Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for Lp-Regularization Using the Multiple Sub-Dictionary Representation.

PubMed

Li, Yunyi; Zhang, Jie; Fan, Shangang; Yang, Jie; Xiong, Jian; Cheng, Xiefeng; Sari, Hikmet; Adachi, Fumiyuki; Gui, Guan

2017-12-15

Both L 1/2 and L 2/3 are two typical non-convex regularizations of L p (0

Adaptive threshold control for auto-rate fallback algorithm in IEEE 802.11 multi-rate WLANs

NASA Astrophysics Data System (ADS)

Wu, Qilin; Lu, Yang; Zhu, Xiaolin; Ge, Fangzhen

2012-03-01

The IEEE 802.11 standard supports multiple rates for data transmission in the physical layer. Nowadays, to improve network performance, a rate adaptation scheme called auto-rate fallback (ARF) is widely adopted in practice. However, ARF scheme suffers performance degradation in multiple contending nodes environments. In this article, we propose a novel rate adaptation scheme called ARF with adaptive threshold control. In multiple contending nodes environment, the proposed scheme can effectively mitigate the frame collision effect on rate adaptation decision by adaptively adjusting rate-up and rate-down threshold according to the current collision level. Simulation results show that the proposed scheme can achieve significantly higher throughput than the other existing rate adaptation schemes. Furthermore, the simulation results also demonstrate that the proposed scheme can effectively respond to the varying channel condition.

A lane line segmentation algorithm based on adaptive threshold and connected domain theory

NASA Astrophysics Data System (ADS)

Feng, Hui; Xu, Guo-sheng; Han, Yi; Liu, Yang

2018-04-01

Before detecting cracks and repairs on road lanes, it's necessary to eliminate the influence of lane lines on the recognition result in road lane images. Aiming at the problems caused by lane lines, an image segmentation algorithm based on adaptive threshold and connected domain is proposed. First, by analyzing features like grey level distribution and the illumination of the images, the algorithm uses Hough transform to divide the images into different sections and convert them into binary images separately. It then uses the connected domain theory to amend the outcome of segmentation, remove noises and fill the interior zone of lane lines. Experiments have proved that this method could eliminate the influence of illumination and lane line abrasion, removing noises thoroughly while maintaining high segmentation precision.

EEG Artifact Removal Using a Wavelet Neural Network

NASA Technical Reports Server (NTRS)

Nguyen, Hoang-Anh T.; Musson, John; Li, Jiang; McKenzie, Frederick; Zhang, Guangfan; Xu, Roger; Richey, Carl; Schnell, Tom

2011-01-01

!n this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We. compared the WNN algorithm with .the ICA technique ,and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data.

Robust crop and weed segmentation under uncontrolled outdoor illumination

USDA-ARS?s Scientific Manuscript database

A new machine vision for weed detection was developed from RGB color model images. Processes included in the algorithm for the detection were excessive green conversion, threshold value computation by statistical analysis, adaptive image segmentation by adjusting the threshold value, median filter, ...

An Adaptive Deghosting Method in Neural Network-Based Infrared Detectors Nonuniformity Correction

PubMed Central

Li, Yiyang; Jin, Weiqi; Zhu, Jin; Zhang, Xu; Li, Shuo

2018-01-01

The problems of the neural network-based nonuniformity correction algorithm for infrared focal plane arrays mainly concern slow convergence speed and ghosting artifacts. In general, the more stringent the inhibition of ghosting, the slower the convergence speed. The factors that affect these two problems are the estimated desired image and the learning rate. In this paper, we propose a learning rate rule that combines adaptive threshold edge detection and a temporal gate. Through the noise estimation algorithm, the adaptive spatial threshold is related to the residual nonuniformity noise in the corrected image. The proposed learning rate is used to effectively and stably suppress ghosting artifacts without slowing down the convergence speed. The performance of the proposed technique was thoroughly studied with infrared image sequences with both simulated nonuniformity and real nonuniformity. The results show that the deghosting performance of the proposed method is superior to that of other neural network-based nonuniformity correction algorithms and that the convergence speed is equivalent to the tested deghosting methods. PMID:29342857

An Adaptive Deghosting Method in Neural Network-Based Infrared Detectors Nonuniformity Correction.

PubMed

Li, Yiyang; Jin, Weiqi; Zhu, Jin; Zhang, Xu; Li, Shuo

2018-01-13

The problems of the neural network-based nonuniformity correction algorithm for infrared focal plane arrays mainly concern slow convergence speed and ghosting artifacts. In general, the more stringent the inhibition of ghosting, the slower the convergence speed. The factors that affect these two problems are the estimated desired image and the learning rate. In this paper, we propose a learning rate rule that combines adaptive threshold edge detection and a temporal gate. Through the noise estimation algorithm, the adaptive spatial threshold is related to the residual nonuniformity noise in the corrected image. The proposed learning rate is used to effectively and stably suppress ghosting artifacts without slowing down the convergence speed. The performance of the proposed technique was thoroughly studied with infrared image sequences with both simulated nonuniformity and real nonuniformity. The results show that the deghosting performance of the proposed method is superior to that of other neural network-based nonuniformity correction algorithms and that the convergence speed is equivalent to the tested deghosting methods.

A self-adaptive algorithm for traffic sign detection in motion image based on color and shape features

NASA Astrophysics Data System (ADS)

Zhang, Ka; Sheng, Yehua; Gong, Zhijun; Ye, Chun; Li, Yongqiang; Liang, Cheng

2007-06-01

As an important sub-system in intelligent transportation system (ITS), the detection and recognition of traffic signs from mobile images is becoming one of the hot spots in the international research field of ITS. Considering the problem of traffic sign automatic detection in motion images, a new self-adaptive algorithm for traffic sign detection based on color and shape features is proposed in this paper. Firstly, global statistical color features of different images are computed based on statistics theory. Secondly, some self-adaptive thresholds and special segmentation rules for image segmentation are designed according to these global color features. Then, for red, yellow and blue traffic signs, the color image is segmented to three binary images by these thresholds and rules. Thirdly, if the number of white pixels in the segmented binary image exceeds the filtering threshold, the binary image should be further filtered. Fourthly, the method of gray-value projection is used to confirm top, bottom, left and right boundaries for candidate regions of traffic signs in the segmented binary image. Lastly, if the shape feature of candidate region satisfies the need of real traffic sign, this candidate region is confirmed as the detected traffic sign region. The new algorithm is applied to actual motion images of natural scenes taken by a CCD camera of the mobile photogrammetry system in Nanjing at different time. The experimental results show that the algorithm is not only simple, robust and more adaptive to natural scene images, but also reliable and high-speed on real traffic sign detection.

Adaptive spatial filtering improves speech reception in noise while preserving binaural cues.

PubMed

Bissmeyer, Susan R S; Goldsworthy, Raymond L

2017-09-01

Hearing loss greatly reduces an individual's ability to comprehend speech in the presence of background noise. Over the past decades, numerous signal-processing algorithms have been developed to improve speech reception in these situations for cochlear implant and hearing aid users. One challenge is to reduce background noise while not introducing interaural distortion that would degrade binaural hearing. The present study evaluates a noise reduction algorithm, referred to as binaural Fennec, that was designed to improve speech reception in background noise while preserving binaural cues. Speech reception thresholds were measured for normal-hearing listeners in a simulated environment with target speech generated in front of the listener and background noise originating 90° to the right of the listener. Lateralization thresholds were also measured in the presence of background noise. These measures were conducted in anechoic and reverberant environments. Results indicate that the algorithm improved speech reception thresholds, even in highly reverberant environments. Results indicate that the algorithm also improved lateralization thresholds for the anechoic environment while not affecting lateralization thresholds for the reverberant environments. These results provide clear evidence that this algorithm can improve speech reception in background noise while preserving binaural cues used to lateralize sound.

A Fiber Bragg Grating Interrogation System with Self-Adaption Threshold Peak Detection Algorithm.

PubMed

Zhang, Weifang; Li, Yingwu; Jin, Bo; Ren, Feifei; Wang, Hongxun; Dai, Wei

2018-04-08

A Fiber Bragg Grating (FBG) interrogation system with a self-adaption threshold peak detection algorithm is proposed and experimentally demonstrated in this study. This system is composed of a field programmable gate array (FPGA) and advanced RISC machine (ARM) platform, tunable Fabry-Perot (F-P) filter and optical switch. To improve system resolution, the F-P filter was employed. As this filter is non-linear, this causes the shifting of central wavelengths with the deviation compensated by the parts of the circuit. Time-division multiplexing (TDM) of FBG sensors is achieved by an optical switch, with the system able to realize the combination of 256 FBG sensors. The wavelength scanning speed of 800 Hz can be achieved by a FPGA+ARM platform. In addition, a peak detection algorithm based on a self-adaption threshold is designed and the peak recognition rate is 100%. Experiments with different temperatures were conducted to demonstrate the effectiveness of the system. Four FBG sensors were examined in the thermal chamber without stress. When the temperature changed from 0 °C to 100 °C, the degree of linearity between central wavelengths and temperature was about 0.999 with the temperature sensitivity being 10 pm/°C. The static interrogation precision was able to reach 0.5 pm. Through the comparison of different peak detection algorithms and interrogation approaches, the system was verified to have an optimum comprehensive performance in terms of precision, capacity and speed.

A Fiber Bragg Grating Interrogation System with Self-Adaption Threshold Peak Detection Algorithm

PubMed Central

Zhang, Weifang; Li, Yingwu; Jin, Bo; Ren, Feifei

2018-01-01

A Fiber Bragg Grating (FBG) interrogation system with a self-adaption threshold peak detection algorithm is proposed and experimentally demonstrated in this study. This system is composed of a field programmable gate array (FPGA) and advanced RISC machine (ARM) platform, tunable Fabry–Perot (F–P) filter and optical switch. To improve system resolution, the F–P filter was employed. As this filter is non-linear, this causes the shifting of central wavelengths with the deviation compensated by the parts of the circuit. Time-division multiplexing (TDM) of FBG sensors is achieved by an optical switch, with the system able to realize the combination of 256 FBG sensors. The wavelength scanning speed of 800 Hz can be achieved by a FPGA+ARM platform. In addition, a peak detection algorithm based on a self-adaption threshold is designed and the peak recognition rate is 100%. Experiments with different temperatures were conducted to demonstrate the effectiveness of the system. Four FBG sensors were examined in the thermal chamber without stress. When the temperature changed from 0 °C to 100 °C, the degree of linearity between central wavelengths and temperature was about 0.999 with the temperature sensitivity being 10 pm/°C. The static interrogation precision was able to reach 0.5 pm. Through the comparison of different peak detection algorithms and interrogation approaches, the system was verified to have an optimum comprehensive performance in terms of precision, capacity and speed. PMID:29642507

Data compression using adaptive transform coding. Appendix 1: Item 1. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Rost, Martin Christopher

1988-01-01

Adaptive low-rate source coders are described in this dissertation. These coders adapt by adjusting the complexity of the coder to match the local coding difficulty of the image. This is accomplished by using a threshold driven maximum distortion criterion to select the specific coder used. The different coders are built using variable blocksized transform techniques, and the threshold criterion selects small transform blocks to code the more difficult regions and larger blocks to code the less complex regions. A theoretical framework is constructed from which the study of these coders can be explored. An algorithm for selecting the optimal bit allocation for the quantization of transform coefficients is developed. The bit allocation algorithm is more fully developed, and can be used to achieve more accurate bit assignments than the algorithms currently used in the literature. Some upper and lower bounds for the bit-allocation distortion-rate function are developed. An obtainable distortion-rate function is developed for a particular scalar quantizer mixing method that can be used to code transform coefficients at any rate.

Image segmentation and 3D visualization for MRI mammography

NASA Astrophysics Data System (ADS)

Li, Lihua; Chu, Yong; Salem, Angela F.; Clark, Robert A.

2002-05-01

MRI mammography has a number of advantages, including the tomographic, and therefore three-dimensional (3-D) nature, of the images. It allows the application of MRI mammography to breasts with dense tissue, post operative scarring, and silicon implants. However, due to the vast quantity of images and subtlety of difference in MR sequence, there is a need for reliable computer diagnosis to reduce the radiologist's workload. The purpose of this work was to develop automatic breast/tissue segmentation and visualization algorithms to aid physicians in detecting and observing abnormalities in breast. Two segmentation algorithms were developed: one for breast segmentation, the other for glandular tissue segmentation. In breast segmentation, the MRI image is first segmented using an adaptive growing clustering method. Two tracing algorithms were then developed to refine the breast air and chest wall boundaries of breast. The glandular tissue segmentation was performed using an adaptive thresholding method, in which the threshold value was spatially adaptive using a sliding window. The 3D visualization of the segmented 2D slices of MRI mammography was implemented under IDL environment. The breast and glandular tissue rendering, slicing and animation were displayed.

An adaptive DPCM algorithm for predicting contours in NTSC composite video signals

NASA Astrophysics Data System (ADS)

Cox, N. R.

An adaptive DPCM algorithm is proposed for encoding digitized National Television Systems Committee (NTSC) color video signals. This algorithm essentially predicts picture contours in the composite signal without resorting to component separation. The contour parameters (slope thresholds) are optimized using four 'typical' television frames that have been sampled at three times the color subcarrier frequency. Three variations of the basic predictor are simulated and compared quantitatively with three non-adaptive predictors of similar complexity. By incorporating a dual-word-length coder and buffer memory, high quality color pictures can be encoded at 4.0 bits/pel or 42.95 Mbit/s. The effect of channel error propagation is also investigated.

Hard decoding algorithm for optimizing thresholds under general Markovian noise

NASA Astrophysics Data System (ADS)

Chamberland, Christopher; Wallman, Joel; Beale, Stefanie; Laflamme, Raymond

2017-04-01

Quantum error correction is instrumental in protecting quantum systems from noise in quantum computing and communication settings. Pauli channels can be efficiently simulated and threshold values for Pauli error rates under a variety of error-correcting codes have been obtained. However, realistic quantum systems can undergo noise processes that differ significantly from Pauli noise. In this paper, we present an efficient hard decoding algorithm for optimizing thresholds and lowering failure rates of an error-correcting code under general completely positive and trace-preserving (i.e., Markovian) noise. We use our hard decoding algorithm to study the performance of several error-correcting codes under various non-Pauli noise models by computing threshold values and failure rates for these codes. We compare the performance of our hard decoding algorithm to decoders optimized for depolarizing noise and show improvements in thresholds and reductions in failure rates by several orders of magnitude. Our hard decoding algorithm can also be adapted to take advantage of a code's non-Pauli transversal gates to further suppress noise. For example, we show that using the transversal gates of the 5-qubit code allows arbitrary rotations around certain axes to be perfectly corrected. Furthermore, we show that Pauli twirling can increase or decrease the threshold depending upon the code properties. Lastly, we show that even if the physical noise model differs slightly from the hypothesized noise model used to determine an optimized decoder, failure rates can still be reduced by applying our hard decoding algorithm.

Automatic video shot boundary detection using k-means clustering and improved adaptive dual threshold comparison

NASA Astrophysics Data System (ADS)

Sa, Qila; Wang, Zhihui

2018-03-01

At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.

Adaptive mechanism-based congestion control for networked systems

NASA Astrophysics Data System (ADS)

Liu, Zhi; Zhang, Yun; Chen, C. L. Philip

2013-03-01

In order to assure the communication quality in network systems with heavy traffic and limited bandwidth, a new ATRED (adaptive thresholds random early detection) congestion control algorithm is proposed for the congestion avoidance and resource management of network systems. Different to the traditional AQM (active queue management) algorithms, the control parameters of ATRED are not configured statically, but dynamically adjusted by the adaptive mechanism. By integrating with the adaptive strategy, ATRED alleviates the tuning difficulty of RED (random early detection) and shows a better control on the queue management, and achieve a more robust performance than RED under varying network conditions. Furthermore, a dynamic transmission control protocol-AQM control system using ATRED controller is introduced for the systematic analysis. It is proved that the stability of the network system can be guaranteed when the adaptive mechanism is finely designed. Simulation studies show the proposed ATRED algorithm achieves a good performance in varying network environments, which is superior to the RED and Gentle-RED algorithm, and providing more reliable service under varying network conditions.

A method of camera calibration with adaptive thresholding

NASA Astrophysics Data System (ADS)

Gao, Lei; Yan, Shu-hua; Wang, Guo-chao; Zhou, Chun-lei

2009-07-01

In order to calculate the parameters of the camera correctly, we must figure out the accurate coordinates of the certain points in the image plane. Corners are the important features in the 2D images. Generally speaking, they are the points that have high curvature and lie in the junction of different brightness regions of images. So corners detection has already widely used in many fields. In this paper we use the pinhole camera model and SUSAN corner detection algorithm to calibrate the camera. When using the SUSAN corner detection algorithm, we propose an approach to retrieve the gray difference threshold, adaptively. That makes it possible to pick up the right chessboard inner comers in all kinds of gray contrast. The experiment result based on this method was proved to be feasible.

Establishing a Dynamic Self-Adaptation Learning Algorithm of the BP Neural Network and Its Applications

NASA Astrophysics Data System (ADS)

Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min

2015-12-01

In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.

Improvement and implementation for Canny edge detection algorithm

NASA Astrophysics Data System (ADS)

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.

Evaluation of thresholding techniques for segmenting scaffold images in tissue engineering

NASA Astrophysics Data System (ADS)

Rajagopalan, Srinivasan; Yaszemski, Michael J.; Robb, Richard A.

2004-05-01

Tissue engineering attempts to address the ever widening gap between the demand and supply of organ and tissue transplants using natural and biomimetic scaffolds. The regeneration of specific tissues aided by synthetic materials is dependent on the structural and morphometric properties of the scaffold. These properties can be derived non-destructively using quantitative analysis of high resolution microCT scans of scaffolds. Thresholding of the scanned images into polymeric and porous phase is central to the outcome of the subsequent structural and morphometric analysis. Visual thresholding of scaffolds produced using stochastic processes is inaccurate. Depending on the algorithmic assumptions made, automatic thresholding might also be inaccurate. Hence there is a need to analyze the performance of different techniques and propose alternate ones, if needed. This paper provides a quantitative comparison of different thresholding techniques for segmenting scaffold images. The thresholding algorithms examined include those that exploit spatial information, locally adaptive characteristics, histogram entropy information, histogram shape information, and clustering of gray-level information. The performance of different techniques was evaluated using established criteria, including misclassification error, edge mismatch, relative foreground error, and region non-uniformity. Algorithms that exploit local image characteristics seem to perform much better than those using global information.

Image denoising in mixed Poisson-Gaussian noise.

PubMed

Luisier, Florian; Blu, Thierry; Unser, Michael

2011-03-01

We propose a general methodology (PURE-LET) to design and optimize a wide class of transform-domain thresholding algorithms for denoising images corrupted by mixed Poisson-Gaussian noise. We express the denoising process as a linear expansion of thresholds (LET) that we optimize by relying on a purely data-adaptive unbiased estimate of the mean-squared error (MSE), derived in a non-Bayesian framework (PURE: Poisson-Gaussian unbiased risk estimate). We provide a practical approximation of this theoretical MSE estimate for the tractable optimization of arbitrary transform-domain thresholding. We then propose a pointwise estimator for undecimated filterbank transforms, which consists of subband-adaptive thresholding functions with signal-dependent thresholds that are globally optimized in the image domain. We finally demonstrate the potential of the proposed approach through extensive comparisons with state-of-the-art techniques that are specifically tailored to the estimation of Poisson intensities. We also present denoising results obtained on real images of low-count fluorescence microscopy.

Study on a low complexity adaptive modulation algorithm in OFDM-ROF system with sub-carrier grouping technology

NASA Astrophysics Data System (ADS)

Liu, Chong-xin; Liu, Bo; Zhang, Li-jia; Xin, Xiang-jun; Tian, Qing-hua; Tian, Feng; Wang, Yong-jun; Rao, Lan; Mao, Yaya; Li, Deng-ao

2018-01-01

During the last decade, the orthogonal frequency division multiplexing radio-over-fiber (OFDM-ROF) system with adaptive modulation technology is of great interest due to its capability of raising the spectral efficiency dramatically, reducing the effects of fiber link or wireless channel, and improving the communication quality. In this study, according to theoretical analysis of nonlinear distortion and frequency selective fading on the transmitted signal, a low-complexity adaptive modulation algorithm is proposed in combination with sub-carrier grouping technology. This algorithm achieves the optimal performance of the system by calculating the average combined signal-to-noise ratio of each group and dynamically adjusting the origination modulation format according to the preset threshold and user's requirements. At the same time, this algorithm takes the sub-carrier group as the smallest unit in the initial bit allocation and the subsequent bit adjustment. So, the algorithm complexity is only 1 /M (M is the number of sub-carriers in each group) of Fischer algorithm, which is much smaller than many classic adaptive modulation algorithms, such as Hughes-Hartogs algorithm, Chow algorithm, and is in line with the development direction of green and high speed communication. Simulation results show that the performance of OFDM-ROF system with the improved algorithm is much better than those without adaptive modulation, and the BER of the former achieves 10e1 to 10e2 times lower than the latter when SNR values gets larger. We can obtain that this low complexity adaptive modulation algorithm is extremely useful for the OFDM-ROF system.

An adaptive tensor voting algorithm combined with texture spectrum

NASA Astrophysics Data System (ADS)

Wang, Gang; Su, Qing-tang; Lü, Gao-huan; Zhang, Xiao-feng; Liu, Yu-huan; He, An-zhi

2015-01-01

An adaptive tensor voting algorithm combined with texture spectrum is proposed. The image texture spectrum is used to get the adaptive scale parameter of voting field. Then the texture information modifies both the attenuation coefficient and the attenuation field so that we can use this algorithm to create more significant and correct structures in the original image according to the human visual perception. At the same time, the proposed method can improve the edge extraction quality, which includes decreasing the flocculent region efficiently and making image clear. In the experiment for extracting pavement cracks, the original pavement image is processed by the proposed method which is combined with the significant curve feature threshold procedure, and the resulted image displays the faint crack signals submerged in the complicated background efficiently and clearly.

Adaptive semantic tag mining from heterogeneous clinical research texts.

PubMed

Hao, T; Weng, C

2015-01-01

To develop an adaptive approach to mine frequent semantic tags (FSTs) from heterogeneous clinical research texts. We develop a "plug-n-play" framework that integrates replaceable unsupervised kernel algorithms with formatting, functional, and utility wrappers for FST mining. Temporal information identification and semantic equivalence detection were two example functional wrappers. We first compared this approach's recall and efficiency for mining FSTs from ClinicalTrials.gov to that of a recently published tag-mining algorithm. Then we assessed this approach's adaptability to two other types of clinical research texts: clinical data requests and clinical trial protocols, by comparing the prevalence trends of FSTs across three texts. Our approach increased the average recall and speed by 12.8% and 47.02% respectively upon the baseline when mining FSTs from ClinicalTrials.gov, and maintained an overlap in relevant FSTs with the base- line ranging between 76.9% and 100% for varying FST frequency thresholds. The FSTs saturated when the data size reached 200 documents. Consistent trends in the prevalence of FST were observed across the three texts as the data size or frequency threshold changed. This paper contributes an adaptive tag-mining framework that is scalable and adaptable without sacrificing its recall. This component-based architectural design can be potentially generalizable to improve the adaptability of other clinical text mining methods.

Adaptive thresholding and dynamic windowing method for automatic centroid detection of digital Shack-Hartmann wavefront sensor.

PubMed

Yin, Xiaoming; Li, Xiang; Zhao, Liping; Fang, Zhongping

2009-11-10

A Shack-Hartmann wavefront sensor (SWHS) splits the incident wavefront into many subsections and transfers the distorted wavefront detection into the centroid measurement. The accuracy of the centroid measurement determines the accuracy of the SWHS. Many methods have been presented to improve the accuracy of the wavefront centroid measurement. However, most of these methods are discussed from the point of view of optics, based on the assumption that the spot intensity of the SHWS has a Gaussian distribution, which is not applicable to the digital SHWS. In this paper, we present a centroid measurement algorithm based on the adaptive thresholding and dynamic windowing method by utilizing image processing techniques for practical application of the digital SHWS in surface profile measurement. The method can detect the centroid of each focal spot precisely and robustly by eliminating the influence of various noises, such as diffraction of the digital SHWS, unevenness and instability of the light source, as well as deviation between the centroid of the focal spot and the center of the detection area. The experimental results demonstrate that the algorithm has better precision, repeatability, and stability compared with other commonly used centroid methods, such as the statistical averaging, thresholding, and windowing algorithms.

An improved contrast enhancement algorithm for infrared images based on adaptive double plateaus histogram equalization

NASA Astrophysics Data System (ADS)

Li, Shuo; Jin, Weiqi; Li, Li; Li, Yiyang

2018-05-01

Infrared thermal images can reflect the thermal-radiation distribution of a particular scene. However, the contrast of the infrared images is usually low. Hence, it is generally necessary to enhance the contrast of infrared images in advance to facilitate subsequent recognition and analysis. Based on the adaptive double plateaus histogram equalization, this paper presents an improved contrast enhancement algorithm for infrared thermal images. In the proposed algorithm, the normalized coefficient of variation of the histogram, which characterizes the level of contrast enhancement, is introduced as feedback information to adjust the upper and lower plateau thresholds. The experiments on actual infrared images show that compared to the three typical contrast-enhancement algorithms, the proposed algorithm has better scene adaptability and yields better contrast-enhancement results for infrared images with more dark areas or a higher dynamic range. Hence, it has high application value in contrast enhancement, dynamic range compression, and digital detail enhancement for infrared thermal images.

Low-resolution expression recognition based on central oblique average CS-LBP with adaptive threshold

NASA Astrophysics Data System (ADS)

Han, Sheng; Xi, Shi-qiong; Geng, Wei-dong

2017-11-01

In order to solve the problem of low recognition rate of traditional feature extraction operators under low-resolution images, a novel algorithm of expression recognition is proposed, named central oblique average center-symmetric local binary pattern (CS-LBP) with adaptive threshold (ATCS-LBP). Firstly, the features of face images can be extracted by the proposed operator after pretreatment. Secondly, the obtained feature image is divided into blocks. Thirdly, the histogram of each block is computed independently and all histograms can be connected serially to create a final feature vector. Finally, expression classification is achieved by using support vector machine (SVM) classifier. Experimental results on Japanese female facial expression (JAFFE) database show that the proposed algorithm can achieve a recognition rate of 81.9% when the resolution is as low as 16×16, which is much better than that of the traditional feature extraction operators.

Visual perception system and method for a humanoid robot

NASA Technical Reports Server (NTRS)

Chelian, Suhas E. (Inventor); Linn, Douglas Martin (Inventor); Wampler, II, Charles W. (Inventor); Bridgwater, Lyndon (Inventor); Wells, James W. (Inventor); Mc Kay, Neil David (Inventor)

2012-01-01

A robotic system includes a humanoid robot with robotic joints each moveable using an actuator(s), and a distributed controller for controlling the movement of each of the robotic joints. The controller includes a visual perception module (VPM) for visually identifying and tracking an object in the field of view of the robot under threshold lighting conditions. The VPM includes optical devices for collecting an image of the object, a positional extraction device, and a host machine having an algorithm for processing the image and positional information. The algorithm visually identifies and tracks the object, and automatically adapts an exposure time of the optical devices to prevent feature data loss of the image under the threshold lighting conditions. A method of identifying and tracking the object includes collecting the image, extracting positional information of the object, and automatically adapting the exposure time to thereby prevent feature data loss of the image.

An enhanced fast scanning algorithm for image segmentation

NASA Astrophysics Data System (ADS)

Ismael, Ahmed Naser; Yusof, Yuhanis binti

2015-12-01

Segmentation is an essential and important process that separates an image into regions that have similar characteristics or features. This will transform the image for a better image analysis and evaluation. An important benefit of segmentation is the identification of region of interest in a particular image. Various algorithms have been proposed for image segmentation and this includes the Fast Scanning algorithm which has been employed on food, sport and medical images. It scans all pixels in the image and cluster each pixel according to the upper and left neighbor pixels. The clustering process in Fast Scanning algorithm is performed by merging pixels with similar neighbor based on an identified threshold. Such an approach will lead to a weak reliability and shape matching of the produced segments. This paper proposes an adaptive threshold function to be used in the clustering process of the Fast Scanning algorithm. This function used the gray'value in the image's pixels and variance Also, the level of the image that is more the threshold are converted into intensity values between 0 and 1, and other values are converted into intensity values zero. The proposed enhanced Fast Scanning algorithm is realized on images of the public and private transportation in Iraq. Evaluation is later made by comparing the produced images of proposed algorithm and the standard Fast Scanning algorithm. The results showed that proposed algorithm is faster in terms the time from standard fast scanning.

Application of composite dictionary multi-atom matching in gear fault diagnosis.

PubMed

Cui, Lingli; Kang, Chenhui; Wang, Huaqing; Chen, Peng

2011-01-01

The sparse decomposition based on matching pursuit is an adaptive sparse expression method for signals. This paper proposes an idea concerning a composite dictionary multi-atom matching decomposition and reconstruction algorithm, and the introduction of threshold de-noising in the reconstruction algorithm. Based on the structural characteristics of gear fault signals, a composite dictionary combining the impulse time-frequency dictionary and the Fourier dictionary was constituted, and a genetic algorithm was applied to search for the best matching atom. The analysis results of gear fault simulation signals indicated the effectiveness of the hard threshold, and the impulse or harmonic characteristic components could be separately extracted. Meanwhile, the robustness of the composite dictionary multi-atom matching algorithm at different noise levels was investigated. Aiming at the effects of data lengths on the calculation efficiency of the algorithm, an improved segmented decomposition and reconstruction algorithm was proposed, and the calculation efficiency of the decomposition algorithm was significantly enhanced. In addition it is shown that the multi-atom matching algorithm was superior to the single-atom matching algorithm in both calculation efficiency and algorithm robustness. Finally, the above algorithm was applied to gear fault engineering signals, and achieved good results.

Adaptive geodesic transform for segmentation of vertebrae on CT images

NASA Astrophysics Data System (ADS)

Gaonkar, Bilwaj; Shu, Liao; Hermosillo, Gerardo; Zhan, Yiqiang

2014-03-01

Vertebral segmentation is a critical first step in any quantitative evaluation of vertebral pathology using CT images. This is especially challenging because bone marrow tissue has the same intensity profile as the muscle surrounding the bone. Thus simple methods such as thresholding or adaptive k-means fail to accurately segment vertebrae. While several other algorithms such as level sets may be used for segmentation any algorithm that is clinically deployable has to work in under a few seconds. To address these dual challenges we present here, a new algorithm based on the geodesic distance transform that is capable of segmenting the spinal vertebrae in under one second. To achieve this we extend the theory of the geodesic distance transforms proposed in1 to incorporate high level anatomical knowledge through adaptive weighting of image gradients. Such knowledge may be provided by the user directly or may be automatically generated by another algorithm. We incorporate information 'learnt' using a previously published machine learning algorithm2 to segment the L1 to L5 vertebrae. While we present a particular application here, the adaptive geodesic transform is a generic concept which can be applied to segmentation of other organs as well.

Multiscale computations with a wavelet-adaptive algorithm

NASA Astrophysics Data System (ADS)

Rastigejev, Yevgenii Anatolyevich

A wavelet-based adaptive multiresolution algorithm for the numerical solution of multiscale problems governed by partial differential equations is introduced. The main features of the method include fast algorithms for the calculation of wavelet coefficients and approximation of derivatives on nonuniform stencils. The connection between the wavelet order and the size of the stencil is established. The algorithm is based on the mathematically well established wavelet theory. This allows us to provide error estimates of the solution which are used in conjunction with an appropriate threshold criteria to adapt the collocation grid. The efficient data structures for grid representation as well as related computational algorithms to support grid rearrangement procedure are developed. The algorithm is applied to the simulation of phenomena described by Navier-Stokes equations. First, we undertake the study of the ignition and subsequent viscous detonation of a H2 : O2 : Ar mixture in a one-dimensional shock tube. Subsequently, we apply the algorithm to solve the two- and three-dimensional benchmark problem of incompressible flow in a lid-driven cavity at large Reynolds numbers. For these cases we show that solutions of comparable accuracy as the benchmarks are obtained with more than an order of magnitude reduction in degrees of freedom. The simulations show the striking ability of the algorithm to adapt to a solution having different scales at different spatial locations so as to produce accurate results at a relatively low computational cost.

Peak reduction for commercial buildings using energy storage

NASA Astrophysics Data System (ADS)

Chua, K. H.; Lim, Y. S.; Morris, S.

