performance was complementary to the negative binomial algorithm. The use of both algorithms on real data for a prospective investigation of the whole carcass condemnation rate as a syndromic surveillance indicator could be relevant. Shewart could also be a good option considering its high sensitivity and simplicity of implementation.
Implementation of the Iterative Proportion Fitting Algorithm for Geostatistical Facies Modeling
International Nuclear Information System (INIS)
Li Yupeng; Deutsch, Clayton V.
2012-01-01
In geostatistics, most stochastic algorithm for simulation of categorical variables such as facies or rock types require a conditional probability distribution. The multivariate probability distribution of all the grouped locations including the unsampled location permits calculation of the conditional probability directly based on its definition. In this article, the iterative proportion fitting (IPF) algorithm is implemented to infer this multivariate probability. Using the IPF algorithm, the multivariate probability is obtained by iterative modification to an initial estimated multivariate probability using lower order bivariate probabilities as constraints. The imposed bivariate marginal probabilities are inferred from profiles along drill holes or wells. In the IPF process, a sparse matrix is used to calculate the marginal probabilities from the multivariate probability, which makes the iterative fitting more tractable and practical. This algorithm can be extended to higher order marginal probability constraints as used in multiple point statistics. The theoretical framework is developed and illustrated with estimation and simulation example.
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...
An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm.
Qin, Qin; Li, Jianqing; Yue, Yinggao; Liu, Chengyu
2017-01-01
R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, were tested on the MIT-BIH arrhythmia database and the QT database. Experimental results showed that the proposed algorithm achieved mean sensitivity of 99.39%, positive predictivity of 99.49%, and accuracy of 98.89% on the MIT-BIH arrhythmia database and 99.83%, 99.90%, and 99.73%, respectively, on the QT database. By processing one ECG record, the mean time consumptions were 0.872 s and 0.763 s for the MIT-BIH arrhythmia database and QT database, respectively, yielding 30.6% and 32.9% of time reduction compared to the traditional Pan-Tompkins method.
ICPD-a new peak detection algorithm for LC/MS.
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.
Algorithms for Anomaly Detection - Lecture 1
CERN. Geneva
2017-01-01
The concept of statistical anomalies, or outliers, has fascinated experimentalists since the earliest attempts to interpret data. We want to know why some data points don’t seem to belong with the others: perhaps we want to eliminate spurious or unrepresentative data from our model. Or, the anomalies themselves may be what we are interested in: an outlier could represent the symptom of a disease, an attack on a computer network, a scientific discovery, or even an unfaithful partner. We start with some general considerations, such as the relationship between clustering and anomaly detection, the choice between supervised and unsupervised methods, and the difference between global and local anomalies. Then we will survey the most representative anomaly detection algorithms, highlighting what kind of data each approach is best suited to, and discussing their limitations. We will finish with a discussion of the difficulties of anomaly detection in high-dimensional data and some new directions for anomaly detec...
Algorithms for Anomaly Detection - Lecture 2
CERN. Geneva
2017-01-01
The concept of statistical anomalies, or outliers, has fascinated experimentalists since the earliest attempts to interpret data. We want to know why some data points don’t seem to belong with the others: perhaps we want to eliminate spurious or unrepresentative data from our model. Or, the anomalies themselves may be what we are interested in: an outlier could represent the symptom of a disease, an attack on a computer network, a scientific discovery, or even an unfaithful partner. We start with some general considerations, such as the relationship between clustering and anomaly detection, the choice between supervised and unsupervised methods, and the difference between global and local anomalies. Then we will survey the most representative anomaly detection algorithms, highlighting what kind of data each approach is best suited to, and discussing their limitations. We will finish with a discussion of the difficulties of anomaly detection in high-dimensional data and some new directions for anomaly detec...
VHDL Implementation of Feature-Extraction Algorithm for the PANDA Electromagnetic Calorimeter
Kavatsyuk, M.; Guliyev, E.; Lemmens, P. J. J.; Löhner, H.; Tambave, G.
2010-01-01
The feature-extraction algorithm, developed for the digital front-end electronics of the electromagnetic calorimeter of the PANDA detector at the future FAIR facility, is implemented in VHDL for a commercial 16 bit 100 MHz sampling ADC. The use of modified firmware with the running on-line
VHDL implementation of feature-extraction algorithm for the PANDA electromagnetic calorimeter
Guliyev, E.; Kavatsyuk, M.; Lemmens, P. J. J.; Tambave, G.; Löhner, H.
2012-01-01
A simple, efficient, and robust feature-extraction algorithm, developed for the digital front-end electronics of the electromagnetic calorimeter of the PANDA spectrometer at FAIR, Darmstadt, is implemented in VHDL for a commercial 16 bit 100 MHz sampling ADC. The source-code is available as an
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).
Developing and Implementing the Data Mining Algorithms in RAVEN
International Nuclear Information System (INIS)
Sen, Ramazan Sonat; Maljovec, Daniel Patrick; Alfonsi, Andrea; Rabiti, Cristian
2015-01-01
The RAVEN code is becoming a comprehensive tool to perform probabilistic risk assessment, uncertainty quantification, and verification and validation. The RAVEN code is being developed to support many programs and to provide a set of methodologies and algorithms for advanced analysis. Scientific computer codes can generate enormous amounts of data. To post-process and analyze such data might, in some cases, take longer than the initial software runtime. Data mining algorithms/methods help in recognizing and understanding patterns in the data, and thus discover knowledge in databases. The methodologies used in the dynamic probabilistic risk assessment or in uncertainty and error quantification analysis couple system/physics codes with simulation controller codes, such as RAVEN. RAVEN introduces both deterministic and stochastic elements into the simulation while the system/physics code model the dynamics deterministically. A typical analysis is performed by sampling values of a set of parameter values. A major challenge in using dynamic probabilistic risk assessment or uncertainty and error quantification analysis for a complex system is to analyze the large number of scenarios generated. Data mining techniques are typically used to better organize and understand data, i.e. recognizing patterns in the data. This report focuses on development and implementation of Application Programming Interfaces (APIs) for different data mining algorithms, and the application of these algorithms to different databases.
Developing and Implementing the Data Mining Algorithms in RAVEN
Energy Technology Data Exchange (ETDEWEB)
Sen, Ramazan Sonat [Idaho National Lab. (INL), Idaho Falls, ID (United States); Maljovec, Daniel Patrick [Idaho National Lab. (INL), Idaho Falls, ID (United States); Alfonsi, Andrea [Idaho National Lab. (INL), Idaho Falls, ID (United States); Rabiti, Cristian [Idaho National Lab. (INL), Idaho Falls, ID (United States)
2015-09-01
The RAVEN code is becoming a comprehensive tool to perform probabilistic risk assessment, uncertainty quantification, and verification and validation. The RAVEN code is being developed to support many programs and to provide a set of methodologies and algorithms for advanced analysis. Scientific computer codes can generate enormous amounts of data. To post-process and analyze such data might, in some cases, take longer than the initial software runtime. Data mining algorithms/methods help in recognizing and understanding patterns in the data, and thus discover knowledge in databases. The methodologies used in the dynamic probabilistic risk assessment or in uncertainty and error quantification analysis couple system/physics codes with simulation controller codes, such as RAVEN. RAVEN introduces both deterministic and stochastic elements into the simulation while the system/physics code model the dynamics deterministically. A typical analysis is performed by sampling values of a set of parameter values. A major challenge in using dynamic probabilistic risk assessment or uncertainty and error quantification analysis for a complex system is to analyze the large number of scenarios generated. Data mining techniques are typically used to better organize and understand data, i.e. recognizing patterns in the data. This report focuses on development and implementation of Application Programming Interfaces (APIs) for different data mining algorithms, and the application of these algorithms to different databases.
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.
Sundareshan, Malur K; Bhattacharjee, Supratik; Inampudi, Radhika; Pang, Ho-Yuen
2002-12-10
Computational complexity is a major impediment to the real-time implementation of image restoration and superresolution algorithms in many applications. Although powerful restoration algorithms have been developed within the past few years utilizing sophisticated mathematical machinery (based on statistical optimization and convex set theory), these algorithms are typically iterative in nature and require a sufficient number of iterations to be executed to achieve the desired resolution improvement that may be needed to meaningfully perform postprocessing image exploitation tasks in practice. Additionally, recent technological breakthroughs have facilitated novel sensor designs (focal plane arrays, for instance) that make it possible to capture megapixel imagery data at video frame rates. A major challenge in the processing of these large-format images is to complete the execution of the image processing steps within the frame capture times and to keep up with the output rate of the sensor so that all data captured by the sensor can be efficiently utilized. Consequently, development of novel methods that facilitate real-time implementation of image restoration and superresolution algorithms is of significant practical interest and is the primary focus of this study. The key to designing computationally efficient processing schemes lies in strategically introducing appropriate preprocessing steps together with the superresolution iterations to tailor optimized overall processing sequences for imagery data of specific formats. For substantiating this assertion, three distinct methods for tailoring a preprocessing filter and integrating it with the superresolution processing steps are outlined. These methods consist of a region-of-interest extraction scheme, a background-detail separation procedure, and a scene-derived information extraction step for implementing a set-theoretic restoration of the image that is less demanding in computation compared with the
GPGPU Implementation of a Genetic Algorithm for Stereo Refinement
Directory of Open Access Journals (Sweden)
Álvaro Arranz
2015-03-01
Full Text Available During the last decade, the general-purpose computing on graphics processing units Graphics (GPGPU has turned out to be a useful tool for speeding up many scientific calculations. Computer vision is known to be one of the fields with more penetration of these new techniques. This paper explores the advantages of using GPGPU implementation to speedup a genetic algorithm used for stereo refinement. The main contribution of this paper is analyzing which genetic operators take advantage of a parallel approach and the description of an efficient state- of-the-art implementation for each one. As a result, speed-ups close to x80 can be achieved, demonstrating to be the only way of achieving close to real-time performance.
Linear array implementation of the EM algorithm for PET image reconstruction
International Nuclear Information System (INIS)
Rajan, K.; Patnaik, L.M.; Ramakrishna, J.
1995-01-01
The PET image reconstruction based on the EM algorithm has several attractive advantages over the conventional convolution back projection algorithms. However, the PET image reconstruction based on the EM algorithm is computationally burdensome for today's single processor systems. In addition, a large memory is required for the storage of the image, projection data, and the probability matrix. Since the computations are easily divided into tasks executable in parallel, multiprocessor configurations are the ideal choice for fast execution of the EM algorithms. In tis study, the authors attempt to overcome these two problems by parallelizing the EM algorithm on a multiprocessor systems. The parallel EM algorithm on a linear array topology using the commercially available fast floating point digital signal processor (DSP) chips as the processing elements (PE's) has been implemented. The performance of the EM algorithm on a 386/387 machine, IBM 6000 RISC workstation, and on the linear array system is discussed and compared. The results show that the computational speed performance of a linear array using 8 DSP chips as PE's executing the EM image reconstruction algorithm is about 15.5 times better than that of the IBM 6000 RISC workstation. The novelty of the scheme is its simplicity. The linear array topology is expandable with a larger number of PE's. The architecture is not dependant on the DSP chip chosen, and the substitution of the latest DSP chip is straightforward and could yield better speed performance
Fischer, M.; Caprio, M.; Cua, G. B.; Heaton, T. H.; Clinton, J. F.; Wiemer, S.
2009-12-01
The Virtual Seismologist (VS) algorithm is a Bayesian approach to earthquake early warning (EEW) being implemented by the Swiss Seismological Service at ETH Zurich. The application of Bayes’ theorem in earthquake early warning states that the most probable source estimate at any given time is a combination of contributions from a likelihood function that evolves in response to incoming data from the on-going earthquake, and selected prior information, which can include factors such as network topology, the Gutenberg-Richter relationship or previously observed seismicity. The VS algorithm was one of three EEW algorithms involved in the California Integrated Seismic Network (CISN) real-time EEW testing and performance evaluation effort. Its compelling real-time performance in California over the last three years has led to its inclusion in the new USGS-funded effort to develop key components of CISN ShakeAlert, a prototype EEW system that could potentially be implemented in California. A significant portion of VS code development was supported by the SAFER EEW project in Europe. We discuss recent enhancements to the VS EEW algorithm. We developed and continue to test a multiple-threshold event detection scheme, which uses different association / location approaches depending on the peak amplitudes associated with an incoming P pick. With this scheme, an event with sufficiently high initial amplitudes can be declared on the basis of a single station, maximizing warning times for damaging events for which EEW is most relevant. Smaller, non-damaging events, which will have lower initial amplitudes, will require more picks to be declared an event to reduce false alarms. This transforms the VS codes from a regional EEW approach reliant on traditional location estimation (and it requirement of at least 4 picks as implemented by the Binder Earthworm phase associator) to a hybrid on-site/regional approach capable of providing a continuously evolving stream of EEW
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 Performance Evaluation of Lightning-NO Algorithms in CMAQ
In the Community Multiscale Air Quality (CMAQv5.2) model, we have implemented two algorithms for lightning NO production; one algorithm is based on the hourly observed cloud-to-ground lightning strike data from National Lightning Detection Network (NLDN) to replace the previous m...
Patton, J.; Yeck, W.; Benz, H.
2017-12-01
The U.S. Geological Survey National Earthquake Information Center (USGS NEIC) is implementing and integrating new signal detection methods such as subspace correlation, continuous beamforming, multi-band picking and automatic phase identification into near-real-time monitoring operations. Leveraging the additional information from these techniques help the NEIC utilize a large and varied network on local to global scales. The NEIC is developing an ordered, rapid, robust, and decentralized framework for distributing seismic detection data as well as a set of formalized formatting standards. These frameworks and standards enable the NEIC to implement a seismic event detection framework that supports basic tasks, including automatic arrival time picking, social media based event detections, and automatic association of different seismic detection data into seismic earthquake events. In addition, this framework enables retrospective detection processing such as automated S-wave arrival time picking given a detected event, discrimination and classification of detected events by type, back-azimuth and slowness calculations, and ensuring aftershock and induced sequence detection completeness. These processes and infrastructure improve the NEIC's capabilities, accuracy, and speed of response. In addition, this same infrastructure provides an improved and convenient structure to support access to automatic detection data for both research and algorithmic development.
Energy Technology Data Exchange (ETDEWEB)
Deptuch, G. W. [AGH-UST, Cracow; Fahim, F. [Fermilab; Grybos, P. [AGH-UST, Cracow; Hoff, J. [Fermilab; Maj, P. [AGH-UST, Cracow; Siddons, D. P. [Brookhaven; Kmon, P. [AGH-UST, Cracow; Trimpl, M. [Fermilab; Zimmerman, T. [Fermilab
2017-05-06
An on-chip implementable algorithm for allocation of an X-ray photon imprint, called a hit, to a single pixel in the presence of charge sharing in a highly segmented pixel detector is described. Its proof-of-principle implementation is also given supported by the results of tests using a highly collimated X-ray photon beam from a synchrotron source. The algorithm handles asynchronous arrivals of X-ray photons. Activation of groups of pixels, comparisons of peak amplitudes of pulses within an active neighborhood and finally latching of the results of these comparisons constitute the three procedural steps of the algorithm. A grouping of pixels to one virtual pixel that recovers composite signals and event driven strobes to control comparisons of fractional signals between neighboring pixels are the actuators of the algorithm. The circuitry necessary to implement the algorithm requires an extensive inter-pixel connection grid of analog and digital signals that are exchanged between pixels. A test-circuit implementation of the algorithm was achieved with a small array of 32×32 pixels and the device was exposed to an 8 keV highly collimated to a diameter of 3 μm X-ray beam. The results of these tests are given in the paper assessing physical implementation of the algorithm.
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.
An epileptic seizures detection algorithm based on the empirical mode decomposition of EEG.
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.
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.
Multi-GPU implementation of a VMAT treatment plan optimization algorithm
International Nuclear Information System (INIS)
Tian, Zhen; Folkerts, Michael; Tan, Jun; Jia, Xun; Jiang, Steve B.; Peng, Fei
2015-01-01
Purpose: Volumetric modulated arc therapy (VMAT) optimization is a computationally challenging problem due to its large data size, high degrees of freedom, and many hardware constraints. High-performance graphics processing units (GPUs) have been used to speed up the computations. However, GPU’s relatively small memory size cannot handle cases with a large dose-deposition coefficient (DDC) matrix in cases of, e.g., those with a large target size, multiple targets, multiple arcs, and/or small beamlet size. The main purpose of this paper is to report an implementation of a column-generation-based VMAT algorithm, previously developed in the authors’ group, on a multi-GPU platform to solve the memory limitation problem. While the column-generation-based VMAT algorithm has been previously developed, the GPU implementation details have not been reported. Hence, another purpose is to present detailed techniques employed for GPU implementation. The authors also would like to utilize this particular problem as an example problem to study the feasibility of using a multi-GPU platform to solve large-scale problems in medical physics. Methods: The column-generation approach generates VMAT apertures sequentially by solving a pricing problem (PP) and a master problem (MP) iteratively. In the authors’ method, the sparse DDC matrix is first stored on a CPU in coordinate list format (COO). On the GPU side, this matrix is split into four submatrices according to beam angles, which are stored on four GPUs in compressed sparse row format. Computation of beamlet price, the first step in PP, is accomplished using multi-GPUs. A fast inter-GPU data transfer scheme is accomplished using peer-to-peer access. The remaining steps of PP and MP problems are implemented on CPU or a single GPU due to their modest problem scale and computational loads. Barzilai and Borwein algorithm with a subspace step scheme is adopted here to solve the MP problem. A head and neck (H and N) cancer case is
Multi-GPU implementation of a VMAT treatment plan optimization algorithm
Energy Technology Data Exchange (ETDEWEB)
Tian, Zhen, E-mail: Zhen.Tian@UTSouthwestern.edu, E-mail: Xun.Jia@UTSouthwestern.edu, E-mail: Steve.Jiang@UTSouthwestern.edu; Folkerts, Michael; Tan, Jun; Jia, Xun, E-mail: Zhen.Tian@UTSouthwestern.edu, E-mail: Xun.Jia@UTSouthwestern.edu, E-mail: Steve.Jiang@UTSouthwestern.edu; Jiang, Steve B., E-mail: Zhen.Tian@UTSouthwestern.edu, E-mail: Xun.Jia@UTSouthwestern.edu, E-mail: Steve.Jiang@UTSouthwestern.edu [Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas 75390 (United States); Peng, Fei [Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213 (United States)
2015-06-15
Purpose: Volumetric modulated arc therapy (VMAT) optimization is a computationally challenging problem due to its large data size, high degrees of freedom, and many hardware constraints. High-performance graphics processing units (GPUs) have been used to speed up the computations. However, GPU’s relatively small memory size cannot handle cases with a large dose-deposition coefficient (DDC) matrix in cases of, e.g., those with a large target size, multiple targets, multiple arcs, and/or small beamlet size. The main purpose of this paper is to report an implementation of a column-generation-based VMAT algorithm, previously developed in the authors’ group, on a multi-GPU platform to solve the memory limitation problem. While the column-generation-based VMAT algorithm has been previously developed, the GPU implementation details have not been reported. Hence, another purpose is to present detailed techniques employed for GPU implementation. The authors also would like to utilize this particular problem as an example problem to study the feasibility of using a multi-GPU platform to solve large-scale problems in medical physics. Methods: The column-generation approach generates VMAT apertures sequentially by solving a pricing problem (PP) and a master problem (MP) iteratively. In the authors’ method, the sparse DDC matrix is first stored on a CPU in coordinate list format (COO). On the GPU side, this matrix is split into four submatrices according to beam angles, which are stored on four GPUs in compressed sparse row format. Computation of beamlet price, the first step in PP, is accomplished using multi-GPUs. A fast inter-GPU data transfer scheme is accomplished using peer-to-peer access. The remaining steps of PP and MP problems are implemented on CPU or a single GPU due to their modest problem scale and computational loads. Barzilai and Borwein algorithm with a subspace step scheme is adopted here to solve the MP problem. A head and neck (H and N) cancer case is
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.
Automatic metal parts inspection: Use of thermographic images and anomaly detection algorithms
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.
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.
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.
Power Analysis of Energy Efficient DES Algorithm and Implementation on 28nm FPGA
DEFF Research Database (Denmark)
Thind, Vandana; Pandey, Bishwajeet; Hussain, Dil muhammed Akbar
2016-01-01
In this work, we have done power analysis ofData Encryption Standard (DES) algorithm using Xilinx ISE software development kit. We have analyzed the amount of power utilized by selective components on board i.e., FPGA Artix-7, where DES algorithm is implemented. The components taken into consider......In this work, we have done power analysis ofData Encryption Standard (DES) algorithm using Xilinx ISE software development kit. We have analyzed the amount of power utilized by selective components on board i.e., FPGA Artix-7, where DES algorithm is implemented. The components taken...... into consideration areclock power, logic power, signals power, IOs power, leakage powerand supply power (dynamic and quiescent). We have used four different WLAN frequencies (2.4 GHz, 3.6 GHz, 4.9GHz, and 5.9 GHz) and four different IO standards like HSTL-I, HSTL-II, HSTL-II-18, HSTL-I-18 for power analysis. We have...... achieved13-47% saving in power at different frequencies and withdifferent energy efficient HSTL IO standard. We calculated the percentage change in the IO power with respect to the mean values of IO power at four different frequencies. We notified that there is minimum of -37.5% and maximum of +35...
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.
Infrared small target detection technology based on OpenCV
Liu, Lei; Huang, Zhijian
2013-09-01
Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. In this paper, some basic principles and the implementing flow charts of a series of algorithms for target detection are described. These algorithms are traditional two-frame difference method, improved three-frame difference method, background estimate and frame difference fusion method, and building background with neighborhood mean method. On the foundation of above works, an infrared target detection software platform which is developed by OpenCV and MFC is introduced. Three kinds of tracking algorithms are integrated in this software. In order to explain the software clearly, the framework and the function are described in this paper. At last, the experiments are performed for some real-life IR images. The whole algorithm implementing processes and results are analyzed, and those algorithms for detection targets are evaluated from the two aspects of subjective and objective. The results prove that the proposed method has satisfying detection effectiveness and robustness. Meanwhile, it has high detection efficiency and can be used for real-time detection.
Implementation of a parallel algorithm for spherical SN calculations on the IBM 3090
International Nuclear Information System (INIS)
Haghighat, A.; Lawrence, R.D.
1989-01-01
Parallel S N algorithms based on domain decomposition in angle are straightforward to develop in Cartesian geometry because the computation of the angular fluxes for a specific discrete ordinate can be performed independently of all other angles. This is not the case for curvilinear geometries, where the angular redistribution component of the discretized streaming operator results in coupling between angular fluxes along adjacent discrete ordinates. Previously, the authors developed a parallel algorithm for S N calculations in spherical geometry and examined its iterative convergence for criticality and detector problems with differing scattering/absorption ratios. In this paper, the authors describe the implementation of the algorithm on an IBM 3090 Model 400 (four processors) and present computational results illustrating the efficiency of the algorithm relative to serial execution
An improved non-uniformity correction algorithm and its GPU parallel implementation
Cheng, Kuanhong; Zhou, Huixin; Qin, Hanlin; Zhao, Dong; Qian, Kun; Rong, Shenghui
2018-05-01
The performance of SLP-THP based non-uniformity correction algorithm is seriously affected by the result of SLP filter, which always leads to image blurring and ghosting artifacts. To address this problem, an improved SLP-THP based non-uniformity correction method with curvature constraint was proposed. Here we put forward a new way to estimate spatial low frequency component. First, the details and contours of input image were obtained respectively by minimizing local Gaussian curvature and mean curvature of image surface. Then, the guided filter was utilized to combine these two parts together to get the estimate of spatial low frequency component. Finally, we brought this SLP component into SLP-THP method to achieve non-uniformity correction. The performance of proposed algorithm was verified by several real and simulated infrared image sequences. The experimental results indicated that the proposed algorithm can reduce the non-uniformity without detail losing. After that, a GPU based parallel implementation that runs 150 times faster than CPU was presented, which showed the proposed algorithm has great potential for real time application.
International Nuclear Information System (INIS)
Roche-Lima, Abiel; Thulasiram, Ruppa K
2012-01-01
Finite automata, in which each transition is augmented with an output label in addition to the familiar input label, are considered finite-state transducers. Transducers have been used to analyze some fundamental issues in bioinformatics. Weighted finite-state transducers have been proposed to pairwise alignments of DNA and protein sequences; as well as to develop kernels for computational biology. Machine learning algorithms for conditional transducers have been implemented and used for DNA sequence analysis. Transducer learning algorithms are based on conditional probability computation. It is calculated by using techniques, such as pair-database creation, normalization (with Maximum-Likelihood normalization) and parameters optimization (with Expectation-Maximization - EM). These techniques are intrinsically costly for computation, even worse when are applied to bioinformatics, because the databases sizes are large. In this work, we describe a parallel implementation of an algorithm to learn conditional transducers using these techniques. The algorithm is oriented to bioinformatics applications, such as alignments, phylogenetic trees, and other genome evolution studies. Indeed, several experiences were developed using the parallel and sequential algorithm on Westgrid (specifically, on the Breeze cluster). As results, we obtain that our parallel algorithm is scalable, because execution times are reduced considerably when the data size parameter is increased. Another experience is developed by changing precision parameter. In this case, we obtain smaller execution times using the parallel algorithm. Finally, number of threads used to execute the parallel algorithm on the Breezy cluster is changed. In this last experience, we obtain as result that speedup is considerably increased when more threads are used; however there is a convergence for number of threads equal to or greater than 16.
International Nuclear Information System (INIS)
Bastiens, K.; Lemahieu, I.
1994-01-01
The application of a maximum entropy reconstruction algorithm to PET images requires a lot of computing resources. A parallel implementation could seriously reduce the execution time. However, programming a parallel application is still a non trivial task, needing specialized people. In this paper a programming environment based on a visual programming language is used for a parallel implementation of the reconstruction algorithm. This programming environment allows less experienced programmers to use the performance of multiprocessor systems. (authors)
A GPU implementation of a track-repeating algorithm for proton radiotherapy dose calculations
International Nuclear Information System (INIS)
Yepes, Pablo P; Mirkovic, Dragan; Taddei, Phillip J
2010-01-01
An essential component in proton radiotherapy is the algorithm to calculate the radiation dose to be delivered to the patient. The most common dose algorithms are fast but they are approximate analytical approaches. However their level of accuracy is not always satisfactory, especially for heterogeneous anatomical areas, like the thorax. Monte Carlo techniques provide superior accuracy; however, they often require large computation resources, which render them impractical for routine clinical use. Track-repeating algorithms, for example the fast dose calculator, have shown promise for achieving the accuracy of Monte Carlo simulations for proton radiotherapy dose calculations in a fraction of the computation time. We report on the implementation of the fast dose calculator for proton radiotherapy on a card equipped with graphics processor units (GPUs) rather than on a central processing unit architecture. This implementation reproduces the full Monte Carlo and CPU-based track-repeating dose calculations within 2%, while achieving a statistical uncertainty of 2% in less than 1 min utilizing one single GPU card, which should allow real-time accurate dose calculations.
PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta.
Chaudhury, Sidhartha; Lyskov, Sergey; Gray, Jeffrey J
2010-03-01
PyRosetta is a stand-alone Python-based implementation of the Rosetta molecular modeling package that allows users to write custom structure prediction and design algorithms using the major Rosetta sampling and scoring functions. PyRosetta contains Python bindings to libraries that define Rosetta functions including those for accessing and manipulating protein structure, calculating energies and running Monte Carlo-based simulations. PyRosetta can be used in two ways: (i) interactively, using iPython and (ii) script-based, using Python scripting. Interactive mode contains a number of help features and is ideal for beginners while script-mode is best suited for algorithm development. PyRosetta has similar computational performance to Rosetta, can be easily scaled up for cluster applications and has been implemented for algorithms demonstrating protein docking, protein folding, loop modeling and design. PyRosetta is a stand-alone package available at http://www.pyrosetta.org under the Rosetta license which is free for academic and non-profit users. A tutorial, user's manual and sample scripts demonstrating usage are also available on the web site.
Low-Cost Super-Resolution Algorithms Implementation Over a HW/SW Video Compression Platform
Directory of Open Access Journals (Sweden)
Llopis Rafael Peset
2006-01-01
Full Text Available Two approaches are presented in this paper to improve the quality of digital images over the sensor resolution using super-resolution techniques: iterative super-resolution (ISR and noniterative super-resolution (NISR algorithms. The results show important improvements in the image quality, assuming that sufficient sample data and a reasonable amount of aliasing are available at the input images. These super-resolution algorithms have been implemented over a codesign video compression platform developed by Philips Research, performing minimal changes on the overall hardware architecture. In this way, a novel and feasible low-cost implementation has been obtained by using the resources encountered in a generic hybrid video encoder. Although a specific video codec platform has been used, the methodology presented in this paper is easily extendable to any other video encoder architectures. Finally a comparison in terms of memory, computational load, and image quality for both algorithms, as well as some general statements about the final impact of the sampling process on the quality of the super-resolved (SR image, are also presented.
Algorithms for detecting and analysing autocatalytic sets.