2017-11-01

Battery-based energy storage has emerged as a cost-effective solution for peak reduction due to the decrement of battery’s price. In this study, a battery-based energy storage system is developed and implemented to achieve an optimal peak reduction for commercial customers with the limited energy capacity of the energy storage. The energy storage system is formed by three bi-directional power converter rated at 5 kVA and a battery bank with capacity of 64 kWh. Three control algorithms, namely fixed-threshold, adaptive-threshold, and fuzzy-based control algorithms have been developed and implemented into the energy storage system in a campus building. The control algorithms are evaluated and compared under different load conditions. The overall experimental results show that the fuzzy-based controller is the most effective algorithm among the three controllers in peak reduction. The fuzzy-based control algorithm is capable of incorporating a priori qualitative knowledge and expertise about the load characteristic of the buildings as well as the useable energy without over-discharging the batteries.

An adaptive tracker for ShipIR/NTCS

NASA Astrophysics Data System (ADS)

Ramaswamy, Srinivasan; Vaitekunas, David A.

2015-05-01

A key component in any image-based tracking system is the adaptive tracking algorithm used to segment the image into potential targets, rank-and-select the best candidate target, and the gating of the selected target to further improve tracker performance. This paper will describe a new adaptive tracker algorithm added to the naval threat countermeasure simulator (NTCS) of the NATO-standard ship signature model (ShipIR). The new adaptive tracking algorithm is an optional feature used with any of the existing internal NTCS or user-defined seeker algorithms (e.g., binary centroid, intensity centroid, and threshold intensity centroid). The algorithm segments the detected pixels into clusters, and the smallest set of clusters that meet the detection criterion is obtained by using a knapsack algorithm to identify the set of clusters that should not be used. The rectangular area containing the chosen clusters defines an inner boundary, from which a weighted centroid is calculated as the aim-point. A track-gate is then positioned around the clusters, taking into account the rate of change of the bounding area and compensating for any gimbal displacement. A sequence of scenarios is used to test the new tracking algorithm on a generic unclassified DDG ShipIR model, with and without flares, and demonstrate how some of the key seeker signals are impacted by both the ship and flare intrinsic signatures.

An artifacts removal post-processing for epiphyseal region-of-interest (EROI) localization in automated bone age assessment (BAA)

PubMed Central

2011-01-01

Background Segmentation is the most crucial part in the computer-aided bone age assessment. A well-known type of segmentation performed in the system is adaptive segmentation. While providing better result than global thresholding method, the adaptive segmentation produces a lot of unwanted noise that could affect the latter process of epiphysis extraction. Methods A proposed method with anisotropic diffusion as pre-processing and a novel Bounded Area Elimination (BAE) post-processing algorithm to improve the algorithm of ossification site localization technique are designed with the intent of improving the adaptive segmentation result and the region-of interest (ROI) localization accuracy. Results The results are then evaluated by quantitative analysis and qualitative analysis using texture feature evaluation. The result indicates that the image homogeneity after anisotropic diffusion has improved averagely on each age group for 17.59%. Results of experiments showed that the smoothness has been improved averagely 35% after BAE algorithm and the improvement of ROI localization has improved for averagely 8.19%. The MSSIM has improved averagely 10.49% after performing the BAE algorithm on the adaptive segmented hand radiograph. Conclusions The result indicated that hand radiographs which have undergone anisotropic diffusion have greatly reduced the noise in the segmented image and the result as well indicated that the BAE algorithm proposed is capable of removing the artifacts generated in adaptive segmentation. PMID:21952080

Expanded envelope concepts for aircraft control-element failure detection and identification

NASA Technical Reports Server (NTRS)

Weiss, Jerold L.; Hsu, John Y.

1988-01-01

The purpose of this effort was to develop and demonstrate concepts for expanding the envelope of failure detection and isolation (FDI) algorithms for aircraft-path failures. An algorithm which uses analytic-redundancy in the form of aerodynamic force and moment balance equations was used. Because aircraft-path FDI uses analytical models, there is a tradeoff between accuracy and the ability to detect and isolate failures. For single flight condition operation, design and analysis methods are developed to deal with this robustness problem. When the departure from the single flight condition is significant, algorithm adaptation is necessary. Adaptation requirements for the residual generation portion of the FDI algorithm are interpreted as the need for accurate, large-motion aero-models, over a broad range of velocity and altitude conditions. For the decision-making part of the algorithm, adaptation may require modifications to filtering operations, thresholds, and projection vectors that define the various hypothesis tests performed in the decision mechanism. Methods of obtaining and evaluating adequate residual generation and decision-making designs have been developed. The application of the residual generation ideas to a high-performance fighter is demonstrated by developing adaptive residuals for the AFTI-F-16 and simulating their behavior under a variety of maneuvers using the results of a NASA F-16 simulation.

Method and apparatus for detection of catalyst failure on-board a motor vehicle using a dual oxygen sensor and an algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clemmens, W.B.; Koupal, J.W.; Sabourin, M.A.

1993-07-20

Apparatus is described for detecting motor vehicle exhaust gas catalytic converter deterioration comprising a first exhaust gas oxygen sensor adapted for communication with an exhaust stream before passage of the exhaust stream through a catalytic converter and a second exhaust gas oxygen sensor adapted for communication with the exhaust stream after passage of the exhaust stream through the catalytic converter, an on-board vehicle computational means, said computational means adapted to accept oxygen content signals from the before and after catalytic converter oxygen sensors and adapted to generate signal threshold values, said computational means adapted to compare over repeated time intervalsmore » the oxygen content signals to the signal threshold values and to store the output of the compared oxygen content signals, and in response after a specified number of time intervals for a specified mode of motor vehicle operation to determine and indicate a level of catalyst deterioration.« less

Ripple distribution for nonlinear fiber-optic channels.

PubMed

Sorokina, Mariia; Sygletos, Stylianos; Turitsyn, Sergei

2017-02-06

We demonstrate data rates above the threshold imposed by nonlinearity on conventional optical signals by applying novel probability distribution, which we call ripple distribution, adapted to the properties of the fiber channel. Our results offer a new direction for signal coding, modulation and practical nonlinear distortions compensation algorithms.

VirSSPA- a virtual reality tool for surgical planning workflow.

PubMed

Suárez, C; Acha, B; Serrano, C; Parra, C; Gómez, T

2009-03-01

A virtual reality tool, called VirSSPA, was developed to optimize the planning of surgical processes. Segmentation algorithms for Computed Tomography (CT) images: a region growing procedure was used for soft tissues and a thresholding algorithm was implemented to segment bones. The algorithms operate semiautomati- cally since they only need seed selection with the mouse on each tissue segmented by the user. The novelty of the paper is the adaptation of an enhancement method based on histogram thresholding applied to CT images for surgical planning, which simplifies subsequent segmentation. A substantial improvement of the virtual reality tool VirSSPA was obtained with these algorithms. VirSSPA was used to optimize surgical planning, to decrease the time spent on surgical planning and to improve operative results. The success rate increases due to surgeons being able to see the exact extent of the patient's ailment. This tool can decrease operating room time, thus resulting in reduced costs. Virtual simulation was effective for optimizing surgical planning, which could, consequently, result in improved outcomes with reduced costs.

Incrementing data quality of multi-frequency echograms using the Adaptive Wiener Filter (AWF) denoising algorithm

NASA Astrophysics Data System (ADS)

Peña, M.

2016-10-01

Achieving acceptable signal-to-noise ratio (SNR) can be difficult when working in sparsely populated waters and/or when species have low scattering such as fluid filled animals. The increasing use of higher frequencies and the study of deeper depths in fisheries acoustics, as well as the use of commercial vessels, is raising the need to employ good denoising algorithms. The use of a lower Sv threshold to remove noise or unwanted targets is not suitable in many cases and increases the relative background noise component in the echogram, demanding more effectiveness from denoising algorithms. The Adaptive Wiener Filter (AWF) denoising algorithm is presented in this study. The technique is based on the AWF commonly used in digital photography and video enhancement. The algorithm firstly increments the quality of the data with a variance-dependent smoothing, before estimating the noise level as the envelope of the Sv minima. The AWF denoising algorithm outperforms existing algorithms in the presence of gaussian, speckle and salt & pepper noise, although impulse noise needs to be previously removed. Cleaned echograms present homogenous echotraces with outlined edges.

An improved finger-vein recognition algorithm based on template matching

NASA Astrophysics Data System (ADS)

Liu, Yueyue; Di, Si; Jin, Jian; Huang, Daoping

2016-10-01

Finger-vein recognition has became the most popular biometric identify methods. The investigation on the recognition algorithms always is the key point in this field. So far, there are many applicable algorithms have been developed. However, there are still some problems in practice, such as the variance of the finger position which may lead to the image distortion and shifting; during the identification process, some matching parameters determined according to experience may also reduce the adaptability of algorithm. Focus on above mentioned problems, this paper proposes an improved finger-vein recognition algorithm based on template matching. In order to enhance the robustness of the algorithm for the image distortion, the least squares error method is adopted to correct the oblique finger. During the feature extraction, local adaptive threshold method is adopted. As regard as the matching scores, we optimized the translation preferences as well as matching distance between the input images and register images on the basis of Naoto Miura algorithm. Experimental results indicate that the proposed method can improve the robustness effectively under the finger shifting and rotation conditions.

Crosswalk navigation for people with visual impairments on a wearable device

NASA Astrophysics Data System (ADS)

Cheng, Ruiqi; Wang, Kaiwei; Yang, Kailun; Long, Ningbo; Hu, Weijian; Chen, Hao; Bai, Jian; Liu, Dong

2017-09-01

Detecting and reminding of crosswalks at urban intersections is one of the most important demands for people with visual impairments. A real-time crosswalk detection algorithm, adaptive extraction and consistency analysis (AECA), is proposed. Compared with existing algorithms, which detect crosswalks in ideal scenarios, the AECA algorithm performs better in challenging scenarios, such as crosswalks at far distances, low-contrast crosswalks, pedestrian occlusion, various illuminances, and the limited resources of portable PCs. Bright stripes of crosswalks are extracted by adaptive thresholding, and are gathered to form crosswalks by consistency analysis. On the testing dataset, the proposed algorithm achieves a precision of 84.6% and a recall of 60.1%, which are higher than the bipolarity-based algorithm. The position and orientation of crosswalks are conveyed to users by voice prompts so as to align themselves with crosswalks and walk along crosswalks. The field tests carried out in various practical scenarios prove the effectiveness and reliability of the proposed navigation approach.

Algorithm for improving psychophysical threshold estimates by detecting sustained inattention in experiments using PEST.

PubMed

Rinderknecht, Mike D; Ranzani, Raffaele; Popp, Werner L; Lambercy, Olivier; Gassert, Roger

2018-05-10

Psychophysical procedures are applied in various fields to assess sensory thresholds. During experiments, sampled psychometric functions are usually assumed to be stationary. However, perception can be altered, for example by loss of attention to the presentation of stimuli, leading to biased data, which results in poor threshold estimates. The few existing approaches attempting to identify non-stationarities either detect only whether there was a change in perception, or are not suitable for experiments with a relatively small number of trials (e.g., [Formula: see text] 300). We present a method to detect inattention periods on a trial-by-trial basis with the aim of improving threshold estimates in psychophysical experiments using the adaptive sampling procedure Parameter Estimation by Sequential Testing (PEST). The performance of the algorithm was evaluated in computer simulations modeling inattention, and tested in a behavioral experiment on proprioceptive difference threshold assessment in 20 stroke patients, a population where attention deficits are likely to be present. Simulations showed that estimation errors could be reduced by up to 77% for inattentive subjects, even in sequences with less than 100 trials. In the behavioral data, inattention was detected in 14% of assessments, and applying the proposed algorithm resulted in reduced test-retest variability in 73% of these corrected assessments pairs. The novel algorithm complements existing approaches and, besides being applicable post hoc, could also be used online to prevent collection of biased data. This could have important implications in assessment practice by shortening experiments and improving estimates, especially for clinical settings.

A Region Tracking-Based Vehicle Detection Algorithm in Nighttime Traffic Scenes

PubMed Central

Wang, Jianqiang; Sun, Xiaoyan; Guo, Junbin

2013-01-01

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.

Time-of-Travel Methods for Measuring Optical Flow on Board a Micro Flying Robot

PubMed Central

Vanhoutte, Erik; Mafrica, Stefano; Ruffier, Franck; Bootsma, Reinoud J.; Serres, Julien

2017-01-01

For use in autonomous micro air vehicles, visual sensors must not only be small, lightweight and insensitive to light variations; on-board autopilots also require fast and accurate optical flow measurements over a wide range of speeds. Using an auto-adaptive bio-inspired Michaelis–Menten Auto-adaptive Pixel (M2APix) analog silicon retina, in this article, we present comparative tests of two optical flow calculation algorithms operating under lighting conditions from 6×10−7 to 1.6×10−2 W·cm−2 (i.e., from 0.2 to 12,000 lux for human vision). Contrast “time of travel” between two adjacent light-sensitive pixels was determined by thresholding and by cross-correlating the two pixels’ signals, with measurement frequency up to 5 kHz for the 10 local motion sensors of the M2APix sensor. While both algorithms adequately measured optical flow between 25 ∘/s and 1000 ∘/s, thresholding gave rise to a lower precision, especially due to a larger number of outliers at higher speeds. Compared to thresholding, cross-correlation also allowed for a higher rate of optical flow output (99 Hz and 1195 Hz, respectively) but required substantially more computational resources. PMID:28287484

Predicting missing values in a home care database using an adaptive uncertainty rule method.

PubMed

Konias, S; Gogou, G; Bamidis, P D; Vlahavas, I; Maglaveras, N

2005-01-01

Contemporary literature illustrates an abundance of adaptive algorithms for mining association rules. However, most literature is unable to deal with the peculiarities, such as missing values and dynamic data creation, that are frequently encountered in fields like medicine. This paper proposes an uncertainty rule method that uses an adaptive threshold for filling missing values in newly added records. A new approach for mining uncertainty rules and filling missing values is proposed, which is in turn particularly suitable for dynamic databases, like the ones used in home care systems. In this study, a new data mining method named FiMV (Filling Missing Values) is illustrated based on the mined uncertainty rules. Uncertainty rules have quite a similar structure to association rules and are extracted by an algorithm proposed in previous work, namely AURG (Adaptive Uncertainty Rule Generation). The main target was to implement an appropriate method for recovering missing values in a dynamic database, where new records are continuously added, without needing to specify any kind of thresholds beforehand. The method was applied to a home care monitoring system database. Randomly, multiple missing values for each record's attributes (rate 5-20% by 5% increments) were introduced in the initial dataset. FiMV demonstrated 100% completion rates with over 90% success in each case, while usual approaches, where all records with missing values are ignored or thresholds are required, experienced significantly reduced completion and success rates. It is concluded that the proposed method is appropriate for the data-cleaning step of the Knowledge Discovery process in databases. The latter, containing much significance for the output efficiency of any data mining technique, can improve the quality of the mined information.

Performance comparison of two resolution modeling PET reconstruction algorithms in terms of physical figures of merit used in quantitative imaging.

PubMed

Matheoud, R; Ferrando, O; Valzano, S; Lizio, D; Sacchetti, G; Ciarmiello, A; Foppiano, F; Brambilla, M

2015-07-01

Resolution modeling (RM) of PET systems has been introduced in iterative reconstruction algorithms for oncologic PET. The RM recovers the loss of resolution and reduces the associated partial volume effect. While these methods improved the observer performance, particularly in the detection of small and faint lesions, their impact on quantification accuracy still requires thorough investigation. The aim of this study was to characterize the performances of the RM algorithms under controlled conditions simulating a typical (18)F-FDG oncologic study, using an anthropomorphic phantom and selected physical figures of merit, used for image quantification. Measurements were performed on Biograph HiREZ (B_HiREZ) and Discovery 710 (D_710) PET/CT scanners and reconstructions were performed using the standard iterative reconstructions and the RM algorithms associated to each scanner: TrueX and SharpIR, respectively. RM determined a significant improvement in contrast recovery for small targets (≤17 mm diameter) only for the D_710 scanner. The maximum standardized uptake value (SUVmax) increased when RM was applied using both scanners. The SUVmax of small targets was on average lower with the B_HiREZ than with the D_710. Sharp IR improved the accuracy of SUVmax determination, whilst TrueX showed an overestimation of SUVmax for sphere dimensions greater than 22 mm. The goodness of fit of adaptive threshold algorithms worsened significantly when RM algorithms were employed for both scanners. Differences in general quantitative performance were observed for the PET scanners analyzed. Segmentation of PET images using adaptive threshold algorithms should not be undertaken in conjunction with RM reconstructions. Copyright © 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Clinical evaluation of pacemaker automatic capture management and atrioventricular interval extension algorithm.

PubMed

Chen, Ke-ping; Xu, Geng; Wu, Shulin; Tang, Baopeng; Wang, Li; Zhang, Shu

2013-03-01

The present study was to assess the accuracy of automatic atrial and ventricular capture management (ACM and VCM) in determining pacing threshold and the performance of a second-generation automatic atrioventricular (AV) interval extension algorithm for reducing unnecessary ventricular pacing. A total of 398 patients at 32 centres who received an EnPulse dual-chamber pacing/dual-chamber adaptive rate pacing pacemaker (Medtronic, Minneapolis, MN, USA) were enrolled. The last amplitude thresholds as measured by ACM and VCM prior to the 6-month follow-up were compared with manually measured thresholds. Device diagnostics were used to evaluate ACM and VCM and the percentage of ventricular pacing with and without the AV extension algorithm. Modelling was performed to assess longevity gains relating to the use of automaticity features. Atrial and ventricular capture management performed accurately and reliably provided complete capture management in 97% of studied patients. The AV interval extension algorithm reduced the median per cent of right ventricular pacing in patients with sinus node dysfunction from 99.7 to 1.5% at 6-month follow-up and in patients with intermittent AV block (excluding persistent 3° AV block) from 99.9 to 50.2%. On the basis of validated modelling, estimated device longevity could potentially be extended by 1.9 years through the use of the capture management and AV interval extension features. Both ACM and VCM features reliably measured thresholds in nearly all patients; the AV extension algorithm significantly reduced ventricular pacing; and the use of pacemaker automaticity features potentially extends device longevity.

Method of Improved Fuzzy Contrast Combined Adaptive Threshold in NSCT for Medical Image Enhancement

PubMed Central

Yang, Jie; Kasabov, Nikola

2017-01-01

Noises and artifacts are introduced to medical images due to acquisition techniques and systems. This interference leads to low contrast and distortion in images, which not only impacts the effectiveness of the medical image but also seriously affects the clinical diagnoses. This paper proposes an algorithm for medical image enhancement based on the nonsubsampled contourlet transform (NSCT), which combines adaptive threshold and an improved fuzzy set. First, the original image is decomposed into the NSCT domain with a low-frequency subband and several high-frequency subbands. Then, a linear transformation is adopted for the coefficients of the low-frequency component. An adaptive threshold method is used for the removal of high-frequency image noise. Finally, the improved fuzzy set is used to enhance the global contrast and the Laplace operator is used to enhance the details of the medical images. Experiments and simulation results show that the proposed method is superior to existing methods of image noise removal, improves the contrast of the image significantly, and obtains a better visual effect. PMID:28744464

Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods.

PubMed

Li, Mengshan; Zhang, Huaijing; Chen, Bingsheng; Wu, Yan; Guan, Lixin

2018-03-05

The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained. The improved PSO algorithm was applied in the training of radial basis function artificial neural network (RBF ANN) model and the selection of molecular descriptors. A quantitative structure-activity relationship model based on RBF ANN trained by the improved PSO algorithm was proposed to predict the pKa values of 74 kinds of neutral and basic drugs and then validated by another database containing 20 molecules. The validation results showed that the model had a good prediction performance. The absolute average relative error, root mean square error, and squared correlation coefficient were 0.3105, 0.0411, and 0.9685, respectively. The model can be used as a reference for exploring other quantitative structure-activity relationships.

Ripple FPN reduced algorithm based on temporal high-pass filter and hardware implementation

NASA Astrophysics Data System (ADS)

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.

Image segmentation algorithm based on improved PCNN

NASA Astrophysics Data System (ADS)

Chen, Hong; Wu, Chengdong; Yu, Xiaosheng; Wu, Jiahui

2017-11-01

A modified simplified Pulse Coupled Neural Network (PCNN) model is proposed in this article based on simplified PCNN. Some work have done to enrich this model, such as imposing restrictions items of the inputs, improving linking inputs and internal activity of PCNN. A self-adaptive parameter setting method of linking coefficient and threshold value decay time constant is proposed here, too. At last, we realized image segmentation algorithm for five pictures based on this proposed simplified PCNN model and PSO. Experimental results demonstrate that this image segmentation algorithm is much better than method of SPCNN and OTSU.

A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band.

PubMed

Trong Bui, Duong; Nguyen, Nhan Duc; Jeong, Gu-Min

2018-06-25

Human activity recognition and pedestrian dead reckoning are an interesting field because of their importance utilities in daily life healthcare. Currently, these fields are facing many challenges, one of which is the lack of a robust algorithm with high performance. This paper proposes a new method to implement a robust step detection and adaptive distance estimation algorithm based on the classification of five daily wrist activities during walking at various speeds using a smart band. The key idea is that the non-parametric adaptive distance estimator is performed after two activity classifiers and a robust step detector. In this study, two classifiers perform two phases of recognizing five wrist activities during walking. Then, a robust step detection algorithm, which is integrated with an adaptive threshold, peak and valley correction algorithm, is applied to the classified activities to detect the walking steps. In addition, the misclassification activities are fed back to the previous layer. Finally, three adaptive distance estimators, which are based on a non-parametric model of the average walking speed, calculate the length of each strike. The experimental results show that the average classification accuracy is about 99%, and the accuracy of the step detection is 98.7%. The error of the estimated distance is 2.2⁻4.2% depending on the type of wrist activities.

An Adaptive Image Enhancement Technique by Combining Cuckoo Search and Particle Swarm Optimization Algorithm

PubMed Central

Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei

2015-01-01

Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper. PMID:25784928

An adaptive image enhancement technique by combining cuckoo search and particle swarm optimization algorithm.

PubMed

Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei

2015-01-01

Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.

SU-F-J-27: Segmentation of Prostate CBCT Images with Implanted Calypso Transponders Using Double Haar Wavelet Transform

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Y; Saleh, Z; Tang, X

Purpose: Segmentation of prostate CBCT images is an essential step towards real-time adaptive radiotherapy. It is challenging For Calypso patients, as more artifacts are generated by the beacon transponders. We herein propose a novel wavelet-based segmentation algorithm for rectum, bladder, and prostate of CBCT images with implanted Calypso transponders. Methods: Five hypofractionated prostate patients with daily CBCT were studied. Each patient had 3 Calypso transponder beacons implanted, and the patients were setup and treated with Calypso tracking system. Two sets of CBCT images from each patient were studied. The structures (i.e. rectum, bladder, and prostate) were contoured by a trainedmore » expert, and these served as ground truth. For a given CBCT, the moving window-based Double Haar transformation is applied first to obtain the wavelet coefficients. Based on a user defined point in the object of interest, a cluster algorithm based adaptive thresholding is applied to the low frequency components of the wavelet coefficients, and a Lee filter theory based adaptive thresholding is applied to the high frequency components. For the next step, the wavelet reconstruction is applied to the thresholded wavelet coefficients. A binary/segmented image of the object of interest is therefore obtained. DICE, sensitivity, inclusiveness and ΔV were used to evaluate the segmentation result. Results: Considering all patients, the bladder has the DICE, sensitivity, inclusiveness, and ΔV ranges of [0.81–0.95], [0.76–0.99], [0.83–0.94], [0.02–0.21]. For prostate, the ranges are [0.77–0.93], [0.84–0.97], [0.68–0.92], [0.1–0.46]. For rectum, the ranges are [0.72–0.93], [0.57–0.99], [0.73–0.98], [0.03–0.42]. Conclusion: The proposed algorithm appeared effective segmenting prostate CBCT images with the present of the Calypso artifacts. However, it is not robust in two scenarios: 1) rectum with significant amount of gas; 2) prostate with very low contrast. Model based algorithm might improve the segmentation in these two scenarios.« less

Real time tracking by LOPF algorithm with mixture model

NASA Astrophysics Data System (ADS)

Meng, Bo; Zhu, Ming; Han, Guangliang; Wu, Zhiguo

2007-11-01

A new particle filter-the Local Optimum Particle Filter (LOPF) algorithm is presented for tracking object accurately and steadily in visual sequences in real time which is a challenge task in computer vision field. In order to using the particles efficiently, we first use Sobel algorithm to extract the profile of the object. Then, we employ a new Local Optimum algorithm to auto-initialize some certain number of particles from these edge points as centre of the particles. The main advantage we do this in stead of selecting particles randomly in conventional particle filter is that we can pay more attentions on these more important optimum candidates and reduce the unnecessary calculation on those negligible ones, in addition we can overcome the conventional degeneracy phenomenon in a way and decrease the computational costs. Otherwise, the threshold is a key factor that affecting the results very much. So here we adapt an adaptive threshold choosing method to get the optimal Sobel result. The dissimilarities between the target model and the target candidates are expressed by a metric derived from the Bhattacharyya coefficient. Here, we use both the counter cue to select the particles and the color cur to describe the targets as the mixture target model. The effectiveness of our scheme is demonstrated by real visual tracking experiments. Results from simulations and experiments with real video data show the improved performance of the proposed algorithm when compared with that of the standard particle filter. The superior performance is evident when the target encountering the occlusion in real video where the standard particle filter usually fails.

Active control of impulsive noise with symmetric α-stable distribution based on an improved step-size normalized adaptive algorithm

NASA Astrophysics Data System (ADS)

Zhou, Yali; Zhang, Qizhi; Yin, Yixin

2015-05-01

In this paper, active control of impulsive noise with symmetric α-stable (SαS) distribution is studied. A general step-size normalized filtered-x Least Mean Square (FxLMS) algorithm is developed based on the analysis of existing algorithms, and the Gaussian distribution function is used to normalize the step size. Compared with existing algorithms, the proposed algorithm needs neither the parameter selection and thresholds estimation nor the process of cost function selection and complex gradient computation. Computer simulations have been carried out to suggest that the proposed algorithm is effective for attenuating SαS impulsive noise, and then the proposed algorithm has been implemented in an experimental ANC system. Experimental results show that the proposed scheme has good performance for SαS impulsive noise attenuation.

A wavelet-based adaptive fusion algorithm of infrared polarization imaging

NASA Astrophysics Data System (ADS)

Yang, Wei; Gu, Guohua; Chen, Qian; Zeng, Haifang

2011-08-01

The purpose of infrared polarization image is to highlight man-made target from a complex natural background. For the infrared polarization images can significantly distinguish target from background with different features, this paper presents a wavelet-based infrared polarization image fusion algorithm. The method is mainly for image processing of high-frequency signal portion, as for the low frequency signal, the original weighted average method has been applied. High-frequency part is processed as follows: first, the source image of the high frequency information has been extracted by way of wavelet transform, then signal strength of 3*3 window area has been calculated, making the regional signal intensity ration of source image as a matching measurement. Extraction method and decision mode of the details are determined by the decision making module. Image fusion effect is closely related to the setting threshold of decision making module. Compared to the commonly used experiment way, quadratic interpolation optimization algorithm is proposed in this paper to obtain threshold. Set the endpoints and midpoint of the threshold searching interval as initial interpolation nodes, and compute the minimum quadratic interpolation function. The best threshold can be obtained by comparing the minimum quadratic interpolation function. A series of image quality evaluation results show this method has got improvement in fusion effect; moreover, it is not only effective for some individual image, but also for a large number of images.

Thresholds for conservation and management: structured decision making as a conceptual framework

USGS Publications Warehouse

Nichols, James D.; Eaton, Mitchell J.; Martin, Julien; Edited by Guntenspergen, Glenn R.

2014-01-01

changes in system dynamics. They are frequently incorporated into ecological models used to project system responses to management actions. Utility thresholds are components of management objectives and are values of state or performance variables at which small changes yield substantial changes in the value of the management outcome. Decision thresholds are values of system state variables at which small changes prompt changes in management actions in order to reach specified management objectives. Decision thresholds are derived from the other components of the decision process.We advocate a structured decision making (SDM) approach within which the following components are identified: objectives (possibly including utility thresholds), potential actions, models (possibly including ecological thresholds), monitoring program, and a solution algorithm (which produces decision thresholds). Adaptive resource management (ARM) is described as a special case of SDM developed for recurrent decision problems that are characterized by uncertainty. We believe that SDM, in general, and ARM, in particular, provide good approaches to conservation and management. Use of SDM and ARM also clarifies the distinct roles of ecological thresholds, utility thresholds, and decision thresholds in informed decision processes.

MO-DE-207A-12: Toward Patient-Specific 4DCT Reconstruction Using Adaptive Velocity Binning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morris, E.D.; Glide-Hurst, C.; Wayne State University, Detroit, MI

2016-06-15

Purpose: While 4DCT provides organ/tumor motion information, it often samples data over 10–20 breathing cycles. For patients presenting with compromised pulmonary function, breathing patterns can change over the acquisition time, potentially leading to tumor delineation discrepancies. This work introduces a novel adaptive velocity-modulated binning (AVB) 4DCT algorithm that modulates the reconstruction based on the respiratory waveform, yielding a patient-specific 4DCT solution. Methods: AVB was implemented in a research reconstruction configuration. After filtering the respiratory waveform, the algorithm examines neighboring data to a phase reconstruction point and the temporal gate is widened until the difference between the reconstruction point and waveformmore » exceeds a threshold value—defined as percent difference between maximum/minimum waveform amplitude. The algorithm only impacts reconstruction if the gate width exceeds a set minimum temporal width required for accurate reconstruction. A sensitivity experiment of threshold values (0.5, 1, 5, 10, and 12%) was conducted to examine the interplay between threshold, signal to noise ratio (SNR), and image sharpness for phantom and several patient 4DCT cases using ten-phase reconstructions. Individual phase reconstructions were examined. Subtraction images and regions of interest were compared to quantify changes in SNR. Results: AVB increased signal in reconstructed 4DCT slices for respiratory waveforms that met the prescribed criteria. For the end-exhale phases, where the respiratory velocity is low, patient data revealed a threshold of 0.5% demonstrated increased SNR in the AVB reconstructions. For intermediate breathing phases, threshold values were required to be >10% to notice appreciable changes in CT intensity with AVB. AVB reconstructions exhibited appreciably higher SNR and reduced noise in regions of interest that were photon deprived such as the liver. Conclusion: We demonstrated that patient-specific velocity-based 4DCT reconstruction is feasible. Image noise was reduced with AVB, suggesting potential applications for low-dose acquisitions and to improve 4DCT reconstruction for irregular breathing patients. The submitting institution holds research agreements with Philips Healthcare.« less

Object tracking algorithm based on the color histogram probability distribution

NASA Astrophysics Data System (ADS)

Li, Ning; Lu, Tongwei; Zhang, Yanduo

2018-04-01

In order to resolve tracking failure resulted from target's being occlusion and follower jamming caused by objects similar to target in the background, reduce the influence of light intensity. This paper change HSV and YCbCr color channel correction the update center of the target, continuously updated image threshold self-adaptive target detection effect, Clustering the initial obstacles is roughly range, shorten the threshold range, maximum to detect the target. In order to improve the accuracy of detector, this paper increased the Kalman filter to estimate the target state area. The direction predictor based on the Markov model is added to realize the target state estimation under the condition of background color interference and enhance the ability of the detector to identify similar objects. The experimental results show that the improved algorithm more accurate and faster speed of processing.

Fast parallel MR image reconstruction via B1-based, adaptive restart, iterative soft thresholding algorithms (BARISTA).

PubMed

Muckley, Matthew J; Noll, Douglas C; Fessler, Jeffrey A

2015-02-01

Sparsity-promoting regularization is useful for combining compressed sensing assumptions with parallel MRI for reducing scan time while preserving image quality. Variable splitting algorithms are the current state-of-the-art algorithms for SENSE-type MR image reconstruction with sparsity-promoting regularization. These methods are very general and have been observed to work with almost any regularizer; however, the tuning of associated convergence parameters is a commonly-cited hindrance in their adoption. Conversely, majorize-minimize algorithms based on a single Lipschitz constant have been observed to be slow in shift-variant applications such as SENSE-type MR image reconstruction since the associated Lipschitz constants are loose bounds for the shift-variant behavior. This paper bridges the gap between the Lipschitz constant and the shift-variant aspects of SENSE-type MR imaging by introducing majorizing matrices in the range of the regularizer matrix. The proposed majorize-minimize methods (called BARISTA) converge faster than state-of-the-art variable splitting algorithms when combined with momentum acceleration and adaptive momentum restarting. Furthermore, the tuning parameters associated with the proposed methods are unitless convergence tolerances that are easier to choose than the constraint penalty parameters required by variable splitting algorithms.

Fast Parallel MR Image Reconstruction via B1-based, Adaptive Restart, Iterative Soft Thresholding Algorithms (BARISTA)

PubMed Central

Noll, Douglas C.; Fessler, Jeffrey A.

2014-01-01

Sparsity-promoting regularization is useful for combining compressed sensing assumptions with parallel MRI for reducing scan time while preserving image quality. Variable splitting algorithms are the current state-of-the-art algorithms for SENSE-type MR image reconstruction with sparsity-promoting regularization. These methods are very general and have been observed to work with almost any regularizer; however, the tuning of associated convergence parameters is a commonly-cited hindrance in their adoption. Conversely, majorize-minimize algorithms based on a single Lipschitz constant have been observed to be slow in shift-variant applications such as SENSE-type MR image reconstruction since the associated Lipschitz constants are loose bounds for the shift-variant behavior. This paper bridges the gap between the Lipschitz constant and the shift-variant aspects of SENSE-type MR imaging by introducing majorizing matrices in the range of the regularizer matrix. The proposed majorize-minimize methods (called BARISTA) converge faster than state-of-the-art variable splitting algorithms when combined with momentum acceleration and adaptive momentum restarting. Furthermore, the tuning parameters associated with the proposed methods are unitless convergence tolerances that are easier to choose than the constraint penalty parameters required by variable splitting algorithms. PMID:25330484

Comparing Binaural Pre-processing Strategies II: Speech Intelligibility of Bilateral Cochlear Implant Users.

PubMed

Baumgärtel, Regina M; Hu, Hongmei; Krawczyk-Becker, Martin; Marquardt, Daniel; Herzke, Tobias; Coleman, Graham; Adiloğlu, Kamil; Bomke, Katrin; Plotz, Karsten; Gerkmann, Timo; Doclo, Simon; Kollmeier, Birger; Hohmann, Volker; Dietz, Mathias

2015-12-30

Several binaural audio signal enhancement algorithms were evaluated with respect to their potential to improve speech intelligibility in noise for users of bilateral cochlear implants (CIs). 50% speech reception thresholds (SRT50) were assessed using an adaptive procedure in three distinct, realistic noise scenarios. All scenarios were highly nonstationary, complex, and included a significant amount of reverberation. Other aspects, such as the perfectly frontal target position, were idealized laboratory settings, allowing the algorithms to perform better than in corresponding real-world conditions. Eight bilaterally implanted CI users, wearing devices from three manufacturers, participated in the study. In all noise conditions, a substantial improvement in SRT50 compared to the unprocessed signal was observed for most of the algorithms tested, with the largest improvements generally provided by binaural minimum variance distortionless response (MVDR) beamforming algorithms. The largest overall improvement in speech intelligibility was achieved by an adaptive binaural MVDR in a spatially separated, single competing talker noise scenario. A no-pre-processing condition and adaptive differential microphones without a binaural link served as the two baseline conditions. SRT50 improvements provided by the binaural MVDR beamformers surpassed the performance of the adaptive differential microphones in most cases. Speech intelligibility improvements predicted by instrumental measures were shown to account for some but not all aspects of the perceptually obtained SRT50 improvements measured in bilaterally implanted CI users. © The Author(s) 2015.

Comparing Binaural Pre-processing Strategies II

PubMed Central

Hu, Hongmei; Krawczyk-Becker, Martin; Marquardt, Daniel; Herzke, Tobias; Coleman, Graham; Adiloğlu, Kamil; Bomke, Katrin; Plotz, Karsten; Gerkmann, Timo; Doclo, Simon; Kollmeier, Birger; Hohmann, Volker; Dietz, Mathias

2015-01-01

Several binaural audio signal enhancement algorithms were evaluated with respect to their potential to improve speech intelligibility in noise for users of bilateral cochlear implants (CIs). 50% speech reception thresholds (SRT50) were assessed using an adaptive procedure in three distinct, realistic noise scenarios. All scenarios were highly nonstationary, complex, and included a significant amount of reverberation. Other aspects, such as the perfectly frontal target position, were idealized laboratory settings, allowing the algorithms to perform better than in corresponding real-world conditions. Eight bilaterally implanted CI users, wearing devices from three manufacturers, participated in the study. In all noise conditions, a substantial improvement in SRT50 compared to the unprocessed signal was observed for most of the algorithms tested, with the largest improvements generally provided by binaural minimum variance distortionless response (MVDR) beamforming algorithms. The largest overall improvement in speech intelligibility was achieved by an adaptive binaural MVDR in a spatially separated, single competing talker noise scenario. A no-pre-processing condition and adaptive differential microphones without a binaural link served as the two baseline conditions. SRT50 improvements provided by the binaural MVDR beamformers surpassed the performance of the adaptive differential microphones in most cases. Speech intelligibility improvements predicted by instrumental measures were shown to account for some but not all aspects of the perceptually obtained SRT50 improvements measured in bilaterally implanted CI users. PMID:26721921

Anti-dynamic-crosstalk method for single photon LIDAR detection

NASA Astrophysics Data System (ADS)

Zhang, Fan; Liu, Qiang; Gong, Mali; Fu, Xing

2017-11-01

With increasing number of vehicles equipped with light detection and ranging (LIDAR), crosstalk is identified as a critical and urgent issue in the range detection for active collision avoidance. Chaotic pulse position modulation (CPPM) applied in the transmitting pulse train has been shown to prevent crosstalk as well as range ambiguity. However, static and unified strategy on discrimination threshold and the number of accumulated pulse is not valid against crosstalk with varying number of sources and varying intensity of each source. This paper presents an adaptive algorithm to distinguish the target echo from crosstalk with dynamic and unknown level of intensity in the context of intelligent vehicles. New strategy is given based on receiver operating characteristics (ROC) curves that consider the detection requirements of the probability of detection and false alarm for the scenario with varying crosstalk. In the adaptive algorithm, the detected results are compared by the new strategy with both the number of accumulated pulses and the threshold being raised step by step, so that the target echo can be exactly identified from crosstalk with the dynamic and unknown level of intensity. The validity of the algorithm has been verified through the experiments with a single photon detector and the time correlated single photo counting (TCSPC) technique, demonstrating a marked drop in required shots for identifying the target compared with static and unified strategy

Integrated segmentation of cellular structures

NASA Astrophysics Data System (ADS)

Ajemba, Peter; Al-Kofahi, Yousef; Scott, Richard; Donovan, Michael; Fernandez, Gerardo

2011-03-01

Automatic segmentation of cellular structures is an essential step in image cytology and histology. Despite substantial progress, better automation and improvements in accuracy and adaptability to novel applications are needed. In applications utilizing multi-channel immuno-fluorescence images, challenges include misclassification of epithelial and stromal nuclei, irregular nuclei and cytoplasm boundaries, and over and under-segmentation of clustered nuclei. Variations in image acquisition conditions and artifacts from nuclei and cytoplasm images often confound existing algorithms in practice. In this paper, we present a robust and accurate algorithm for jointly segmenting cell nuclei and cytoplasm using a combination of ideas to reduce the aforementioned problems. First, an adaptive process that includes top-hat filtering, Eigenvalues-of-Hessian blob detection and distance transforms is used to estimate the inverse illumination field and correct for intensity non-uniformity in the nuclei channel. Next, a minimum-error-thresholding based binarization process and seed-detection combining Laplacian-of-Gaussian filtering constrained by a distance-map-based scale selection is used to identify candidate seeds for nuclei segmentation. The initial segmentation using a local maximum clustering algorithm is refined using a minimum-error-thresholding technique. Final refinements include an artifact removal process specifically targeted at lumens and other problematic structures and a systemic decision process to reclassify nuclei objects near the cytoplasm boundary as epithelial or stromal. Segmentation results were evaluated using 48 realistic phantom images with known ground-truth. The overall segmentation accuracy exceeds 94%. The algorithm was further tested on 981 images of actual prostate cancer tissue. The artifact removal process worked in 90% of cases. The algorithm has now been deployed in a high-volume histology analysis application.

An adaptive Kalman filter technique for context-aware heart rate monitoring.

PubMed

Xu, Min; Goldfain, Albert; Dellostritto, Jim; Iyengar, Satish

2012-01-01

Traditional physiological monitoring systems convert a person's vital sign waveforms, such as heart rate, respiration rate and blood pressure, into meaningful information by comparing the instant reading with a preset threshold or a baseline without considering the contextual information of the person. It would be beneficial to incorporate the contextual data such as activity status of the person to the physiological data in order to obtain a more accurate representation of a person's physiological status. In this paper, we proposed an algorithm based on adaptive Kalman filter that describes the heart rate response with respect to different activity levels. It is towards our final goal of intelligent detection of any abnormality in the person's vital signs. Experimental results are provided to demonstrate the feasibility of the algorithm.

Adaptive truncation of matrix decompositions and efficient estimation of NMR relaxation distributions

NASA Astrophysics Data System (ADS)

Teal, Paul D.; Eccles, Craig

2015-04-01

The two most successful methods of estimating the distribution of nuclear magnetic resonance relaxation times from two dimensional data are data compression followed by application of the Butler-Reeds-Dawson algorithm, and a primal-dual interior point method using preconditioned conjugate gradient. Both of these methods have previously been presented using a truncated singular value decomposition of matrices representing the exponential kernel. In this paper it is shown that other matrix factorizations are applicable to each of these algorithms, and that these illustrate the different fundamental principles behind the operation of the algorithms. These are the rank-revealing QR (RRQR) factorization and the LDL factorization with diagonal pivoting, also known as the Bunch-Kaufman-Parlett factorization. It is shown that both algorithms can be improved by adaptation of the truncation as the optimization process progresses, improving the accuracy as the optimal value is approached. A variation on the interior method viz, the use of barrier function instead of the primal-dual approach, is found to offer considerable improvement in terms of speed and reliability. A third type of algorithm, related to the algorithm known as Fast iterative shrinkage-thresholding algorithm, is applied to the problem. This method can be efficiently formulated without the use of a matrix decomposition.

Adaptive and accelerated tracking-learning-detection

NASA Astrophysics Data System (ADS)

Guo, Pengyu; Li, Xin; Ding, Shaowen; Tian, Zunhua; Zhang, Xiaohu

2013-08-01

An improved online long-term visual tracking algorithm, named adaptive and accelerated TLD (AA-TLD) based on Tracking-Learning-Detection (TLD) which is a novel tracking framework has been introduced in this paper. The improvement focuses on two aspects, one is adaption, which makes the algorithm not dependent on the pre-defined scanning grids by online generating scale space, and the other is efficiency, which uses not only algorithm-level acceleration like scale prediction that employs auto-regression and moving average (ARMA) model to learn the object motion to lessen the detector's searching range and the fixed number of positive and negative samples that ensures a constant retrieving time, but also CPU and GPU parallel technology to achieve hardware acceleration. In addition, in order to obtain a better effect, some TLD's details are redesigned, which uses a weight including both normalized correlation coefficient and scale size to integrate results, and adjusts distance metric thresholds online. A contrastive experiment on success rate, center location error and execution time, is carried out to show a performance and efficiency upgrade over state-of-the-art TLD with partial TLD datasets and Shenzhou IX return capsule image sequences. The algorithm can be used in the field of video surveillance to meet the need of real-time video tracking.

A novel evaluation of two related and two independent algorithms for eye movement classification during reading.

PubMed

Friedman, Lee; Rigas, Ioannis; Abdulin, Evgeny; Komogortsev, Oleg V

2018-05-15

Nystrӧm and Holmqvist have published a method for the classification of eye movements during reading (ONH) (Nyström & Holmqvist, 2010). When we applied this algorithm to our data, the results were not satisfactory, so we modified the algorithm (now the MNH) to better classify our data. The changes included: (1) reducing the amount of signal filtering, (2) excluding a new type of noise, (3) removing several adaptive thresholds and replacing them with fixed thresholds, (4) changing the way that the start and end of each saccade was determined, (5) employing a new algorithm for detecting PSOs, and (6) allowing a fixation period to either begin or end with noise. A new method for the evaluation of classification algorithms is presented. It was designed to provide comprehensive feedback to an algorithm developer, in a time-efficient manner, about the types and numbers of classification errors that an algorithm produces. This evaluation was conducted by three expert raters independently, across 20 randomly chosen recordings, each classified by both algorithms. The MNH made many fewer errors in determining when saccades start and end, and it also detected some fixations and saccades that the ONH did not. The MNH fails to detect very small saccades. We also evaluated two additional algorithms: the EyeLink Parser and a more current, machine-learning-based algorithm. The EyeLink Parser tended to find more saccades that ended too early than did the other methods, and we found numerous problems with the output of the machine-learning-based algorithm.

An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm.

PubMed

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.

An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm

PubMed Central

Qin, Qin

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. PMID:29104745

Edge enhancement and noise suppression for infrared image based on feature analysis

NASA Astrophysics Data System (ADS)

Jiang, Meng

2018-06-01

Infrared images are often suffering from background noise, blurred edges, few details and low signal-to-noise ratios. To improve infrared image quality, it is essential to suppress noise and enhance edges simultaneously. To realize it in this paper, we propose a novel algorithm based on feature analysis in shearlet domain. Firstly, as one of multi-scale geometric analysis (MGA), we introduce the theory and superiority of shearlet transform. Secondly, after analyzing the defects of traditional thresholding technique to suppress noise, we propose a novel feature extraction distinguishing image structures from noise well and use it to improve the traditional thresholding technique. Thirdly, with computing the correlations between neighboring shearlet coefficients, the feature attribute maps identifying the weak detail and strong edges are completed to improve the generalized unsharped masking (GUM). At last, experiment results with infrared images captured in different scenes demonstrate that the proposed algorithm suppresses noise efficiently and enhances image edges adaptively.

Confronting Decision Cliffs: Diagnostic Assessment of Multi-Objective Evolutionary Algorithms' Performance for Addressing Uncertain Environmental Thresholds

NASA Astrophysics Data System (ADS)

Ward, V. L.; Singh, R.; Reed, P. M.; Keller, K.

2014-12-01

As water resources problems typically involve several stakeholders with conflicting objectives, multi-objective evolutionary algorithms (MOEAs) are now key tools for understanding management tradeoffs. Given the growing complexity of water planning problems, it is important to establish if an algorithm can consistently perform well on a given class of problems. This knowledge allows the decision analyst to focus on eliciting and evaluating appropriate problem formulations. This study proposes a multi-objective adaptation of the classic environmental economics "Lake Problem" as a computationally simple but mathematically challenging MOEA benchmarking problem. The lake problem abstracts a fictional town on a lake which hopes to maximize its economic benefit without degrading the lake's water quality to a eutrophic (polluted) state through excessive phosphorus loading. The problem poses the challenge of maintaining economic activity while confronting the uncertainty of potentially crossing a nonlinear and potentially irreversible pollution threshold beyond which the lake is eutrophic. Objectives for optimization are maximizing economic benefit from lake pollution, maximizing water quality, maximizing the reliability of remaining below the environmental threshold, and minimizing the probability that the town will have to drastically change pollution policies in any given year. The multi-objective formulation incorporates uncertainty with a stochastic phosphorus inflow abstracting non-point source pollution. We performed comprehensive diagnostics using 6 algorithms: Borg, MOEAD, eMOEA, eNSGAII, GDE3, and NSGAII to ascertain their controllability, reliability, efficiency, and effectiveness. The lake problem abstracts elements of many current water resources and climate related management applications where there is the potential for crossing irreversible, nonlinear thresholds. We show that many modern MOEAs can fail on this test problem, indicating its suitability as a useful and nontrivial benchmarking problem.

Robust adaptive 3-D segmentation of vessel laminae from fluorescence confocal microscope images and parallel GPU implementation.

PubMed

Narayanaswamy, Arunachalam; Dwarakapuram, Saritha; Bjornsson, Christopher S; Cutler, Barbara M; Shain, William; Roysam, Badrinath

2010-03-01

This paper presents robust 3-D algorithms to segment vasculature that is imaged by labeling laminae, rather than the lumenal volume. The signal is weak, sparse, noisy, nonuniform, low-contrast, and exhibits gaps and spectral artifacts, so adaptive thresholding and Hessian filtering based methods are not effective. The structure deviates from a tubular geometry, so tracing algorithms are not effective. We propose a four step approach. The first step detects candidate voxels using a robust hypothesis test based on a model that assumes Poisson noise and locally planar geometry. The second step performs an adaptive region growth to extract weakly labeled and fine vessels while rejecting spectral artifacts. To enable interactive visualization and estimation of features such as statistical confidence, local curvature, local thickness, and local normal, we perform the third step. In the third step, we construct an accurate mesh representation using marching tetrahedra, volume-preserving smoothing, and adaptive decimation algorithms. To enable topological analysis and efficient validation, we describe a method to estimate vessel centerlines using a ray casting and vote accumulation algorithm which forms the final step of our algorithm. Our algorithm lends itself to parallel processing, and yielded an 8 x speedup on a graphics processor (GPU). On synthetic data, our meshes had average error per face (EPF) values of (0.1-1.6) voxels per mesh face for peak signal-to-noise ratios from (110-28 dB). Separately, the error from decimating the mesh to less than 1% of its original size, the EPF was less than 1 voxel/face. When validated on real datasets, the average recall and precision values were found to be 94.66% and 94.84%, respectively.

Orion MPCV Touchdown Detection Threshold Development and Testing

NASA Technical Reports Server (NTRS)

Daum, Jared; Gay, Robert

2013-01-01

A robust method of detecting Orion Multi-Purpose Crew Vehicle (MPCV) splashdown is necessary to ensure crew and hardware safety during descent and after touchdown. The proposed method uses a triple redundant system to inhibit Reaction Control System (RCS) thruster firings, detach parachute risers from the vehicle, and transition to the post-landing segment of the Flight Software (FSW). An in-depth trade study was completed to determine optimal characteristics of the touchdown detection method resulting in an algorithm monitoring filtered, lever-arm corrected, 200 Hz Inertial Measurement Unit (IMU) vehicle acceleration magnitude data against a tunable threshold using persistence counter logic. Following the design of the algorithm, high fidelity environment and vehicle simulations, coupled with the actual vehicle FSW, were used to tune the acceleration threshold and persistence counter value to result in adequate performance in detecting touchdown and sufficient safety margin against early detection while descending under parachutes. An analytical approach including Kriging and adaptive sampling allowed for a sufficient number of finite element analysis (FEA) impact simulations to be completed using minimal computation time. The combination of a persistence counter of 10 and an acceleration threshold of approximately 57.3 ft/s2 resulted in an impact performance factor of safety (FOS) of 1.0 and a safety FOS of approximately 2.6 for touchdown declaration. An RCS termination acceleration threshold of approximately 53.1 ft/s(exp)2 with a persistence counter of 10 resulted in an increased impact performance FOS of 1.2 at the expense of a lowered under-parachutes safety factor of 2.2. The resulting tuned algorithm was then tested on data from eight Capsule Parachute Assembly System (CPAS) flight tests, showing an experimental minimum safety FOS of 6.1. The formulated touchdown detection algorithm will be flown on the Orion MPCV FSW during the Exploration Flight Test 1 (EFT-1) mission in the second half of 2014.

Differentially Private Histogram Publication For Dynamic Datasets: An Adaptive Sampling Approach

PubMed Central

Li, Haoran; Jiang, Xiaoqian; Xiong, Li; Liu, Jinfei

2016-01-01

Differential privacy has recently become a de facto standard for private statistical data release. Many algorithms have been proposed to generate differentially private histograms or synthetic data. However, most of them focus on “one-time” release of a static dataset and do not adequately address the increasing need of releasing series of dynamic datasets in real time. A straightforward application of existing histogram methods on each snapshot of such dynamic datasets will incur high accumulated error due to the composibility of differential privacy and correlations or overlapping users between the snapshots. In this paper, we address the problem of releasing series of dynamic datasets in real time with differential privacy, using a novel adaptive distance-based sampling approach. Our first method, DSFT, uses a fixed distance threshold and releases a differentially private histogram only when the current snapshot is sufficiently different from the previous one, i.e., with a distance greater than a predefined threshold. Our second method, DSAT, further improves DSFT and uses a dynamic threshold adaptively adjusted by a feedback control mechanism to capture the data dynamics. Extensive experiments on real and synthetic datasets demonstrate that our approach achieves better utility than baseline methods and existing state-of-the-art methods. PMID:26973795

Motion Cueing Algorithm Development: Piloted Performance Testing of the Cueing Algorithms

NASA Technical Reports Server (NTRS)

Houck, Jacob A. (Technical Monitor); Telban, Robert J.; Cardullo, Frank M.; Kelly, Lon C.

2005-01-01

The relative effectiveness in simulating aircraft maneuvers with both current and newly developed motion cueing algorithms was assessed with an eleven-subject piloted performance evaluation conducted on the NASA Langley Visual Motion Simulator (VMS). In addition to the current NASA adaptive algorithm, two new cueing algorithms were evaluated: the optimal algorithm and the nonlinear algorithm. The test maneuvers included a straight-in approach with a rotating wind vector, an offset approach with severe turbulence and an on/off lateral gust that occurs as the aircraft approaches the runway threshold, and a takeoff both with and without engine failure after liftoff. The maneuvers were executed with each cueing algorithm with added visual display delay conditions ranging from zero to 200 msec. Two methods, the quasi-objective NASA Task Load Index (TLX), and power spectral density analysis of pilot control, were used to assess pilot workload. Piloted performance parameters for the approach maneuvers, the vertical velocity upon touchdown and the runway touchdown position, were also analyzed but did not show any noticeable difference among the cueing algorithms. TLX analysis reveals, in most cases, less workload and variation among pilots with the nonlinear algorithm. Control input analysis shows pilot-induced oscillations on a straight-in approach were less prevalent compared to the optimal algorithm. The augmented turbulence cues increased workload on an offset approach that the pilots deemed more realistic compared to the NASA adaptive algorithm. The takeoff with engine failure showed the least roll activity for the nonlinear algorithm, with the least rudder pedal activity for the optimal algorithm.

An adaptive approach to the physical annealing strategy for simulated annealing

NASA Astrophysics Data System (ADS)

Hasegawa, M.

2013-02-01

A new and reasonable method for adaptive implementation of simulated annealing (SA) is studied on two types of random traveling salesman problems. The idea is based on the previous finding on the search characteristics of the threshold algorithms, that is, the primary role of the relaxation dynamics in their finite-time optimization process. It is shown that the effective temperature for optimization can be predicted from the system's behavior analogous to the stabilization phenomenon occurring in the heating process starting from a quenched solution. The subsequent slow cooling near the predicted point draws out the inherent optimizing ability of finite-time SA in more straightforward manner than the conventional adaptive approach.

Impact of view reduction in CT on radiation dose for patients

NASA Astrophysics Data System (ADS)

Parcero, E.; Flores, L.; Sánchez, M. G.; Vidal, V.; Verdú, G.

2017-08-01

Iterative methods have become a hot topic of research in computed tomography (CT) imaging because of their capacity to resolve the reconstruction problem from a limited number of projections. This allows the reduction of radiation exposure on patients during the data acquisition. The reconstruction time and the high radiation dose imposed on patients are the two major drawbacks in CT. To solve them effectively we adapted the method for sparse linear equations and sparse least squares (LSQR) with soft threshold filtering (STF) and the fast iterative shrinkage-thresholding algorithm (FISTA) to computed tomography reconstruction. The feasibility of the proposed methods is demonstrated numerically.

A collaborative approach to developing an electronic health record phenotyping algorithm for drug-induced liver injury

PubMed Central

Overby, Casey Lynnette; Pathak, Jyotishman; Gottesman, Omri; Haerian, Krystl; Perotte, Adler; Murphy, Sean; Bruce, Kevin; Johnson, Stephanie; Talwalkar, Jayant; Shen, Yufeng; Ellis, Steve; Kullo, Iftikhar; Chute, Christopher; Friedman, Carol; Bottinger, Erwin; Hripcsak, George; Weng, Chunhua

2013-01-01

Objective To describe a collaborative approach for developing an electronic health record (EHR) phenotyping algorithm for drug-induced liver injury (DILI). Methods We analyzed types and causes of differences in DILI case definitions provided by two institutions—Columbia University and Mayo Clinic; harmonized two EHR phenotyping algorithms; and assessed the performance, measured by sensitivity, specificity, positive predictive value, and negative predictive value, of the resulting algorithm at three institutions except that sensitivity was measured only at Columbia University. Results Although these sites had the same case definition, their phenotyping methods differed by selection of liver injury diagnoses, inclusion of drugs cited in DILI cases, laboratory tests assessed, laboratory thresholds for liver injury, exclusion criteria, and approaches to validating phenotypes. We reached consensus on a DILI phenotyping algorithm and implemented it at three institutions. The algorithm was adapted locally to account for differences in populations and data access. Implementations collectively yielded 117 algorithm-selected cases and 23 confirmed true positive cases. Discussion Phenotyping for rare conditions benefits significantly from pooling data across institutions. Despite the heterogeneity of EHRs and varied algorithm implementations, we demonstrated the portability of this algorithm across three institutions. The performance of this algorithm for identifying DILI was comparable with other computerized approaches to identify adverse drug events. Conclusions Phenotyping algorithms developed for rare and complex conditions are likely to require adaptive implementation at multiple institutions. Better approaches are also needed to share algorithms. Early agreement on goals, data sources, and validation methods may improve the portability of the algorithms. PMID:23837993

Smoothness within ruggedness: the role of neutrality in adaptation.

PubMed Central

Huynen, M A; Stadler, P F; Fontana, W

1996-01-01

RNA secondary structure folding algorithms predict the existence of connected networks of RNA sequences with identical structure. On such networks, evolving populations split into subpopulations, which diffuse independently in sequence space. This demands a distinction between two mutation thresholds: one at which genotypic information is lost and one at which phenotypic information is lost. In between, diffusion enables the search of vast areas in genotype space while still preserving the dominant phenotype. By this dynamic the success of phenotypic adaptation becomes much less sensitive to the initial conditions in genotype space. Images Fig. 2 PMID:8552647

Nonuniformity correction for an infrared focal plane array based on diamond search block matching.

PubMed

Sheng-Hui, Rong; Hui-Xin, Zhou; Han-Lin, Qin; Rui, Lai; Kun, Qian

2016-05-01

In scene-based nonuniformity correction algorithms, artificial ghosting and image blurring degrade the correction quality severely. In this paper, an improved algorithm based on the diamond search block matching algorithm and the adaptive learning rate is proposed. First, accurate transform pairs between two adjacent frames are estimated by the diamond search block matching algorithm. Then, based on the error between the corresponding transform pairs, the gradient descent algorithm is applied to update correction parameters. During the process of gradient descent, the local standard deviation and a threshold are utilized to control the learning rate to avoid the accumulation of matching error. Finally, the nonuniformity correction would be realized by a linear model with updated correction parameters. The performance of the proposed algorithm is thoroughly studied with four real infrared image sequences. Experimental results indicate that the proposed algorithm can reduce the nonuniformity with less ghosting artifacts in moving areas and can also overcome the problem of image blurring in static areas.

Novel image processing method study for a label-free optical biosensor

NASA Astrophysics Data System (ADS)

Yang, Chenhao; Wei, Li'an; Yang, Rusong; Feng, Ying

2015-10-01

Optical biosensor is generally divided into labeled type and label-free type, the former mainly contains fluorescence labeled method and radioactive-labeled method, while fluorescence-labeled method is more mature in the application. The mainly image processing methods of fluorescent-labeled biosensor includes smooth filtering, artificial gridding and constant thresholding. Since some fluorescent molecules may influence the biological reaction, label-free methods have been the main developing direction of optical biosensors nowadays. The using of wider field of view and larger angle of incidence light path which could effectively improve the sensitivity of the label-free biosensor also brought more difficulties in image processing, comparing with the fluorescent-labeled biosensor. Otsu's method is widely applied in machine vision, etc, which choose the threshold to minimize the intraclass variance of the thresholded black and white pixels. It's capacity-constrained with the asymmetrical distribution of images as a global threshold segmentation. In order to solve the irregularity of light intensity on the transducer, we improved the algorithm. In this paper, we present a new image processing algorithm based on a reflectance modulation biosensor platform, which mainly comprises the design of sliding normalization algorithm for image rectification and utilizing the improved otsu's method for image segmentation, in order to implement automatic recognition of target areas. Finally we used adaptive gridding method extracting the target parameters for analysis. Those methods could improve the efficiency of image processing, reduce human intervention, enhance the reliability of experiments and laid the foundation for the realization of high throughput of label-free optical biosensors.

Adaptive Waveform Correlation Detectors for Arrays: Algorithms for Autonomous Calibration

DTIC Science & Technology

2007-09-01

March 17, 2005. The seismic signals from both master and detected events are followed by infrasound arrivals. Note the long duration of the...correlation coefficient traces with a significant array -gain. A detected event that is co-located with the master event will record the same time-difference...estimating the detection threshold reduction for a range of highly repeating seismic sources using arrays of different configurations and at different

Complexity reduction in the H.264/AVC using highly adaptive fast mode decision based on macroblock motion activity

NASA Astrophysics Data System (ADS)

Abdellah, Skoudarli; Mokhtar, Nibouche; Amina, Serir

2015-11-01

The H.264/AVC video coding standard is used in a wide range of applications from video conferencing to high-definition television according to its high compression efficiency. This efficiency is mainly acquired from the newly allowed prediction schemes including variable block modes. However, these schemes require a high complexity to select the optimal mode. Consequently, complexity reduction in the H.264/AVC encoder has recently become a very challenging task in the video compression domain, especially when implementing the encoder in real-time applications. Fast mode decision algorithms play an important role in reducing the overall complexity of the encoder. In this paper, we propose an adaptive fast intermode algorithm based on motion activity, temporal stationarity, and spatial homogeneity. This algorithm predicts the motion activity of the current macroblock from its neighboring blocks and identifies temporal stationary regions and spatially homogeneous regions using adaptive threshold values based on content video features. Extensive experimental work has been done in high profile, and results show that the proposed source-coding algorithm effectively reduces the computational complexity by 53.18% on average compared with the reference software encoder, while maintaining the high-coding efficiency of H.264/AVC by incurring only 0.097 dB in total peak signal-to-noise ratio and 0.228% increment on the total bit rate.

Segmentation Approach Towards Phase-Contrast Microscopic Images of Activated Sludge to Monitor the Wastewater Treatment.

PubMed

Khan, Muhammad Burhan; Nisar, Humaira; Ng, Choon Aun; Yeap, Kim Ho; Lai, Koon Chun

2017-12-01

Image processing and analysis is an effective tool for monitoring and fault diagnosis of activated sludge (AS) wastewater treatment plants. The AS image comprise of flocs (microbial aggregates) and filamentous bacteria. In this paper, nine different approaches are proposed for image segmentation of phase-contrast microscopic (PCM) images of AS samples. The proposed strategies are assessed for their effectiveness from the perspective of microscopic artifacts associated with PCM. The first approach uses an algorithm that is based on the idea that different color space representation of images other than red-green-blue may have better contrast. The second uses an edge detection approach. The third strategy, employs a clustering algorithm for the segmentation and the fourth applies local adaptive thresholding. The fifth technique is based on texture-based segmentation and the sixth uses watershed algorithm. The seventh adopts a split-and-merge approach. The eighth employs Kittler's thresholding. Finally, the ninth uses a top-hat and bottom-hat filtering-based technique. The approaches are assessed, and analyzed critically with reference to the artifacts of PCM. Gold approximations of ground truth images are prepared to assess the segmentations. Overall, the edge detection-based approach exhibits the best results in terms of accuracy, and the texture-based algorithm in terms of false negative ratio. The respective scenarios are explained for suitability of edge detection and texture-based algorithms.

Diving at altitude: from definition to practice.

PubMed

Egi, S Murat; Pieri, Massimo; Marroni, Alessandro

2014-01-01

Diving above sea level has different motivations for recreational, military, commercial and scientific activities. Despite the apparently wide practice of inland diving, there are three major discrepancies about diving at altitude: threshold elevation that requires changes in sea level procedures; upper altitude limit of the applicability of these modifications; and independent validation of altitude adaptation methods of decompression algorithms. The first problem is solved by converting the normal fluctuation in barometric pressure to an altitude equivalent. Based on the barometric variations recorded from a meteorological center, it is possible to suggest 600 meters as a threshold for classifying a dive as an "altitude" dive. The second problem is solved by proposing the threshold altitude of aviation (2,400 meters) to classify "high" altitude dives. The DAN (Divers Alert Network) Europe diving database (DB) is analyzed to solve the third problem. The database consists of 65,050 dives collected from different dive computers. A total of 1,467 dives were found to be classified as altitude dives. However, by checking the elevation according to the logged geographical coordinates, 1,284 dives were disqualified because the altitude setting had been used as a conservative setting by the dive computer despite the fact that the dive was made at sea level. Furthermore, according to the description put forward in this manuscript, 72 dives were disqualified because the surface level elevation is lower than 600 meters. The number of field data (111 dives) is still very low to use for the validation of any particular method of altitude adaptation concerning decompression algorithms.