Hordijk, Wim; Smith, Joshua I; Steel, Mike
2015-01-01
Autocatalytic sets are considered to be fundamental to the origin of life. Prior theoretical and computational work on the existence and properties of these sets has relied on a fast algorithm for detectingself-sustaining autocatalytic sets in chemical reaction systems. Here, we introduce and apply a modified version and several extensions of the basic algorithm: (i) a modification aimed at reducing the number of calls to the computationally most expensive part of the algorithm, (ii) the application of a previously introduced extension of the basic algorithm to sample the smallest possible autocatalytic sets within a reaction network, and the application of a statistical test which provides a probable lower bound on the number of such smallest sets, (iii) the introduction and application of another extension of the basic algorithm to detect autocatalytic sets in a reaction system where molecules can also inhibit (as well as catalyse) reactions, (iv) a further, more abstract, extension of the theory behind searching for autocatalytic sets. (i) The modified algorithm outperforms the original one in the number of calls to the computationally most expensive procedure, which, in some cases also leads to a significant improvement in overall running time, (ii) our statistical test provides strong support for the existence of very large numbers (even millions) of minimal autocatalytic sets in a well-studied polymer model, where these minimal sets share about half of their reactions on average, (iii) "uninhibited" autocatalytic sets can be found in reaction systems that allow inhibition, but their number and sizes depend on the level of inhibition relative to the level of catalysis. (i) Improvements in the overall running time when searching for autocatalytic sets can potentially be obtained by using a modified version of the algorithm, (ii) the existence of large numbers of minimal autocatalytic sets can have important consequences for the possible evolvability of
Energy Technology Data Exchange (ETDEWEB)
Ruiz, M.; Barrera, E.; Gonzalez, J.; Melendez, R. [Grupo de Investigacion en Instrumentacion y Acustica Aplicada - Universidad Politecnica de Madrid (Spain); Vega, J.; Ratta, G.; Gonzalez, S. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Murari, A. [Consorzio RFX - Associazione EURATOM ENEA per la Fusione, Padova (Italy)
2009-07-01
A basic requirement of the data acquisition systems used in long pulse fusion experiments is to detect events of interest in the acquired signals in real time. Developing such applications is usually a complex task, so it is necessary to develop a set of hardware and software tools that simplify their implementation. An example of these tools is the Intelligent Test and Measurement System (ITMS), which offers distributed data acquisition, distribution and real time processing capabilities with advanced, but easy to use, software tools that simplify application development and system setup. This poster presents the application of the ITMS platform to solve the problem of detecting L/H and H/L transitions in real time based on the use of efficient pattern recognition algorithms. The system architecture used to implement this solution and its performance evaluation are discussed. The system consists of the following elements: a) the ITMS, which is used both to implement the data acquisition and real time LH-HL detection, and to simulate the working environment by reproducing real signals obtained from JET database; b) an event detector, developed using advanced pattern recognition algorithms, that will continuously analyze the information of the acquired signals in order to detect the L/H and H/L transitions; and c) a host computer with several software tools developed with LabVIEW, JAVA and JINI to simplify the development of these complex experiments. These tools allow the user to describe data acquisition and processing tasks using state machines defined in SCXML. These state machines become part of the system setup information, so the behaviour of the system can be changed subsequently by simply modifying these state machines. All the events and key detected are stored by the host computer. This document is a poster. (authors)
D'Angelo, Gianni; Rampone, Salvatore
2014-01-01
The huge quantity of data produced in Biomedical research needs sophisticated algorithmic methodologies for its storage, analysis, and processing. High Performance Computing (HPC) appears as a magic bullet in this challenge. However, several hard to solve parallelization and load balancing problems arise in this context. Here we discuss the HPC-oriented implementation of a general purpose learning algorithm, originally conceived for DNA analysis and recently extended to treat uncertainty on data (U-BRAIN). The U-BRAIN algorithm is a learning algorithm that finds a Boolean formula in disjunctive normal form (DNF), of approximately minimum complexity, that is consistent with a set of data (instances) which may have missing bits. The conjunctive terms of the formula are computed in an iterative way by identifying, from the given data, a family of sets of conditions that must be satisfied by all the positive instances and violated by all the negative ones; such conditions allow the computation of a set of coefficients (relevances) for each attribute (literal), that form a probability distribution, allowing the selection of the term literals. The great versatility that characterizes it, makes U-BRAIN applicable in many of the fields in which there are data to be analyzed. However the memory and the execution time required by the running are of O(n(3)) and of O(n(5)) order, respectively, and so, the algorithm is unaffordable for huge data sets. We find mathematical and programming solutions able to lead us towards the implementation of the algorithm U-BRAIN on parallel computers. First we give a Dynamic Programming model of the U-BRAIN algorithm, then we minimize the representation of the relevances. When the data are of great size we are forced to use the mass memory, and depending on where the data are actually stored, the access times can be quite different. According to the evaluation of algorithmic efficiency based on the Disk Model, in order to reduce the costs of
Chen, Fangyue; Chen, Guanrong Ron; He, Guolong; Xu, Xiubin; He, Qinbin
2009-10-01
Universal perceptron (UP), a generalization of Rosenblatt's perceptron, is considered in this paper, which is capable of implementing all Boolean functions (BFs). In the classification of BFs, there are: 1) linearly separable Boolean function (LSBF) class, 2) parity Boolean function (PBF) class, and 3) non-LSBF and non-PBF class. To implement these functions, UP takes different kinds of simple topological structures in which each contains at most one hidden layer along with the smallest possible number of hidden neurons. Inspired by the concept of DNA sequences in biological systems, a novel learning algorithm named DNA-like learning is developed, which is able to quickly train a network with any prescribed BF. The focus is on performing LSBF and PBF by a single-layer perceptron (SLP) with the new algorithm. Two criteria for LSBF and PBF are proposed, respectively, and a new measure for a BF, named nonlinearly separable degree (NLSD), is introduced. In the sense of this measure, the PBF is the most complex one. The new algorithm has many advantages including, in particular, fast running speed, good robustness, and no need of considering the convergence property. For example, the number of iterations and computations in implementing the basic 2-bit logic operations such as AND, OR, and XOR by using the new algorithm is far smaller than the ones needed by using other existing algorithms such as error-correction (EC) and backpropagation (BP) algorithms. Moreover, the synaptic weights and threshold values derived from UP can be directly used in designing of the template of cellular neural networks (CNNs), which has been considered as a new spatial-temporal sensory computing paradigm.
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.
Implementation of Chaid Algorithm: A Hotel Case
Directory of Open Access Journals (Sweden)
Celal Hakan Kagnicioglu
2016-01-01
Full Text Available Today, companies are planning their own activities depending on efficiency and effectiveness. In order to have plans for the future activities they need historical data coming from outside and inside of the companies. However, this data is in huge amounts to understand easily. Since, this huge amount of data creates complexity in business for many industries like hospitality industry, reliable, accurate and fast access to this data is to be one of the greatest problems. Besides, management of this data is another big problem. In order to analyze this huge amount of data, Data Mining (DM tools, can be used effectively. In this study, after giving brief definition about fundamentals of data mining, Chi Squared Automatic Interaction Detection (CHAID algorithm, one of the mostly used DM tool, will be introduced. By CHAID algorithm, the most used materials in room cleaning process and the relations of these materials based on in a five star hotel data are tried to be determined. At the end of the analysis, it is seen that while some variables have strong relation with the number of rooms cleaned in the hotel, the others have no or weak relation.
Implementation of Chaid Algorithm: A Hotel Case
Directory of Open Access Journals (Sweden)
Celal Hakan Kağnicioğlu
2014-11-01
Full Text Available Today, companies are planning their own activities depending on efficiency and effectiveness. In order to have plans for the future activities they need historical data coming from outside and inside of the companies. However, this data is in huge amounts to understand easily. Since, this huge amount of data creates complexity in business for many industries like hospitality industry, reliable, accurate and fast access to this data is to be one of the greatest problems. Besides, management of this data is another big problem. In order to analyze this huge amount of data, Data Mining (DM tools, can be used effectively. In this study, after giving brief definition about fundamentals of data mining, Chi Squared Automatic Interaction Detection (CHAID algorithm, one of the mostly used DM tool, will be introduced. By CHAID algorithm, the most used materials in room cleaning process and the relations of these materials based on in a five star hotel data are tried to be determined. At the end of the analysis, it is seen that while some variables have strong relation with the number of rooms cleaned in the hotel, the others have no or weak relation.
Kume, Teruyoshi; Kim, Byeong-Keuk; Waseda, Katsuhisa; Sathyanarayana, Shashidhar; Li, Wenguang; Teo, Tat-Jin; Yock, Paul G; Fitzgerald, Peter J; Honda, Yasuhiro
2013-02-01
The aim of this study was to evaluate a new fully automated lumen border tracing system based on a novel multifrequency processing algorithm. We developed the multifrequency processing method to enhance arterial lumen detection by exploiting the differential scattering characteristics of blood and arterial tissue. The implementation of the method can be integrated into current intravascular ultrasound (IVUS) hardware. This study was performed in vivo with conventional 40-MHz IVUS catheters (Atlantis SR Pro™, Boston Scientific Corp, Natick, MA) in 43 clinical patients with coronary artery disease. A total of 522 frames were randomly selected, and lumen areas were measured after automatically tracing lumen borders with the new tracing system and a commercially available tracing system (TraceAssist™) referred to as the "conventional tracing system." The data assessed by the two automated systems were compared with the results of manual tracings by experienced IVUS analysts. New automated lumen measurements showed better agreement with manual lumen area tracings compared with those of the conventional tracing system (correlation coefficient: 0.819 vs. 0.509). When compared against manual tracings, the new algorithm also demonstrated improved systematic error (mean difference: 0.13 vs. -1.02 mm(2) ) and random variability (standard deviation of difference: 2.21 vs. 4.02 mm(2) ) compared with the conventional tracing system. This preliminary study showed that the novel fully automated tracing system based on the multifrequency processing algorithm can provide more accurate lumen border detection than current automated tracing systems and thus, offer a more reliable quantitative evaluation of lumen geometry. Copyright © 2011 Wiley Periodicals, Inc.
DEFF Research Database (Denmark)
Dollerup, Niels; Jepsen, Michael S.; Damkilde, Lars
2013-01-01
The artide describes a robust and effective implementation of the interior point optimization algorithm. The adopted method includes a precalculation step, which reduces the number of variables by fulfilling the equilibrium equations a priori. This work presents an improved implementation of the ...
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)
Energy Technology Data Exchange (ETDEWEB)
Bastiens, K; Lemahieu, I [University of Ghent - ELIS Department, St. Pietersnieuwstraat 41, B-9000 Ghent (Belgium)
1994-12-31
The application of a maximum entropy reconstruction algorithm to PET images requires a lot of computing resources. A parallel implementation could seriously reduce the execution time. However, programming a parallel application is still a non trivial task, needing specialized people. In this paper a programming environment based on a visual programming language is used for a parallel implementation of the reconstruction algorithm. This programming environment allows less experienced programmers to use the performance of multiprocessor systems. (authors). 8 refs, 3 figs, 1 tab.
International Nuclear Information System (INIS)
Lu Dawei; Peng Xinhua; Du Jiangfeng; Zhu Jing; Zou Ping; Yu Yihua; Zhang Shanmin; Chen Qun
2010-01-01
An important quantum search algorithm based on the quantum random walk performs an oracle search on a database of N items with O(√(phN)) calls, yielding a speedup similar to the Grover quantum search algorithm. The algorithm was implemented on a quantum information processor of three-qubit liquid-crystal nuclear magnetic resonance (NMR) in the case of finding 1 out of 4, and the diagonal elements' tomography of all the final density matrices was completed with comprehensible one-dimensional NMR spectra. The experimental results agree well with the theoretical predictions.
Fall detection using supervised machine learning algorithms: A comparative study
Zerrouki, Nabil; Harrou, Fouzi; Houacine, Amrane; Sun, Ying
2017-01-01
Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches which are: Naïve Bayes, K nearest neighbor, neural network, and support vector machine. The analysis of the classification power associated to these most widely utilized algorithms is conducted on two fall detection databases namely FDD and URFD. Since the performance of the classification algorithm is inherently dependent on the features, we extracted and used the same features for all classifiers. The classification evaluation is conducted using different state of the art statistical measures such as the overall accuracy, the F-measure coefficient, and the area under ROC curve (AUC) value.
A novel through-wall respiration detection algorithm using UWB radar.
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.
Fall detection using supervised machine learning algorithms: A comparative study
Zerrouki, Nabil
2017-01-05
Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches which are: Naïve Bayes, K nearest neighbor, neural network, and support vector machine. The analysis of the classification power associated to these most widely utilized algorithms is conducted on two fall detection databases namely FDD and URFD. Since the performance of the classification algorithm is inherently dependent on the features, we extracted and used the same features for all classifiers. The classification evaluation is conducted using different state of the art statistical measures such as the overall accuracy, the F-measure coefficient, and the area under ROC curve (AUC) value.
Detection of cracks in shafts with the Approximated Entropy algorithm
Sampaio, Diego Luchesi; Nicoletti, Rodrigo
2016-05-01
The Approximate Entropy is a statistical calculus used primarily in the fields of Medicine, Biology, and Telecommunication for classifying and identifying complex signal data. In this work, an Approximate Entropy algorithm is used to detect cracks in a rotating shaft. The signals of the cracked shaft are obtained from numerical simulations of a de Laval rotor with breathing cracks modelled by the Fracture Mechanics. In this case, one analysed the vertical displacements of the rotor during run-up transients. The results show the feasibility of detecting cracks from 5% depth, irrespective of the unbalance of the rotating system and crack orientation in the shaft. The results also show that the algorithm can differentiate the occurrence of crack only, misalignment only, and crack + misalignment in the system. However, the algorithm is sensitive to intrinsic parameters p (number of data points in a sample vector) and f (fraction of the standard deviation that defines the minimum distance between two sample vectors), and good results are only obtained by appropriately choosing their values according to the sampling rate of the signal.
High-frequency asymptotics of the local vertex function. Algorithmic implementations
Energy Technology Data Exchange (ETDEWEB)
Tagliavini, Agnese; Wentzell, Nils [Institut fuer Theoretische Physik, Eberhard Karls Universitaet, 72076 Tuebingen (Germany); Institute for Solid State Physics, Vienna University of Technology, 1040 Vienna (Austria); Li, Gang; Rohringer, Georg; Held, Karsten; Toschi, Alessandro [Institute for Solid State Physics, Vienna University of Technology, 1040 Vienna (Austria); Taranto, Ciro [Institute for Solid State Physics, Vienna University of Technology, 1040 Vienna (Austria); Max Planck Institute for Solid State Research, D-70569 Stuttgart (Germany); Andergassen, Sabine [Institut fuer Theoretische Physik, Eberhard Karls Universitaet, 72076 Tuebingen (Germany)
2016-07-01
Local vertex functions are a crucial ingredient of several forefront many-body algorithms in condensed matter physics. However, the full treatment of their frequency dependence poses a huge limitation to the numerical performance. A significant advancement requires an efficient treatment of the high-frequency asymptotic behavior of the vertex functions. We here provide a detailed diagrammatic analysis of the high-frequency asymptotic structures and their physical interpretation. Based on these insights, we propose a frequency parametrization, which captures the whole high-frequency asymptotics for arbitrary values of the local Coulomb interaction and electronic density. We present its algorithmic implementation in many-body solvers based on parquet-equations as well as functional renormalization group schemes and assess its validity by comparing our results for the single impurity Anderson model with exact diagonalization calculations.
Real time implementation of a linear predictive coding algorithm on digital signal processor DSP32C
International Nuclear Information System (INIS)
Sheikh, N.M.; Usman, S.R.; Fatima, S.
2002-01-01
Pulse Code Modulation (PCM) has been widely used in speech coding. However, due to its high bit rate. PCM has severe limitations in application where high spectral efficiency is desired, for example, in mobile communication, CD quality broadcasting system etc. These limitation have motivated research in bit rate reduction techniques. Linear predictive coding (LPC) is one of the most powerful complex techniques for bit rate reduction. With the introduction of powerful digital signal processors (DSP) it is possible to implement the complex LPC algorithm in real time. In this paper we present a real time implementation of the LPC algorithm on AT and T's DSP32C at a sampling frequency of 8192 HZ. Application of the LPC algorithm on two speech signals is discussed. Using this implementation , a bit rate reduction of 1:3 is achieved for better than tool quality speech, while a reduction of 1.16 is possible for speech quality required in military applications. (author)
Further optimization of SeDDaRA blind image deconvolution algorithm and its DSP implementation
Wen, Bo; Zhang, Qiheng; Zhang, Jianlin
2011-11-01
Efficient algorithm for blind image deconvolution and its high-speed implementation is of great value in practice. Further optimization of SeDDaRA is developed, from algorithm structure to numerical calculation methods. The main optimization covers that, the structure's modularization for good implementation feasibility, reducing the data computation and dependency of 2D-FFT/IFFT, and acceleration of power operation by segmented look-up table. Then the Fast SeDDaRA is proposed and specialized for low complexity. As the final implementation, a hardware system of image restoration is conducted by using the multi-DSP parallel processing. Experimental results show that, the processing time and memory demand of Fast SeDDaRA decreases 50% at least; the data throughput of image restoration system is over 7.8Msps. The optimization is proved efficient and feasible, and the Fast SeDDaRA is able to support the real-time application.