A dual-adaptive support-based stereo matching algorithm

NASA Astrophysics Data System (ADS)

Zhang, Yin; Zhang, Yun

2017-07-01

Many stereo matching algorithms use fixed color thresholds and a rigid cross skeleton to segment supports (viz., Cross method), which, however, does not work well for different images. To address this issue, this paper proposes a novel dual adaptive support (viz., DAS)-based stereo matching method, which uses both appearance and shape information of a local region to segment supports automatically, and, then, integrates the DAS-based cost aggregation with the absolute difference plus census transform cost, scanline optimization and disparity refinement to develop a stereo matching system. The performance of the DAS method is also evaluated in the Middlebury benchmark and by comparing with the Cross method. The results show that the average error for the DAS method 25.06% lower than that for the Cross method, indicating that the proposed method is more accurate, with fewer parameters and suitable for parallel computing.

A Well-Tempered Hybrid Method for Solving Challenging Time-Dependent Density Functional Theory (TDDFT) Systems.

PubMed

Kasper, Joseph M; Williams-Young, David B; Vecharynski, Eugene; Yang, Chao; Li, Xiaosong

2018-04-10

The time-dependent Hartree-Fock (TDHF) and time-dependent density functional theory (TDDFT) equations allow one to probe electronic resonances of a system quickly and inexpensively. However, the iterative solution of the eigenvalue problem can be challenging or impossible to converge, using standard methods such as the Davidson algorithm for spectrally dense regions in the interior of the spectrum, as are common in X-ray absorption spectroscopy (XAS). More robust solvers, such as the generalized preconditioned locally harmonic residual (GPLHR) method, can alleviate this problem, but at the expense of higher average computational cost. A hybrid method is proposed which adapts to the problem in order to maximize computational performance while providing the superior convergence of GPLHR. In addition, a modification to the GPLHR algorithm is proposed to adaptively choose the shift parameter to enforce a convergence of states above a predefined energy threshold.

Fast and automatic algorithm for optic disc extraction in retinal images using principle-component-analysis-based preprocessing and curvelet transform.

PubMed

Shahbeig, Saleh; Pourghassem, Hossein

2013-01-01

Optic disc or optic nerve (ON) head extraction in retinal images has widespread applications in retinal disease diagnosis and human identification in biometric systems. This paper introduces a fast and automatic algorithm for detecting and extracting the ON region accurately from the retinal images without the use of the blood-vessel information. In this algorithm, to compensate for the destructive changes of the illumination and also enhance the contrast of the retinal images, we estimate the illumination of background and apply an adaptive correction function on the curvelet transform coefficients of retinal images. In other words, we eliminate the fault factors and pave the way to extract the ON region exactly. Then, we detect the ON region from retinal images using the morphology operators based on geodesic conversions, by applying a proper adaptive correction function on the reconstructed image's curvelet transform coefficients and a novel powerful criterion. Finally, using a local thresholding on the detected area of the retinal images, we extract the ON region. The proposed algorithm is evaluated on available images of DRIVE and STARE databases. The experimental results indicate that the proposed algorithm obtains an accuracy rate of 100% and 97.53% for the ON extractions on DRIVE and STARE databases, respectively.

Speckle Noise Reduction in Optical Coherence Tomography Using Two-dimensional Curvelet-based Dictionary Learning.

PubMed

Esmaeili, Mahdad; Dehnavi, Alireza Mehri; Rabbani, Hossein; Hajizadeh, Fedra

2017-01-01

The process of interpretation of high-speed optical coherence tomography (OCT) images is restricted due to the large speckle noise. To address this problem, this paper proposes a new method using two-dimensional (2D) curvelet-based K-SVD algorithm for speckle noise reduction and contrast enhancement of intra-retinal layers of 2D spectral-domain OCT images. For this purpose, we take curvelet transform of the noisy image. In the next step, noisy sub-bands of different scales and rotations are separately thresholded with an adaptive data-driven thresholding method, then, each thresholded sub-band is denoised based on K-SVD dictionary learning with a variable size initial dictionary dependent on the size of curvelet coefficients' matrix in each sub-band. We also modify each coefficient matrix to enhance intra-retinal layers, with noise suppression at the same time. We demonstrate the ability of the proposed algorithm in speckle noise reduction of 100 publically available OCT B-scans with and without non-neovascular age-related macular degeneration (AMD), and improvement of contrast-to-noise ratio from 1.27 to 5.12 and mean-to-standard deviation ratio from 3.20 to 14.41 are obtained.

Implementation and performance evaluation of acoustic denoising algorithms for UAV

NASA Astrophysics Data System (ADS)

Chowdhury, Ahmed Sony Kamal

Unmanned Aerial Vehicles (UAVs) have become popular alternative for wildlife monitoring and border surveillance applications. Elimination of the UAV's background noise and classifying the target audio signal effectively are still a major challenge. The main goal of this thesis is to remove UAV's background noise by means of acoustic denoising techniques. Existing denoising algorithms, such as Adaptive Least Mean Square (LMS), Wavelet Denoising, Time-Frequency Block Thresholding, and Wiener Filter, were implemented and their performance evaluated. The denoising algorithms were evaluated for average Signal to Noise Ratio (SNR), Segmental SNR (SSNR), Log Likelihood Ratio (LLR), and Log Spectral Distance (LSD) metrics. To evaluate the effectiveness of the denoising algorithms on classification of target audio, we implemented Support Vector Machine (SVM) and Naive Bayes classification algorithms. Simulation results demonstrate that LMS and Discrete Wavelet Transform (DWT) denoising algorithm offered superior performance than other algorithms. Finally, we implemented the LMS and DWT algorithms on a DSP board for hardware evaluation. Experimental results showed that LMS algorithm's performance is robust compared to DWT for various noise types to classify target audio signals.

Spike-Threshold Adaptation Predicted by Membrane Potential Dynamics In Vivo

PubMed Central

Fontaine, Bertrand; Peña, José Luis; Brette, Romain

2014-01-01

Neurons encode information in sequences of spikes, which are triggered when their membrane potential crosses a threshold. In vivo, the spiking threshold displays large variability suggesting that threshold dynamics have a profound influence on how the combined input of a neuron is encoded in the spiking. Threshold variability could be explained by adaptation to the membrane potential. However, it could also be the case that most threshold variability reflects noise and processes other than threshold adaptation. Here, we investigated threshold variation in auditory neurons responses recorded in vivo in barn owls. We found that spike threshold is quantitatively predicted by a model in which the threshold adapts, tracking the membrane potential at a short timescale. As a result, in these neurons, slow voltage fluctuations do not contribute to spiking because they are filtered by threshold adaptation. More importantly, these neurons can only respond to input spikes arriving together on a millisecond timescale. These results demonstrate that fast adaptation to the membrane potential captures spike threshold variability in vivo. PMID:24722397

Compressively sampled MR image reconstruction using generalized thresholding iterative algorithm

NASA Astrophysics Data System (ADS)

Elahi, Sana; kaleem, Muhammad; Omer, Hammad

2018-01-01

Compressed sensing (CS) is an emerging area of interest in Magnetic Resonance Imaging (MRI). CS is used for the reconstruction of the images from a very limited number of samples in k-space. This significantly reduces the MRI data acquisition time. One important requirement for signal recovery in CS is the use of an appropriate non-linear reconstruction algorithm. It is a challenging task to choose a reconstruction algorithm that would accurately reconstruct the MR images from the under-sampled k-space data. Various algorithms have been used to solve the system of non-linear equations for better image quality and reconstruction speed in CS. In the recent past, iterative soft thresholding algorithm (ISTA) has been introduced in CS-MRI. This algorithm directly cancels the incoherent artifacts produced because of the undersampling in k -space. This paper introduces an improved iterative algorithm based on p -thresholding technique for CS-MRI image reconstruction. The use of p -thresholding function promotes sparsity in the image which is a key factor for CS based image reconstruction. The p -thresholding based iterative algorithm is a modification of ISTA, and minimizes non-convex functions. It has been shown that the proposed p -thresholding iterative algorithm can be used effectively to recover fully sampled image from the under-sampled data in MRI. The performance of the proposed method is verified using simulated and actual MRI data taken at St. Mary's Hospital, London. The quality of the reconstructed images is measured in terms of peak signal-to-noise ratio (PSNR), artifact power (AP), and structural similarity index measure (SSIM). The proposed approach shows improved performance when compared to other iterative algorithms based on log thresholding, soft thresholding and hard thresholding techniques at different reduction factors.

Outlier detection for particle image velocimetry data using a locally estimated noise variance

NASA Astrophysics Data System (ADS)

Lee, Yong; Yang, Hua; Yin, ZhouPing

2017-03-01

This work describes an adaptive spatial variable threshold outlier detection algorithm for raw gridded particle image velocimetry data using a locally estimated noise variance. This method is an iterative procedure, and each iteration is composed of a reference vector field reconstruction step and an outlier detection step. We construct the reference vector field using a weighted adaptive smoothing method (Garcia 2010 Comput. Stat. Data Anal. 54 1167-78), and the weights are determined in the outlier detection step using a modified outlier detector (Ma et al 2014 IEEE Trans. Image Process. 23 1706-21). A hard decision on the final weights of the iteration can produce outlier labels of the field. The technical contribution is that the spatial variable threshold motivation is embedded in the modified outlier detector with a locally estimated noise variance in an iterative framework for the first time. It turns out that a spatial variable threshold is preferable to a single spatial constant threshold in complicated flows such as vortex flows or turbulent flows. Synthetic cellular vortical flows with simulated scattered or clustered outliers are adopted to evaluate the performance of our proposed method in comparison with popular validation approaches. This method also turns out to be beneficial in a real PIV measurement of turbulent flow. The experimental results demonstrated that the proposed method yields the competitive performance in terms of outlier under-detection count and over-detection count. In addition, the outlier detection method is computational efficient and adaptive, requires no user-defined parameters, and corresponding implementations are also provided in supplementary materials.

A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation.

PubMed

Wang, Rui; Zhou, Yongquan; Zhao, Chengyan; Wu, Haizhou

2015-01-01

Multi-threshold image segmentation is a powerful image processing technique that is used for the preprocessing of pattern recognition and computer vision. However, traditional multilevel thresholding methods are computationally expensive because they involve exhaustively searching the optimal thresholds to optimize the objective functions. To overcome this drawback, this paper proposes a flower pollination algorithm with a randomized location modification. The proposed algorithm is used to find optimal threshold values for maximizing Otsu's objective functions with regard to eight medical grayscale images. When benchmarked against other state-of-the-art evolutionary algorithms, the new algorithm proves itself to be robust and effective through numerical experimental results including Otsu's objective values and standard deviations.

Acoustical source reconstruction from non-synchronous sequential measurements by Fast Iterative Shrinkage Thresholding Algorithm

NASA Astrophysics Data System (ADS)

Yu, Liang; Antoni, Jerome; Leclere, Quentin; Jiang, Weikang

2017-11-01

Acoustical source reconstruction is a typical inverse problem, whose minimum frequency of reconstruction hinges on the size of the array and maximum frequency depends on the spacing distance between the microphones. For the sake of enlarging the frequency of reconstruction and reducing the cost of an acquisition system, Cyclic Projection (CP), a method of sequential measurements without reference, was recently investigated (JSV,2016,372:31-49). In this paper, the Propagation based Fast Iterative Shrinkage Thresholding Algorithm (Propagation-FISTA) is introduced, which improves CP in two aspects: (1) the number of acoustic sources is no longer needed and the only making assumption is that of a "weakly sparse" eigenvalue spectrum; (2) the construction of the spatial basis is much easier and adaptive to practical scenarios of acoustical measurements benefiting from the introduction of propagation based spatial basis. The proposed Propagation-FISTA is first investigated with different simulations and experimental setups and is next illustrated with an industrial case.

Modified Mahalanobis Taguchi System for Imbalance Data Classification

PubMed Central

2017-01-01

The Mahalanobis Taguchi System (MTS) is considered one of the most promising binary classification algorithms to handle imbalance data. Unfortunately, MTS lacks a method for determining an efficient threshold for the binary classification. In this paper, a nonlinear optimization model is formulated based on minimizing the distance between MTS Receiver Operating Characteristics (ROC) curve and the theoretical optimal point named Modified Mahalanobis Taguchi System (MMTS). To validate the MMTS classification efficacy, it has been benchmarked with Support Vector Machines (SVMs), Naive Bayes (NB), Probabilistic Mahalanobis Taguchi Systems (PTM), Synthetic Minority Oversampling Technique (SMOTE), Adaptive Conformal Transformation (ACT), Kernel Boundary Alignment (KBA), Hidden Naive Bayes (HNB), and other improved Naive Bayes algorithms. MMTS outperforms the benchmarked algorithms especially when the imbalance ratio is greater than 400. A real life case study on manufacturing sector is used to demonstrate the applicability of the proposed model and to compare its performance with Mahalanobis Genetic Algorithm (MGA). PMID:28811820

Assessing and Adapting LiDAR-Derived Pit-Free Canopy Height Model Algorithm for Sites with Varying Vegetation Structure

NASA Astrophysics Data System (ADS)

Scholl, V.; Hulslander, D.; Goulden, T.; Wasser, L. A.

2015-12-01

Spatial and temporal monitoring of vegetation structure is important to the ecological community. Airborne Light Detection and Ranging (LiDAR) systems are used to efficiently survey large forested areas. From LiDAR data, three-dimensional models of forests called canopy height models (CHMs) are generated and used to estimate tree height. A common problem associated with CHMs is data pits, where LiDAR pulses penetrate the top of the canopy, leading to an underestimation of vegetation height. The National Ecological Observatory Network (NEON) currently implements an algorithm to reduce data pit frequency, which requires two height threshold parameters, increment size and range ceiling. CHMs are produced at a series of height increments up to a height range ceiling and combined to produce a CHM with reduced pits (referred to as a "pit-free" CHM). The current implementation uses static values for the height increment and ceiling (5 and 15 meters, respectively). To facilitate the generation of accurate pit-free CHMs across diverse NEON sites with varying vegetation structure, the impacts of adjusting the height threshold parameters were investigated through development of an algorithm which dynamically selects the height increment and ceiling. A series of pit-free CHMs were generated using three height range ceilings and four height increment values for three ecologically different sites. Height threshold parameters were found to change CHM-derived tree heights up to 36% compared to original CHMs. The extent of the parameters' influence on modelled tree heights was greater than expected, which will be considered during future CHM data product development at NEON. (A) Aerial image of Harvard National Forest, (B) standard CHM containing pits, appearing as black speckles, (C) a pit-free CHM created with the static algorithm implementation, and (D) a pit-free CHM created through varying the height threshold ceiling up to 82 m and the increment to 1 m.

Speech perception at positive signal-to-noise ratios using adaptive adjustment of time compression.

PubMed

Schlueter, Anne; Brand, Thomas; Lemke, Ulrike; Nitzschner, Stefan; Kollmeier, Birger; Holube, Inga

2015-11-01

Positive signal-to-noise ratios (SNRs) characterize listening situations most relevant for hearing-impaired listeners in daily life and should therefore be considered when evaluating hearing aid algorithms. For this, a speech-in-noise test was developed and evaluated, in which the background noise is presented at fixed positive SNRs and the speech rate (i.e., the time compression of the speech material) is adaptively adjusted. In total, 29 younger and 12 older normal-hearing, as well as 24 older hearing-impaired listeners took part in repeated measurements. Younger normal-hearing and older hearing-impaired listeners conducted one of two adaptive methods which differed in adaptive procedure and step size. Analysis of the measurements with regard to list length and estimation strategy for thresholds resulted in a practical method measuring the time compression for 50% recognition. This method uses time-compression adjustment and step sizes according to Versfeld and Dreschler [(2002). J. Acoust. Soc. Am. 111, 401-408], with sentence scoring, lists of 30 sentences, and a maximum likelihood method for threshold estimation. Evaluation of the procedure showed that older participants obtained higher test-retest reliability compared to younger participants. Depending on the group of listeners, one or two lists are required for training prior to data collection.

An Application of Reassigned Time-Frequency Representations for Seismic Noise/Signal Decomposition

NASA Astrophysics Data System (ADS)

Mousavi, S. M.; Langston, C. A.

2016-12-01

Seismic data recorded by surface arrays are often strongly contaminated by unwanted noise. This background noise makes the detection of small magnitude events difficult. An automatic method for seismic noise/signal decomposition is presented based upon an enhanced time-frequency representation. Synchrosqueezing is a time-frequency reassignment method aimed at sharpening a time-frequency picture. Noise can be distinguished from the signal and suppressed more easily in this reassigned domain. The threshold level is estimated using a general cross validation approach that does not rely on any prior knowledge about the noise level. Efficiency of thresholding has been improved by adding a pre-processing step based on higher order statistics and a post-processing step based on adaptive hard-thresholding. In doing so, both accuracy and speed of the denoising have been improved compared to our previous algorithms (Mousavi and Langston, 2016a, 2016b; Mousavi et al., 2016). The proposed algorithm can either kill the noise (either white or colored) and keep the signal or kill the signal and keep the noise. Hence, It can be used in either normal denoising applications or in ambient noise studies. Application of the proposed method on synthetic and real seismic data shows the effectiveness of the method for denoising/designaling of local microseismic, and ocean bottom seismic data. References: Mousavi, S.M., C. A. Langston., and S. P. Horton (2016), Automatic Microseismic Denoising and Onset Detection Using the Synchrosqueezed-Continuous Wavelet Transform. Geophysics. 81, V341-V355, doi: 10.1190/GEO2015-0598.1. Mousavi, S.M., and C. A. Langston (2016a), Hybrid Seismic Denoising Using Higher-Order Statistics and Improved Wavelet Block Thresholding. Bull. Seismol. Soc. Am., 106, doi: 10.1785/0120150345. Mousavi, S.M., and C.A. Langston (2016b), Adaptive noise estimation and suppression for improving microseismic event detection, Journal of Applied Geophysics., doi: http://dx.doi.org/10.1016/j.jappgeo.2016.06.008.

Improved Bat Algorithm Applied to Multilevel Image Thresholding

PubMed Central

2014-01-01

Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed. PMID:25165733

Treatment thresholds for osteoporosis and reimbursability criteria: perspectives associated with fracture risk-assessment tools.

PubMed

Adami, Silvano; Bertoldo, Francesco; Gatti, Davide; Minisola, Giovanni; Rossini, Maurizio; Sinigaglia, Luigi; Varenna, Massimo

2013-09-01

The definition of osteoporosis was based for several years on bone mineral density values, which were used by most guidelines for defining treatment thresholds. The availability of tools for the estimation of fracture risk, such as FRAX™ or its adapted Italian version, DeFRA, is providing a way to grade osteoporosis severity. By applying these new tools, the criteria identified in Italy for treatment reimbursability (e.g., "Nota 79") are confirmed as extremely conservative. The new fracture risk-assessment tools provide continuous risk values that can be used by health authorities (or "payers") for identifying treatment thresholds. FRAX estimates the risk for "major osteoporotic fractures," which are not counted in registered fracture trials. Here, we elaborate an algorithm to convert vertebral and nonvertebral fractures to the "major fractures" of FRAX, and this allows a cost-effectiveness assessment for each drug.

Texture orientation-based algorithm for detecting infrared maritime targets.

PubMed

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.

Robust crop and weed segmentation under uncontrolled outdoor illumination.

PubMed

Jeon, Hong Y; Tian, Lei F; Zhu, Heping

2011-01-01

An image processing algorithm for detecting individual weeds was developed and evaluated. Weed detection processes included were normalized excessive green conversion, statistical threshold value estimation, adaptive image segmentation, median filter, morphological feature calculation and Artificial Neural Network (ANN). The developed algorithm was validated for its ability to identify and detect weeds and crop plants under uncontrolled outdoor illuminations. A machine vision implementing field robot captured field images under outdoor illuminations and the image processing algorithm automatically processed them without manual adjustment. The errors of the algorithm, when processing 666 field images, ranged from 2.1 to 2.9%. The ANN correctly detected 72.6% of crop plants from the identified plants, and considered the rest as weeds. However, the ANN identification rates for crop plants were improved up to 95.1% by addressing the error sources in the algorithm. The developed weed detection and image processing algorithm provides a novel method to identify plants against soil background under the uncontrolled outdoor illuminations, and to differentiate weeds from crop plants. Thus, the proposed new machine vision and processing algorithm may be useful for outdoor applications including plant specific direct applications (PSDA).

A Multi-Anatomical Retinal Structure Segmentation System for Automatic Eye Screening Using Morphological Adaptive Fuzzy Thresholding

PubMed Central

Elleithy, Khaled; Elleithy, Abdelrahman

2018-01-01

Eye exam can be as efficacious as physical one in determining health concerns. Retina screening can be the very first clue for detecting a variety of hidden health issues including pre-diabetes and diabetes. Through the process of clinical diagnosis and prognosis; ophthalmologists rely heavily on the binary segmented version of retina fundus image; where the accuracy of segmented vessels, optic disc, and abnormal lesions extremely affects the diagnosis accuracy which in turn affect the subsequent clinical treatment steps. This paper proposes an automated retinal fundus image segmentation system composed of three segmentation subsystems follow same core segmentation algorithm. Despite of broad difference in features and characteristics; retinal vessels, optic disc, and exudate lesions are extracted by each subsystem without the need for texture analysis or synthesis. For sake of compact diagnosis and complete clinical insight, our proposed system can detect these anatomical structures in one session with high accuracy even in pathological retina images. The proposed system uses a robust hybrid segmentation algorithm combines adaptive fuzzy thresholding and mathematical morphology. The proposed system is validated using four benchmark datasets: DRIVE and STARE (vessels), DRISHTI-GS (optic disc), and DIARETDB1 (exudates lesions). Competitive segmentation performance is achieved, outperforming a variety of up-to-date systems and demonstrating the capacity to deal with other heterogeneous anatomical structures. PMID:29888146

A segmentation method for lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise

PubMed Central

Zhang, Wei; Zhang, Xiaolong; Qiang, Yan; Tian, Qi; Tang, Xiaoxian

2017-01-01

The fast and accurate segmentation of lung nodule image sequences is the basis of subsequent processing and diagnostic analyses. However, previous research investigating nodule segmentation algorithms cannot entirely segment cavitary nodules, and the segmentation of juxta-vascular nodules is inaccurate and inefficient. To solve these problems, we propose a new method for the segmentation of lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise (DBSCAN). First, our method uses three-dimensional computed tomography image features of the average intensity projection combined with multi-scale dot enhancement for preprocessing. Hexagonal clustering and morphological optimized sequential linear iterative clustering (HMSLIC) for sequence image oversegmentation is then proposed to obtain superpixel blocks. The adaptive weight coefficient is then constructed to calculate the distance required between superpixels to achieve precise lung nodules positioning and to obtain the subsequent clustering starting block. Moreover, by fitting the distance and detecting the change in slope, an accurate clustering threshold is obtained. Thereafter, a fast DBSCAN superpixel sequence clustering algorithm, which is optimized by the strategy of only clustering the lung nodules and adaptive threshold, is then used to obtain lung nodule mask sequences. Finally, the lung nodule image sequences are obtained. The experimental results show that our method rapidly, completely and accurately segments various types of lung nodule image sequences. PMID:28880916

Hair segmentation using adaptive threshold from edge and branch length measures.

PubMed

Lee, Ian; Du, Xian; Anthony, Brian

2017-10-01

Non-invasive imaging techniques allow the monitoring of skin structure and diagnosis of skin diseases in clinical applications. However, hair in skin images hampers the imaging and classification of the skin structure of interest. Although many hair segmentation methods have been proposed for digital hair removal, a major challenge in hair segmentation remains in detecting hairs that are thin, overlapping, of similar contrast or color to underlying skin, or overlaid on highly-textured skin structure. To solve the problem, we present an automatic hair segmentation method that uses edge density (ED) and mean branch length (MBL) to measure hair. First, hair is detected by the integration of top-hat transform and modified second-order Gaussian filter. Second, we employ a robust adaptive threshold of ED and MBL to generate a hair mask. Third, the hair mask is refined by k-NN classification of hair and skin pixels. The proposed algorithm was tested using two datasets of healthy skin images and lesion images respectively. These datasets were taken from different imaging platforms in various illumination levels and varying skin colors. We compared the hair detection and segmentation results from our algorithm and six other hair segmentation methods of state of the art. Our method exhibits high value of sensitivity: 75% and specificity: 95%, which indicates significantly higher accuracy and better balance between true positive and false positive detection than the other methods. Published by Elsevier Ltd.

iFall: an Android application for fall monitoring and response.

PubMed

Sposaro, Frank; Tyson, Gary

2009-01-01

Injuries due to falls are among the leading causes of hospitalization in elderly persons, often resulting in a rapid decline in quality of life or death. Rapid response can improve the patients outcome, but this is often lacking when the injured person lives alone and the nature of the injury complicates calling for help. This paper presents an alert system for fall detection using common commercially available electronic devices to both detect the fall and alert authorities. We use an Android-based smart phone with an integrated tri-axial accelerometer. Data from the accelerometer is evaluated with several threshold based algorithms and position data to determine a fall. The threshold is adaptive based on user provided parameters such as: height, weight, and level of activity. The algorithm adapts to unique movements that a phone experiences as opposed to similar systems which require users to mount accelerometers to their chest or trunk. If a fall is suspected a notification is raised requiring the user's response. If the user does not respond, the system alerts pre-specified social contacts with an informational message via SMS. If a contact responds the system commits an audible notification, automatically connects, and enables the speakerphone. If a social contact confirms a fall, an appropriate emergency service is alerted. Our system provides a realizable, cost effective solution to fall detection using a simple graphical interface while not overwhelming the user with uncomfortable sensors.

The threshold algorithm: Description of the methodology and new developments

NASA Astrophysics Data System (ADS)

Neelamraju, Sridhar; Oligschleger, Christina; Schön, J. Christian

2017-10-01

Understanding the dynamics of complex systems requires the investigation of their energy landscape. In particular, the flow of probability on such landscapes is a central feature in visualizing the time evolution of complex systems. To obtain such flows, and the concomitant stable states of the systems and the generalized barriers among them, the threshold algorithm has been developed. Here, we describe the methodology of this approach starting from the fundamental concepts in complex energy landscapes and present recent new developments, the threshold-minimization algorithm and the molecular dynamics threshold algorithm. For applications of these new algorithms, we draw on landscape studies of three disaccharide molecules: lactose, maltose, and sucrose.

Automatic management of atrial and ventricular stimulation in a contemporary unselected population of pacemaker recipients: the ESSENTIAL Registry.

PubMed

Biffi, Mauro; Bertini, Matteo; Saporito, Davide; Belotti, Giuseppina; Quartieri, Fabio; Piancastelli, Maurizio; Pucci, Angelo; Boggian, Giulio; Mazzocca, Gian Franco; Giorgi, Davide; Diotallevi, Paolo; Diemberger, Igor; Martignani, Cristian; Pancaldi, Stefano; Ziacchi, Matteo; Marcantoni, Lina; Toselli, Tiziano; Attala, Simone; Iori, Matteo; Bottoni, Nicola; Argnani, Selina; Tomasi, Corrado; Sassone, Biagio; Boriani, Giuseppe

2016-10-01

We investigated the applicability of the Ventricular Capture Control (VCC) and Atrial Capture Control (ACC) algorithms for automatic management of cardiac stimulation featured by Biotronik pacemakers in a broad, unselected population of pacemaker recipients. Ventricular Capture Control and Atrial Capture Control were programmed to work at a maximum adapted output voltage as 4.8 V in consecutive recipients of Biotronik pacemakers. Ambulatory threshold measurements were made 1 and 12 months after pacemaker implant/replacement in all possible pacing/sensing configurations, and were compared with manual measurements. Among 542 patients aged 80 (73-85) years, 382 had a pacemaker implant and 160 a pacemaker replacement. Ventricular Capture Control could work at long term in 97% of patients irrespectively of pacing indication, lead type, and lead service life, performance being superior with discordant pacing/sensing configurations. Atrial Capture Control could work in 93% of patients at 4.8 V maximum adapted voltage and at any pulse width, regardless of pacing indication, lead type, and service life. At 12-month follow-up, a ventricular threshold increase ≥1.5 V had occurred in 4.4% of patients uneventfully owing to VCC functioning. Projected pacemaker longevity at 1 month was strongly correlated with the 12-month estimate, and exceeded 13 years in >60% of patients. These algorithms for automatic management of pacing output ensure patient safety in the event of a huge increase of pacing threshold, while enabling maximization of battery longevity. Their applicability is quite broad in an unselected pacemaker population irrespectively of lead choice and service of life. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2016. For permissions please email: journals.permissions@oup.com.

Policy Tree Optimization for Adaptive Management of Water Resources Systems

NASA Astrophysics Data System (ADS)

Herman, J. D.; Giuliani, M.

2016-12-01

Water resources systems must cope with irreducible uncertainty in supply and demand, requiring policy alternatives capable of adapting to a range of possible future scenarios. Recent studies have developed adaptive policies based on "signposts" or "tipping points", which are threshold values of indicator variables that signal a change in policy. However, there remains a need for a general method to optimize the choice of indicators and their threshold values in a way that is easily interpretable for decision makers. Here we propose a conceptual framework and computational algorithm to design adaptive policies as a tree structure (i.e., a hierarchical set of logical rules) using a simulation-optimization approach based on genetic programming. We demonstrate the approach using Folsom Reservoir, California as a case study, in which operating policies must balance the risk of both floods and droughts. Given a set of feature variables, such as reservoir level, inflow observations and forecasts, and time of year, the resulting policy defines the conditions under which flood control and water supply hedging operations should be triggered. Importantly, the tree-based rule sets are easy to interpret for decision making, and can be compared to historical operating policies to understand the adaptations needed under possible climate change scenarios. Several remaining challenges are discussed, including the empirical convergence properties of the method, and extensions to irreversible decisions such as infrastructure. Policy tree optimization, and corresponding open-source software, provide a generalizable, interpretable approach to designing adaptive policies under uncertainty for water resources systems.

The influence of different signal-to-background ratios on spatial resolution and F18-FDG-PET quantification using point spread function and time-of-flight reconstruction.

PubMed

Rogasch, Julian Mm; Hofheinz, Frank; Lougovski, Alexandr; Furth, Christian; Ruf, Juri; Großer, Oliver S; Mohnike, Konrad; Hass, Peter; Walke, Mathias; Amthauer, Holger; Steffen, Ingo G

2014-12-01

F18-fluorodeoxyglucose positron-emission tomography (FDG-PET) reconstruction algorithms can have substantial influence on quantitative image data used, e.g., for therapy planning or monitoring in oncology. We analyzed radial activity concentration profiles of differently reconstructed FDG-PET images to determine the influence of varying signal-to-background ratios (SBRs) on the respective spatial resolution, activity concentration distribution, and quantification (standardized uptake value [SUV], metabolic tumor volume [MTV]). Measurements were performed on a Siemens Biograph mCT 64 using a cylindrical phantom containing four spheres (diameter, 30 to 70 mm) filled with F18-FDG applying three SBRs (SBR1, 16:1; SBR2, 6:1; SBR3, 2:1). Images were reconstructed employing six algorithms (filtered backprojection [FBP], FBP + time-of-flight analysis [FBP + TOF], 3D-ordered subset expectation maximization [3D-OSEM], 3D-OSEM + TOF, point spread function [PSF], PSF + TOF). Spatial resolution was determined by fitting the convolution of the object geometry with a Gaussian point spread function to radial activity concentration profiles. MTV delineation was performed using fixed thresholds and semiautomatic background-adapted thresholding (ROVER, ABX, Radeberg, Germany). The pairwise Wilcoxon test revealed significantly higher spatial resolutions for PSF + TOF (up to 4.0 mm) compared to PSF, FBP, FBP + TOF, 3D-OSEM, and 3D-OSEM + TOF at all SBRs (each P < 0.05) with the highest differences for SBR1 decreasing to the lowest for SBR3. Edge elevations in radial activity profiles (Gibbs artifacts) were highest for PSF and PSF + TOF declining with decreasing SBR (PSF + TOF largest sphere; SBR1, 6.3%; SBR3, 2.7%). These artifacts induce substantial SUVmax overestimation compared to the reference SUV for PSF algorithms at SBR1 and SBR2 leading to substantial MTV underestimation in threshold-based segmentation. In contrast, both PSF algorithms provided the lowest deviation of SUVmean from reference SUV at SBR1 and SBR2. At high contrast, the PSF algorithms provided the highest spatial resolution and lowest SUVmean deviation from the reference SUV. In contrast, both algorithms showed the highest deviations in SUVmax and threshold-based MTV definition. At low contrast, all investigated reconstruction algorithms performed approximately equally. The use of PSF algorithms for quantitative PET data, e.g., for target volume definition or in serial PET studies, should be performed with caution - especially if comparing SUV of lesions with high and low contrasts.

Twelve automated thresholding methods for segmentation of PET images: a phantom study.