GPU implementations of online track finding algorithms at PANDA
Energy Technology Data Exchange (ETDEWEB)
Herten, Andreas; Stockmanns, Tobias; Ritman, James [Institut fuer Kernphysik, Forschungszentrum Juelich GmbH (Germany); Adinetz, Andrew; Pleiter, Dirk [Juelich Supercomputing Centre, Forschungszentrum Juelich GmbH (Germany); Kraus, Jiri [NVIDIA GmbH (Germany); Collaboration: PANDA-Collaboration
2014-07-01
The PANDA experiment is a hadron physics experiment that will investigate antiproton annihilation in the charm quark mass region. The experiment is now being constructed as one of the main parts of the FAIR facility. At an event rate of 2 . 10{sup 7}/s a data rate of 200 GB/s is expected. A reduction of three orders of magnitude is required in order to save the data for further offline analysis. Since signal and background processes at PANDA have similar signatures, no hardware-level trigger is foreseen for the experiment. Instead, a fast online event filter is substituting this element. We investigate the possibility of using graphics processing units (GPUs) for the online tracking part of this task. Researched algorithms are a Hough Transform, a track finder involving Riemann surfaces, and the novel, PANDA-specific Triplet Finder. This talk shows selected advances in the implementations as well as performance evaluations of the GPU tracking algorithms to be used at the PANDA experiment.
Kodiak: An Implementation Framework for Branch and Bound Algorithms
Smith, Andrew P.; Munoz, Cesar A.; Narkawicz, Anthony J.; Markevicius, Mantas
2015-01-01
Recursive branch and bound algorithms are often used to refine and isolate solutions to several classes of global optimization problems. A rigorous computation framework for the solution of systems of equations and inequalities involving nonlinear real arithmetic over hyper-rectangular variable and parameter domains is presented. It is derived from a generic branch and bound algorithm that has been formally verified, and utilizes self-validating enclosure methods, namely interval arithmetic and, for polynomials and rational functions, Bernstein expansion. Since bounds computed by these enclosure methods are sound, this approach may be used reliably in software verification tools. Advantage is taken of the partial derivatives of the constraint functions involved in the system, firstly to reduce the branching factor by the use of bisection heuristics and secondly to permit the computation of bifurcation sets for systems of ordinary differential equations. The associated software development, Kodiak, is presented, along with examples of three different branch and bound problem types it implements.
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.
International Nuclear Information System (INIS)
Wood, Thomas W.; Heasler, Patrick G.; Daly, Don S.
2010-01-01
Almost all of the 'architectures' for radiation detection systems in Department of Energy (DOE) and other USG programs rely on some version of layered detector deployment. Efficacy analyses of layered (or more generally extended) detection systems in many contexts often assume statistical independence among detection events and thus predict monotonically increasing system performance with the addition of detection layers. We show this to be a false conclusion for the ROC curves typical of most current technology gamma detectors, and more generally show that statistical independence is often an unwarranted assumption for systems in which there is ambiguity about the objects to be detected. In such systems, a model of correlation among detection events allows optimization of system algorithms for interpretation of detector signals. These algorithms are framed as optimal discriminant functions in joint signal space, and may be applied to gross counting or spectroscopic detector systems. We have shown how system algorithms derived from this model dramatically improve detection probabilities compared to the standard serial detection operating paradigm for these systems. These results would not surprise anyone who has confronted the problem of correlated errors (or failure rates) in the analogous contexts, but is seems to be largely underappreciated among those analyzing the radiation detection problem - independence is widely assumed and experimental studies typical fail to measure correlation. This situation, if not rectified, will lead to several unfortunate results. Including overconfidence in system efficacy, overinvestment in layers of similar technology, and underinvestment in diversity among detection assets.
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
Validation of an automated seizure detection algorithm for term neonates
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
A Novel Method to Implement the Matrix Pencil Super Resolution Algorithm for Indoor Positioning
Directory of Open Access Journals (Sweden)
Tariq Jamil Saifullah Khanzada
2011-10-01
Full Text Available This article highlights the estimation of the results for the algorithms implemented in order to estimate the delays and distances for the indoor positioning system. The data sets for the transmitted and received signals are captured at a typical outdoor and indoor area. The estimation super resolution algorithms are applied. Different state of art and super resolution techniques based algorithms are applied to avail the optimal estimates of the delays and distances between the transmitted and received signals and a novel method for matrix pencil algorithm is devised. The algorithms perform variably at different scenarios of transmitted and received positions. Two scenarios are experienced, for the single antenna scenario the super resolution techniques like ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique and theMatrix Pencil algorithms give optimal performance compared to the conventional techniques. In two antenna scenario RootMUSIC and Matrix Pencil algorithm performed better than other algorithms for the distance estimation, however, the accuracy of all the algorithms is worst than the single antenna scenario. In all cases our devised Matrix Pencil algorithm achieved the best estimation results.
Implementation and statistical analysis of Metropolis algorithm for SU(3)
International Nuclear Information System (INIS)
Katznelson, E.; Nobile, A.
1984-12-01
In this paper we study the statistical properties of an implementation of the Metropolis algorithm for SU(3) gauge theory. It is shown that the results have normal distribution. We demonstrate that in this case error analysis can be carried on in a simple way and we show that applying it to both the measurement strategy and the output data analysis has an important influence on the performance and reliability of the simulation. (author)
Implementation of Human Trafficking Education and Treatment Algorithm in the Emergency Department.
Egyud, Amber; Stephens, Kimberly; Swanson-Bierman, Brenda; DiCuccio, Marge; Whiteman, Kimberly
2017-11-01
Health care professionals have not been successful in recognizing or rescuing victims of human trafficking. The purpose of this project was to implement a screening system and treatment algorithm in the emergency department to improve the identification and rescue of victims of human trafficking. The lack of recognition by health care professionals is related to inadequate education and training tools and confusion with other forms of violence such as trauma and sexual assault. A multidisciplinary team was formed to assess the evidence related to human trafficking and make recommendations for practice. After receiving education, staff completed a survey about knowledge gained from the training. An algorithm for identification and treatment of sex trafficking victims was implemented and included a 2-pronged identification approach: (1) medical red flags created by a risk-assessment tool embedded in the electronic health record and (2) a silent notification process. Outcome measures were the number of victims who were identified either by the medical red flags or by silent notification and were offered and accepted intervention. Survey results indicated that 75% of participants reported that the education improved their competence level. The results demonstrated that an education and treatment algorithm may be an effective strategy to improve recognition. One patient was identified as an actual victim of human trafficking; the remaining patients reported other forms of abuse. Education and a treatment algorithm were effective strategies to improve recognition and rescue of human trafficking victims and increase identification of other forms of abuse. Copyright © 2017 Emergency Nurses Association. Published by Elsevier Inc. All rights reserved.
Cloud detection algorithm comparison and validation for operational Landsat data products
Foga, Steven Curtis; Scaramuzza, Pat; Guo, Song; Zhu, Zhe; Dilley, Ronald; Beckmann, Tim; Schmidt, Gail L.; Dwyer, John L.; Hughes, MJ; Laue, Brady
2017-01-01
Clouds are a pervasive and unavoidable issue in satellite-borne optical imagery. Accurate, well-documented, and automated cloud detection algorithms are necessary to effectively leverage large collections of remotely sensed data. The Landsat project is uniquely suited for comparative validation of cloud assessment algorithms because the modular architecture of the Landsat ground system allows for quick evaluation of new code, and because Landsat has the most comprehensive manual truth masks of any current satellite data archive. Currently, the Landsat Level-1 Product Generation System (LPGS) uses separate algorithms for determining clouds, cirrus clouds, and snow and/or ice probability on a per-pixel basis. With more bands onboard the Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) satellite, and a greater number of cloud masking algorithms, the U.S. Geological Survey (USGS) is replacing the current cloud masking workflow with a more robust algorithm that is capable of working across multiple Landsat sensors with minimal modification. Because of the inherent error from stray light and intermittent data availability of TIRS, these algorithms need to operate both with and without thermal data. In this study, we created a workflow to evaluate cloud and cloud shadow masking algorithms using cloud validation masks manually derived from both Landsat 7 Enhanced Thematic Mapper Plus (ETM +) and Landsat 8 OLI/TIRS data. We created a new validation dataset consisting of 96 Landsat 8 scenes, representing different biomes and proportions of cloud cover. We evaluated algorithm performance by overall accuracy, omission error, and commission error for both cloud and cloud shadow. We found that CFMask, C code based on the Function of Mask (Fmask) algorithm, and its confidence bands have the best overall accuracy among the many algorithms tested using our validation data. The Artificial Thermal-Automated Cloud Cover Algorithm (AT-ACCA) is the most accurate
Derivation and implementation of a cone-beam reconstruction algorithm for nonplanar orbits
International Nuclear Information System (INIS)
Kudo, Hiroyuki; Saito, Tsuneo
1994-01-01
Smith and Grangeat derived a cone-beam inversion formula that can be applied when a nonplanar orbit satisfying the completeness condition is used. Although Grangeat's inversion formula is mathematically different from Smith's, they have similar overall structures to each other. The contribution of this paper is two-fold. First, based on the derivation of Smith, the authors point out that Grangeat's inversion formula and Smith's can be conveniently described using a single formula (the Smith-Grangeat inversion formula) that is in the form of space-variant filtering followed by cone-beam backprojection. Furthermore, the resulting formula is reformulated for data acquisition systems with a planar detector to obtain a new reconstruction algorithm. Second, the authors make two significant modifications to the new algorithm to reduce artifacts and numerical errors encountered in direct implementation of the new algorithm. As for exactness of the new algorithm, the following fact can be stated. The algorithm based on Grangeat's intermediate function is exact for any complete orbit, whereas that based on Smith's intermediate function should be considered as an approximate inverse excepting the special case where almost every plane in 3-D space meets the orbit. The validity of the new algorithm is demonstrated by simulation studies
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 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.
A computerized algorithm for arousal detection in healthy adults and patients with Parkinson disease
DEFF Research Database (Denmark)
Sørensen, Gertrud Laura; Jennum, Poul; Kempfner, Jacob
2012-01-01
arousals from non-rapid eye movement (REM) and REM sleep, independent of the subject's age and disease. The proposed algorithm uses features from EEG, EMG, and the manual sleep stage scoring as input to a feed-forward artificial neural network (ANN). The performance of the algorithm has been assessed using......Arousals occur from all sleep stages and can be identified as abrupt electroencephalogram (EEG) and electromyogram (EMG) changes. Manual scoring of arousals is time consuming with low interscore agreement. The aim of this study was to design an arousal detection algorithm capable of detecting...
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.
Stochastic Resonance algorithms to enhance damage detection in bearing faults
Directory of Open Access Journals (Sweden)
Castiglione Roberto
2015-01-01
Full Text Available Stochastic Resonance is a phenomenon, studied and mainly exploited in telecommunication, which permits the amplification and detection of weak signals by the assistance of noise. The first papers on this technique are dated early 80 s and were developed to explain the periodically recurrent ice ages. Other applications mainly concern neuroscience, biology, medicine and obviously signal analysis and processing. Recently, some researchers have applied the technique for detecting faults in mechanical systems and bearings. In this paper, we try to better understand the conditions of applicability and which is the best algorithm to be adopted for these purposes. In fact, to get the methodology profitable and efficient to enhance the signal spikes due to fault in rings and balls/rollers of bearings, some parameters have to be properly selected. This is a problem since in system identification this procedure should be as blind as possible. Two algorithms are analysed: the first exploits classical SR with three parameters mutually dependent, while the other uses Woods-Saxon potential, with three parameters yet but holding a different meaning. The comparison of the performances of the two algorithms and the optimal choice of their parameters are the scopes of this paper. Algorithms are tested on simulated and experimental data showing an evident capacity of increasing the signal to noise ratio.
Implementation of Robert's Coping with Labor Algorithm© in a large tertiary care facility.
Fairchild, Esther; Roberts, Leissa; Zelman, Karen; Michelli, Shelley; Hastings-Tolsma, Marie
2017-07-01
to implement use of Roberts' Coping with Labor Algorithm © (CWLA) with laboring women in a large tertiary care facility. this was a quality improvement project to implement an alternate approach to pain assessment during labor. It included system assessment for change readiness, implementation of the algorithm across a 6-week period, evaluation of usefulness by nursing staff, and determination of sustained change at one month. Stakeholder Theory (Friedman and Miles, 2002) and Deming's (1982) Plan-Do-Check-Act Cycle, as adapted by Roberts et al (2010), provided the framework for project implementation. the project was undertaken on a labor and delivery (L&D) unit of a large tertiary care facility in a southwestern state in the USA. The unit had 19 suites with close to 6000 laboring patients each year. full, part-time, and per diem Registered Nurse (RN) staff (N=80), including a subset (n=18) who served as the pilot group and champions for implementing the change. a majority of RNs held a positive attitude toward use of the CWLA to assess laboring women's coping with the pain of labor as compared to a Numeric Rating Scale (NRS). RNs reported usefulness in using the CWLA with patients from a wide variety of ethnicities. A pre-existing well-developed team which advocated for evidence-based practice on the unit proved to be a significant strength which promoted rapid change in practice. this work provides important knowledge supporting use of the CWLA in a large tertiary care facility and an approach for effectively implementing that change. Strengths identified in this project contributed to rapid implementation and could be emulated in other facilities. Participant reports support usefulness of the CWLA with patients of varied ethnicity. Assessment of change sustainability at 1 and 6 months demonstrated widespread use of the algorithm though long-term determination is yet needed. Copyright © 2017 Elsevier Ltd. All rights reserved.
A Flexible VHDL Floating Point Module for Control Algorithm Implementation in Space Applications
Padierna, A.; Nicoleau, C.; Sanchez, J.; Hidalgo, I.; Elvira, S.
2012-08-01
The implementation of control loops for space applications is an area with great potential. However, the characteristics of this kind of systems, such as its wide dynamic range of numeric values, make inadequate the use of fixed-point algorithms.However, because the generic chips available for the treatment of floating point data are, in general, not qualified to operate in space environments and the possibility of using an IP module in a FPGA/ASIC qualified for space is not viable due to the low amount of logic cells available for these type of devices, it is necessary to find a viable alternative.For these reasons, in this paper a VHDL Floating Point Module is presented. This proposal allows the design and execution of floating point algorithms with acceptable occupancy to be implemented in FPGAs/ASICs qualified for space environments.
PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta
Chaudhury, Sidhartha; Lyskov, Sergey; Gray, Jeffrey J.
2010-01-01
Summary: PyRosetta is a stand-alone Python-based implementation of the Rosetta molecular modeling package that allows users to write custom structure prediction and design algorithms using the major Rosetta sampling and scoring functions. PyRosetta contains Python bindings to libraries that define Rosetta functions including those for accessing and manipulating protein structure, calculating energies and running Monte Carlo-based simulations. PyRosetta can be used in two ways: (i) interactively, using iPython and (ii) script-based, using Python scripting. Interactive mode contains a number of help features and is ideal for beginners while script-mode is best suited for algorithm development. PyRosetta has similar computational performance to Rosetta, can be easily scaled up for cluster applications and has been implemented for algorithms demonstrating protein docking, protein folding, loop modeling and design. Availability: PyRosetta is a stand-alone package available at http://www.pyrosetta.org under the Rosetta license which is free for academic and non-profit users. A tutorial, user's manual and sample scripts demonstrating usage are also available on the web site. Contact: pyrosetta@graylab.jhu.edu PMID:20061306
Fu, Haohao; Shao, Xueguang; Chipot, Christophe; Cai, Wensheng
2016-08-09
Proper use of the adaptive biasing force (ABF) algorithm in free-energy calculations needs certain prerequisites to be met, namely, that the Jacobian for the metric transformation and its first derivative be available and the coarse variables be independent and fully decoupled from any holonomic constraint or geometric restraint, thereby limiting singularly the field of application of the approach. The extended ABF (eABF) algorithm circumvents these intrinsic limitations by applying the time-dependent bias onto a fictitious particle coupled to the coarse variable of interest by means of a stiff spring. However, with the current implementation of eABF in the popular molecular dynamics engine NAMD, a trajectory-based post-treatment is necessary to derive the underlying free-energy change. Usually, such a posthoc analysis leads to a decrease in the reliability of the free-energy estimates due to the inevitable loss of information, as well as to a drop in efficiency, which stems from substantial read-write accesses to file systems. We have developed a user-friendly, on-the-fly code for performing eABF simulations within NAMD. In the present contribution, this code is probed in eight illustrative examples. The performance of the algorithm is compared with traditional ABF, on the one hand, and the original eABF implementation combined with a posthoc analysis, on the other hand. Our results indicate that the on-the-fly eABF algorithm (i) supplies the correct free-energy landscape in those critical cases where the coarse variables at play are coupled to either each other or to geometric restraints or holonomic constraints, (ii) greatly improves the reliability of the free-energy change, compared to the outcome of a posthoc analysis, and (iii) represents a negligible additional computational effort compared to regular ABF. Moreover, in the proposed implementation, guidelines for choosing two parameters of the eABF algorithm, namely the stiffness of the spring and the mass
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.
Tilton, James C.; Plaza, Antonio J. (Editor); Chang, Chein-I. (Editor)
2008-01-01
The hierarchical image segmentation algorithm (referred to as HSEG) is a hybrid of hierarchical step-wise optimization (HSWO) and constrained spectral clustering that produces a hierarchical set of image segmentations. HSWO is an iterative approach to region grooving segmentation in which the optimal image segmentation is found at N(sub R) regions, given a segmentation at N(sub R+1) regions. HSEG's addition of constrained spectral clustering makes it a computationally intensive algorithm, for all but, the smallest of images. To counteract this, a computationally efficient recursive approximation of HSEG (called RHSEG) has been devised. Further improvements in processing speed are obtained through a parallel implementation of RHSEG. This chapter describes this parallel implementation and demonstrates its computational efficiency on a Landsat Thematic Mapper test scene.
A New FPGA Architecture of FAST and BRIEF Algorithm for On-Board Corner Detection and Matching.
Huang, Jingjin; Zhou, Guoqing; Zhou, Xiang; Zhang, Rongting
2018-03-28
Although some researchers have proposed the Field Programmable Gate Array (FPGA) architectures of Feature From Accelerated Segment Test (FAST) and Binary Robust Independent Elementary Features (BRIEF) algorithm, there is no consideration of image data storage in these traditional architectures that will result in no image data that can be reused by the follow-up algorithms. This paper proposes a new FPGA architecture that considers the reuse of sub-image data. In the proposed architecture, a remainder-based method is firstly designed for reading the sub-image, a FAST detector and a BRIEF descriptor are combined for corner detection and matching. Six pairs of satellite images with different textures, which are located in the Mentougou district, Beijing, China, are used to evaluate the performance of the proposed architecture. The Modelsim simulation results found that: (i) the proposed architecture is effective for sub-image reading from DDR3 at a minimum cost; (ii) the FPGA implementation is corrected and efficient for corner detection and matching, such as the average value of matching rate of natural areas and artificial areas are approximately 67% and 83%, respectively, which are close to PC's and the processing speed by FPGA is approximately 31 and 2.5 times faster than those by PC processing and by GPU processing, respectively.
Massanes, Francesc; Cadennes, Marie; Brankov, Jovan G
2011-07-01
In this paper we describe and evaluate a fast implementation of a classical block matching motion estimation algorithm for multiple Graphical Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) computing engine. The implemented block matching algorithm (BMA) uses summed absolute difference (SAD) error criterion and full grid search (FS) for finding optimal block displacement. In this evaluation we compared the execution time of a GPU and CPU implementation for images of various sizes, using integer and non-integer search grids.The results show that use of a GPU card can shorten computation time by a factor of 200 times for integer and 1000 times for a non-integer search grid. The additional speedup for non-integer search grid comes from the fact that GPU has built-in hardware for image interpolation. Further, when using multiple GPU cards, the presented evaluation shows the importance of the data splitting method across multiple cards, but an almost linear speedup with a number of cards is achievable.In addition we compared execution time of the proposed FS GPU implementation with two existing, highly optimized non-full grid search CPU based motion estimations methods, namely implementation of the Pyramidal Lucas Kanade Optical flow algorithm in OpenCV and Simplified Unsymmetrical multi-Hexagon search in H.264/AVC standard. In these comparisons, FS GPU implementation still showed modest improvement even though the computational complexity of FS GPU implementation is substantially higher than non-FS CPU implementation.We also demonstrated that for an image sequence of 720×480 pixels in resolution, commonly used in video surveillance, the proposed GPU implementation is sufficiently fast for real-time motion estimation at 30 frames-per-second using two NVIDIA C1060 Tesla GPU cards.
Arctic lead detection using a waveform mixture algorithm from CryoSat-2 data
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.
Algorithm of parallel: hierarchical transformation and its implementation on FPGA
Timchenko, Leonid I.; Petrovskiy, Mykola S.; Kokryatskay, Natalia I.; Barylo, Alexander S.; Dembitska, Sofia V.; Stepanikuk, Dmytro S.; Suleimenov, Batyrbek; Zyska, Tomasz; Uvaysova, Svetlana; Shedreyeva, Indira
2017-08-01
In this paper considers the algorithm of laser beam spots image classification in atmospheric-optical transmission systems. It discusses the need for images filtering using adaptive methods, using, for example, parallel-hierarchical networks. The article also highlights the need to create high-speed memory devices for such networks. Implementation and simulation results of the developed method based on the PLD are demonstrated, which shows that the presented method gives 15-20% better prediction results than similar methods.
Statistical Assessment of Gene Fusion Detection Algorithms using RNASequencing Data
Varadan, V.; Janevski, A.; Kamalakaran, S.; Banerjee, N.; Harris, L.; Dimitrova, D.
2012-01-01
The detection and quantification of fusion transcripts has both biological and clinical implications. RNA sequencing technology provides a means for unbiased and high resolution characterization of fusion transcript information in tissue samples. We evaluated two fusiondetection algorithms,
A combinational fast algorithm for image reconstruction
International Nuclear Information System (INIS)
Wu Zhongquan
1987-01-01
A combinational fast algorithm has been developed in order to increase the speed of reconstruction. First, an interpolation method based on B-spline functions is used in image reconstruction. Next, the influence of the boundary conditions assumed here on the interpolation of filtered projections and on the image reconstruction is discussed. It is shown that this boundary condition has almost no influence on the image in the central region of the image space, because the error of interpolation rapidly decreases by a factor of ten in shifting two pixels from the edge toward the center. In addition, a fast algorithm for computing the detecting angle has been used with the mentioned interpolation algorithm, and the cost for detecting angle computaton is reduced by a factor of two. The implementation results show that in the same subjective and objective fidelity, the computational cost for the interpolation using this algorithm is about one-twelfth of the conventional algorithm
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 75Ã¢Â€Â‰MHz, communicating with dedicated hardware over FSL links.
Microcontroller-based real-time QRS detection.
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.
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.
Software Piracy Detection Model Using Ant Colony Optimization Algorithm
Astiqah Omar, Nor; Zakuan, Zeti Zuryani Mohd; Saian, Rizauddin
2017-06-01
Internet enables information to be accessible anytime and anywhere. This scenario creates an environment whereby information can be easily copied. Easy access to the internet is one of the factors which contribute towards piracy in Malaysia as well as the rest of the world. According to a survey conducted by Compliance Gap BSA Global Software Survey in 2013 on software piracy, found out that 43 percent of the software installed on PCs around the world was not properly licensed, the commercial value of the unlicensed installations worldwide was reported to be 62.7 billion. Piracy can happen anywhere including universities. Malaysia as well as other countries in the world is faced with issues of piracy committed by the students in universities. Piracy in universities concern about acts of stealing intellectual property. It can be in the form of software piracy, music piracy, movies piracy and piracy of intellectual materials such as books, articles and journals. This scenario affected the owner of intellectual property as their property is in jeopardy. This study has developed a classification model for detecting software piracy. The model was developed using a swarm intelligence algorithm called the Ant Colony Optimization algorithm. The data for training was collected by a study conducted in Universiti Teknologi MARA (Perlis). Experimental results show that the model detection accuracy rate is better as compared to J48 algorithm.
FPGA Based Low Power DES Algorithm Design And Implementation using HTML Technology
DEFF Research Database (Denmark)
Thind, Vandana; Pandey, Bishwajeet; Kalia, Kartik
2016-01-01
In this particular work, we have done power analysis of DES algorithm implemented on 28nm FPGA using HTML (H-HSUL, T-TTL, M-MOBILE_DDR, L-LVCMOS) technology. In this research, we have used high performance software Xilinx ISE where we have selected four different IO Standards i.e. MOBILE_DDR, HSUL...
International Nuclear Information System (INIS)
Taraglio, S.; Massaioli, F.
1995-08-01
A parallel implementation of a library to build and train Multi Layer Perceptrons via the Back Propagation algorithm is presented. The target machine is the SIMD massively parallel supercomputer Quadrics. Performance measures are provided on three different machines with different number of processors, for two network examples. A sample source code is given
A study and implementation of algorithm for automatic ECT result comparison
International Nuclear Information System (INIS)
Jang, You Hyun; Nam, Min Woo; Kim, In Chul; Joo, Kyung Mun; Kim, Jong Seog
2012-01-01
Automatic ECT Result Comparison Algorithm was developed and implemented with computer language to remove the human error in manual comparison with many data. The structures of two ECT Program (Eddy net and ECT IDS) that have unique file structure were analyzed to open file and upload data in PC memory. Comparison algorithm was defined graphically for easy PC programming language conversion. Automatic Result Program was programmed with C language that is suitable for future code management and has object oriented programming structure and fast development potential. Automatic Result Program has MS Excel file exporting function that is useful to use external S/W for additional analysis and intuitive result visualization function with color mapping in user friendly fashion that helps analyze efficiently
A study and implementation of algorithm for automatic ECT result comparison
Energy Technology Data Exchange (ETDEWEB)
Jang, You Hyun; Nam, Min Woo; Kim, In Chul; Joo, Kyung Mun; Kim, Jong Seog [Central Research Institute, Daejeon (Korea, Republic of)
2012-10-15
Automatic ECT Result Comparison Algorithm was developed and implemented with computer language to remove the human error in manual comparison with many data. The structures of two ECT Program (Eddy net and ECT IDS) that have unique file structure were analyzed to open file and upload data in PC memory. Comparison algorithm was defined graphically for easy PC programming language conversion. Automatic Result Program was programmed with C language that is suitable for future code management and has object oriented programming structure and fast development potential. Automatic Result Program has MS Excel file exporting function that is useful to use external S/W for additional analysis and intuitive result visualization function with color mapping in user friendly fashion that helps analyze efficiently.
GillespieSSA: Implementing the Gillespie Stochastic Simulation Algorithm in R
Directory of Open Access Journals (Sweden)
Mario Pineda-Krch
2008-02-01
Full Text Available The deterministic dynamics of populations in continuous time are traditionally described using coupled, first-order ordinary differential equations. While this approach is accurate for large systems, it is often inadequate for small systems where key species may be present in small numbers or where key reactions occur at a low rate. The Gillespie stochastic simulation algorithm (SSA is a procedure for generating time-evolution trajectories of finite populations in continuous time and has become the standard algorithm for these types of stochastic models. This article presents a simple-to-use and flexible framework for implementing the SSA using the high-level statistical computing language R and the package GillespieSSA. Using three ecological models as examples (logistic growth, Rosenzweig-MacArthur predator-prey model, and Kermack-McKendrick SIRS metapopulation model, this paper shows how a deterministic model can be formulated as a finite-population stochastic model within the framework of SSA theory and how it can be implemented in R. Simulations of the stochastic models are performed using four different SSA Monte Carlo methods: one exact method (Gillespie's direct method; and three approximate methods (explicit, binomial, and optimized tau-leap methods. Comparison of simulation results confirms that while the time-evolution trajectories obtained from the different SSA methods are indistinguishable, the approximate methods are up to four orders of magnitude faster than the exact methods.
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
REAL-TIME OBJECT DETECTION IN PARALLEL THROUGH ATOMIC TRANSACTIONS
Directory of Open Access Journals (Sweden)
K Sivakumar
2016-11-01
Full Text Available Object detection and tracking is important operation involved in embedded systems like video surveillance, Traffic monitoring, campus security system, machine vision applications and other areas. Detecting and tracking multiple objects in a video or image is challenging problem in machine vision and computer vision based embedded systems. Implementation of such a object detection and tracking systems are done in sequential way of processing and also it was implemented using hardware synthesize tools like verilog HDL with FPGA, achieves considerably lesser performance in speed and it does support lesser atomic transactions. There are many object detection and tracking algorithm were proposed and implemented, among them background subtraction is one of them. This paper proposes a implementation of detecting and tracking multiple objects based on background subtraction algorithm using java and .NET and also discuss about the architecture concept for object detection through atomic transactional, modern hardware synthesizes language called Bluespec.
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.
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.
Memetic algorithms for de novo motif-finding in biomedical sequences.
Bi, Chengpeng
2012-09-01
The objectives of this study are to design and implement a new memetic algorithm for de novo motif discovery, which is then applied to detect important signals hidden in various biomedical molecular sequences. In this paper, memetic algorithms are developed and tested in de novo motif-finding problems. Several strategies in the algorithm design are employed that are to not only efficiently explore the multiple sequence local alignment space, but also effectively uncover the molecular signals. As a result, there are a number of key features in the implementation of the memetic motif-finding algorithm (MaMotif), including a chromosome replacement operator, a chromosome alteration-aware local search operator, a truncated local search strategy, and a stochastic operation of local search imposed on individual learning. To test the new algorithm, we compare MaMotif with a few of other similar algorithms using simulated and experimental data including genomic DNA, primary microRNA sequences (let-7 family), and transmembrane protein sequences. The new memetic motif-finding algorithm is successfully implemented in C++, and exhaustively tested with various simulated and real biological sequences. In the simulation, it shows that MaMotif is the most time-efficient algorithm compared with others, that is, it runs 2 times faster than the expectation maximization (EM) method and 16 times faster than the genetic algorithm-based EM hybrid. In both simulated and experimental testing, results show that the new algorithm is compared favorably or superior to other algorithms. Notably, MaMotif is able to successfully discover the transcription factors' binding sites in the chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-Seq) data, correctly uncover the RNA splicing signals in gene expression, and precisely find the highly conserved helix motif in the transmembrane protein sequences, as well as rightly detect the palindromic segments in the primary micro
Neural network fusion capabilities for efficient implementation of tracking algorithms
Sundareshan, Malur K.; Amoozegar, Farid
1997-03-01
The ability to efficiently fuse information of different forms to facilitate intelligent decision making is one of the major capabilities of trained multilayer neural networks that is now being recognized. While development of innovative adaptive control algorithms for nonlinear dynamical plants that attempt to exploit these capabilities seems to be more popular, a corresponding development of nonlinear estimation algorithms using these approaches, particularly for application in target surveillance and guidance operations, has not received similar attention. We describe the capabilities and functionality of neural network algorithms for data fusion and implementation of tracking filters. To discuss details and to serve as a vehicle for quantitative performance evaluations, the illustrative case of estimating the position and velocity of surveillance targets is considered. Efficient target- tracking algorithms that can utilize data from a host of sensing modalities and are capable of reliably tracking even uncooperative targets executing fast and complex maneuvers are of interest in a number of applications. The primary motivation for employing neural networks in these applications comes from the efficiency with which more features extracted from different sensor measurements can be utilized as inputs for estimating target maneuvers. A system architecture that efficiently integrates the fusion capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described. The innovation lies in the way the fusion of multisensor data is accomplished to facilitate improved estimation without increasing the computational complexity of the dynamical state estimator itself.