PubMed

Prieto, Elena; Lecumberri, Pablo; Pagola, Miguel; Gómez, Marisol; Bilbao, Izaskun; Ecay, Margarita; Peñuelas, Iván; Martí-Climent, Josep M

2012-06-21

Tumor volume delineation over positron emission tomography (PET) images is of great interest for proper diagnosis and therapy planning. However, standard segmentation techniques (manual or semi-automated) are operator dependent and time consuming while fully automated procedures are cumbersome or require complex mathematical development. The aim of this study was to segment PET images in a fully automated way by implementing a set of 12 automated thresholding algorithms, classical in the fields of optical character recognition, tissue engineering or non-destructive testing images in high-tech structures. Automated thresholding algorithms select a specific threshold for each image without any a priori spatial information of the segmented object or any special calibration of the tomograph, as opposed to usual thresholding methods for PET. Spherical (18)F-filled objects of different volumes were acquired on clinical PET/CT and on a small animal PET scanner, with three different signal-to-background ratios. Images were segmented with 12 automatic thresholding algorithms and results were compared with the standard segmentation reference, a threshold at 42% of the maximum uptake. Ridler and Ramesh thresholding algorithms based on clustering and histogram-shape information, respectively, provided better results that the classical 42%-based threshold (p < 0.05). We have herein demonstrated that fully automated thresholding algorithms can provide better results than classical PET segmentation tools.

Twelve automated thresholding methods for segmentation of PET images: a phantom study

NASA Astrophysics Data System (ADS)

Prieto, Elena; Lecumberri, Pablo; Pagola, Miguel; Gómez, Marisol; Bilbao, Izaskun; Ecay, Margarita; Peñuelas, Iván; Martí-Climent, Josep M.

2012-06-01

Tumor volume delineation over positron emission tomography (PET) images is of great interest for proper diagnosis and therapy planning. However, standard segmentation techniques (manual or semi-automated) are operator dependent and time consuming while fully automated procedures are cumbersome or require complex mathematical development. The aim of this study was to segment PET images in a fully automated way by implementing a set of 12 automated thresholding algorithms, classical in the fields of optical character recognition, tissue engineering or non-destructive testing images in high-tech structures. Automated thresholding algorithms select a specific threshold for each image without any a priori spatial information of the segmented object or any special calibration of the tomograph, as opposed to usual thresholding methods for PET. Spherical 18F-filled objects of different volumes were acquired on clinical PET/CT and on a small animal PET scanner, with three different signal-to-background ratios. Images were segmented with 12 automatic thresholding algorithms and results were compared with the standard segmentation reference, a threshold at 42% of the maximum uptake. Ridler and Ramesh thresholding algorithms based on clustering and histogram-shape information, respectively, provided better results that the classical 42%-based threshold (p < 0.05). We have herein demonstrated that fully automated thresholding algorithms can provide better results than classical PET segmentation tools.

Robust dynamic myocardial perfusion CT deconvolution for accurate residue function estimation via adaptive-weighted tensor total variation regularization: a preclinical study.

PubMed

Zeng, Dong; Gong, Changfei; Bian, Zhaoying; Huang, Jing; Zhang, Xinyu; Zhang, Hua; Lu, Lijun; Niu, Shanzhou; Zhang, Zhang; Liang, Zhengrong; Feng, Qianjin; Chen, Wufan; Ma, Jianhua

2016-11-21

Dynamic myocardial perfusion computed tomography (MPCT) is a promising technique for quick diagnosis and risk stratification of coronary artery disease. However, one major drawback of dynamic MPCT imaging is the heavy radiation dose to patients due to its dynamic image acquisition protocol. In this work, to address this issue, we present a robust dynamic MPCT deconvolution algorithm via adaptive-weighted tensor total variation (AwTTV) regularization for accurate residue function estimation with low-mA s data acquisitions. For simplicity, the presented method is termed 'MPD-AwTTV'. More specifically, the gains of the AwTTV regularization over the original tensor total variation regularization are from the anisotropic edge property of the sequential MPCT images. To minimize the associative objective function we propose an efficient iterative optimization strategy with fast convergence rate in the framework of an iterative shrinkage/thresholding algorithm. We validate and evaluate the presented algorithm using both digital XCAT phantom and preclinical porcine data. The preliminary experimental results have demonstrated that the presented MPD-AwTTV deconvolution algorithm can achieve remarkable gains in noise-induced artifact suppression, edge detail preservation, and accurate flow-scaled residue function and MPHM estimation as compared with the other existing deconvolution algorithms in digital phantom studies, and similar gains can be obtained in the porcine data experiment.

Robust dynamic myocardial perfusion CT deconvolution for accurate residue function estimation via adaptive-weighted tensor total variation regularization: a preclinical study

NASA Astrophysics Data System (ADS)

Zeng, Dong; Gong, Changfei; Bian, Zhaoying; Huang, Jing; Zhang, Xinyu; Zhang, Hua; Lu, Lijun; Niu, Shanzhou; Zhang, Zhang; Liang, Zhengrong; Feng, Qianjin; Chen, Wufan; Ma, Jianhua

2016-11-01

Dynamic myocardial perfusion computed tomography (MPCT) is a promising technique for quick diagnosis and risk stratification of coronary artery disease. However, one major drawback of dynamic MPCT imaging is the heavy radiation dose to patients due to its dynamic image acquisition protocol. In this work, to address this issue, we present a robust dynamic MPCT deconvolution algorithm via adaptive-weighted tensor total variation (AwTTV) regularization for accurate residue function estimation with low-mA s data acquisitions. For simplicity, the presented method is termed ‘MPD-AwTTV’. More specifically, the gains of the AwTTV regularization over the original tensor total variation regularization are from the anisotropic edge property of the sequential MPCT images. To minimize the associative objective function we propose an efficient iterative optimization strategy with fast convergence rate in the framework of an iterative shrinkage/thresholding algorithm. We validate and evaluate the presented algorithm using both digital XCAT phantom and preclinical porcine data. The preliminary experimental results have demonstrated that the presented MPD-AwTTV deconvolution algorithm can achieve remarkable gains in noise-induced artifact suppression, edge detail preservation, and accurate flow-scaled residue function and MPHM estimation as compared with the other existing deconvolution algorithms in digital phantom studies, and similar gains can be obtained in the porcine data experiment.

A community detection algorithm based on structural similarity

NASA Astrophysics Data System (ADS)

Guo, Xuchao; Hao, Xia; Liu, Yaqiong; Zhang, Li; Wang, Lu

2017-09-01

In order to further improve the efficiency and accuracy of community detection algorithm, a new algorithm named SSTCA (the community detection algorithm based on structural similarity with threshold) is proposed. In this algorithm, the structural similarities are taken as the weights of edges, and the threshold k is considered to remove multiple edges whose weights are less than the threshold, and improve the computational efficiency. Tests were done on the Zachary’s network, Dolphins’ social network and Football dataset by the proposed algorithm, and compared with GN and SSNCA algorithm. The results show that the new algorithm is superior to other algorithms in accuracy for the dense networks and the operating efficiency is improved obviously.

Twofold processing for denoising ultrasound medical images.

PubMed

Kishore, P V V; Kumar, K V V; Kumar, D Anil; Prasad, M V D; Goutham, E N D; Rahul, R; Krishna, C B S Vamsi; Sandeep, Y

2015-01-01

Ultrasound medical (US) imaging non-invasively pictures inside of a human body for disease diagnostics. Speckle noise attacks ultrasound images degrading their visual quality. A twofold processing algorithm is proposed in this work to reduce this multiplicative speckle noise. First fold used block based thresholding, both hard (BHT) and soft (BST), on pixels in wavelet domain with 8, 16, 32 and 64 non-overlapping block sizes. This first fold process is a better denoising method for reducing speckle and also inducing object of interest blurring. The second fold process initiates to restore object boundaries and texture with adaptive wavelet fusion. The degraded object restoration in block thresholded US image is carried through wavelet coefficient fusion of object in original US mage and block thresholded US image. Fusion rules and wavelet decomposition levels are made adaptive for each block using gradient histograms with normalized differential mean (NDF) to introduce highest level of contrast between the denoised pixels and the object pixels in the resultant image. Thus the proposed twofold methods are named as adaptive NDF block fusion with hard and soft thresholding (ANBF-HT and ANBF-ST). The results indicate visual quality improvement to an interesting level with the proposed twofold processing, where the first fold removes noise and second fold restores object properties. Peak signal to noise ratio (PSNR), normalized cross correlation coefficient (NCC), edge strength (ES), image quality Index (IQI) and structural similarity index (SSIM), measure the quantitative quality of the twofold processing technique. Validation of the proposed method is done by comparing with anisotropic diffusion (AD), total variational filtering (TVF) and empirical mode decomposition (EMD) for enhancement of US images. The US images are provided by AMMA hospital radiology labs at Vijayawada, India.

An adaptive staircase procedure for the E-Prime programming environment.

PubMed

Hairston, W David; Maldjian, Joseph A

2009-01-01

Many studies need to determine a subject's threshold for a given task. This can be achieved efficiently using an adaptive staircase procedure. While the logic and algorithms for staircases have been well established, the few pre-programmed routines currently available to researchers require at least moderate programming experience to integrate into new paradigms and experimental settings. Here, we describe a freely distributed routine developed for the E-Prime programming environment that can be easily integrated into any experimental protocol with only a basic understanding of E-Prime. An example experiment (visual temporal-order-judgment task) where subjects report the order of occurrence of two circles illustrates the behavior and consistency of the routine.

A fuzzy optimal threshold technique for medical images

NASA Astrophysics Data System (ADS)

Thirupathi Kannan, Balaji; Krishnasamy, Krishnaveni; Pradeep Kumar Kenny, S.

2012-01-01

A new fuzzy based thresholding method for medical images especially cervical cytology images having blob and mosaic structures is proposed in this paper. Many existing thresholding algorithms may segment either blob or mosaic images but there aren't any single algorithm that can do both. In this paper, an input cervical cytology image is binarized, preprocessed and the pixel value with minimum Fuzzy Gaussian Index is identified as an optimal threshold value and used for segmentation. The proposed technique is tested on various cervical cytology images having blob or mosaic structures, compared with various existing algorithms and proved better than the existing algorithms.

Unsupervised texture image segmentation by improved neural network ART2

NASA Technical Reports Server (NTRS)

Wang, Zhiling; Labini, G. Sylos; Mugnuolo, R.; Desario, Marco

1994-01-01

We here propose a segmentation algorithm of texture image for a computer vision system on a space robot. An improved adaptive resonance theory (ART2) for analog input patterns is adapted to classify the image based on a set of texture image features extracted by a fast spatial gray level dependence method (SGLDM). The nonlinear thresholding functions in input layer of the neural network have been constructed by two parts: firstly, to reduce the effects of image noises on the features, a set of sigmoid functions is chosen depending on the types of the feature; secondly, to enhance the contrast of the features, we adopt fuzzy mapping functions. The cluster number in output layer can be increased by an autogrowing mechanism constantly when a new pattern happens. Experimental results and original or segmented pictures are shown, including the comparison between this approach and K-means algorithm. The system written in C language is performed on a SUN-4/330 sparc-station with an image board IT-150 and a CCD camera.

Evolving autonomous learning in cognitive networks.

PubMed

Sheneman, Leigh; Hintze, Arend

2017-12-01

There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. These methods have been previously combined, particularly in artificial neural networks using an external objective feedback mechanism. We adapt this approach to Markov Brains, which are evolvable networks of probabilistic and deterministic logic gates. Prior to this work MB could only adapt from one generation to the other, so we introduce feedback gates which augment their ability to learn during their lifetime. We show that Markov Brains can incorporate these feedback gates in such a way that they do not rely on an external objective feedback signal, but instead can generate internal feedback that is then used to learn. This results in a more biologically accurate model of the evolution of learning, which will enable us to study the interplay between evolution and learning and could be another step towards autonomously learning machines.

Directional hearing aid using hybrid adaptive beamformer (HAB) and binaural ITE array

NASA Astrophysics Data System (ADS)

Shaw, Scott T.; Larow, Andy J.; Gibian, Gary L.; Sherlock, Laguinn P.; Schulein, Robert

2002-05-01

A directional hearing aid algorithm called the Hybrid Adaptive Beamformer (HAB), developed for NIH/NIA, can be applied to many different microphone array configurations. In this project the HAB algorithm was applied to a new array employing in-the-ear microphones at each ear (HAB-ITE), to see if previous HAB performance could be achieved with a more cosmetically acceptable package. With diotic output, the average benefit in threshold SNR was 10.9 dB for three HoH and 11.7 dB for five normal-hearing subjects. These results are slightly better than previous results of equivalent tests with a 3-in. array. With an innovative binaural fitting, a small benefit beyond that provided by diotic adaptive beamforming was observed: 12.5 dB for HoH and 13.3 dB for normal-hearing subjects, a 1.6 dB improvement over the diotic presentation. Subjectively, the binaural fitting preserved binaural hearing abilities, giving the user a sense of space, and providing left-right localization. Thus the goal of creating an adaptive beamformer that simultaneously provides excellent noise reduction and binaural hearing was achieved. Further work remains before the HAB-ITE can be incorporated into a real product, optimizing binaural adaptive beamforming, and integrating the concept with other technologies to produce a viable product prototype. [Work supported by NIH/NIDCD.

Visual Tracking via Sparse and Local Linear Coding.

PubMed

Wang, Guofeng; Qin, Xueying; Zhong, Fan; Liu, Yue; Li, Hongbo; Peng, Qunsheng; Yang, Ming-Hsuan

2015-11-01

The state search is an important component of any object tracking algorithm. Numerous algorithms have been proposed, but stochastic sampling methods (e.g., particle filters) are arguably one of the most effective approaches. However, the discretization of the state space complicates the search for the precise object location. In this paper, we propose a novel tracking algorithm that extends the state space of particle observations from discrete to continuous. The solution is determined accurately via iterative linear coding between two convex hulls. The algorithm is modeled by an optimal function, which can be efficiently solved by either convex sparse coding or locality constrained linear coding. The algorithm is also very flexible and can be combined with many generic object representations. Thus, we first use sparse representation to achieve an efficient searching mechanism of the algorithm and demonstrate its accuracy. Next, two other object representation models, i.e., least soft-threshold squares and adaptive structural local sparse appearance, are implemented with improved accuracy to demonstrate the flexibility of our algorithm. Qualitative and quantitative experimental results demonstrate that the proposed tracking algorithm performs favorably against the state-of-the-art methods in dynamic scenes.

ADAPTIVE THRESHOLD LOGIC.

DTIC Science & Technology

The design and construction of a 16 variable threshold logic gate with adaptable weights is described. The operating characteristics of tape wound...and sizes as well as for the 16 input adaptive threshold logic gate. (Author)

Textual and visual content-based anti-phishing: a Bayesian approach.

PubMed

Zhang, Haijun; Liu, Gang; Chow, Tommy W S; Liu, Wenyin

2011-10-01

A novel framework using a Bayesian approach for content-based phishing web page detection is presented. Our model takes into account textual and visual contents to measure the similarity between the protected web page and suspicious web pages. A text classifier, an image classifier, and an algorithm fusing the results from classifiers are introduced. An outstanding feature of this paper is the exploration of a Bayesian model to estimate the matching threshold. This is required in the classifier for determining the class of the web page and identifying whether the web page is phishing or not. In the text classifier, the naive Bayes rule is used to calculate the probability that a web page is phishing. In the image classifier, the earth mover's distance is employed to measure the visual similarity, and our Bayesian model is designed to determine the threshold. In the data fusion algorithm, the Bayes theory is used to synthesize the classification results from textual and visual content. The effectiveness of our proposed approach was examined in a large-scale dataset collected from real phishing cases. Experimental results demonstrated that the text classifier and the image classifier we designed deliver promising results, the fusion algorithm outperforms either of the individual classifiers, and our model can be adapted to different phishing cases. © 2011 IEEE

Entropy-guided switching trimmed mean deviation-boosted anisotropic diffusion filter

NASA Astrophysics Data System (ADS)

Nnolim, Uche A.

2016-07-01

An effective anisotropic diffusion (AD) mean filter variant is proposed for filtering of salt-and-pepper impulse noise. The implemented filter is robust to impulse noise ranging from low to high density levels. The algorithm involves a switching scheme in addition to utilizing the unsymmetric trimmed mean/median deviation to filter image noise while greatly preserving image edges, regardless of impulse noise density (ND). It operates with threshold parameters selected manually or adaptively estimated from the image statistics. It is further combined with the partial differential equations (PDE)-based AD for edge preservation at high NDs to enhance the properties of the trimmed mean filter. Based on experimental results, the proposed filter easily and consistently outperforms the median filter and its other variants ranging from simple to complex filter structures, especially the known PDE-based variants. In addition, the switching scheme and threshold calculation enables the filter to avoid smoothing an uncorrupted image, and filtering is activated only when impulse noise is present. Ultimately, the particular properties of the filter make its combination with the AD algorithm a unique and powerful edge-preservation smoothing filter at high-impulse NDs.

An adaptive surface filter for airborne laser scanning point clouds by means of regularization and bending energy

NASA Astrophysics Data System (ADS)

Hu, Han; Ding, Yulin; Zhu, Qing; Wu, Bo; Lin, Hui; Du, Zhiqiang; Zhang, Yeting; Zhang, Yunsheng

2014-06-01

The filtering of point clouds is a ubiquitous task in the processing of airborne laser scanning (ALS) data; however, such filtering processes are difficult because of the complex configuration of the terrain features. The classical filtering algorithms rely on the cautious tuning of parameters to handle various landforms. To address the challenge posed by the bundling of different terrain features into a single dataset and to surmount the sensitivity of the parameters, in this study, we propose an adaptive surface filter (ASF) for the classification of ALS point clouds. Based on the principle that the threshold should vary in accordance to the terrain smoothness, the ASF embeds bending energy, which quantitatively depicts the local terrain structure to self-adapt the filter threshold automatically. The ASF employs a step factor to control the data pyramid scheme in which the processing window sizes are reduced progressively, and the ASF gradually interpolates thin plate spline surfaces toward the ground with regularization to handle noise. Using the progressive densification strategy, regularization and self-adaption, both performance improvement and resilience to parameter tuning are achieved. When tested against the benchmark datasets provided by ISPRS, the ASF performs the best in comparison with all other filtering methods, yielding an average total error of 2.85% when optimized and 3.67% when using the same parameter set.

Influence of aging on thermal and vibratory thresholds of quantitative sensory testing.

PubMed

Lin, Yea-Huey; Hsieh, Song-Chou; Chao, Chi-Chao; Chang, Yang-Chyuan; Hsieh, Sung-Tsang

2005-09-01

Quantitative sensory testing has become a common approach to evaluate thermal and vibratory thresholds in various types of neuropathies. To understand the effect of aging on sensory perception, we measured warm, cold, and vibratory thresholds by performing quantitative sensory testing on a population of 484 normal subjects (175 males and 309 females), aged 48.61 +/- 14.10 (range 20-86) years. Sensory thresholds of the hand and foot were measured with two algorithms: the method of limits (Limits) and the method of level (Level). Thresholds measured by Limits are reaction-time-dependent, while those measured by Level are independent of reaction time. In addition, we explored (1) the correlations of thresholds between these two algorithms, (2) the effect of age on differences in thresholds between algorithms, and (3) differences in sensory thresholds between the two test sites. Age was consistently and significantly correlated with sensory thresholds of all tested modalities measured by both algorithms on multivariate regression analysis compared with other factors, including gender, body height, body weight, and body mass index. When thresholds were plotted against age, slopes differed between sensory thresholds of the hand and those of the foot: for the foot, slopes were steeper compared with those for the hand for each sensory modality. Sensory thresholds of both test sites measured by Level were highly correlated with those measured by Limits, and thresholds measured by Limits were higher than those measured by Level. Differences in sensory thresholds between the two algorithms were also correlated with age: thresholds of the foot were higher than those of the hand for each sensory modality. This difference in thresholds (measured with both Level and Limits) between the hand and foot was also correlated with age. These findings suggest that age is the most significant factor in determining sensory thresholds compared with the other factors of gender and anthropometric parameters, and this provides a foundation for investigating the neurobiologic significance of aging on the processing of sensory stimuli.

The Hilbert-Huang Transform-Based Denoising Method for the TEM Response of a PRBS Source Signal

NASA Astrophysics Data System (ADS)

Hai, Li; Guo-qiang, Xue; Pan, Zhao; Hua-sen, Zhong; Khan, Muhammad Younis

2016-08-01

The denoising process is critical in processing transient electromagnetic (TEM) sounding data. For the full waveform pseudo-random binary sequences (PRBS) response, an inadequate noise estimation may result in an erroneous interpretation. We consider the Hilbert-Huang transform (HHT) and its application to suppress the noise in the PRBS response. The focus is on the thresholding scheme to suppress the noise and the analysis of the signal based on its Hilbert time-frequency representation. The method first decomposes the signal into the intrinsic mode function, and then, inspired by the thresholding scheme in wavelet analysis; an adaptive and interval thresholding is conducted to set to zero all the components in intrinsic mode function which are lower than a threshold related to the noise level. The algorithm is based on the characteristic of the PRBS response. The HHT-based denoising scheme is tested on the synthetic and field data with the different noise levels. The result shows that the proposed method has a good capability in denoising and detail preservation.

The Greenhouse Gas Climate Change Initiative (GHG-CCI): comparative validation of GHG-CCI SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT CO2 and CH4 retrieval algorithm products with measurements from the TCCON

NASA Astrophysics Data System (ADS)

Dils, B.; Buchwitz, M.; Reuter, M.; Schneising, O.; Boesch, H.; Parker, R.; Guerlet, S.; Aben, I.; Blumenstock, T.; Burrows, J. P.; Butz, A.; Deutscher, N. M.; Frankenberg, C.; Hase, F.; Hasekamp, O. P.; Heymann, J.; De Mazière, M.; Notholt, J.; Sussmann, R.; Warneke, T.; Griffith, D.; Sherlock, V.; Wunch, D.

2014-06-01

Column-averaged dry-air mole fractions of carbon dioxide and methane have been retrieved from spectra acquired by the TANSO-FTS (Thermal And Near-infrared Sensor for carbon Observations-Fourier Transform Spectrometer) and SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography) instruments on board GOSAT (Greenhouse gases Observing SATellite) and ENVISAT (ENVIronmental SATellite), respectively, using a range of European retrieval algorithms. These retrievals have been compared with data from ground-based high-resolution Fourier transform spectrometers (FTSs) from the Total Carbon Column Observing Network (TCCON). The participating algorithms are the weighting function modified differential optical absorption spectroscopy (DOAS) algorithm (WFMD, University of Bremen), the Bremen optimal estimation DOAS algorithm (BESD, University of Bremen), the iterative maximum a posteriori DOAS (IMAP, Jet Propulsion Laboratory (JPL) and Netherlands Institute for Space Research algorithm (SRON)), the proxy and full-physics versions of SRON's RemoTeC algorithm (SRPR and SRFP, respectively) and the proxy and full-physics versions of the University of Leicester's adaptation of the OCO (Orbiting Carbon Observatory) algorithm (OCPR and OCFP, respectively). The goal of this algorithm inter-comparison was to identify strengths and weaknesses of the various so-called round- robin data sets generated with the various algorithms so as to determine which of the competing algorithms would proceed to the next round of the European Space Agency's (ESA) Greenhouse Gas Climate Change Initiative (GHG-CCI) project, which is the generation of the so-called Climate Research Data Package (CRDP), which is the first version of the Essential Climate Variable (ECV) "greenhouse gases" (GHGs). For XCO2, all algorithms reach the precision requirements for inverse modelling (< 8 ppm), with only WFMD having a lower precision (4.7 ppm) than the other algorithm products (2.4-2.5 ppm). When looking at the seasonal relative accuracy (SRA, variability of the bias in space and time), none of the algorithms have reached the demanding < 0.5 ppm threshold. For XCH4, the precision for both SCIAMACHY products (50.2 ppb for IMAP and 76.4 ppb for WFMD) fails to meet the < 34 ppb threshold for inverse modelling, but note that this work focusses on the period after the 2005 SCIAMACHY detector degradation. The GOSAT XCH4 precision ranges between 18.1 and 14.0 ppb. Looking at the SRA, all GOSAT algorithm products reach the < 10 ppm threshold (values ranging between 5.4 and 6.2 ppb). For SCIAMACHY, IMAP and WFMD have a SRA of 17.2 and 10.5 ppb, respectively.

Adaptive re-tracking algorithm for retrieval of water level variations and wave heights from satellite altimetry data for middle-sized inland water bodies

NASA Astrophysics Data System (ADS)

Troitskaya, Yuliya; Lebedev, Sergey; Soustova, Irina; Rybushkina, Galina; Papko, Vladislav; Baidakov, Georgy; Panyutin, Andrey

One of the recent applications of satellite altimetry originally designed for measurements of the sea level [1] is associated with remote investigation of the water level of inland waters: lakes, rivers, reservoirs [2-7]. The altimetry data re-tracking algorithms developed for open ocean conditions (e.g. Ocean-1,2) [1] often cannot be used in these cases, since the radar return is significantly contaminated by reflection from the land. The problem of minimization of errors in the water level retrieval for inland waters from altimetry measurements can be resolved by re-tracking satellite altimetry data. Recently, special re-tracking algorithms have been actively developed for re-processing altimetry data in the coastal zone when reflection from land strongly affects echo shapes: threshold re-tracking, The other methods of re-tracking (threshold re-tracking, beta-re-tracking, improved threshold re-tracking) were developed in [9-11]. The latest development in this field is PISTACH product [12], in which retracking bases on the classification of typical forms of telemetric waveforms in the coastal zones and inland water bodies. In this paper a novel method of regional adaptive re-tracking based on constructing a theoretical model describing the formation of telemetric waveforms by reflection from the piecewise constant model surface corresponding to the geography of the region is considered. It was proposed in [13, 14], where the algorithm for assessing water level in inland water bodies and in the coastal zone of the ocean with an error of about 10-15 cm was constructed. The algorithm includes four consecutive steps: - constructing a local piecewise model of a reflecting surface in the neighbourhood of the reservoir; - solving a direct problem by calculating the reflected waveforms within the framework of the model; - imposing restrictions and validity criteria for the algorithm based on waveform modelling; - solving the inverse problem by retrieving a tracking point by the improved threshold algorithm. The possibility of determination of significant wave height (SWH) in the lakes through a two-step adaptive retracking is also studied. Calculation of the parameter SWH for Gorky Reservoir from May 2010 to March 2014 showed the anomalously high values of SWH, derived from altimetry data [15], which means that the calibration of this SWH for inland waters is required. Calibration ground measurements were performed at Gorky reservoir in 2011-2013, when wave height, wind speed and air temperature were collected by equipment placed on a buoy [15] collocated with Jason-1 and Jason-2 altimetry data acquisition. The results obtained on the basis of standard algorithm and method for adaptive re-tracking at Rybinsk , Gorky , Kuibyshev , Saratov and Volgograd reservoirs and middle-sized lakes of Russia: Chany, Segozero, Hanko, Oneko, Beloye, water areas of which are intersected by the Jason-1,2 tracks, were compared and their correlation with the observed data of hydrological stations in reservoirs and lakes was investigated. It was noted that the Volgograd reservoir regional re-tracking to determine the water level , while the standard GDR data are practically absent. REFERENCES [1] AVISO/Altimetry. User Handbook. Merged TOPEX/ POSEIDON Products. Edition 3.0. AVISO. Toulouse., 1996. [2] C.M. Birkett et al., “Surface water dynamics in the Amazon Basin: Application of satellite radar altimetry,” J. Geophys. Res., vol. 107, pp. 8059, 2002. [3] G. Brown, “The average impulse response of a rough surface and its applications,” IEEE Trans. Antennas Propagat., vol. 25, pp. 67-74, 1977. [4] I.O. Campos et al., “Temporal variations of river basin waters from Topex/Poseidon satellite altimetry. Application to the Amazon basin,” Earth and Planetary Sciences, vol. 333, pp. 633-643, 2001. [5] A.V. Kouraev et al., “Ob’ river discharge from TOPEX/Poseidon satellite altimetry (1992-2002),” Rem. Sens. Environ., vol. 93, pp. 238-245, 2004. [6] S.A. Lebedev, and A.G. Kostianoy, Satellite Altimetry of the Caspian Sea. Moscow : MORE, [in Russian], 2005. [7] P. A. M. Berry et al., “Global inland water monitoring from multi-mission altimetry”, Geophys. Res. Lett., vol. 32, pp. L16401, 2005. [8] S. Calmant, and F. Seyler, “Continental surface waters from satellite altimetry,” Geosciences C.R., vol. 338, pp. 1113-1122, 2006. [9] C. H. Davis, “A robust threshold retracking algorithm for measuring ice sheet surface elevation change from satellite radar altimeters,” IEEE Trans. Geosci. Remote Sens., vol. 35, pp. 974-979, 1997. [10] X. Deng, and W. E. Featherstone, “A coastal retracking system for satellite radar altimeter waveforms: Application to ERS-2 around Australia,” J. Geophys. Res., vol. 111, pp. C06012, 2006. [11] J. Guo et al., “Lake level variations monitored with satellite altimetry waveform retracking,” IEEE J. Sel. Topics Appl. Earth Obs., vol. 2(2), pp. 80-86, 2009. [12] Mercier F., Rosmorduc V., Carrere L., Thibaut P., Coastal and Hydrology Altimetry product (PISTACH) handbook. Version 1.0. 2010. [13] Yu.Troitskaya et al., “Satellite altimetry of inland water bodies,” Water Resources, vol. 39(2), pp.184-199, 2012. [14] Yu.Troitskaya et al., “Adaptive retracking of Jason-1 altimetry data for inland waters: the example of the Gorky Reservoir”, Int. J. Rem. Sens., vol. 33, pp. 7559-7578, 2012. [15] Yu.Troitskaya et al., , "Adaptive retracking of Jason-1,2 satellite altimetry data for the Volga river reservoirs ," IEEE J. Sel. Topics Appl. Earth Obs., issue 99, 2013, doi: 10.1109/JSTARS.2013.2267092.

High precision automated face localization in thermal images: oral cancer dataset as test case

NASA Astrophysics Data System (ADS)

Chakraborty, M.; Raman, S. K.; Mukhopadhyay, S.; Patsa, S.; Anjum, N.; Ray, J. G.

2017-02-01

Automated face detection is the pivotal step in computer vision aided facial medical diagnosis and biometrics. This paper presents an automatic, subject adaptive framework for accurate face detection in the long infrared spectrum on our database for oral cancer detection consisting of malignant, precancerous and normal subjects of varied age group. Previous works on oral cancer detection using Digital Infrared Thermal Imaging(DITI) reveals that patients and normal subjects differ significantly in their facial thermal distribution. Therefore, it is a challenging task to formulate a completely adaptive framework to veraciously localize face from such a subject specific modality. Our model consists of first extracting the most probable facial regions by minimum error thresholding followed by ingenious adaptive methods to leverage the horizontal and vertical projections of the segmented thermal image. Additionally, the model incorporates our domain knowledge of exploiting temperature difference between strategic locations of the face. To our best knowledge, this is the pioneering work on detecting faces in thermal facial images comprising both patients and normal subjects. Previous works on face detection have not specifically targeted automated medical diagnosis; face bounding box returned by those algorithms are thus loose and not apt for further medical automation. Our algorithm significantly outperforms contemporary face detection algorithms in terms of commonly used metrics for evaluating face detection accuracy. Since our method has been tested on challenging dataset consisting of both patients and normal subjects of diverse age groups, it can be seamlessly adapted in any DITI guided facial healthcare or biometric applications.

Surgical motion characterization in simulated needle insertion procedures

NASA Astrophysics Data System (ADS)

Holden, Matthew S.; Ungi, Tamas; Sargent, Derek; McGraw, Robert C.; Fichtinger, Gabor

2012-02-01

PURPOSE: Evaluation of surgical performance in image-guided needle insertions is of emerging interest, to both promote patient safety and improve the efficiency and effectiveness of training. The purpose of this study was to determine if a Markov model-based algorithm can more accurately segment a needle-based surgical procedure into its five constituent tasks than a simple threshold-based algorithm. METHODS: Simulated needle trajectories were generated with known ground truth segmentation by a synthetic procedural data generator, with random noise added to each degree of freedom of motion. The respective learning algorithms were trained, and then tested on different procedures to determine task segmentation accuracy. In the threshold-based algorithm, a change in tasks was detected when the needle crossed a position/velocity threshold. In the Markov model-based algorithm, task segmentation was performed by identifying the sequence of Markov models most likely to have produced the series of observations. RESULTS: For amplitudes of translational noise greater than 0.01mm, the Markov model-based algorithm was significantly more accurate in task segmentation than the threshold-based algorithm (82.3% vs. 49.9%, p<0.001 for amplitude 10.0mm). For amplitudes less than 0.01mm, the two algorithms produced insignificantly different results. CONCLUSION: Task segmentation of simulated needle insertion procedures was improved by using a Markov model-based algorithm as opposed to a threshold-based algorithm for procedures involving translational noise.

Text extraction via an edge-bounded averaging and a parametric character model

NASA Astrophysics Data System (ADS)

Fan, Jian

2003-01-01

We present a deterministic text extraction algorithm that relies on three basic assumptions: color/luminance uniformity of the interior region, closed boundaries of sharp edges and the consistency of local contrast. The algorithm is basically independent of the character alphabet, text layout, font size and orientation. The heart of this algorithm is an edge-bounded averaging for the classification of smooth regions that enhances robustness against noise without sacrificing boundary accuracy. We have also developed a verification process to clean up the residue of incoherent segmentation. Our framework provides a symmetric treatment for both regular and inverse text. We have proposed three heuristics for identifying the type of text from a cluster consisting of two types of pixel aggregates. Finally, we have demonstrated the advantages of the proposed algorithm over adaptive thresholding and block-based clustering methods in terms of boundary accuracy, segmentation coherency, and capability to identify inverse text and separate characters from background patches.

Application of image recognition algorithms for statistical description of nano- and microstructured surfaces

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mărăscu, V.; Dinescu, G.; Faculty of Physics, University of Bucharest, 405 Atomistilor Street, Bucharest-Magurele

In this paper we propose a statistical approach for describing the self-assembling of sub-micronic polystyrene beads on silicon surfaces, as well as the evolution of surface topography due to plasma treatments. Algorithms for image recognition are used in conjunction with Scanning Electron Microscopy (SEM) imaging of surfaces. In a first step, greyscale images of the surface covered by the polystyrene beads are obtained. Further, an adaptive thresholding method was applied for obtaining binary images. The next step consisted in automatic identification of polystyrene beads dimensions, by using Hough transform algorithm, according to beads radius. In order to analyze the uniformitymore » of the self–assembled polystyrene beads, the squared modulus of 2-dimensional Fast Fourier Transform (2- D FFT) was applied. By combining these algorithms we obtain a powerful and fast statistical tool for analysis of micro and nanomaterials with aspect features regularly distributed on surface upon SEM examination.« less

Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model.

PubMed

Hsieh, Chia-Yeh; Liu, Kai-Chun; Huang, Chih-Ning; Chu, Woei-Chyn; Chan, Chia-Tai

2017-02-08

Falls are the primary cause of accidents for the elderly in the living environment. Reducing hazards in the living environment and performing exercises for training balance and muscles are the common strategies for fall prevention. However, falls cannot be avoided completely; fall detection provides an alarm that can decrease injuries or death caused by the lack of rescue. The automatic fall detection system has opportunities to provide real-time emergency alarms for improving the safety and quality of home healthcare services. Two common technical challenges are also tackled in order to provide a reliable fall detection algorithm, including variability and ambiguity. We propose a novel hierarchical fall detection algorithm involving threshold-based and knowledge-based approaches to detect a fall event. The threshold-based approach efficiently supports the detection and identification of fall events from continuous sensor data. A multiphase fall model is utilized, including free fall, impact, and rest phases for the knowledge-based approach, which identifies fall events and has the potential to deal with the aforementioned technical challenges of a fall detection system. Seven kinds of falls and seven types of daily activities arranged in an experiment are used to explore the performance of the proposed fall detection algorithm. The overall performances of the sensitivity, specificity, precision, and accuracy using a knowledge-based algorithm are 99.79%, 98.74%, 99.05% and 99.33%, respectively. The results show that the proposed novel hierarchical fall detection algorithm can cope with the variability and ambiguity of the technical challenges and fulfill the reliability, adaptability, and flexibility requirements of an automatic fall detection system with respect to the individual differences.

Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model

PubMed Central

Hsieh, Chia-Yeh; Liu, Kai-Chun; Huang, Chih-Ning; Chu, Woei-Chyn; Chan, Chia-Tai

2017-01-01

Falls are the primary cause of accidents for the elderly in the living environment. Reducing hazards in the living environment and performing exercises for training balance and muscles are the common strategies for fall prevention. However, falls cannot be avoided completely; fall detection provides an alarm that can decrease injuries or death caused by the lack of rescue. The automatic fall detection system has opportunities to provide real-time emergency alarms for improving the safety and quality of home healthcare services. Two common technical challenges are also tackled in order to provide a reliable fall detection algorithm, including variability and ambiguity. We propose a novel hierarchical fall detection algorithm involving threshold-based and knowledge-based approaches to detect a fall event. The threshold-based approach efficiently supports the detection and identification of fall events from continuous sensor data. A multiphase fall model is utilized, including free fall, impact, and rest phases for the knowledge-based approach, which identifies fall events and has the potential to deal with the aforementioned technical challenges of a fall detection system. Seven kinds of falls and seven types of daily activities arranged in an experiment are used to explore the performance of the proposed fall detection algorithm. The overall performances of the sensitivity, specificity, precision, and accuracy using a knowledge-based algorithm are 99.79%, 98.74%, 99.05% and 99.33%, respectively. The results show that the proposed novel hierarchical fall detection algorithm can cope with the variability and ambiguity of the technical challenges and fulfill the reliability, adaptability, and flexibility requirements of an automatic fall detection system with respect to the individual differences. PMID:28208694

Surgical wound segmentation based on adaptive threshold edge detection and genetic algorithm

NASA Astrophysics Data System (ADS)

Shih, Hsueh-Fu; Ho, Te-Wei; Hsu, Jui-Tse; Chang, Chun-Che; Lai, Feipei; Wu, Jin-Ming

2017-02-01

Postsurgical wound care has a great impact on patients' prognosis. It often takes few days, even few weeks, for the wound to stabilize, which incurs a great cost of health care and nursing resources. To assess the wound condition and diagnosis, it is important to segment out the wound region for further analysis. However, the scenario of this strategy often consists of complicated background and noise. In this study, we propose a wound segmentation algorithm based on Canny edge detector and genetic algorithm with an unsupervised evaluation function. The results were evaluated by the 112 clinical images, and 94.3% of images were correctly segmented. The judgment was based on the evaluation of experimented medical doctors. This capability to extract complete wound regions, makes it possible to conduct further image analysis such as intelligent recovery evaluation and automatic infection requirements.

Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination

PubMed Central

Jeon, Hong Y.; Tian, Lei F.; Zhu, Heping

2011-01-01

An image processing algorithm for detecting individual weeds was developed and evaluated. Weed detection processes included were normalized excessive green conversion, statistical threshold value estimation, adaptive image segmentation, median filter, morphological feature calculation and Artificial Neural Network (ANN). The developed algorithm was validated for its ability to identify and detect weeds and crop plants under uncontrolled outdoor illuminations. A machine vision implementing field robot captured field images under outdoor illuminations and the image processing algorithm automatically processed them without manual adjustment. The errors of the algorithm, when processing 666 field images, ranged from 2.1 to 2.9%. The ANN correctly detected 72.6% of crop plants from the identified plants, and considered the rest as weeds. However, the ANN identification rates for crop plants were improved up to 95.1% by addressing the error sources in the algorithm. The developed weed detection and image processing algorithm provides a novel method to identify plants against soil background under the uncontrolled outdoor illuminations, and to differentiate weeds from crop plants. Thus, the proposed new machine vision and processing algorithm may be useful for outdoor applications including plant specific direct applications (PSDA). PMID:22163954

Adaptive sequential Bayesian classification using Page's test

NASA Astrophysics Data System (ADS)

Lynch, Robert S., Jr.; Willett, Peter K.

2002-03-01

In this paper, the previously introduced Mean-Field Bayesian Data Reduction Algorithm is extended for adaptive sequential hypothesis testing utilizing Page's test. In general, Page's test is well understood as a method of detecting a permanent change in distribution associated with a sequence of observations. However, the relationship between detecting a change in distribution utilizing Page's test with that of classification and feature fusion is not well understood. Thus, the contribution of this work is based on developing a method of classifying an unlabeled vector of fused features (i.e., detect a change to an active statistical state) as quickly as possible given an acceptable mean time between false alerts. In this case, the developed classification test can be thought of as equivalent to performing a sequential probability ratio test repeatedly until a class is decided, with the lower log-threshold of each test being set to zero and the upper log-threshold being determined by the expected distance between false alerts. It is of interest to estimate the delay (or, related stopping time) to a classification decision (the number of time samples it takes to classify the target), and the mean time between false alerts, as a function of feature selection and fusion by the Mean-Field Bayesian Data Reduction Algorithm. Results are demonstrated by plotting the delay to declaring the target class versus the mean time between false alerts, and are shown using both different numbers of simulated training data and different numbers of relevant features for each class.

Category-Specific Comparison of Univariate Alerting Methods for Biosurveillance Decision Support

PubMed Central

Elbert, Yevgeniy; Hung, Vivian; Burkom, Howard

2013-01-01

Objective For a multi-source decision support application, we sought to match univariate alerting algorithms to surveillance data types to optimize detection performance. Introduction Temporal alerting algorithms commonly used in syndromic surveillance systems are often adjusted for data features such as cyclic behavior but are subject to overfitting or misspecification errors when applied indiscriminately. In a project for the Armed Forces Health Surveillance Center to enable multivariate decision support, we obtained 4.5 years of out-patient, prescription and laboratory test records from all US military treatment facilities. A proof-of-concept project phase produced 16 events with multiple evidence corroboration for comparison of alerting algorithms for detection performance. We used the representative streams from each data source to compare sensitivity of 6 algorithms to injected spikes, and we used all data streams from 16 known events to compare them for detection timeliness. Methods The six methods compared were: Holt-Winters generalized exponential smoothing method (1)automated choice between daily methods, regression and an exponential weighted moving average (2)adaptive daily Shewhart-type chartadaptive one-sided daily CUSUMEWMA applied to 7-day means with a trend correction; and7-day temporal scan statistic Sensitivity testing: We conducted comparative sensitivity testing for categories of time series with similar scales and seasonal behavior. We added multiples of the standard deviation of each time series as single-day injects in separate algorithm runs. For each candidate method, we then used as a sensitivity measure the proportion of these runs for which the output of each algorithm was below alerting thresholds estimated empirically for each algorithm using simulated data streams. We identified the algorithm(s) whose sensitivity was most consistently high for each data category. For each syndromic query applied to each data source (outpatient, lab test orders, and prescriptions), 502 authentic time series were derived, one for each reporting treatment facility. Data categories were selected in order to group time series with similar expected algorithm performance: Median > 100 < Median ≤ 10Median = 0Lag 7 Autocorrelation Coefficient ≥ 0.2Lag 7 Autocorrelation Coefficient < 0.2 Timeliness testing: For the timeliness testing, we avoided artificiality of simulated signals by measuring alerting detection delays in the 16 corroborated outbreaks. The multiple time series from these events gave a total of 141 time series with outbreak intervals for timeliness testing. The following measures were computed to quantify timeliness of detection: Median Detection Delay – median number of days to detect the outbreak.Penalized Mean Detection Delay –mean number of days to detect the outbreak with outbreak misses penalized as 1 day plus the maximum detection time. Results Based on the injection results, the Holt-Winters algorithm was most sensitive among time series with positive medians. The adaptive CUSUM and the Shewhart methods were most sensitive for data streams with median zero. Table 1 provides timeliness results using the 141 outbreak-associated streams on sparse (Median=0) and non-sparse data categories. [Insert table #1 here] Data median Detection Delay, days Holt-winters Regression EWMA Adaptive Shewhart Adaptive CUSUM 7-day Trend-adj. EWMA 7-day Temporal Scan Median 0 Median 3 2 4 2 4.5 2 Penalized Mean 7.2 7 6.6 6.2 7.3 7.6 Median >0 Median 2 2 2.5 2 6 4 Penalized Mean 6.1 7 7.2 7.1 7.7 6.6 The gray shading in the table 1 indicates methods with shortest detection delays for sparse and non-sparse data streams. The Holt-Winters method was again superior for non-sparse data. For data with median=0, the adaptive CUSUM was superior for a daily false alarm probability of 0.01, but the Shewhart method was timelier for more liberal thresholds. Conclusions Both kinds of detection performance analysis showed the method based on Holt-Winters exponential smoothing superior on non-sparse time series with day-of-week effects. The adaptive CUSUM and She-whart methods proved optimal on sparse data and data without weekly patterns.

Enabling the extended compact genetic algorithm for real-parameter optimization by using adaptive discretization.

PubMed

Chen, Ying-ping; Chen, Chao-Hong

2010-01-01

An adaptive discretization method, called split-on-demand (SoD), enables estimation of distribution algorithms (EDAs) for discrete variables to solve continuous optimization problems. SoD randomly splits a continuous interval if the number of search points within the interval exceeds a threshold, which is decreased at every iteration. After the split operation, the nonempty intervals are assigned integer codes, and the search points are discretized accordingly. As an example of using SoD with EDAs, the integration of SoD and the extended compact genetic algorithm (ECGA) is presented and numerically examined. In this integration, we adopt a local search mechanism as an optional component of our back end optimization engine. As a result, the proposed framework can be considered as a memetic algorithm, and SoD can potentially be applied to other memetic algorithms. The numerical experiments consist of two parts: (1) a set of benchmark functions on which ECGA with SoD and ECGA with two well-known discretization methods: the fixed-height histogram (FHH) and the fixed-width histogram (FWH) are compared; (2) a real-world application, the economic dispatch problem, on which ECGA with SoD is compared to other methods. The experimental results indicate that SoD is a better discretization method to work with ECGA. Moreover, ECGA with SoD works quite well on the economic dispatch problem and delivers solutions better than the best known results obtained by other methods in existence.

Threshold automatic selection hybrid phase unwrapping algorithm for digital holographic microscopy

NASA Astrophysics Data System (ADS)

Zhou, Meiling; Min, Junwei; Yao, Baoli; Yu, Xianghua; Lei, Ming; Yan, Shaohui; Yang, Yanlong; Dan, Dan

2015-01-01

Conventional quality-guided (QG) phase unwrapping algorithm is hard to be applied to digital holographic microscopy because of the long execution time. In this paper, we present a threshold automatic selection hybrid phase unwrapping algorithm that combines the existing QG algorithm and the flood-filled (FF) algorithm to solve this problem. The original wrapped phase map is divided into high- and low-quality sub-maps by selecting a threshold automatically, and then the FF and QG unwrapping algorithms are used in each level to unwrap the phase, respectively. The feasibility of the proposed method is proved by experimental results, and the execution speed is shown to be much faster than that of the original QG unwrapping algorithm.

Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding

PubMed Central

Resnik, Andrey; Celikel, Tansu; Englitz, Bernhard

2016-01-01

Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information. PMID:27304526

Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding.

PubMed

Huang, Chao; Resnik, Andrey; Celikel, Tansu; Englitz, Bernhard

2016-06-01

Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information.

Space Object Maneuver Detection Algorithms Using TLE Data

NASA Astrophysics Data System (ADS)

Pittelkau, M.

2016-09-01

An important aspect of Space Situational Awareness (SSA) is detection of deliberate and accidental orbit changes of space objects. Although space surveillance systems detect orbit maneuvers within their tracking algorithms, maneuver data are not readily disseminated for general use. However, two-line element (TLE) data is available and can be used to detect maneuvers of space objects. This work is an attempt to improve upon existing TLE-based maneuver detection algorithms. Three adaptive maneuver detection algorithms are developed and evaluated: The first is a fading-memory Kalman filter, which is equivalent to the sliding-window least-squares polynomial fit, but computationally more efficient and adaptive to the noise in the TLE data. The second algorithm is based on a sample cumulative distribution function (CDF) computed from a histogram of the magnitude-squared |V|2 of change-in-velocity vectors (V), which is computed from the TLE data. A maneuver detection threshold is computed from the median estimated from the CDF, or from the CDF and a specified probability of false alarm. The third algorithm is a median filter. The median filter is the simplest of a class of nonlinear filters called order statistics filters, which is within the theory of robust statistics. The output of the median filter is practically insensitive to outliers, or large maneuvers. The median of the |V|2 data is proportional to the variance of the V, so the variance is estimated from the output of the median filter. A maneuver is detected when the input data exceeds a constant times the estimated variance.

Automatic treatment plan re-optimization for adaptive radiotherapy guided with the initial plan DVHs.

PubMed

Li, Nan; Zarepisheh, Masoud; Uribe-Sanchez, Andres; Moore, Kevin; Tian, Zhen; Zhen, Xin; Graves, Yan Jiang; Gautier, Quentin; Mell, Loren; Zhou, Linghong; Jia, Xun; Jiang, Steve

2013-12-21

Adaptive radiation therapy (ART) can reduce normal tissue toxicity and/or improve tumor control through treatment adaptations based on the current patient anatomy. Developing an efficient and effective re-planning algorithm is an important step toward the clinical realization of ART. For the re-planning process, manual trial-and-error approach to fine-tune planning parameters is time-consuming and is usually considered unpractical, especially for online ART. It is desirable to automate this step to yield a plan of acceptable quality with minimal interventions. In ART, prior information in the original plan is available, such as dose-volume histogram (DVH), which can be employed to facilitate the automatic re-planning process. The goal of this work is to develop an automatic re-planning algorithm to generate a plan with similar, or possibly better, DVH curves compared with the clinically delivered original plan. Specifically, our algorithm iterates the following two loops. An inner loop is the traditional fluence map optimization, in which we optimize a quadratic objective function penalizing the deviation of the dose received by each voxel from its prescribed or threshold dose with a set of fixed voxel weighting factors. In outer loop, the voxel weighting factors in the objective function are adjusted according to the deviation of the current DVH curves from those in the original plan. The process is repeated until the DVH curves are acceptable or maximum iteration step is reached. The whole algorithm is implemented on GPU for high efficiency. The feasibility of our algorithm has been demonstrated with three head-and-neck cancer IMRT cases, each having an initial planning CT scan and another treatment CT scan acquired in the middle of treatment course. Compared with the DVH curves in the original plan, the DVH curves in the resulting plan using our algorithm with 30 iterations are better for almost all structures. The re-optimization process takes about 30 s using our in-house optimization engine.

Shear wave speed estimation by adaptive random sample consensus method.

PubMed

Lin, Haoming; Wang, Tianfu; Chen, Siping

2014-01-01

This paper describes a new method for shear wave velocity estimation that is capable of extruding outliers automatically without preset threshold. The proposed method is an adaptive random sample consensus (ARANDSAC) and the metric used here is finding the certain percentage of inliers according to the closest distance criterion. To evaluate the method, the simulation and phantom experiment results were compared using linear regression with all points (LRWAP) and radon sum transform (RS) method. The assessment reveals that the relative biases of mean estimation are 20.00%, 4.67% and 5.33% for LRWAP, ARANDSAC and RS respectively for simulation, 23.53%, 4.08% and 1.08% for phantom experiment. The results suggested that the proposed ARANDSAC algorithm is accurate in shear wave speed estimation.

Fission gas bubble identification using MATLAB's image processing toolbox

DOE Office of Scientific and Technical Information (OSTI.GOV)

Collette, R.

Automated image processing routines have the potential to aid in the fuel performance evaluation process by eliminating bias in human judgment that may vary from person-to-person or sample-to-sample. This study presents several MATLAB based image analysis routines designed for fission gas void identification in post-irradiation examination of uranium molybdenum (U–Mo) monolithic-type plate fuels. Frequency domain filtration, enlisted as a pre-processing technique, can eliminate artifacts from the image without compromising the critical features of interest. This process is coupled with a bilateral filter, an edge-preserving noise removal technique aimed at preparing the image for optimal segmentation. Adaptive thresholding proved to bemore » the most consistent gray-level feature segmentation technique for U–Mo fuel microstructures. The Sauvola adaptive threshold technique segments the image based on histogram weighting factors in stable contrast regions and local statistics in variable contrast regions. Once all processing is complete, the algorithm outputs the total fission gas void count, the mean void size, and the average porosity. The final results demonstrate an ability to extract fission gas void morphological data faster, more consistently, and at least as accurately as manual segmentation methods. - Highlights: •Automated image processing can aid in the fuel qualification process. •Routines are developed to characterize fission gas bubbles in irradiated U–Mo fuel. •Frequency domain filtration effectively eliminates FIB curtaining artifacts. •Adaptive thresholding proved to be the most accurate segmentation method. •The techniques established are ready to be applied to large scale data extraction testing.« less

Comparative advantages of novel algorithms using MSR threshold and MSR difference threshold for biclustering gene expression data.

PubMed

Das, Shyama; Idicula, Sumam Mary

2011-01-01

The goal of biclustering in gene expression data matrix is to find a submatrix such that the genes in the submatrix show highly correlated activities across all conditions in the submatrix. A measure called mean squared residue (MSR) is used to simultaneously evaluate the coherence of rows and columns within the submatrix. MSR difference is the incremental increase in MSR when a gene or condition is added to the bicluster. In this chapter, three biclustering algorithms using MSR threshold (MSRT) and MSR difference threshold (MSRDT) are experimented and compared. All these methods use seeds generated from K-Means clustering algorithm. Then these seeds are enlarged by adding more genes and conditions. The first algorithm makes use of MSRT alone. Both the second and third algorithms make use of MSRT and the newly introduced concept of MSRDT. Highly coherent biclusters are obtained using this concept. In the third algorithm, a different method is used to calculate the MSRDT. The results obtained on bench mark datasets prove that these algorithms are better than many of the metaheuristic algorithms.

Contributions of adaptation currents to dynamic spike threshold on slow timescales: Biophysical insights from conductance-based models

NASA Astrophysics Data System (ADS)

Yi, Guosheng; Wang, Jiang; Wei, Xile; Deng, Bin; Li, Huiyan; Che, Yanqiu

2017-06-01

Spike-frequency adaptation (SFA) mediated by various adaptation currents, such as voltage-gated K+ current (IM), Ca2+-gated K+ current (IAHP), or Na+-activated K+ current (IKNa), exists in many types of neurons, which has been shown to effectively shape their information transmission properties on slow timescales. Here we use conductance-based models to investigate how the activation of three adaptation currents regulates the threshold voltage for action potential (AP) initiation during the course of SFA. It is observed that the spike threshold gets depolarized and the rate of membrane depolarization (dV/dt) preceding AP is reduced as adaptation currents reduce firing rate. It is indicated that the presence of inhibitory adaptation currents enables the neuron to generate a dynamic threshold inversely correlated with preceding dV/dt on slower timescales than fast dynamics of AP generation. By analyzing the interactions of ionic currents at subthreshold potentials, we find that the activation of adaptation currents increase the outward level of net membrane current prior to AP initiation, which antagonizes inward Na+ to result in a depolarized threshold and lower dV/dt from one AP to the next. Our simulations demonstrate that the threshold dynamics on slow timescales is a secondary effect caused by the activation of adaptation currents. These findings have provided a biophysical interpretation of the relationship between adaptation currents and spike threshold.

Lane identification and path planning for autonomous mobile robots

NASA Astrophysics Data System (ADS)

McKeon, Robert T.; Paulik, Mark; Krishnan, Mohan

2006-10-01

This work has been performed in conjunction with the University of Detroit Mercy's (UDM) ECE Department autonomous vehicle entry in the 2006 Intelligent Ground Vehicle Competition (www.igvc.org). The IGVC challenges engineering students to design autonomous vehicles and compete in a variety of unmanned mobility competitions. The course to be traversed in the competition consists of a lane demarcated by painted lines on grass with the possibility of one of the two lines being deliberately left out over segments of the course. The course also consists of other challenging artifacts such as sandpits, ramps, potholes, and colored tarps that alter the color composition of scenes, and obstacles set up using orange and white construction barrels. This paper describes a composite lane edge detection approach that uses three algorithms to implement noise filters enabling increased removal of noise prior to the application of image thresholding. The first algorithm uses a row-adaptive statistical filter to establish an intensity floor followed by a global threshold based on a reverse cumulative intensity histogram and a priori knowledge about lane thickness and separation. The second method first improves the contrast of the image by implementing an arithmetic combination of the blue plane (RGB format) and a modified saturation plane (HSI format). A global threshold is then applied based on the mean of the intensity image and a user-defined offset. The third method applies the horizontal component of the Sobel mask to a modified gray scale of the image, followed by a thresholding method similar to the one used in the second method. The Hough transform is applied to each of the resulting binary images to select the most probable line candidates. Finally, a heuristics-based confidence interval is determined, and the results sent on to a separate fuzzy polar-based navigation algorithm, which fuses the image data with that produced by a laser scanner (for obstacle detection).

Stable Extraction of Threshold Voltage Using Transconductance Change Method for CMOS Modeling, Simulation and Characterization

NASA Astrophysics Data System (ADS)

Choi, Woo Young; Woo, Dong-Soo; Choi, Byung Yong; Lee, Jong Duk; Park, Byung-Gook

2004-04-01

We proposed a stable extraction algorithm for threshold voltage using transconductance change method by optimizing node interval. With the algorithm, noise-free gm2 (=dgm/dVGS) profiles can be extracted within one-percent error, which leads to more physically-meaningful threshold voltage calculation by the transconductance change method. The extracted threshold voltage predicts the gate-to-source voltage at which the surface potential is within kT/q of φs=2φf+VSB. Our algorithm makes the transconductance change method more practical by overcoming noise problem. This threshold voltage extraction algorithm yields the threshold roll-off behavior of nanoscale metal oxide semiconductor field effect transistor (MOSFETs) accurately and makes it possible to calculate the surface potential φs at any other point on the drain-to-source current (IDS) versus gate-to-source voltage (VGS) curve. It will provide us with a useful analysis tool in the field of device modeling, simulation and characterization.

A MATLAB toolbox for the efficient estimation of the psychometric function using the updated maximum-likelihood adaptive procedure.

PubMed

Shen, Yi; Dai, Wei; Richards, Virginia M

2015-03-01

A MATLAB toolbox for the efficient estimation of the threshold, slope, and lapse rate of the psychometric function is described. The toolbox enables the efficient implementation of the updated maximum-likelihood (UML) procedure. The toolbox uses an object-oriented architecture for organizing the experimental variables and computational algorithms, which provides experimenters with flexibility in experimental design and data management. Descriptions of the UML procedure and the UML Toolbox are provided, followed by toolbox use examples. Finally, guidelines and recommendations of parameter configurations are given.

Designing an Algorithm for Cancerous Tissue Segmentation Using Adaptive K-means Cluttering and Discrete Wavelet Transform.

PubMed

Rezaee, Kh; Haddadnia, J

2013-09-01

Breast cancer is currently one of the leading causes of death among women worldwide. The diagnosis and separation of cancerous tumors in mammographic images require accuracy, experience and time, and it has always posed itself as a major challenge to the radiologists and physicians. This paper proposes a new algorithm which draws on discrete wavelet transform and adaptive K-means techniques to transmute the medical images implement the tumor estimation and detect breast cancer tumors in mammograms in early stages. It also allows the rapid processing of the input data. In the first step, after designing a filter, the discrete wavelet transform is applied to the input images and the approximate coefficients of scaling components are constructed. Then, the different parts of image are classified in continuous spectrum. In the next step, by using adaptive K-means algorithm for initializing and smart choice of clusters' number, the appropriate threshold is selected. Finally, the suspicious cancerous mass is separated by implementing the image processing techniques. We Received 120 mammographic images in LJPEG format, which had been scanned in Gray-Scale with 50 microns size, 3% noise and 20% INU from clinical data taken from two medical databases (mini-MIAS and DDSM). The proposed algorithm detected tumors at an acceptable level with an average accuracy of 92.32% and sensitivity of 90.24%. Also, the Kappa coefficient was approximately 0.85, which proved the suitable reliability of the system performance. The exact positioning of the cancerous tumors allows the radiologist to determine the stage of disease progression and suggest an appropriate treatment in accordance with the tumor growth. The low PPV and high NPV of the system is a warranty of the system and both clinical specialists and patients can trust its output.

Dynamic Network Selection for Multicast Services in Wireless Cooperative Networks

NASA Astrophysics Data System (ADS)

Chen, Liang; Jin, Le; He, Feng; Cheng, Hanwen; Wu, Lenan

In next generation mobile multimedia communications, different wireless access networks are expected to cooperate. However, it is a challenging task to choose an optimal transmission path in this scenario. This paper focuses on the problem of selecting the optimal access network for multicast services in the cooperative mobile and broadcasting networks. An algorithm is proposed, which considers multiple decision factors and multiple optimization objectives. An analytic hierarchy process (AHP) method is applied to schedule the service queue and an artificial neural network (ANN) is used to improve the flexibility of the algorithm. Simulation results show that by applying the AHP method, a group of weight ratios can be obtained to improve the performance of multiple objectives. And ANN method is effective to adaptively adjust weight ratios when users' new waiting threshold is generated.

A novel fusion method of improved adaptive LTP and two-directional two-dimensional PCA for face feature extraction

NASA Astrophysics Data System (ADS)

Luo, Yuan; Wang, Bo-yu; Zhang, Yi; Zhao, Li-ming

2018-03-01

In this paper, under different illuminations and random noises, focusing on the local texture feature's defects of a face image that cannot be completely described because the threshold of local ternary pattern (LTP) cannot be calculated adaptively, a local three-value model of improved adaptive local ternary pattern (IALTP) is proposed. Firstly, the difference function between the center pixel and the neighborhood pixel weight is established to obtain the statistical characteristics of the central pixel and the neighborhood pixel. Secondly, the adaptively gradient descent iterative function is established to calculate the difference coefficient which is defined to be the threshold of the IALTP operator. Finally, the mean and standard deviation of the pixel weight of the local region are used as the coding mode of IALTP. In order to reflect the overall properties of the face and reduce the dimension of features, the two-directional two-dimensional PCA ((2D)2PCA) is adopted. The IALTP is used to extract local texture features of eyes and mouth area. After combining the global features and local features, the fusion features (IALTP+) are obtained. The experimental results on the Extended Yale B and AR standard face databases indicate that under different illuminations and random noises, the algorithm proposed in this paper is more robust than others, and the feature's dimension is smaller. The shortest running time reaches 0.329 6 s, and the highest recognition rate reaches 97.39%.

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

PubMed Central

Hatt, Mathieu; Lee, John A.; Schmidtlein, Charles R.; Naqa, Issam El; Caldwell, Curtis; De Bernardi, Elisabetta; Lu, Wei; Das, Shiva; Geets, Xavier; Gregoire, Vincent; Jeraj, Robert; MacManus, Michael P.; Mawlawi, Osama R.; Nestle, Ursula; Pugachev, Andrei B.; Schöder, Heiko; Shepherd, Tony; Spezi, Emiliano; Visvikis, Dimitris; Zaidi, Habib; Kirov, Assen S.

2017-01-01

Purpose The purpose of this educational report is to provide an overview of the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application. Approach A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET-AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto-segmentation are addressed. Findings A large number of PET-AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi-automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal-to-noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol. Conclusions Available comparison studies suggest that PET-AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET-AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET-AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to evaluating both existing and future PET-AS algorithms needs to be designed, to aid clinicians in evaluating and selecting PET-AS algorithms and to establish performance limits for their acceptance for clinical use. The initial steps toward designing and building such a standard are undertaken by the task group members. PMID:28120467

A generalized linear integrate-and-fire neural model produces diverse spiking behaviors.

PubMed

Mihalaş, Stefan; Niebur, Ernst

2009-03-01

For simulations of neural networks, there is a trade-off between the size of the network that can be simulated and the complexity of the model used for individual neurons. In this study, we describe a generalization of the leaky integrate-and-fire model that produces a wide variety of spiking behaviors while still being analytically solvable between firings. For different parameter values, the model produces spiking or bursting, tonic, phasic or adapting responses, depolarizing or hyperpolarizing after potentials and so forth. The model consists of a diagonalizable set of linear differential equations describing the time evolution of membrane potential, a variable threshold, and an arbitrary number of firing-induced currents. Each of these variables is modified by an update rule when the potential reaches threshold. The variables used are intuitive and have biological significance. The model's rich behavior does not come from the differential equations, which are linear, but rather from complex update rules. This single-neuron model can be implemented using algorithms similar to the standard integrate-and-fire model. It is a natural match with event-driven algorithms for which the firing times are obtained as a solution of a polynomial equation.

A Generalized Linear Integrate-and-Fire Neural Model Produces Diverse Spiking Behaviors

PubMed Central

Mihalaş, Ştefan; Niebur, Ernst

2010-01-01

For simulations of neural networks, there is a trade-off between the size of the network that can be simulated and the complexity of the model used for individual neurons. In this study, we describe a generalization of the leaky integrate-and-fire model that produces a wide variety of spiking behaviors while still being analytically solvable between firings. For different parameter values, the model produces spiking or bursting, tonic, phasic or adapting responses, depolarizing or hyperpolarizing after potentials and so forth. The model consists of a diagonalizable set of linear differential equations describing the time evolution of membrane potential, a variable threshold, and an arbitrary number of firing-induced currents. Each of these variables is modified by an update rule when the potential reaches threshold. The variables used are intuitive and have biological significance. The model’s rich behavior does not come from the differential equations, which are linear, but rather from complex update rules. This single-neuron model can be implemented using algorithms similar to the standard integrate-and-fire model. It is a natural match with event-driven algorithms for which the firing times are obtained as a solution of a polynomial equation. PMID:18928368

'Outbreak Gold Standard' selection to provide optimized threshold for infectious diseases early-alert based on China Infectious Disease Automated-alert and Response System.