Directory of Open Access Journals (Sweden)
M. Flach
2017-08-01
Full Text Available Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of extreme climatic events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations like sudden changes in basic characteristics of time series such as the sample mean, the variance, changes in the cycle amplitude, and trends. This artificial experiment is needed as there is no gold standard for the identification of anomalies in real Earth observations. Our results show that a well-chosen feature extraction step (e.g., subtracting seasonal cycles, or dimensionality reduction is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify three detection algorithms (k-nearest neighbors mean distance, kernel density estimation, a recurrence approach and their combinations (ensembles that outperform other multivariate approaches as well as univariate extreme-event detection methods. Our results therefore provide an effective workflow to
An Optimized Structure on FPGA of Key Point Detection in SIFT Algorithm
Directory of Open Access Journals (Sweden)
Xu Chenyu
2016-01-01
Full Text Available SIFT algorithm is the most efficient and powerful algorithm to describe the features of images and it has been applied in many fields. In this paper, we propose an optimized method to realize the hardware implementation of the SIFT algorithm. We mainly discuss the structure of Data Generation here. A pipeline architecture is introduced to accelerate this optimized system. Parameters’ setting and approximation’s controlling in different image qualities and hardware resources are the focus of this paper. The results of experiments fully prove that this structure is real-time and effective, and provide consultative opinion to meet the different situations.
Spreco, A; Eriksson, O; Dahlström, Ö; Timpka, T
2017-07-01
Methods for the detection of influenza epidemics and prediction of their progress have seldom been comparatively evaluated using prospective designs. This study aimed to perform a prospective comparative trial of algorithms for the detection and prediction of increased local influenza activity. Data on clinical influenza diagnoses recorded by physicians and syndromic data from a telenursing service were used. Five detection and three prediction algorithms previously evaluated in public health settings were calibrated and then evaluated over 3 years. When applied on diagnostic data, only detection using the Serfling regression method and prediction using the non-adaptive log-linear regression method showed acceptable performances during winter influenza seasons. For the syndromic data, none of the detection algorithms displayed a satisfactory performance, while non-adaptive log-linear regression was the best performing prediction method. We conclude that evidence was found for that available algorithms for influenza detection and prediction display satisfactory performance when applied on local diagnostic data during winter influenza seasons. When applied on local syndromic data, the evaluated algorithms did not display consistent performance. Further evaluations and research on combination of methods of these types in public health information infrastructures for 'nowcasting' (integrated detection and prediction) of influenza activity are warranted.
Application of the Goertzel’s algorithm in the airgap mixed eccentricity fault detection
Directory of Open Access Journals (Sweden)
Reljić Dejan
2015-01-01
Full Text Available In this paper, a suitable method for the on-line detection of the airgap mixed eccentricity fault in a three-phase cage induction motor has been proposed. The method is based on a Motor Current Signature Analysis (MCSA approach, a technique that is often used for an induction motor condition monitoring and fault diagnosis. It is based on the spectral analysis of the stator line current signal and the frequency identification of specific components, which are created as a result of motor faults. The most commonly used method for the current signal spectral analysis is based on the Fast Fourier transform (FFT. However, due to the complexity and memory demands, the FFT algorithm is not always suitable for real-time systems. Instead of the whole spectrum analysis, this paper suggests only the spectral analysis on the expected airgap fault frequencies employing the Goertzel’s algorithm to predict the magnitude of these frequency components. The method is simple and can be implemented in real-time airgap mixed eccentricity monitoring systems without much computational effort. A low-cost data acquisition system, supported by the LabView software, has been used for the hardware and software implementation of the proposed method. The method has been validated by the laboratory experiments on both the line-connected and the inverter-fed three-phase fourpole cage induction motor operated at the rated frequency and under constant load at a few different values. In addition, the results of the proposed method have been verified through the motor’s vibration signal analysis. [Projekat Ministarstva nauke Republike Srbije, br. III42004
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.
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
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.
Optimized Swinging Door Algorithm for Wind Power Ramp Event Detection: Preprint
Energy Technology Data Exchange (ETDEWEB)
Cui, Mingjian; Zhang, Jie; Florita, Anthony R.; Hodge, Bri-Mathias; Ke, Deping; Sun, Yuanzhang
2015-08-06
Significant wind power ramp events (WPREs) are those that influence the integration of wind power, and they are a concern to the continued reliable operation of the power grid. As wind power penetration has increased in recent years, so has the importance of wind power ramps. In this paper, an optimized swinging door algorithm (SDA) is developed to improve ramp detection performance. Wind power time series data are segmented by the original SDA, and then all significant ramps are detected and merged through a dynamic programming algorithm. An application of the optimized SDA is provided to ascertain the optimal parameter of the original SDA. Measured wind power data from the Electric Reliability Council of Texas (ERCOT) are used to evaluate the proposed optimized SDA.
Model-based fault detection algorithm for photovoltaic system monitoring
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 game theoretic algorithm to detect overlapping community structure in networks
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.
Implementation of QR-decomposition based on CORDIC for unitary MUSIC algorithm
Lounici, Merwan; Luan, Xiaoming; Saadi, Wahab
2013-07-01
The DOA (Direction Of Arrival) estimation with subspace methods such as MUSIC (MUltiple SIgnal Classification) and ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique) is based on an accurate estimation of the eigenvalues and eigenvectors of covariance matrix. QR decomposition is implemented with the Coordinate Rotation DIgital Computer (CORDIC) algorithm. QRD requires only additions and shifts [6], so it is faster and more regular than other methods. In this article the hardware architecture of an EVD (Eigen Value Decomposition) processor based on TSA (triangular systolic array) for QR decomposition is proposed. Using Xilinx System Generator (XSG), the design is implemented and the estimated logic device resource values are presented for different matrix sizes.
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.
Arctic lead detection using a waveform mixture algorithm from CryoSat-2 data
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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.
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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.
Hardware-software face detection system based on multi-block local binary patterns
Acasandrei, Laurentiu; Barriga, Angel
2015-03-01
Face detection is an important aspect for biometrics, video surveillance and human computer interaction. Due to the complexity of the detection algorithms any face detection system requires a huge amount of computational and memory resources. In this communication an accelerated implementation of MB LBP face detection algorithm targeting low frequency, low memory and low power embedded system is presented. The resulted implementation is time deterministic and uses a customizable AMBA IP hardware accelerator. The IP implements the kernel operations of the MB-LBP algorithm and can be used as universal accelerator for MB LBP based applications. The IP employs 8 parallel MB-LBP feature evaluators cores, uses a deterministic bandwidth, has a low area profile and the power consumption is ~95 mW on a Virtex5 XC5VLX50T. The resulted implementation acceleration gain is between 5 to 8 times, while the hardware MB-LBP feature evaluation gain is between 69 and 139 times.
Nguyen, An Hung; Guillemette, Thomas; Lambert, Andrew J.; Pickering, Mark R.; Garratt, Matthew A.
2017-09-01
Image registration is a fundamental image processing technique. It is used to spatially align two or more images that have been captured at different times, from different sensors, or from different viewpoints. There have been many algorithms proposed for this task. The most common of these being the well-known Lucas-Kanade (LK) and Horn-Schunck approaches. However, the main limitation of these approaches is the computational complexity required to implement the large number of iterations necessary for successful alignment of the images. Previously, a multi-pass image interpolation algorithm (MP-I2A) was developed to considerably reduce the number of iterations required for successful registration compared with the LK algorithm. This paper develops a kernel-warping algorithm (KWA), a modified version of the MP-I2A, which requires fewer iterations to successfully register two images and less memory space for the field-programmable gate array (FPGA) implementation than the MP-I2A. These reductions increase feasibility of the implementation of the proposed algorithm on FPGAs with very limited memory space and other hardware resources. A two-FPGA system rather than single FPGA system is successfully developed to implement the KWA in order to compensate insufficiency of hardware resources supported by one FPGA, and increase parallel processing ability and scalability of the system.
Botnet Propagation Via Public Websited Detection Algorithm
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Jonas Juknius
2011-08-01
Full Text Available The networks of compromised and remotely controlled computers (bots are widely used in many Internet fraudulent activities, especially in the distributed denial of service attacks. Brute force gives enormous power to bot masters and makes botnet traffic visible; therefore, some countermeasures might be applied at early stages. Our study focuses on detecting botnet propagation via public websites. The provided algorithm might help with preventing from massive infections when popular web sites are compromised without spreading visual changes used for malware in botnets.Article in English
Using Deep Learning Algorithm to Enhance Image-review Software for Surveillance Cameras
Energy Technology Data Exchange (ETDEWEB)
Cui, Yonggang
2018-05-07
We propose the development of proven deep learning algorithms to flag objects and events of interest in Next Generation Surveillance System (NGSS) surveillance to make IAEA image review more efficient. Video surveillance is one of the core monitoring technologies used by the IAEA Department of Safeguards when implementing safeguards at nuclear facilities worldwide. The current image review software GARS has limited automated functions, such as scene-change detection, black image detection and missing scene analysis, but struggles with highly cluttered backgrounds. A cutting-edge algorithm to be developed in this project will enable efficient and effective searches in images and video streams by identifying and tracking safeguards relevant objects and detect anomalies in their vicinity. In this project, we will develop the algorithm, test it with the IAEA surveillance cameras and data sets collected at simulated nuclear facilities at BNL and SNL, and implement it in a software program for potential integration into the IAEA’s IRAP (Integrated Review and Analysis Program).
Detection of the arcuate fasciculus in congenital amusia depends on the tractography algorithm
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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.
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.
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...
Optimal Seamline Detection for Orthoimage Mosaicking Based on DSM and Improved JPS Algorithm
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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.
Vivó-Truyols, G.; Janssen, H.-G.
2010-01-01
The watershed algorithm is the most common method used for peak detection and integration In two-dimensional chromatography However, the retention time variability in the second dimension may render the algorithm to fail A study calculating the probabilities of failure of the watershed algorithm was
Road Detection by Using a Generalized Hough Transform
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Weifeng Liu
2017-06-01
Full Text Available Road detection plays key roles for remote sensing image analytics. Hough transform (HT is one very typical method for road detection, especially for straight line road detection. Although many variants of Hough transform have been reported, it is still a great challenge to develop a low computational complexity and time-saving Hough transform algorithm. In this paper, we propose a generalized Hough transform (i.e., Radon transform implementation for road detection in remote sensing images. Specifically, we present a dictionary learning method to approximate the Radon transform. The proposed approximation method treats a Radon transform as a linear transform, which then facilitates parallel implementation of the Radon transform for multiple images. To evaluate the proposed algorithm, we conduct extensive experiments on the popular RSSCN7 database for straight road detection. The experimental results demonstrate that our method is superior to the traditional algorithms in terms of accuracy and computing complexity.
A Low-Complexity Joint Detection-Decoding Algorithm for Nonbinary LDPC-Coded Modulation Systems
Wang, Xuepeng; Bai, Baoming; Ma, Xiao
2010-01-01
In this paper, we present a low-complexity joint detection-decoding algorithm for nonbinary LDPC codedmodulation systems. The algorithm combines hard-decision decoding using the message-passing strategy with the signal detector in an iterative manner. It requires low computational complexity, offers good system performance and has a fast rate of decoding convergence. Compared to the q-ary sum-product algorithm (QSPA), it provides an attractive candidate for practical applications of q-ary LDP...
Automated Detection of Craters in Martian Satellite Imagery Using Convolutional Neural Networks
Norman, C. J.; Paxman, J.; Benedix, G. K.; Tan, T.; Bland, P. A.; Towner, M.
2018-04-01
Crater counting is used in determining surface age of planets. We propose improvements to martian Crater Detection Algorithms by implementing an end-to-end detection approach with the possibility of scaling the algorithm planet-wide.
Shot Boundary Detection in Soccer Video using Twin-comparison Algorithm and Dominant Color Region
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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.
Deshpande, Ruchi R.; Fernandez, James; Lee, Joon K.; Chan, Tao; Liu, Brent J.; Huang, H. K.
2010-03-01
Timely detection of Acute Intra-cranial Hemorrhage (AIH) in an emergency environment is essential for the triage of patients suffering from Traumatic Brain Injury. Moreover, the small size of lesions and lack of experience on the reader's part could lead to difficulties in the detection of AIH. A CT based CAD algorithm for the detection of AIH has been developed in order to improve upon the current standard of identification and treatment of AIH. A retrospective analysis of the algorithm has already been carried out with 135 AIH CT studies with 135 matched normal head CT studies from the Los Angeles County General Hospital/ University of Southern California Hospital System (LAC/USC). In the next step, AIH studies have been collected from Walter Reed Army Medical Center, and are currently being processed using the AIH CAD system as part of implementing a multi-site assessment and evaluation of the performance of the algorithm. The sensitivity and specificity numbers from the Walter Reed study will be compared with the numbers from the LAC/USC study to determine if there are differences in the presentation and detection due to the difference in the nature of trauma between the two sites. Simultaneously, a stand-alone system with a user friendly GUI has been developed to facilitate implementation in a clinical setting.
Vibration-Based Damage Detection in Beams by Cooperative Coevolutionary Genetic Algorithm
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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.