PubMed

Wang, Rui-Ping; Jiang, Yong-Gen; Zhao, Gen-Ming; Guo, Xiao-Qin; Michael, Engelgau

2017-12-01

The China Infectious Disease Automated-alert and Response System (CIDARS) was successfully implemented and became operational nationwide in 2008. The CIDARS plays an important role in and has been integrated into the routine outbreak monitoring efforts of the Center for Disease Control (CDC) at all levels in China. In the CIDARS, thresholds are determined using the "Mean+2SD‟ in the early stage which have limitations. This study compared the performance of optimized thresholds defined using the "Mean +2SD‟ method to the performance of 5 novel algorithms to select optimal "Outbreak Gold Standard (OGS)‟ and corresponding thresholds for outbreak detection. Data for infectious disease were organized by calendar week and year. The "Mean+2SD‟, C1, C2, moving average (MA), seasonal model (SM), and cumulative sum (CUSUM) algorithms were applied. Outbreak signals for the predicted value (Px) were calculated using a percentile-based moving window. When the outbreak signals generated by an algorithm were in line with a Px generated outbreak signal for each week, this Px was then defined as the optimized threshold for that algorithm. In this study, six infectious diseases were selected and classified into TYPE A (chickenpox and mumps), TYPE B (influenza and rubella) and TYPE C [hand foot and mouth disease (HFMD) and scarlet fever]. Optimized thresholds for chickenpox (P 55 ), mumps (P 50 ), influenza (P 40 , P 55 , and P 75 ), rubella (P 45 and P 75 ), HFMD (P 65 and P 70 ), and scarlet fever (P 75 and P 80 ) were identified. The C1, C2, CUSUM, SM, and MA algorithms were appropriate for TYPE A. All 6 algorithms were appropriate for TYPE B. C1 and CUSUM algorithms were appropriate for TYPE C. It is critical to incorporate more flexible algorithms as OGS into the CIDRAS and to identify the proper OGS and corresponding recommended optimized threshold by different infectious disease types.

Automatic luminous reflections detector using global threshold with increased luminosity contrast in images

NASA Astrophysics Data System (ADS)

Silva, Ricardo Petri; Naozuka, Gustavo Taiji; Mastelini, Saulo Martiello; Felinto, Alan Salvany

2018-01-01

The incidence of luminous reflections (LR) in captured images can interfere with the color of the affected regions. These regions tend to oversaturate, becoming whitish and, consequently, losing the original color information of the scene. Decision processes that employ images acquired from digital cameras can be impaired by the LR incidence. Such applications include real-time video surgeries, facial, and ocular recognition. This work proposes an algorithm called contrast enhancement of potential LR regions, which is a preprocessing to increase the contrast of potential LR regions, in order to improve the performance of automatic LR detectors. In addition, three automatic detectors were compared with and without the employment of our preprocessing method. The first one is a technique already consolidated in the literature called the Chang-Tseng threshold. We propose two automatic detectors called adapted histogram peak and global threshold. We employed four performance metrics to evaluate the detectors, namely, accuracy, precision, exactitude, and root mean square error. The exactitude metric is developed by this work. Thus, a manually defined reference model was created. The global threshold detector combined with our preprocessing method presented the best results, with an average exactitude rate of 82.47%.

QRS Detection Algorithm for Telehealth Electrocardiogram Recordings.

PubMed

Khamis, Heba; Weiss, Robert; Xie, Yang; Chang, Chan-Wei; Lovell, Nigel H; Redmond, Stephen J

2016-07-01

QRS detection algorithms are needed to analyze electrocardiogram (ECG) recordings generated in telehealth environments. However, the numerous published QRS detectors focus on clean clinical data. Here, a "UNSW" QRS detection algorithm is described that is suitable for clinical ECG and also poorer quality telehealth ECG. The UNSW algorithm generates a feature signal containing information about ECG amplitude and derivative, which is filtered according to its frequency content and an adaptive threshold is applied. The algorithm was tested on clinical and telehealth ECG and the QRS detection performance is compared to the Pan-Tompkins (PT) and Gutiérrez-Rivas (GR) algorithm. For the MIT-BIH Arrhythmia database (virtually artifact free, clinical ECG), the overall sensitivity (Se) and positive predictivity (+P) of the UNSW algorithm was >99%, which was comparable to PT and GR. When applied to the MIT-BIH noise stress test database (clinical ECG with added calibrated noise) after artifact masking, all three algorithms had overall Se >99%, and the UNSW algorithm had higher +P (98%, p < 0.05) than PT and GR. For 250 telehealth ECG records (unsupervised recordings; dry metal electrodes), the UNSW algorithm had 98% Se and 95% +P which was superior to PT (+P: p < 0.001) and GR (Se and +P: p < 0.001). This is the first study to describe a QRS detection algorithm for telehealth data and evaluate it on clinical and telehealth ECG with superior results to published algorithms. The UNSW algorithm could be used to manage increasing telehealth ECG analysis workloads.

The Limits to Adaptation; A Systems Approach

EPA Science Inventory

The Limits to Adaptation: A Systems Approach. The ability to adapt to climate change is delineated by capacity thresholds, after which climate damages begin to overwhelm the adaptation response. Such thresholds depend upon physical properties (natural processes and engineering...

Algorithmic detectability threshold of the stochastic block model

NASA Astrophysics Data System (ADS)

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.

Automatic layer segmentation of H&E microscopic images of mice skin

NASA Astrophysics Data System (ADS)

Hussein, Saif; Selway, Joanne; Jassim, Sabah; Al-Assam, Hisham

2016-05-01

Mammalian skin is a complex organ composed of a variety of cells and tissue types. The automatic detection and quantification of changes in skin structures has a wide range of applications for biological research. To accurately segment and quantify nuclei, sebaceous gland, hair follicles, and other skin structures, there is a need for a reliable segmentation of different skin layers. This paper presents an efficient segmentation algorithm to segment the three main layers of mice skin, namely epidermis, dermis, and subcutaneous layers. It also segments the epidermis layer into two sub layers, basal and cornified layers. The proposed algorithm uses adaptive colour deconvolution technique on H&E stain images to separate different tissue structures, inter-modes and Otsu thresholding techniques were effectively combined to segment the layers. It then uses a set of morphological and logical operations on each layer to removing unwanted objects. A dataset of 7000 H&E microscopic images of mutant and wild type mice were used to evaluate the effectiveness of the algorithm. Experimental results examined by domain experts have confirmed the viability of the proposed algorithms.

Research on AHP decision algorithms based on BP algorithm

NASA Astrophysics Data System (ADS)

Ma, Ning; Guan, Jianhe

2017-10-01

Decision making is the thinking activity that people choose or judge, and scientific decision-making has always been a hot issue in the field of research. Analytic Hierarchy Process (AHP) is a simple and practical multi-criteria and multi-objective decision-making method that combines quantitative and qualitative and can show and calculate the subjective judgment in digital form. In the process of decision analysis using AHP method, the rationality of the two-dimensional judgment matrix has a great influence on the decision result. However, in dealing with the real problem, the judgment matrix produced by the two-dimensional comparison is often inconsistent, that is, it does not meet the consistency requirements. BP neural network algorithm is an adaptive nonlinear dynamic system. It has powerful collective computing ability and learning ability. It can perfect the data by constantly modifying the weights and thresholds of the network to achieve the goal of minimizing the mean square error. In this paper, the BP algorithm is used to deal with the consistency of the two-dimensional judgment matrix of the AHP.

Moving human full body and body parts detection, tracking, and applications on human activity estimation, walking pattern and face recognition

NASA Astrophysics Data System (ADS)

Chen, Hai-Wen; McGurr, Mike

2016-05-01

We have developed a new way for detection and tracking of human full-body and body-parts with color (intensity) patch morphological segmentation and adaptive thresholding for security surveillance cameras. An adaptive threshold scheme has been developed for dealing with body size changes, illumination condition changes, and cross camera parameter changes. Tests with the PETS 2009 and 2014 datasets show that we can obtain high probability of detection and low probability of false alarm for full-body. Test results indicate that our human full-body detection method can considerably outperform the current state-of-the-art methods in both detection performance and computational complexity. Furthermore, in this paper, we have developed several methods using color features for detection and tracking of human body-parts (arms, legs, torso, and head, etc.). For example, we have developed a human skin color sub-patch segmentation algorithm by first conducting a RGB to YIQ transformation and then applying a Subtractive I/Q image Fusion with morphological operations. With this method, we can reliably detect and track human skin color related body-parts such as face, neck, arms, and legs. Reliable body-parts (e.g. head) detection allows us to continuously track the individual person even in the case that multiple closely spaced persons are merged. Accordingly, we have developed a new algorithm to split a merged detection blob back to individual detections based on the detected head positions. Detected body-parts also allow us to extract important local constellation features of the body-parts positions and angles related to the full-body. These features are useful for human walking gait pattern recognition and human pose (e.g. standing or falling down) estimation for potential abnormal behavior and accidental event detection, as evidenced with our experimental tests. Furthermore, based on the reliable head (face) tacking, we have applied a super-resolution algorithm to enhance the face resolution for improved human face recognition performance.

SU-C-9A-01: Parameter Optimization in Adaptive Region-Growing for Tumor Segmentation in PET

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tan, S; Huazhong University of Science and Technology, Wuhan, Hubei; Xue, M

Purpose: To design a reliable method to determine the optimal parameter in the adaptive region-growing (ARG) algorithm for tumor segmentation in PET. Methods: The ARG uses an adaptive similarity criterion m - fσ ≤ I-PET ≤ m + fσ, so that a neighboring voxel is appended to the region based on its similarity to the current region. When increasing the relaxing factor f (f ≥ 0), the resulting volumes monotonically increased with a sharp increase when the region just grew into the background. The optimal f that separates the tumor from the background is defined as the first point withmore » the local maximum curvature on an Error function fitted to the f-volume curve. The ARG was tested on a tumor segmentation Benchmark that includes ten lung cancer patients with 3D pathologic tumor volume as ground truth. For comparison, the widely used 42% and 50% SUVmax thresholding, Otsu optimal thresholding, Active Contours (AC), Geodesic Active Contours (GAC), and Graph Cuts (GC) methods were tested. The dice similarity index (DSI), volume error (VE), and maximum axis length error (MALE) were calculated to evaluate the segmentation accuracy. Results: The ARG provided the highest accuracy among all tested methods. Specifically, the ARG has an average DSI, VE, and MALE of 0.71, 0.29, and 0.16, respectively, better than the absolute 42% thresholding (DSI=0.67, VE= 0.57, and MALE=0.23), the relative 42% thresholding (DSI=0.62, VE= 0.41, and MALE=0.23), the absolute 50% thresholding (DSI=0.62, VE=0.48, and MALE=0.21), the relative 50% thresholding (DSI=0.48, VE=0.54, and MALE=0.26), OTSU (DSI=0.44, VE=0.63, and MALE=0.30), AC (DSI=0.46, VE= 0.85, and MALE=0.47), GAC (DSI=0.40, VE= 0.85, and MALE=0.46) and GC (DSI=0.66, VE= 0.54, and MALE=0.21) methods. Conclusions: The results suggest that the proposed method reliably identified the optimal relaxing factor in ARG for tumor segmentation in PET. This work was supported in part by National Cancer Institute Grant R01 CA172638; The dataset is provided by AAPM TG211.« less

Fission gas bubble identification using MATLAB's image processing toolbox

DOE Office of Scientific and Technical Information (OSTI.GOV)

Collette, R.; King, J.; Keiser, Jr., D.

Automated image processing routines have the potential to aid in the fuel performance evaluation process by eliminating bias in human judgment that may vary from person-to-person or sample-to-sample. In addition, this study presents several MATLAB based image analysis routines designed for fission gas void identification in post-irradiation examination of uranium molybdenum (U–Mo) monolithic-type plate fuels. Frequency domain filtration, enlisted as a pre-processing technique, can eliminate artifacts from the image without compromising the critical features of interest. This process is coupled with a bilateral filter, an edge-preserving noise removal technique aimed at preparing the image for optimal segmentation. Adaptive thresholding provedmore » to be the most consistent gray-level feature segmentation technique for U–Mo fuel microstructures. The Sauvola adaptive threshold technique segments the image based on histogram weighting factors in stable contrast regions and local statistics in variable contrast regions. Once all processing is complete, the algorithm outputs the total fission gas void count, the mean void size, and the average porosity. The final results demonstrate an ability to extract fission gas void morphological data faster, more consistently, and at least as accurately as manual segmentation methods.« less

Mouse epileptic seizure detection with multiple EEG features and simple thresholding technique

NASA Astrophysics Data System (ADS)

Tieng, Quang M.; Anbazhagan, Ashwin; Chen, Min; Reutens, David C.

2017-12-01

Objective. Epilepsy is a common neurological disorder characterized by recurrent, unprovoked seizures. The search for new treatments for seizures and epilepsy relies upon studies in animal models of epilepsy. To capture data on seizures, many applications require prolonged electroencephalography (EEG) with recordings that generate voluminous data. The desire for efficient evaluation of these recordings motivates the development of automated seizure detection algorithms. Approach. A new seizure detection method is proposed, based on multiple features and a simple thresholding technique. The features are derived from chaos theory, information theory and the power spectrum of EEG recordings and optimally exploit both linear and nonlinear characteristics of EEG data. Main result. The proposed method was tested with real EEG data from an experimental mouse model of epilepsy and distinguished seizures from other patterns with high sensitivity and specificity. Significance. The proposed approach introduces two new features: negative logarithm of adaptive correlation integral and power spectral coherence ratio. The combination of these new features with two previously described features, entropy and phase coherence, improved seizure detection accuracy significantly. Negative logarithm of adaptive correlation integral can also be used to compute the duration of automatically detected seizures.

Fission gas bubble identification using MATLAB's image processing toolbox

DOE PAGES

Collette, R.; King, J.; Keiser, Jr., D.; ...

2016-06-08

Automated image processing routines have the potential to aid in the fuel performance evaluation process by eliminating bias in human judgment that may vary from person-to-person or sample-to-sample. In addition, this study presents several MATLAB based image analysis routines designed for fission gas void identification in post-irradiation examination of uranium molybdenum (U–Mo) monolithic-type plate fuels. Frequency domain filtration, enlisted as a pre-processing technique, can eliminate artifacts from the image without compromising the critical features of interest. This process is coupled with a bilateral filter, an edge-preserving noise removal technique aimed at preparing the image for optimal segmentation. Adaptive thresholding provedmore » to be the most consistent gray-level feature segmentation technique for U–Mo fuel microstructures. The Sauvola adaptive threshold technique segments the image based on histogram weighting factors in stable contrast regions and local statistics in variable contrast regions. Once all processing is complete, the algorithm outputs the total fission gas void count, the mean void size, and the average porosity. The final results demonstrate an ability to extract fission gas void morphological data faster, more consistently, and at least as accurately as manual segmentation methods.« less

Change detection of bitemporal multispectral images based on FCM and D-S theory

NASA Astrophysics Data System (ADS)

Shi, Aiye; Gao, Guirong; Shen, Shaohong

2016-12-01

In this paper, we propose a change detection method of bitemporal multispectral images based on the D-S theory and fuzzy c-means (FCM) algorithm. Firstly, the uncertainty and certainty regions are determined by thresholding method applied to the magnitudes of difference image (MDI) and spectral angle information (SAI) of bitemporal images. Secondly, the FCM algorithm is applied to the MDI and SAI in the uncertainty region, respectively. Then, the basic probability assignment (BPA) functions of changed and unchanged classes are obtained by the fuzzy membership values from the FCM algorithm. In addition, the optimal value of fuzzy exponent of FCM is adaptively determined by conflict degree between the MDI and SAI in uncertainty region. Finally, the D-S theory is applied to obtain the new fuzzy partition matrix for uncertainty region and further the change map is obtained. Experiments on bitemporal Landsat TM images and bitemporal SPOT images validate that the proposed method is effective.

Policy tree optimization for adaptive management of water resources systems

NASA Astrophysics Data System (ADS)

Herman, Jonathan; Giuliani, Matteo

2017-04-01

Water resources systems must cope with irreducible uncertainty in supply and demand, requiring policy alternatives capable of adapting to a range of possible future scenarios. Recent studies have developed adaptive policies based on "signposts" or "tipping points" that suggest the need of updating the policy. However, there remains a need for a general method to optimize the choice of the signposts to be used and their threshold values. This work contributes a general framework and computational algorithm to design adaptive policies as a tree structure (i.e., a hierarchical set of logical rules) using a simulation-optimization approach based on genetic programming. Given a set of feature variables (e.g., reservoir level, inflow observations, inflow forecasts), the resulting policy defines both the optimal reservoir operations and the conditions under which such operations should be triggered. We demonstrate the approach using Folsom Reservoir (California) as a case study, in which operating policies must balance the risk of both floods and droughts. Numerical results show that the tree-based policies outperform the ones designed via Dynamic Programming. In addition, they display good adaptive capacity to the changing climate, successfully adapting the reservoir operations across a large set of uncertain climate scenarios.

Improved relocatable over-the-horizon radar detection and tracking using the maximum likelihood adaptive neural system algorithm

NASA Astrophysics Data System (ADS)

Perlovsky, Leonid I.; Webb, Virgil H.; Bradley, Scott R.; Hansen, Christopher A.

1998-07-01

An advanced detection and tracking system is being developed for the U.S. Navy's Relocatable Over-the-Horizon Radar (ROTHR) to provide improved tracking performance against small aircraft typically used in drug-smuggling activities. The development is based on the Maximum Likelihood Adaptive Neural System (MLANS), a model-based neural network that combines advantages of neural network and model-based algorithmic approaches. The objective of the MLANS tracker development effort is to address user requirements for increased detection and tracking capability in clutter and improved track position, heading, and speed accuracy. The MLANS tracker is expected to outperform other approaches to detection and tracking for the following reasons. It incorporates adaptive internal models of target return signals, target tracks and maneuvers, and clutter signals, which leads to concurrent clutter suppression, detection, and tracking (track-before-detect). It is not combinatorial and thus does not require any thresholding or peak picking and can track in low signal-to-noise conditions. It incorporates superresolution spectrum estimation techniques exceeding the performance of conventional maximum likelihood and maximum entropy methods. The unique spectrum estimation method is based on the Einsteinian interpretation of the ROTHR received energy spectrum as a probability density of signal frequency. The MLANS neural architecture and learning mechanism are founded on spectrum models and maximization of the "Einsteinian" likelihood, allowing knowledge of the physical behavior of both targets and clutter to be injected into the tracker algorithms. The paper describes the addressed requirements and expected improvements, theoretical foundations, engineering methodology, and results of the development effort to date.

Cool, warm, and heat-pain detection thresholds: testing methods and inferences about anatomic distribution of receptors.

PubMed

Dyck, P J; Zimmerman, I; Gillen, D A; Johnson, D; Karnes, J L; O'Brien, P C

1993-08-01

We recently found that vibratory detection threshold is greatly influenced by the algorithm of testing. Here, we study the influence of stimulus characteristics and algorithm of testing and estimating threshold on cool (CDT), warm (WDT), and heat-pain (HPDT) detection thresholds. We show that continuously decreasing (for CDT) or increasing (for WDT) thermode temperature to the point at which cooling or warming is perceived and signaled by depressing a response key ("appearance" threshold) overestimates threshold with rapid rates of thermal change. The mean of the appearance and disappearance thresholds also does not perform well for insensitive sites and patients. Pyramidal (or flat-topped pyramidal) stimuli ranging in magnitude, in 25 steps, from near skin temperature to 9 degrees C for 10 seconds (for CDT), from near skin temperature to 45 degrees C for 10 seconds (for WDT), and from near skin temperature to 49 degrees C for 10 seconds (for HPDT) provide ideal stimuli for use in several algorithms of testing and estimating threshold. Near threshold, only the initial direction of thermal change from skin temperature is perceived, and not its return to baseline. Use of steps of stimulus intensity allows the subject or patient to take the needed time to decide whether the stimulus was felt or not (in 4, 2, and 1 stepping algorithms), or whether it occurred in stimulus interval 1 or 2 (in two-alternative forced-choice testing). Thermal thresholds were generally significantly lower with a large (10 cm2) than with a small (2.7 cm2) thermode.(ABSTRACT TRUNCATED AT 250 WORDS)

[Improving speech comprehension using a new cochlear implant speech processor].

PubMed

Müller-Deile, J; Kortmann, T; Hoppe, U; Hessel, H; Morsnowski, A

2009-06-01

The aim of this multicenter clinical field study was to assess the benefits of the new Freedom 24 sound processor for cochlear implant (CI) users implanted with the Nucleus 24 cochlear implant system. The study included 48 postlingually profoundly deaf experienced CI users who demonstrated speech comprehension performance with their current speech processor on the Oldenburg sentence test (OLSA) in quiet conditions of at least 80% correct scores and who were able to perform adaptive speech threshold testing using the OLSA in noisy conditions. Following baseline measures of speech comprehension performance with their current speech processor, subjects were upgraded to the Freedom 24 speech processor. After a take-home trial period of at least 2 weeks, subject performance was evaluated by measuring the speech reception threshold with the Freiburg multisyllabic word test and speech intelligibility with the Freiburg monosyllabic word test at 50 dB and 70 dB in the sound field. The results demonstrated highly significant benefits for speech comprehension with the new speech processor. Significant benefits for speech comprehension were also demonstrated with the new speech processor when tested in competing background noise.In contrast, use of the Abbreviated Profile of Hearing Aid Benefit (APHAB) did not prove to be a suitably sensitive assessment tool for comparative subjective self-assessment of hearing benefits with each processor. Use of the preprocessing algorithm known as adaptive dynamic range optimization (ADRO) in the Freedom 24 led to additional improvements over the standard upgrade map for speech comprehension in quiet and showed equivalent performance in noise. Through use of the preprocessing beam-forming algorithm BEAM, subjects demonstrated a highly significant improved signal-to-noise ratio for speech comprehension thresholds (i.e., signal-to-noise ratio for 50% speech comprehension scores) when tested with an adaptive procedure using the Oldenburg sentences in the clinical setting S(0)N(CI), with speech signal at 0 degrees and noise lateral to the CI at 90 degrees . With the convincing findings from our evaluations of this multicenter study cohort, a trial with the Freedom 24 sound processor for all suitable CI users is recommended. For evaluating the benefits of a new processor, the comparative assessment paradigm used in our study design would be considered ideal for use with individual patients.

Wavelet-based Adaptive Mesh Refinement Method for Global Atmospheric Chemical Transport Modeling

NASA Astrophysics Data System (ADS)

Rastigejev, Y.

2011-12-01

Numerical modeling of global atmospheric chemical transport presents enormous computational difficulties, associated with simulating a wide range of time and spatial scales. The described difficulties are exacerbated by the fact that hundreds of chemical species and thousands of chemical reactions typically are used for chemical kinetic mechanism description. These computational requirements very often forces researches to use relatively crude quasi-uniform numerical grids with inadequate spatial resolution that introduces significant numerical diffusion into the system. It was shown that this spurious diffusion significantly distorts the pollutant mixing and transport dynamics for typically used grid resolution. The described numerical difficulties have to be systematically addressed considering that the demand for fast, high-resolution chemical transport models will be exacerbated over the next decade by the need to interpret satellite observations of tropospheric ozone and related species. In this study we offer dynamically adaptive multilevel Wavelet-based Adaptive Mesh Refinement (WAMR) method for numerical modeling of atmospheric chemical evolution equations. The adaptive mesh refinement is performed by adding and removing finer levels of resolution in the locations of fine scale development and in the locations of smooth solution behavior accordingly. The algorithm is based on the mathematically well established wavelet theory. This allows us to provide error estimates of the solution that are used in conjunction with an appropriate threshold criteria to adapt the non-uniform grid. Other essential features of the numerical algorithm include: an efficient wavelet spatial discretization that allows to minimize the number of degrees of freedom for a prescribed accuracy, a fast algorithm for computing wavelet amplitudes, and efficient and accurate derivative approximations on an irregular grid. The method has been tested for a variety of benchmark problems including numerical simulation of transpacific traveling pollution plumes. The generated pollution plumes are diluted due to turbulent mixing as they are advected downwind. Despite this dilution, it was recently discovered that pollution plumes in the remote troposphere can preserve their identity as well-defined structures for two weeks or more as they circle the globe. Present Global Chemical Transport Models (CTMs) implemented for quasi-uniform grids are completely incapable of reproducing these layered structures due to high numerical plume dilution caused by numerical diffusion combined with non-uniformity of atmospheric flow. It is shown that WAMR algorithm solutions of comparable accuracy as conventional numerical techniques are obtained with more than an order of magnitude reduction in number of grid points, therefore the adaptive algorithm is capable to produce accurate results at a relatively low computational cost. The numerical simulations demonstrate that WAMR algorithm applied the traveling plume problem accurately reproduces the plume dynamics unlike conventional numerical methods that utilizes quasi-uniform numerical grids.

Precision control of eluted activity from a Sr/Rb generator for cardiac positron emission tomography.

PubMed

Klein, R; Adler, A; Beanlands, R S; deKemp, R A

2004-01-01

A rubidium-82 (/sup 82/Rb) elution system is described for use with clinical positron emission tomography. The system is self-calibrating with 1.4% repeatability, independent of generator activity and elution flow rate. Saline flow is switched between a /sup 82/Sr//sup 82/Rb generator and a bypass line to achieve a constant activity elution of /sup 82/Rb. In the present study, pulse width modulation (PWM) of a solenoid valve is compared to simple threshold control as a means to simulate a proportional valve. A predictive-corrective control algorithm is developed which produces a constant activity elution within the constraints of long feedback delay and short elution time. Accurate constant-activity elutions of 10-70% of the total generator activity were demonstrated using the threshold comparison control. The adaptive-corrective control of the PWM valve provided a substantial improvement in precision of the steady-state output.

A MATLAB toolbox for the efficient estimation of the psychometric function using the updated maximum-likelihood adaptive procedure

PubMed Central

Richards, V. M.; Dai, W.

2014-01-01

A MATLAB toolbox for the efficient estimation of the threshold, slope, and lapse rate of the psychometric function is described. The toolbox enables the efficient implementation of the updated maximum-likelihood (UML) procedure. The toolbox uses an object-oriented architecture for organizing the experimental variables and computational algorithms, which provides experimenters with flexibility in experimental design and data management. Descriptions of the UML procedure and the UML Toolbox are provided, followed by toolbox use examples. Finally, guidelines and recommendations of parameter configurations are given. PMID:24671826

Multiple targets detection method in detection of UWB through-wall radar

NASA Astrophysics Data System (ADS)

Yang, Xiuwei; Yang, Chuanfa; Zhao, Xingwen; Tian, Xianzhong

2017-11-01

In this paper, the problems and difficulties encountered in the detection of multiple moving targets by UWB radar are analyzed. The experimental environment and the penetrating radar system are established. An adaptive threshold method based on local area is proposed to effectively filter out clutter interference The objective of the moving target is analyzed, and the false target is further filtered out by extracting the target feature. Based on the correlation between the targets, the target matching algorithm is proposed to improve the detection accuracy. Finally, the effectiveness of the above method is verified by practical experiment.

Language Comprehension in Language-Learning Impaired Children Improved with Acoustically Modified Speech

NASA Astrophysics Data System (ADS)

Tallal, Paula; Miller, Steve L.; Bedi, Gail; Byma, Gary; Wang, Xiaoqin; Nagarajan, Srikantan S.; Schreiner, Christoph; Jenkins, William M.; Merzenich, Michael M.

1996-01-01

A speech processing algorithm was developed to create more salient versions of the rapidly changing elements in the acoustic waveform of speech that have been shown to be deficiently processed by language-learning impaired (LLI) children. LLI children received extensive daily training, over a 4-week period, with listening exercises in which all speech was translated into this synthetic form. They also received daily training with computer "games" designed to adaptively drive improvements in temporal processing thresholds. Significant improvements in speech discrimination and language comprehension abilities were demonstrated in two independent groups of LLI children.

Typical performance of approximation algorithms for NP-hard problems

NASA Astrophysics Data System (ADS)

Takabe, Satoshi; Hukushima, Koji

2016-11-01

Typical performance of approximation algorithms is studied for randomized minimum vertex cover problems. A wide class of random graph ensembles characterized by an arbitrary degree distribution is discussed with the presentation of a theoretical framework. Herein, three approximation algorithms are examined: linear-programming relaxation, loopy-belief propagation, and the leaf-removal algorithm. The former two algorithms are analyzed using a statistical-mechanical technique, whereas the average-case analysis of the last one is conducted using the generating function method. These algorithms have a threshold in the typical performance with increasing average degree of the random graph, below which they find true optimal solutions with high probability. Our study reveals that there exist only three cases, determined by the order of the typical performance thresholds. In addition, we provide some conditions for classification of the graph ensembles and demonstrate explicitly some examples for the difference in thresholds.

Automatic treatment plan re-optimization for adaptive radiotherapy guided with the initial plan DVHs

NASA Astrophysics Data System (ADS)

Li, Nan; Zarepisheh, Masoud; Uribe-Sanchez, Andres; Moore, Kevin; Tian, Zhen; Zhen, Xin; Jiang Graves, Yan; Gautier, Quentin; Mell, Loren; Zhou, Linghong; Jia, Xun; Jiang, Steve

2013-12-01

Adaptive radiation therapy (ART) can reduce normal tissue toxicity and/or improve tumor control through treatment adaptations based on the current patient anatomy. Developing an efficient and effective re-planning algorithm is an important step toward the clinical realization of ART. For the re-planning process, manual trial-and-error approach to fine-tune planning parameters is time-consuming and is usually considered unpractical, especially for online ART. It is desirable to automate this step to yield a plan of acceptable quality with minimal interventions. In ART, prior information in the original plan is available, such as dose-volume histogram (DVH), which can be employed to facilitate the automatic re-planning process. The goal of this work is to develop an automatic re-planning algorithm to generate a plan with similar, or possibly better, DVH curves compared with the clinically delivered original plan. Specifically, our algorithm iterates the following two loops. An inner loop is the traditional fluence map optimization, in which we optimize a quadratic objective function penalizing the deviation of the dose received by each voxel from its prescribed or threshold dose with a set of fixed voxel weighting factors. In outer loop, the voxel weighting factors in the objective function are adjusted according to the deviation of the current DVH curves from those in the original plan. The process is repeated until the DVH curves are acceptable or maximum iteration step is reached. The whole algorithm is implemented on GPU for high efficiency. The feasibility of our algorithm has been demonstrated with three head-and-neck cancer IMRT cases, each having an initial planning CT scan and another treatment CT scan acquired in the middle of treatment course. Compared with the DVH curves in the original plan, the DVH curves in the resulting plan using our algorithm with 30 iterations are better for almost all structures. The re-optimization process takes about 30 s using our in-house optimization engine. This work was originally presented at the 54th AAPM annual meeting in Charlotte, NC, July 29-August 2, 2012.

A new time-adaptive discrete bionic wavelet transform for enhancing speech from adverse noise environment

NASA Astrophysics Data System (ADS)

Palaniswamy, Sumithra; Duraisamy, Prakash; Alam, Mohammad Showkat; Yuan, Xiaohui

2012-04-01

Automatic speech processing systems are widely used in everyday life such as mobile communication, speech and speaker recognition, and for assisting the hearing impaired. In speech communication systems, the quality and intelligibility of speech is of utmost importance for ease and accuracy of information exchange. To obtain an intelligible speech signal and one that is more pleasant to listen, noise reduction is essential. In this paper a new Time Adaptive Discrete Bionic Wavelet Thresholding (TADBWT) scheme is proposed. The proposed technique uses Daubechies mother wavelet to achieve better enhancement of speech from additive non- stationary noises which occur in real life such as street noise and factory noise. Due to the integration of human auditory system model into the wavelet transform, bionic wavelet transform (BWT) has great potential for speech enhancement which may lead to a new path in speech processing. In the proposed technique, at first, discrete BWT is applied to noisy speech to derive TADBWT coefficients. Then the adaptive nature of the BWT is captured by introducing a time varying linear factor which updates the coefficients at each scale over time. This approach has shown better performance than the existing algorithms at lower input SNR due to modified soft level dependent thresholding on time adaptive coefficients. The objective and subjective test results confirmed the competency of the TADBWT technique. The effectiveness of the proposed technique is also evaluated for speaker recognition task under noisy environment. The recognition results show that the TADWT technique yields better performance when compared to alternate methods specifically at lower input SNR.

The Limits to Adaptation: A Systems Approach

EPA Science Inventory

The ability to adapt to climate change is delineated by capacity thresholds, after which climate damages begin to overwhelm the adaptation response. Such thresholds depend upon physical properties (natural processes and engineering parameters), resource constraints (expressed th...