A new comparison of hyperspectral anomaly detection algorithms for real-time applications
Díaz, María.; López, Sebastián.; Sarmiento, Roberto
2016-10-01
Due to the high spectral resolution that remotely sensed hyperspectral images provide, there has been an increasing interest in anomaly detection. The aim of anomaly detection is to stand over pixels whose spectral signature differs significantly from the background spectra. Basically, anomaly detectors mark pixels with a certain score, considering as anomalies those whose scores are higher than a threshold. Receiver Operating Characteristic (ROC) curves have been widely used as an assessment measure in order to compare the performance of different algorithms. ROC curves are graphical plots which illustrate the trade- off between false positive and true positive rates. However, they are limited in order to make deep comparisons due to the fact that they discard relevant factors required in real-time applications such as run times, costs of misclassification and the competence to mark anomalies with high scores. This last fact is fundamental in anomaly detection in order to distinguish them easily from the background without any posterior processing. An extensive set of simulations have been made using different anomaly detection algorithms, comparing their performances and efficiencies using several extra metrics in order to complement ROC curves analysis. Results support our proposal and demonstrate that ROC curves do not provide a good visualization of detection performances for themselves. Moreover, a figure of merit has been proposed in this paper which encompasses in a single global metric all the measures yielded for the proposed additional metrics. Therefore, this figure, named Detection Efficiency (DE), takes into account several crucial types of performance assessment that ROC curves do not consider. Results demonstrate that algorithms with the best detection performances according to ROC curves do not have the highest DE values. Consequently, the recommendation of using extra measures to properly evaluate performances have been supported and justified by
Liu, Wei; Chen, Shu-Ming; Zhang, Jian; Wu, Chun-Wang; Wu, Wei; Chen, Ping-Xing
2015-03-01
It is widely believed that Shor’s factoring algorithm provides a driving force to boost the quantum computing research. However, a serious obstacle to its binary implementation is the large number of quantum gates. Non-binary quantum computing is an efficient way to reduce the required number of elemental gates. Here, we propose optimization schemes for Shor’s algorithm implementation and take a ternary version for factorizing 21 as an example. The optimized factorization is achieved by a two-qutrit quantum circuit, which consists of only two single qutrit gates and one ternary controlled-NOT gate. This two-qutrit quantum circuit is then encoded into the nine lower vibrational states of an ion trapped in a weakly anharmonic potential. Optimal control theory (OCT) is employed to derive the manipulation electric field for transferring the encoded states. The ternary Shor’s algorithm can be implemented in one single step. Numerical simulation results show that the accuracy of the state transformations is about 0.9919. Project supported by the National Natural Science Foundation of China (Grant No. 61205108) and the High Performance Computing (HPC) Foundation of National University of Defense Technology, China.
IMPLEMENTATION OF IMAGE PROCESSING ALGORITHMS AND GLVQ TO TRACK AN OBJECT USING AR.DRONE CAMERA
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Muhammad Nanda Kurniawan
2014-08-01
Full Text Available Abstract In this research, Parrot AR.Drone as an Unmanned Aerial Vehicle (UAV was used to track an object from above. Development of this system utilized some functions from OpenCV library and Robot Operating System (ROS. Techniques that were implemented in the system are image processing al-gorithm (Centroid-Contour Distance (CCD, feature extraction algorithm (Principal Component Analysis (PCA and an artificial neural network algorithm (Generalized Learning Vector Quantization (GLVQ. The final result of this research is a program for AR.Drone to track a moving object on the floor in fast response time that is under 1 second.
An Algorithm for Detection of DVB-T Signals Based on Their Second-Order Statistics
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Pierre Jallon
2008-03-01
Full Text Available We propose in this paper a detection algorithm based on a cost function that jointly tests the correlation induced by the cyclic prefix and the fact that this correlation is time-periodic. In the first part of the paper, the cost function is introduced and some analytical results are given. In particular, the noise and multipath channel impacts on its values are theoretically analysed. In a second part of the paper, some asymptotic results are derived. A first exploitation of these results is used to build a detection test based on the false alarm probability. These results are also used to evaluate the impact of the number of cycle frequencies taken into account in the cost function on the detection performances. Thanks to numerical estimations, we have been able to estimate that the proposed algorithm detects DVB-T signals with an SNR of Ã¢ÂˆÂ’12Ã¢Â€Â‰dB. As a comparison, and in the same context, the detection algorithm proposed by the 802.22 WG in 2006 is able to detect these signals with an SNR of Ã¢ÂˆÂ’8Ã¢Â€Â‰dB.
An effective detection algorithm for region duplication forgery in digital images
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.
A New FPGA Architecture of FAST and BRIEF Algorithm for On-Board Corner Detection and Matching
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Jingjin Huang
2018-03-01
Full Text Available Although some researchers have proposed the Field Programmable Gate Array (FPGA architectures of Feature From Accelerated Segment Test (FAST and Binary Robust Independent Elementary Features (BRIEF algorithm, there is no consideration of image data storage in these traditional architectures that will result in no image data that can be reused by the follow-up algorithms. This paper proposes a new FPGA architecture that considers the reuse of sub-image data. In the proposed architecture, a remainder-based method is firstly designed for reading the sub-image, a FAST detector and a BRIEF descriptor are combined for corner detection and matching. Six pairs of satellite images with different textures, which are located in the Mentougou district, Beijing, China, are used to evaluate the performance of the proposed architecture. The Modelsim simulation results found that: (i the proposed architecture is effective for sub-image reading from DDR3 at a minimum cost; (ii the FPGA implementation is corrected and efficient for corner detection and matching, such as the average value of matching rate of natural areas and artificial areas are approximately 67% and 83%, respectively, which are close to PC’s and the processing speed by FPGA is approximately 31 and 2.5 times faster than those by PC processing and by GPU processing, respectively.
Killingsworth, Christopher D; Taylor, Steven M; Patterson, Mark A; Weinberg, Jordan A; McGwin, Gerald; Melton, Sherry M; Reiff, Donald A; Kerby, Jeffrey D; Rue, Loring W; Jordan, William D; Passman, Marc A
2010-05-01
Although contrast venography is the standard imaging method for inferior vena cava (IVC) filter insertion, intravascular ultrasound (IVUS) imaging is a safe and effective option that allows for bedside filter placement and is especially advantageous for immobilized critically ill patients by limiting resource use, risk of transportation, and cost. This study reviewed the effectiveness of a prospectively implemented algorithm for IVUS-guided IVC filter placement in this high-risk population. Current evidence-based guidelines were used to create a clinical decision algorithm for IVUS-guided IVC filter placement in critically ill patients. After a defined lead-in phase to allow dissemination of techniques, the algorithm was prospectively implemented on January 1, 2008. Data were collected for 1 year using accepted reporting standards and a quality assurance review performed based on intent-to-treat at 6, 12, and 18 months. As defined in the prospectively implemented algorithm, 109 patients met criteria for IVUS-directed bedside IVC filter placement. Technical feasibility was 98.1%. Only 2 patients had inadequate IVUS visualization for bedside filter placement and required subsequent placement in the endovascular suite. Technical success, defined as proper deployment in an infrarenal position, was achieved in 104 of the remaining 107 patients (97.2%). The filter was permanent in 21 (19.6%) and retrievable in 86 (80.3%). The single-puncture technique was used in 101 (94.4%), with additional dual access required in 6 (5.6%). Periprocedural complications were rare but included malpositioning requiring retrieval and repositioning in three patients, filter tilt >/=15 degrees in two, and arteriovenous fistula in one. The 30-day mortality rate for the bedside group was 5.5%, with no filter-related deaths. Successful placement of IVC filters using IVUS-guided imaging at the bedside in critically ill patients can be established through an evidence-based prospectively
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 Implementation Of Elias Delta Code And ElGamal Algorithm In Image Compression And Security
Rachmawati, Dian; Andri Budiman, Mohammad; Saffiera, Cut Amalia
2018-01-01
In data transmission such as transferring an image, confidentiality, integrity, and efficiency of data storage aspects are highly needed. To maintain the confidentiality and integrity of data, one of the techniques used is ElGamal. The strength of this algorithm is found on the difficulty of calculating discrete logs in a large prime modulus. ElGamal belongs to the class of Asymmetric Key Algorithm and resulted in enlargement of the file size, therefore data compression is required. Elias Delta Code is one of the compression algorithms that use delta code table. The image was first compressed using Elias Delta Code Algorithm, then the result of the compression was encrypted by using ElGamal algorithm. Prime test was implemented using Agrawal Biswas Algorithm. The result showed that ElGamal method could maintain the confidentiality and integrity of data with MSE and PSNR values 0 and infinity. The Elias Delta Code method generated compression ratio and space-saving each with average values of 62.49%, and 37.51%.
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Oscar Arley Riveros
2017-01-01
Full Text Available Introduction: This paper aims to present the design of a mobile application involving NFC technology and a collaborative recommendation algorithm under the K-neighbors technique, allowing to observe personalized suggestions for each client. Objective: Design and develop a mobile application, using NFC technologies and K-Neighbors Technique in a recommendation algorithm, for a Procurement System. Methodology: The process followed for the design and development of the application focuses on: • Review of the state of the art in mobile shopping systems. • State-of-the-art construction in the use of NFC technology and AI techniques for recommending systems focused on K-Neighbors Algorithms • Proposed system design • Parameterization and implementation of the K-Neighbors Technique and integration of NFC Technology • Proposed System Implementation and Testing. Results: Among the results obtained are detailed: • Mobile application that integrates Android, NFC Technologies and a Technique of Algorithm Recommendation • Parameterization of the K-Neighbors Technique, to be used within the recommended algorithm. • Implementation of functional requirements that allow the generation of personalized recommendations for purchase to the user, user ratings Conclusions: The k-neighbors technique in a recommendation algorithm allows the client to provide a series of recommendations with a level of security, since this algorithm performs calculations taking into account multiple parameters and contrasts the results obtained for other users, finding the articles with a Greater degree of similarity with the customer profile. This algorithm starts from a sample of similar, complementary and other unrelated products, applying its respective formulation, we obtain that the recommendation is made only with the complementary products that obtained higher qualification; Making a big difference with most recommending systems on the market, which are limited to
A Robust Vision-based Runway Detection and Tracking Algorithm for Automatic UAV Landing
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.
Detecting Hijacked Journals by Using Classification Algorithms.
Andoohgin Shahri, Mona; Jazi, Mohammad Davarpanah; Borchardt, Glenn; Dadkhah, Mehdi
2018-04-01
Invalid journals are recent challenges in the academic world and many researchers are unacquainted with the phenomenon. The number of victims appears to be accelerating. Researchers might be suspicious of predatory journals because they have unfamiliar names, but hijacked journals are imitations of well-known, reputable journals whose websites have been hijacked. Hijacked journals issue calls for papers via generally laudatory emails that delude researchers into paying exorbitant page charges for publication in a nonexistent journal. This paper presents a method for detecting hijacked journals by using a classification algorithm. The number of published articles exposing hijacked journals is limited and most of them use simple techniques that are limited to specific journals. Hence we needed to amass Internet addresses and pertinent data for analyzing this type of attack. We inspected the websites of 104 scientific journals by using a classification algorithm that used criteria common to reputable journals. We then prepared a decision tree that we used to test five journals we knew were authentic and five we knew were hijacked.
An implementation of signal processing algorithms for ultrasonic NDE
International Nuclear Information System (INIS)
Ericsson, L.; Stepinski, T.
1994-01-01
Probability of detection flaws during ultrasonic pulse-echo inspection is often limited by the presence of backscattered echoes from the material structure. A digital signal processing technique for removal of this material noise, referred to as split spectrum processing (SSP), has been developed and verified using laboratory experiments during the last decade. The authors have performed recently a limited scale evaluation of various SSP techniques for ultrasonic signals acquired during the inspection of welds in austenitic steel. They have obtained very encouraging results that indicate promising capabilities of the SSP for inspection of nuclear power plants. Thus, a more extensive investigation of the technique using large amounts of ultrasonic data is motivated. This analysis should employ different combinations of materials, flaws and transducers. Due to the considerable number of ultrasonic signals required to verify the technique for future practical use, a custom-made computer software is necessary. At the request of the Swedish nuclear power industry the authors have developed such a program package. The program provides a user-friendly graphical interface and is intended for processing of B-scan data in a flexible way. Assembled in the program are a number of signal processing algorithms including traditional Split Spectrum Processing and the more recent Cut Spectrum Processing algorithm developed by them. The program and some results obtained using the various algorithms are presented in the paper
TESTING BAYESIAN ALGORITHMS TO DETECT GENETIC STRUCTURE IN TWO CLOSELY RELATED OAK TAXA
Directory of Open Access Journals (Sweden)
Cristian Mihai Enescu
2013-12-01
Full Text Available The aim of this study was to test the Bayesian algorithm implemented in the software STRUCTURE in order to detect the number of clusters, by using microsatellite data from four oak species. Several assignment models, with or without a priori grouping of individuals to species, were proposed. Better results were obtained by using the sampling location information and when only two taxa were analyzed. Particularly, pedunculate oak and sessile oak formed distinct clusters whatever the assignment model we use. By contrast, no separation between the two oaks from series Lanuginosae was observed. This can be explained, on one hand, by the small sampling size for Italian oak, or by the genetic similarities of the two pubescent oaks, namely Quercus pubescens and Q. virgiliana, on the other hand. Our findings support the hypothesis according which Italian oak is an intraspecific taxonomic unit of pubescent oak.
The Efficacy of Epidemic Algorithms on Detecting Node Replicas in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Narasimha Shashidhar
2015-12-01
Full Text Available A node replication attack against a wireless sensor network involves surreptitious efforts by an adversary to insert duplicate sensor nodes into the network while avoiding detection. Due to the lack of tamper-resistant hardware and the low cost of sensor nodes, launching replication attacks takes little effort to carry out. Naturally, detecting these replica nodes is a very important task and has been studied extensively. In this paper, we propose a novel distributed, randomized sensor duplicate detection algorithm called Discard to detect node replicas in group-deployed wireless sensor networks. Our protocol is an epidemic, self-organizing duplicate detection scheme, which exhibits emergent properties. Epidemic schemes have found diverse applications in distributed computing: load balancing, topology management, audio and video streaming, computing aggregate functions, failure detection, network and resource monitoring, to name a few. To the best of our knowledge, our algorithm is the first attempt at exploring the potential of this paradigm to detect replicas in a wireless sensor network. Through analysis and simulation, we show that our scheme achieves robust replica detection with substantially lower communication, computational and storage requirements than prior schemes in the literature.
Spatial-time-state fusion algorithm for defect detection through eddy current pulsed thermography
Xiao, Xiang; Gao, Bin; Woo, Wai Lok; Tian, Gui Yun; Xiao, Xiao Ting
2018-05-01
Eddy Current Pulsed Thermography (ECPT) has received extensive attention due to its high sensitive of detectability on surface and subsurface cracks. However, it remains as a difficult challenge in unsupervised detection as to identify defects without knowing any prior knowledge. This paper presents a spatial-time-state features fusion algorithm to obtain fully profile of the defects by directional scanning. The proposed method is intended to conduct features extraction by using independent component analysis (ICA) and automatic features selection embedding genetic algorithm. Finally, the optimal feature of each step is fused to obtain defects reconstruction by applying common orthogonal basis extraction (COBE) method. Experiments have been conducted to validate the study and verify the efficacy of the proposed method on blind defect detection.
A Scalable Gaussian Process Analysis Algorithm for Biomass Monitoring
Energy Technology Data Exchange (ETDEWEB)
Chandola, Varun [ORNL; Vatsavai, Raju [ORNL
2011-01-01
Biomass monitoring is vital for studying the carbon cycle of earth's ecosystem and has several significant implications, especially in the context of understanding climate change and its impacts. Recently, several change detection methods have been proposed to identify land cover changes in temporal profiles (time series) of vegetation collected using remote sensing instruments, but do not satisfy one or both of the two requirements of the biomass monitoring problem, i.e., {\\em operating in online mode} and {\\em handling periodic time series}. In this paper, we adapt Gaussian process regression to detect changes in such time series in an online fashion. While Gaussian process (GP) have been widely used as a kernel based learning method for regression and classification, their applicability to massive spatio-temporal data sets, such as remote sensing data, has been limited owing to the high computational costs involved. We focus on addressing the scalability issues associated with the proposed GP based change detection algorithm. This paper makes several significant contributions. First, we propose a GP based online time series change detection algorithm and demonstrate its effectiveness in detecting different types of changes in {\\em Normalized Difference Vegetation Index} (NDVI) data obtained from a study area in Iowa, USA. Second, we propose an efficient Toeplitz matrix based solution which significantly improves the computational complexity and memory requirements of the proposed GP based method. Specifically, the proposed solution can analyze a time series of length $t$ in $O(t^2)$ time while maintaining a $O(t)$ memory footprint, compared to the $O(t^3)$ time and $O(t^2)$ memory requirement of standard matrix manipulation based methods. Third, we describe a parallel version of the proposed solution which can be used to simultaneously analyze a large number of time series. We study three different parallel implementations: using threads, MPI, and a
An implementation of differential evolution algorithm for inversion of geoelectrical data
Balkaya, Çağlayan
2013-11-01
Differential evolution (DE), a population-based evolutionary algorithm (EA) has been implemented to invert self-potential (SP) and vertical electrical sounding (VES) data sets. The algorithm uses three operators including mutation, crossover and selection similar to genetic algorithm (GA). Mutation is the most important operator for the success of DE. Three commonly used mutation strategies including DE/best/1 (strategy 1), DE/rand/1 (strategy 2) and DE/rand-to-best/1 (strategy 3) were applied together with a binomial type crossover. Evolution cycle of DE was realized without boundary constraints. For the test studies performed with SP data, in addition to both noise-free and noisy synthetic data sets two field data sets observed over the sulfide ore body in the Malachite mine (Colorado) and over the ore bodies in the Neem-Ka Thana cooper belt (India) were considered. VES test studies were carried out using synthetically produced resistivity data representing a three-layered earth model and a field data set example from Gökçeada (Turkey), which displays a seawater infiltration problem. Mutation strategies mentioned above were also extensively tested on both synthetic and field data sets in consideration. Of these, strategy 1 was found to be the most effective strategy for the parameter estimation by providing less computational cost together with a good accuracy. The solutions obtained by DE for the synthetic cases of SP were quite consistent with particle swarm optimization (PSO) which is a more widely used population-based optimization algorithm than DE in geophysics. Estimated parameters of SP and VES data were also compared with those obtained from Metropolis-Hastings (M-H) sampling algorithm based on simulated annealing (SA) without cooling to clarify uncertainties in the solutions. Comparison to the M-H algorithm shows that DE performs a fast approximate posterior sampling for the case of low-dimensional inverse geophysical problems.
Zhang, Yuli; Han, Jun; Weng, Xinqian; He, Zhongzhu; Zeng, Xiaoyang
This paper presents an Application Specific Instruction-set Processor (ASIP) for the SHA-3 BLAKE algorithm family by instruction set extensions (ISE) from an RISC (reduced instruction set computer) processor. With a design space exploration for this ASIP to increase the performance and reduce the area cost, we accomplish an efficient hardware and software implementation of BLAKE algorithm. The special instructions and their well-matched hardware function unit improve the calculation of the key section of the algorithm, namely G-functions. Also, relaxing the time constraint of the special function unit can decrease its hardware cost, while keeping the high data throughput of the processor. Evaluation results reveal the ASIP achieves 335Mbps and 176Mbps for BLAKE-256 and BLAKE-512. The extra area cost is only 8.06k equivalent gates. The proposed ASIP outperforms several software approaches on various platforms in cycle per byte. In fact, both high throughput and low hardware cost achieved by this programmable processor are comparable to that of ASIC implementations.
Implementation of a Wavefront-Sensing Algorithm
Smith, Jeffrey S.; Dean, Bruce; Aronstein, David
2013-01-01
A computer program has been written as a unique implementation of an image-based wavefront-sensing algorithm reported in "Iterative-Transform Phase Retrieval Using Adaptive Diversity" (GSC-14879-1), NASA Tech Briefs, Vol. 31, No. 4 (April 2007), page 32. This software was originally intended for application to the James Webb Space Telescope, but is also applicable to other segmented-mirror telescopes. The software is capable of determining optical-wavefront information using, as input, a variable number of irradiance measurements collected in defocus planes about the best focal position. The software also uses input of the geometrical definition of the telescope exit pupil (otherwise denoted the pupil mask) to identify the locations of the segments of the primary telescope mirror. From the irradiance data and mask information, the software calculates an estimate of the optical wavefront (a measure of performance) of the telescope generally and across each primary mirror segment specifically. The software is capable of generating irradiance data, wavefront estimates, and basis functions for the full telescope and for each primary-mirror segment. Optionally, each of these pieces of information can be measured or computed outside of the software and incorporated during execution of the software.
Energy Technology Data Exchange (ETDEWEB)
Madduri, Kamesh; Ediger, David; Jiang, Karl; Bader, David A.; Chavarria-Miranda, Daniel
2009-02-15
We present a new lock-free parallel algorithm for computing betweenness centralityof massive small-world networks. With minor changes to the data structures, ouralgorithm also achieves better spatial cache locality compared to previous approaches. Betweenness centrality is a key algorithm kernel in HPCS SSCA#2, a benchmark extensively used to evaluate the performance of emerging high-performance computing architectures for graph-theoretic computations. We design optimized implementations of betweenness centrality and the SSCA#2 benchmark for two hardware multithreaded systems: a Cray XMT system with the Threadstorm processor, and a single-socket Sun multicore server with the UltraSPARC T2 processor. For a small-world network of 134 million vertices and 1.073 billion edges, the 16-processor XMT system and the 8-core Sun Fire T5120 server achieve TEPS scores (an algorithmic performance count for the SSCA#2 benchmark) of 160 million and 90 million respectively, which corresponds to more than a 2X performance improvement over the previous parallel implementations. To better characterize the performance of these multithreaded systems, we correlate the SSCA#2 performance results with data from the memory-intensive STREAM and RandomAccess benchmarks. Finally, we demonstrate the applicability of our implementation to analyze massive real-world datasets by computing approximate betweenness centrality for a large-scale IMDb movie-actor network.
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
DEFF Research Database (Denmark)
Saadi, Dorthe Bodholt; Egstrup, Kenneth; Branebjerg, Jens
2012-01-01
We have designed and optimized an automatic QRS complex detection algorithm for electrocardiogram (ECG) signals recorded with the DELTA ePatch platform. The algorithm is able to automatically switch between single-channel and multi-channel analysis mode. This preliminary study includes data from ...
International Nuclear Information System (INIS)
Schellhorn, M; Rosenberger, M; Correns, M; Blau, M; Goepfert, A; Rueckwardt, M; Linss, G
2010-01-01
Within the field of industrial image processing the use of colour cameras becomes ever more common. Increasingly the established black and white cameras are replaced by economical single-chip colour cameras with Bayer pattern. The use of the additional colour information is particularly important for recognition or inspection. Become interesting however also for the geometric metrology, if measuring tasks can be solved more robust or more exactly. However only few suitable algorithms are available, in order to detect edges with the necessary precision. All attempts require however additional computation expenditure. On the basis of a new filter for edge detection in colour images with subpixel precision, the implementation on a pre-processing hardware platform is presented. Hardware implemented filters offer the advantage that they can be used easily with existing measuring software, since after the filtering a single channel image is present, which unites the information of all colour channels. Advanced field programmable gate arrays represent an ideal platform for the parallel processing of multiple channels. The effective implementation presupposes however a high programming expenditure. On the example of the colour filter implementation, arising problems are analyzed and the chosen solution method is presented.
Directory of Open Access Journals (Sweden)
Faryal Shamsi
2017-12-01
Full Text Available This Analysis and Design of Algorithm is considered as a compulsory course in the field of Computer Science. It increases the logical and problem solving skills of the students and make their solutions efficient in terms of time and space. These objectives can only be achieved if a student practically implements what he or she has studied throughout the course. But if the contents of this course are merely studied and rarely practiced then the actual goals of the course is not fulfilled. This article will explore the extent of practical implementation of the course of analysis and design of algorithm. Problems faced by the computer science community and major barriers in the field are also enumerated. Finally, some recommendations are made to overcome the obstacles in the practical implementation of analysis and design of algorithms.
Implementation of the CA-CFAR algorithm for pulsed-doppler radar on a GPU architecture
CSIR Research Space (South Africa)
Venter, CJ
2011-12-01
Full Text Available /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Implementation of the CA-CFAR Algorithm for Pulsed...
Liu, Zhi; Zhou, Baotong; Zhang, Changnian
2017-03-01
Vehicle-mounted panoramic system is important safety assistant equipment for driving. However, traditional systems only render fixed top-down perspective view of limited view field, which may have potential safety hazard. In this paper, a texture mapping algorithm for 3D vehicle-mounted panoramic system is introduced, and an implementation of the algorithm utilizing OpenGL ES library based on Android smart platform is presented. Initial experiment results show that the proposed algorithm can render a good 3D panorama, and has the ability to change view point freely.
Implementation of PHENIX trigger algorithms on massively parallel computers
International Nuclear Information System (INIS)
Petridis, A.N.; Wohn, F.K.
1995-01-01
The event selection requirements of contemporary high energy and nuclear physics experiments are met by the introduction of on-line trigger algorithms which identify potentially interesting events and reduce the data acquisition rate to levels that are manageable by the electronics. Such algorithms being parallel in nature can be simulated off-line using massively parallel computers. The PHENIX experiment intends to investigate the possible existence of a new phase of matter called the quark gluon plasma which has been theorized to have existed in very early stages of the evolution of the universe by studying collisions of heavy nuclei at ultra-relativistic energies. Such interactions can also reveal important information regarding the structure of the nucleus and mandate a thorough investigation of the simpler proton-nucleus collisions at the same energies. The complexity of PHENIX events and the need to analyze and also simulate them at rates similar to the data collection ones imposes enormous computation demands. This work is a first effort to implement PHENIX trigger algorithms on parallel computers and to study the feasibility of using such machines to run the complex programs necessary for the simulation of the PHENIX detector response. Fine and coarse grain approaches have been studied and evaluated. Depending on the application the performance of a massively parallel computer can be much better or much worse than that of a serial workstation. A comparison between single instruction and multiple instruction computers is also made and possible applications of the single instruction machines to high energy and nuclear physics experiments are outlined. copyright 1995 American Institute of Physics
Searching Algorithms Implemented on Probabilistic Systolic Arrays
Czech Academy of Sciences Publication Activity Database
Kramosil, Ivan
1996-01-01
Roč. 25, č. 1 (1996), s. 7-45 ISSN 0308-1079 R&D Projects: GA ČR GA201/93/0781 Keywords : searching algorithms * probabilistic algorithms * systolic arrays * parallel algorithms Impact factor: 0.214, year: 1996
FPGA based hardware optimized implementation of signal processing system for LFM pulsed radar
Azim, Noor ul; Jun, Wang
2016-11-01
Signal processing is one of the main parts of any radar system. Different signal processing algorithms are used to extract information about different parameters like range, speed, direction etc, of a target in the field of radar communication. This paper presents LFM (Linear Frequency Modulation) pulsed radar signal processing algorithms which are used to improve target detection, range resolution and to estimate the speed of a target. Firstly, these algorithms are simulated in MATLAB to verify the concept and theory. After the conceptual verification in MATLAB, the simulation is converted into implementation on hardware using Xilinx FPGA. Chosen FPGA is Xilinx Virtex-6 (XC6LVX75T). For hardware implementation pipeline optimization is adopted and also other factors are considered for resources optimization in the process of implementation. Focusing algorithms in this work for improving target detection, range resolution and speed estimation are hardware optimized fast convolution processing based pulse compression and pulse Doppler processing.
Airport Traffic Conflict Detection and Resolution Algorithm Evaluation
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.
Autopiquer - a Robust and Reliable Peak Detection Algorithm for Mass Spectrometry.
Kilgour, David P A; Hughes, Sam; Kilgour, Samantha L; Mackay, C Logan; Palmblad, Magnus; Tran, Bao Quoc; Goo, Young Ah; Ernst, Robert K; Clarke, David J; Goodlett, David R
2017-02-01
We present a simple algorithm for robust and unsupervised peak detection by determining a noise threshold in isotopically resolved mass spectrometry data. Solving this problem will greatly reduce the subjective and time-consuming manual picking of mass spectral peaks and so will prove beneficial in many research applications. The Autopiquer approach uses autocorrelation to test for the presence of (isotopic) structure in overlapping windows across the spectrum. Within each window, a noise threshold is optimized to remove the most unstructured data, whilst keeping as much of the (isotopic) structure as possible. This algorithm has been successfully demonstrated for both peak detection and spectral compression on data from many different classes of mass spectrometer and for different sample types, and this approach should also be extendible to other types of data that contain regularly spaced discrete peaks. Graphical Abstract ᅟ.
The Sustainable Technology Division has recently completed an implementation of the U.S. EPA's Waste Reduction (WAR) Algorithm that can be directly accessed from a Cape-Open compliant process modeling environment. The WAR Algorithm add-in can be used in AmsterChem's COFE (Cape-Op...
Schneider, Nadine; Sayle, Roger A; Landrum, Gregory A
2015-10-26
Finding a canonical ordering of the atoms in a molecule is a prerequisite for generating a unique representation of the molecule. The canonicalization of a molecule is usually accomplished by applying some sort of graph relaxation algorithm, the most common of which is the Morgan algorithm. There are known issues with that algorithm that lead to noncanonical atom orderings as well as problems when it is applied to large molecules like proteins. Furthermore, each cheminformatics toolkit or software provides its own version of a canonical ordering, most based on unpublished algorithms, which also complicates the generation of a universal unique identifier for molecules. We present an alternative canonicalization approach that uses a standard stable-sorting algorithm instead of a Morgan-like index. Two new invariants that allow canonical ordering of molecules with dependent chirality as well as those with highly symmetrical cyclic graphs have been developed. The new approach proved to be robust and fast when tested on the 1.45 million compounds of the ChEMBL 20 data set in different scenarios like random renumbering of input atoms or SMILES round tripping. Our new algorithm is able to generate a canonical order of the atoms of protein molecules within a few milliseconds. The novel algorithm is implemented in the open-source cheminformatics toolkit RDKit. With this paper, we provide a reference Python implementation of the algorithm that could easily be integrated in any cheminformatics toolkit. This provides a first step toward a common standard for canonical atom ordering to generate a universal unique identifier for molecules other than InChI.
An adaptive algorithm for the detection of microcalcifications in simulated low-dose mammography
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
An improved data clustering algorithm for outlier detection
Directory of Open Access Journals (Sweden)
Anant Agarwal
2016-12-01
Full Text Available Data mining is the extraction of hidden predictive information from large databases. This is a technology with potential to study and analyze useful information present in data. Data objects which do not usually fit into the general behavior of the data are termed as outliers. Outlier Detection in databases has numerous applications such as fraud detection, customized marketing, and the search for terrorism. By definition, outliers are rare occurrences and hence represent a small portion of the data. However, the use of Outlier Detection for various purposes is not an easy task. This research proposes a modified PAM for detecting outliers. The proposed technique has been implemented in JAVA. The results produced by the proposed technique are found better than existing technique in terms of outliers detected and time complexity.
Hougardy, Stefan
2016-01-01
Algorithms play an increasingly important role in nearly all fields of mathematics. This book allows readers to develop basic mathematical abilities, in particular those concerning the design and analysis of algorithms as well as their implementation. It presents not only fundamental algorithms like the sieve of Eratosthenes, the Euclidean algorithm, sorting algorithms, algorithms on graphs, and Gaussian elimination, but also discusses elementary data structures, basic gr