Threshold Assessment of Gear Diagnostic Tools on Flight and Test Rig Data

NASA Technical Reports Server (NTRS)

Dempsey, Paula J.; Mosher, Marianne; Huff, Edward M.

2003-01-01

A method for defining thresholds for vibration-based algorithms that provides the minimum number of false alarms while maintaining sensitivity to gear damage was developed. This analysis focused on two vibration based gear damage detection algorithms, FM4 and MSA. This method was developed using vibration data collected during surface fatigue tests performed in a spur gearbox rig. The thresholds were defined based on damage progression during tests with damage. The thresholds false alarm rates were then evaluated on spur gear tests without damage. Next, the same thresholds were applied to flight data from an OH-58 helicopter transmission. Results showed that thresholds defined in test rigs can be used to define thresholds in flight to correctly classify the transmission operation as normal.

Characterizing Decision-Analysis Performances of Risk Prediction Models Using ADAPT Curves.

PubMed

Lee, Wen-Chung; Wu, Yun-Chun

2016-01-01

The area under the receiver operating characteristic curve is a widely used index to characterize the performance of diagnostic tests and prediction models. However, the index does not explicitly acknowledge the utilities of risk predictions. Moreover, for most clinical settings, what counts is whether a prediction model can guide therapeutic decisions in a way that improves patient outcomes, rather than to simply update probabilities.Based on decision theory, the authors propose an alternative index, the "average deviation about the probability threshold" (ADAPT).An ADAPT curve (a plot of ADAPT value against the probability threshold) neatly characterizes the decision-analysis performances of a risk prediction model.Several prediction models can be compared for their ADAPT values at a chosen probability threshold, for a range of plausible threshold values, or for the whole ADAPT curves. This should greatly facilitate the selection of diagnostic tests and prediction models.

Bilevel thresholding of sliced image of sludge floc.

PubMed

Chu, C P; Lee, D J

2004-02-15

This work examined the feasibility of employing various thresholding algorithms to determining the optimal bilevel thresholding value for estimating the geometric parameters of sludge flocs from the microtome sliced images and from the confocal laser scanning microscope images. Morphological information extracted from images depends on the bilevel thresholding value. According to the evaluation on the luminescence-inverted images and fractal curves (quadric Koch curve and Sierpinski carpet), Otsu's method yields more stable performance than other histogram-based algorithms and is chosen to obtain the porosity. The maximum convex perimeter method, however, can probe the shapes and spatial distribution of the pores among the biomass granules in real sludge flocs. A combined algorithm is recommended for probing the sludge floc structure.

Directional Histogram Ratio at Random Probes: A Local Thresholding Criterion for Capillary Images

PubMed Central

Lu, Na; Silva, Jharon; Gu, Yu; Gerber, Scott; Wu, Hulin; Gelbard, Harris; Dewhurst, Stephen; Miao, Hongyu

2013-01-01

With the development of micron-scale imaging techniques, capillaries can be conveniently visualized using methods such as two-photon and whole mount microscopy. However, the presence of background staining, leaky vessels and the diffusion of small fluorescent molecules can lead to significant complexity in image analysis and loss of information necessary to accurately quantify vascular metrics. One solution to this problem is the development of accurate thresholding algorithms that reliably distinguish blood vessels from surrounding tissue. Although various thresholding algorithms have been proposed, our results suggest that without appropriate pre- or post-processing, the existing approaches may fail to obtain satisfactory results for capillary images that include areas of contamination. In this study, we propose a novel local thresholding algorithm, called directional histogram ratio at random probes (DHR-RP). This method explicitly considers the geometric features of tube-like objects in conducting image binarization, and has a reliable performance in distinguishing small vessels from either clean or contaminated background. Experimental and simulation studies suggest that our DHR-RP algorithm is superior over existing thresholding methods. PMID:23525856

Maximum-likelihood spectral estimation and adaptive filtering techniques with application to airborne Doppler weather radar. Thesis Technical Report No. 20

NASA Technical Reports Server (NTRS)

Lai, Jonathan Y.

1994-01-01

This dissertation focuses on the signal processing problems associated with the detection of hazardous windshears using airborne Doppler radar when weak weather returns are in the presence of strong clutter returns. In light of the frequent inadequacy of spectral-processing oriented clutter suppression methods, we model a clutter signal as multiple sinusoids plus Gaussian noise, and propose adaptive filtering approaches that better capture the temporal characteristics of the signal process. This idea leads to two research topics in signal processing: (1) signal modeling and parameter estimation, and (2) adaptive filtering in this particular signal environment. A high-resolution, low SNR threshold maximum likelihood (ML) frequency estimation and signal modeling algorithm is devised and proves capable of delineating both the spectral and temporal nature of the clutter return. Furthermore, the Least Mean Square (LMS) -based adaptive filter's performance for the proposed signal model is investigated, and promising simulation results have testified to its potential for clutter rejection leading to more accurate estimation of windspeed thus obtaining a better assessment of the windshear hazard.

Real-Time Adaptive Control Allocation Applied to a High Performance Aircraft

NASA Technical Reports Server (NTRS)

Davidson, John B.; Lallman, Frederick J.; Bundick, W. Thomas

2001-01-01

Abstract This paper presents the development and application of one approach to the control of aircraft with large numbers of control effectors. This approach, referred to as real-time adaptive control allocation, combines a nonlinear method for control allocation with actuator failure detection and isolation. The control allocator maps moment (or angular acceleration) commands into physical control effector commands as functions of individual control effectiveness and availability. The actuator failure detection and isolation algorithm is a model-based approach that uses models of the actuators to predict actuator behavior and an adaptive decision threshold to achieve acceptable false alarm/missed detection rates. This integrated approach provides control reconfiguration when an aircraft is subjected to actuator failure, thereby improving maneuverability and survivability of the degraded aircraft. This method is demonstrated on a next generation military aircraft Lockheed-Martin Innovative Control Effector) simulation that has been modified to include a novel nonlinear fluid flow control control effector based on passive porosity. Desktop and real-time piloted simulation results demonstrate the performance of this integrated adaptive control allocation approach.

Face verification with balanced thresholds.

PubMed

Yan, Shuicheng; Xu, Dong; Tang, Xiaoou

2007-01-01

The process of face verification is guided by a pre-learned global threshold, which, however, is often inconsistent with class-specific optimal thresholds. It is, hence, beneficial to pursue a balance of the class-specific thresholds in the model-learning stage. In this paper, we present a new dimensionality reduction algorithm tailored to the verification task that ensures threshold balance. This is achieved by the following aspects. First, feasibility is guaranteed by employing an affine transformation matrix, instead of the conventional projection matrix, for dimensionality reduction, and, hence, we call the proposed algorithm threshold balanced transformation (TBT). Then, the affine transformation matrix, constrained as the product of an orthogonal matrix and a diagonal matrix, is optimized to improve the threshold balance and classification capability in an iterative manner. Unlike most algorithms for face verification which are directly transplanted from face identification literature, TBT is specifically designed for face verification and clarifies the intrinsic distinction between these two tasks. Experiments on three benchmark face databases demonstrate that TBT significantly outperforms the state-of-the-art subspace techniques for face verification.

New developments in supra-threshold perimetry.

PubMed

Henson, David B; Artes, Paul H

2002-09-01

To describe a series of recent enhancements to supra-threshold perimetry. Computer simulations were used to develop an improved algorithm (HEART) for the setting of the supra-threshold test intensity at the beginning of a field test, and to evaluate the relationship between various pass/fail criteria and the test's performance (sensitivity and specificity) and how they compare with modern threshold perimetry. Data were collected in optometric practices to evaluate HEART and to assess how the patient's response times can be analysed to detect false positive response errors in visual field test results. The HEART algorithm shows improved performance (reduced between-eye differences) over current algorithms. A pass/fail criterion of '3 stimuli seen of 3-5 presentations' at each test location reduces test/retest variability and combines high sensitivity and specificity. A large percentage of false positive responses can be detected by comparing their latencies to the average response time of a patient. Optimised supra-threshold visual field tests can perform as well as modern threshold techniques. Such tests may be easier to perform for novice patients, compared with the more demanding threshold tests.

The effects of segmentation algorithms on the measurement of 18F-FDG PET texture parameters in non-small cell lung cancer.

PubMed

Bashir, Usman; Azad, Gurdip; Siddique, Muhammad Musib; Dhillon, Saana; Patel, Nikheel; Bassett, Paul; Landau, David; Goh, Vicky; Cook, Gary

2017-12-01

Measures of tumour heterogeneity derived from 18-fluoro-2-deoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT) scans are increasingly reported as potential biomarkers of non-small cell lung cancer (NSCLC) for classification and prognostication. Several segmentation algorithms have been used to delineate tumours, but their effects on the reproducibility and predictive and prognostic capability of derived parameters have not been evaluated. The purpose of our study was to retrospectively compare various segmentation algorithms in terms of inter-observer reproducibility and prognostic capability of texture parameters derived from non-small cell lung cancer (NSCLC) 18 F-FDG PET/CT images. Fifty three NSCLC patients (mean age 65.8 years; 31 males) underwent pre-chemoradiotherapy 18 F-FDG PET/CT scans. Three readers segmented tumours using freehand (FH), 40% of maximum intensity threshold (40P), and fuzzy locally adaptive Bayesian (FLAB) algorithms. Intraclass correlation coefficient (ICC) was used to measure the inter-observer variability of the texture features derived by the three segmentation algorithms. Univariate cox regression was used on 12 commonly reported texture features to predict overall survival (OS) for each segmentation algorithm. Model quality was compared across segmentation algorithms using Akaike information criterion (AIC). 40P was the most reproducible algorithm (median ICC 0.9; interquartile range [IQR] 0.85-0.92) compared with FLAB (median ICC 0.83; IQR 0.77-0.86) and FH (median ICC 0.77; IQR 0.7-0.85). On univariate cox regression analysis, 40P found 2 out of 12 variables, i.e. first-order entropy and grey-level co-occurence matrix (GLCM) entropy, to be significantly associated with OS; FH and FLAB found 1, i.e., first-order entropy. For each tested variable, survival models for all three segmentation algorithms were of similar quality, exhibiting comparable AIC values with overlapping 95% CIs. Compared with both FLAB and FH, segmentation with 40P yields superior inter-observer reproducibility of texture features. Survival models generated by all three segmentation algorithms are of at least equivalent utility. Our findings suggest that a segmentation algorithm using a 40% of maximum threshold is acceptable for texture analysis of 18 F-FDG PET in NSCLC.

Impact of tumor size and tracer uptake heterogeneity in (18)F-FDG PET and CT non-small cell lung cancer tumor delineation.

PubMed

Hatt, Mathieu; Cheze-le Rest, Catherine; van Baardwijk, Angela; Lambin, Philippe; Pradier, Olivier; Visvikis, Dimitris

2011-11-01

The objectives of this study were to investigate the relationship between CT- and (18)F-FDG PET-based tumor volumes in non-small cell lung cancer (NSCLC) and the impact of tumor size and uptake heterogeneity on various approaches to delineating uptake on PET images. Twenty-five NSCLC cancer patients with (18)F-FDG PET/CT were considered. Seventeen underwent surgical resection of their tumor, and the maximum diameter was measured. Two observers manually delineated the tumors on the CT images and the tumor uptake on the corresponding PET images, using a fixed threshold at 50% of the maximum (T(50)), an adaptive threshold methodology, and the fuzzy locally adaptive Bayesian (FLAB) algorithm. Maximum diameters of the delineated volumes were compared with the histopathology reference when available. The volumes of the tumors were compared, and correlations between the anatomic volume and PET uptake heterogeneity and the differences between delineations were investigated. All maximum diameters measured on PET and CT images significantly correlated with the histopathology reference (r > 0.89, P < 0.0001). Significant differences were observed among the approaches: CT delineation resulted in large overestimation (+32% ± 37%), whereas all delineations on PET images resulted in underestimation (from -15% ± 17% for T(50) to -4% ± 8% for FLAB) except manual delineation (+8% ± 17%). Overall, CT volumes were significantly larger than PET volumes (55 ± 74 cm(3) for CT vs. from 18 ± 25 to 47 ± 76 cm(3) for PET). A significant correlation was found between anatomic tumor size and heterogeneity (larger lesions were more heterogeneous). Finally, the more heterogeneous the tumor uptake, the larger was the underestimation of PET volumes by threshold-based techniques. Volumes based on CT images were larger than those based on PET images. Tumor size and tracer uptake heterogeneity have an impact on threshold-based methods, which should not be used for the delineation of cases of large heterogeneous NSCLC, as these methods tend to largely underestimate the spatial extent of the functional tumor in such cases. For an accurate delineation of PET volumes in NSCLC, advanced image segmentation algorithms able to deal with tracer uptake heterogeneity should be preferred.

Surface-Height Determination of Crevassed Glaciers-Mathematical Principles of an Autoadaptive Density-Dimension Algorithm and Validation Using ICESat-2 Simulator (SIMPL) Data

NASA Technical Reports Server (NTRS)

Herzfeld, Ute C.; Trantow, Thomas M.; Harding, David; Dabney, Philip W.

2017-01-01

Glacial acceleration is a main source of uncertainty in sea-level-change assessment. Measurement of ice-surface heights with a spatial and temporal resolution that not only allows elevation-change calculation, but also captures ice-surface morphology and its changes is required to aid in investigations of the geophysical processes associated with glacial acceleration.The Advanced Topographic Laser Altimeter System aboard NASAs future ICESat-2 Mission (launch 2017) will implement multibeam micropulse photon-counting lidar altimetry aimed at measuring ice-surface heights at 0.7-m along-track spacing. The instrument is designed to resolve spatial and temporal variability of rapidly changing glaciers and ice sheets and the Arctic sea ice. The new technology requires the development of a new mathematical algorithm for the retrieval of height information.We introduce the density-dimension algorithm (DDA) that utilizes the radial basis function to calculate a weighted density as a form of data aggregation in the photon cloud and considers density an additional dimension as an aid in auto-adaptive threshold determination. The auto-adaptive capability of the algorithm is necessary to separate returns from noise and signal photons under changing environmental conditions. The algorithm is evaluated using data collected with an ICESat-2 simulator instrument, the Slope Imaging Multi-polarization Photon-counting Lidar, over the heavily crevassed Giesecke Braer in Northwestern Greenland in summer 2015. Results demonstrate that ICESat-2 may be expected to provide ice-surface height measurements over crevassed glaciers and other complex ice surfaces. The DDA is generally applicable for the analysis of airborne and spaceborne micropulse photon-counting lidar data over complex and simple surfaces.

Formulating face verification with semidefinite programming.

PubMed

Yan, Shuicheng; Liu, Jianzhuang; Tang, Xiaoou; Huang, Thomas S

2007-11-01

This paper presents a unified solution to three unsolved problems existing in face verification with subspace learning techniques: selection of verification threshold, automatic determination of subspace dimension, and deducing feature fusing weights. In contrast to previous algorithms which search for the projection matrix directly, our new algorithm investigates a similarity metric matrix (SMM). With a certain verification threshold, this matrix is learned by a semidefinite programming approach, along with the constraints of the kindred pairs with similarity larger than the threshold, and inhomogeneous pairs with similarity smaller than the threshold. Then, the subspace dimension and the feature fusing weights are simultaneously inferred from the singular value decomposition of the derived SMM. In addition, the weighted and tensor extensions are proposed to further improve the algorithmic effectiveness and efficiency, respectively. Essentially, the verification is conducted within an affine subspace in this new algorithm and is, hence, called the affine subspace for verification (ASV). Extensive experiments show that the ASV can achieve encouraging face verification accuracy in comparison to other subspace algorithms, even without the need to explore any parameters.

Prediction Study on Anti-Slide Control of Railway Vehicle Based on RBF Neural Networks

NASA Astrophysics Data System (ADS)

Yang, Lijun; Zhang, Jimin

While railway vehicle braking, Anti-slide control system will detect operating status of each wheel-sets e.g. speed difference and deceleration etc. Once the detected value on some wheel-set is over pre-defined threshold, brake effort on such wheel-set will be adjusted automatically to avoid blocking. Such method takes effect on guarantee safety operation of vehicle and avoid wheel-set flatness, however it cannot adapt itself to the rail adhesion variation. While wheel-sets slide, the operating status is chaotic time series with certain law, and can be predicted with the law and experiment data in certain time. The predicted values can be used as the input reference signals of vehicle anti-slide control system, to judge and control the slide status of wheel-sets. In this article, the RBF neural networks is taken to predict wheel-set slide status in multi-step with weight vector adjusted based on online self-adaptive algorithm, and the center & normalizing parameters of active function of the hidden unit of RBF neural networks' hidden layer computed with K-means clustering algorithm. With multi-step prediction simulation, the predicted signal with appropriate precision can be used by anti-slide system to trace actively and adjust wheel-set slide tendency, so as to adapt to wheel-rail adhesion variation and reduce the risk of wheel-set blocking.

Comparison of algorithms of testing for use in automated evaluation of sensation.

PubMed

Dyck, P J; Karnes, J L; Gillen, D A; O'Brien, P C; Zimmerman, I R; Johnson, D M

1990-10-01

Estimates of vibratory detection threshold may be used to detect, characterize, and follow the course of sensory abnormality in neurologic disease. The approach is especially useful in epidemiologic and controlled clinical trials. We studied which algorithm of testing and finding threshold should be used in automatic systems by comparing among algorithms and stimulus conditions for the index finger of healthy subjects and for the great toe of patients with mild neuropathy. Appearance thresholds obtained by linear ramps increasing at a rate less than 4.15 microns/sec provided accurate and repeatable thresholds compared with thresholds obtained by forced-choice testing. These rates would be acceptable if only sensitive sites were studied, but they were too slow for use in automatic testing of insensitive parts. Appearance thresholds obtained by fast linear rates (4.15 or 16.6 microns/sec) overestimated threshold, especially for sensitive parts. Use of the mean of appearance and disappearance thresholds, with the stimulus increasing exponentially at rates of 0.5 or 1.0 just noticeable difference (JND) units per second, and interspersion of null stimuli, Békésy with null stimuli, provided accurate, repeatable, and fast estimates of threshold for sensitive parts. Despite the good performance of Békésy testing, we prefer forced choice for evaluation of the sensation of patients with neuropathy.

Parallel Density-Based Clustering for Discovery of Ionospheric Phenomena

NASA Astrophysics Data System (ADS)

Pankratius, V.; Gowanlock, M.; Blair, D. M.

2015-12-01

Ionospheric total electron content maps derived from global networks of dual-frequency GPS receivers can reveal a plethora of ionospheric features in real-time and are key to space weather studies and natural hazard monitoring. However, growing data volumes from expanding sensor networks are making manual exploratory studies challenging. As the community is heading towards Big Data ionospheric science, automation and Computer-Aided Discovery become indispensable tools for scientists. One problem of machine learning methods is that they require domain-specific adaptations in order to be effective and useful for scientists. Addressing this problem, our Computer-Aided Discovery approach allows scientists to express various physical models as well as perturbation ranges for parameters. The search space is explored through an automated system and parallel processing of batched workloads, which finds corresponding matches and similarities in empirical data. We discuss density-based clustering as a particular method we employ in this process. Specifically, we adapt Density-Based Spatial Clustering of Applications with Noise (DBSCAN). This algorithm groups geospatial data points based on density. Clusters of points can be of arbitrary shape, and the number of clusters is not predetermined by the algorithm; only two input parameters need to be specified: (1) a distance threshold, (2) a minimum number of points within that threshold. We discuss an implementation of DBSCAN for batched workloads that is amenable to parallelization on manycore architectures such as Intel's Xeon Phi accelerator with 60+ general-purpose cores. This manycore parallelization can cluster large volumes of ionospheric total electronic content data quickly. Potential applications for cluster detection include the visualization, tracing, and examination of traveling ionospheric disturbances or other propagating phenomena. Acknowledgments. We acknowledge support from NSF ACI-1442997 (PI V. Pankratius).

Role of extrinsic noise in the sensitivity of the rod pathway: rapid dark adaptation of nocturnal vision in humans.

PubMed

Reeves, Adam; Grayhem, Rebecca

2016-03-01

Rod-mediated 500 nm test spots were flashed in Maxwellian view at 5 deg eccentricity, both on steady 10.4 deg fields of intensities (I) from 0.00001 to 1.0 scotopic troland (sc td) and from 0.2 s to 1 s after extinguishing the field. On dim fields, thresholds of tiny (5') tests were proportional to √I (Rose-DeVries law), while thresholds after extinction fell within 0.6 s to the fully dark-adapted absolute threshold. Thresholds of large (1.3 deg) tests were proportional to I (Weber law) and extinction thresholds, to √I. rod thresholds are elevated by photon-driven noise from dim fields that disappears at field extinction; large spot thresholds are additionally elevated by neural light adaptation proportional to √I. At night, recovery from dimly lit fields is fast, not slow.

Negative Difference Resistance and Its Application to Construct Boolean Logic Circuits

NASA Astrophysics Data System (ADS)

Nikodem, Maciej; Bawiec, Marek A.; Surmacz, Tomasz R.

Electronic circuits based on nanodevices and quantum effect are the future of logic circuits design. Today's technology allows constructing resonant tunneling diodes, quantum cellular automata and nanowires/nanoribbons that are the elementary components of threshold gates. However, synthesizing a threshold circuit for an arbitrary logic function is still a challenging task where no efficient algorithms exist. This paper focuses on Generalised Threshold Gates (GTG), giving the overview of threshold circuit synthesis methods and presenting an algorithm that considerably simplifies the task in case of GTG circuits.

Multi-observation PET image analysis for patient follow-up quantitation and therapy assessment

NASA Astrophysics Data System (ADS)

David, S.; Visvikis, D.; Roux, C.; Hatt, M.

2011-09-01

In positron emission tomography (PET) imaging, an early therapeutic response is usually characterized by variations of semi-quantitative parameters restricted to maximum SUV measured in PET scans during the treatment. Such measurements do not reflect overall tumor volume and radiotracer uptake variations. The proposed approach is based on multi-observation image analysis for merging several PET acquisitions to assess tumor metabolic volume and uptake variations. The fusion algorithm is based on iterative estimation using a stochastic expectation maximization (SEM) algorithm. The proposed method was applied to simulated and clinical follow-up PET images. We compared the multi-observation fusion performance to threshold-based methods, proposed for the assessment of the therapeutic response based on functional volumes. On simulated datasets the adaptive threshold applied independently on both images led to higher errors than the ASEM fusion and on clinical datasets it failed to provide coherent measurements for four patients out of seven due to aberrant delineations. The ASEM method demonstrated improved and more robust estimation of the evaluation leading to more pertinent measurements. Future work will consist in extending the methodology and applying it to clinical multi-tracer datasets in order to evaluate its potential impact on the biological tumor volume definition for radiotherapy applications.

Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion

PubMed Central

Zhao, Yuanshen; Gong, Liang; Huang, Yixiang; Liu, Chengliang

2016-01-01

Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost. PMID:26840313

Fully Automated Detection of Cloud and Aerosol Layers in the CALIPSO Lidar Measurements

NASA Technical Reports Server (NTRS)

Vaughan, Mark A.; Powell, Kathleen A.; Kuehn, Ralph E.; Young, Stuart A.; Winker, David M.; Hostetler, Chris A.; Hunt, William H.; Liu, Zhaoyan; McGill, Matthew J.; Getzewich, Brian J.

2009-01-01

Accurate knowledge of the vertical and horizontal extent of clouds and aerosols in the earth s atmosphere is critical in assessing the planet s radiation budget and for advancing human understanding of climate change issues. To retrieve this fundamental information from the elastic backscatter lidar data acquired during the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, a selective, iterated boundary location (SIBYL) algorithm has been developed and deployed. SIBYL accomplishes its goals by integrating an adaptive context-sensitive profile scanner into an iterated multiresolution spatial averaging scheme. This paper provides an in-depth overview of the architecture and performance of the SIBYL algorithm. It begins with a brief review of the theory of target detection in noise-contaminated signals, and an enumeration of the practical constraints levied on the retrieval scheme by the design of the lidar hardware, the geometry of a space-based remote sensing platform, and the spatial variability of the measurement targets. Detailed descriptions are then provided for both the adaptive threshold algorithm used to detect features of interest within individual lidar profiles and the fully automated multiresolution averaging engine within which this profile scanner functions. The resulting fusion of profile scanner and averaging engine is specifically designed to optimize the trade-offs between the widely varying signal-to-noise ratio of the measurements and the disparate spatial resolutions of the detection targets. Throughout the paper, specific algorithm performance details are illustrated using examples drawn from the existing CALIPSO dataset. Overall performance is established by comparisons to existing layer height distributions obtained by other airborne and space-based lidars.

Automated detection of open magnetic field regions in EUV images

NASA Astrophysics Data System (ADS)

Krista, Larisza Diana; Reinard, Alysha

2016-05-01

Open magnetic regions on the Sun are either long-lived (coronal holes) or transient (dimmings) in nature, but both appear as dark regions in EUV images. For this reason their detection can be done in a similar way. As coronal holes are often large and long-lived in comparison to dimmings, their detection is more straightforward. The Coronal Hole Automated Recognition and Monitoring (CHARM) algorithm detects coronal holes using EUV images and a magnetogram. The EUV images are used to identify dark regions, and the magnetogam allows us to determine if the dark region is unipolar - a characteristic of coronal holes. There is no temporal sensitivity in this process, since coronal hole lifetimes span days to months. Dimming regions, however, emerge and disappear within hours. Hence, the time and location of a dimming emergence need to be known to successfully identify them and distinguish them from regular coronal holes. Currently, the Coronal Dimming Tracker (CoDiT) algorithm is semi-automated - it requires the dimming emergence time and location as an input. With those inputs we can identify the dimming and track it through its lifetime. CoDIT has also been developed to allow the tracking of dimmings that split or merge - a typical feature of dimmings.The advantage of these particular algorithms is their ability to adapt to detecting different types of open field regions. For coronal hole detection, each full-disk solar image is processed individually to determine a threshold for the image, hence, we are not limited to a single pre-determined threshold. For dimming regions we also allow individual thresholds for each dimming, as they can differ substantially. This flexibility is necessary for a subjective analysis of the studied regions. These algorithms were developed with the goal to allow us better understand the processes that give rise to eruptive and non-eruptive open field regions. We aim to study how these regions evolve over time and what environmental factors influence their growth and decay over short and long time-periods (days to solar cycles).

Analysis of image thresholding segmentation algorithms based on swarm intelligence

NASA Astrophysics Data System (ADS)

Zhang, Yi; Lu, Kai; Gao, Yinghui; Yang, Bo

2013-03-01

Swarm intelligence-based image thresholding segmentation algorithms are playing an important role in the research field of image segmentation. In this paper, we briefly introduce the theories of four existing image segmentation algorithms based on swarm intelligence including fish swarm algorithm, artificial bee colony, bacteria foraging algorithm and particle swarm optimization. Then some image benchmarks are tested in order to show the differences of the segmentation accuracy, time consumption, convergence and robustness for Salt & Pepper noise and Gaussian noise of these four algorithms. Through these comparisons, this paper gives qualitative analyses for the performance variance of the four algorithms. The conclusions in this paper would give a significant guide for the actual image segmentation.

[A peak recognition algorithm designed for chromatographic peaks of transformer oil].

PubMed

Ou, Linjun; Cao, Jian

2014-09-01

In the field of the chromatographic peak identification of the transformer oil, the traditional first-order derivative requires slope threshold to achieve peak identification. In terms of its shortcomings of low automation and easy distortion, the first-order derivative method was improved by applying the moving average iterative method and the normalized analysis techniques to identify the peaks. Accurate identification of the chromatographic peaks was realized through using multiple iterations of the moving average of signal curves and square wave curves to determine the optimal value of the normalized peak identification parameters, combined with the absolute peak retention times and peak window. The experimental results show that this algorithm can accurately identify the peaks and is not sensitive to the noise, the chromatographic peak width or the peak shape changes. It has strong adaptability to meet the on-site requirements of online monitoring devices of dissolved gases in transformer oil.

Automated Bone Screw Tightening to Adaptive Levels of Stripping Torque.

PubMed

Reynolds, Karen J; Mohtar, Aaron A; Cleek, Tammy M; Ryan, Melissa K; Hearn, Trevor C

2017-06-01

To use relationships between tightening parameters, related to bone quality, to develop an automated system that determines and controls the level of screw tightening. An algorithm relating current at head contact (IHC) to current at construct failure (Imax) was developed. The algorithm was used to trigger cessation of screw insertion at a predefined tightening level, in real time, between head contact and maximum current. The ability of the device to stop at the predefined level was assessed. The mean (±SD) current at which screw insertion ceased was calculated to be [51.47 ± 9.75% × (Imax - IHC)] + IHC, with no premature bone failures. A smart screwdriver was developed that uses the current from the motor driving the screw to predict the current at which the screw will strip the bone threads. The device was implemented and was able to achieve motor shut-off and cease tightening at a predefined threshold, with no premature bone failures.

Synergy of adaptive thresholds and multiple transmitters in free-space optical communication.

PubMed

Louthain, James A; Schmidt, Jason D

2010-04-26

Laser propagation through extended turbulence causes severe beam spread and scintillation. Airborne laser communication systems require special considerations in size, complexity, power, and weight. Rather than using bulky, costly, adaptive optics systems, we reduce the variability of the received signal by integrating a two-transmitter system with an adaptive threshold receiver to average out the deleterious effects of turbulence. In contrast to adaptive optics approaches, systems employing multiple transmitters and adaptive thresholds exhibit performance improvements that are unaffected by turbulence strength. Simulations of this system with on-off-keying (OOK) showed that reducing the scintillation variations with multiple transmitters improves the performance of low-frequency adaptive threshold estimators by 1-3 dB. The combination of multiple transmitters and adaptive thresholding provided at least a 10 dB gain over implementing only transmitter pointing and receiver tilt correction for all three high-Rytov number scenarios. The scenario with a spherical-wave Rytov number R=0.20 enjoyed a 13 dB reduction in the required SNR for BER's between 10(-5) to 10(-3), consistent with the code gain metric. All five scenarios between 0.06 and 0.20 Rytov number improved to within 3 dB of the SNR of the lowest Rytov number scenario.

Longitudinal changes in femur bone mineral density after spinal cord injury: effects of slice placement and peel method

PubMed Central

Dudley-Javoroski, S.

2010-01-01

Summary Surveillance of femur metaphysis bone mineral density (BMD) decline after spinal cord injury (SCI) may be subject to slice placement error of 2.5%. Adaptations to anti-osteoporosis measures should exceed this potential source of error. Image analysis parameters likewise affect BMD output and should be selected strategically in longitudinal studies. Introduction Understanding the longitudinal changes in bone mineral density (BMD) after spinal cord injury (SCI) is important when assessing new interventions. We determined the longitudinal effect of SCI on BMD of the femur metaphysis. To facilitate interpretation of longitudinal outcomes, we (1) determined the BMD difference associated with erroneous peripheral quantitative computed tomography (pQCT) slice placement, and (2) determined the effect of operator-selected pQCT peel algorithms on BMD. Methods pQCT images were obtained from the femur metaphysis (12% of length from distal end) of adult subjects with and without SCI. Slice placement errors were simulated at 3 mm intervals and were processed in two ways (threshold-based vs. concentric peel). Results BMD demonstrated a rapid decline over 2 years post-injury. BMD differences attributable to operator-selected peel methods were large (17.3% for subjects with SCI). Conclusions Femur metaphysis BMD declines after SCI in a manner similar to other anatomic sites. Concentric (percentage-based) peel methods may be most appropriate when special sensitivity is required to detect BMD adaptations. Threshold-based methods may be more appropriate when asymmetric adaptations are observed. PMID:19707702

Designing an Algorithm for Cancerous Tissue Segmentation Using Adaptive K-means Cluttering and Discrete Wavelet Transform

PubMed Central

Rezaee, Kh.; Haddadnia, J.

2013-01-01

Background: Breast cancer is currently one of the leading causes of death among women worldwide. The diagnosis and separation of cancerous tumors in mammographic images require accuracy, experience and time, and it has always posed itself as a major challenge to the radiologists and physicians. Objective: This paper proposes a new algorithm which draws on discrete wavelet transform and adaptive K-means techniques to transmute the medical images implement the tumor estimation and detect breast cancer tumors in mammograms in early stages. It also allows the rapid processing of the input data. Method: In the first step, after designing a filter, the discrete wavelet transform is applied to the input images and the approximate coefficients of scaling components are constructed. Then, the different parts of image are classified in continuous spectrum. In the next step, by using adaptive K-means algorithm for initializing and smart choice of clusters’ number, the appropriate threshold is selected. Finally, the suspicious cancerous mass is separated by implementing the image processing techniques. Results: We Received 120 mammographic images in LJPEG format, which had been scanned in Gray-Scale with 50 microns size, 3% noise and 20% INU from clinical data taken from two medical databases (mini-MIAS and DDSM). The proposed algorithm detected tumors at an acceptable level with an average accuracy of 92.32% and sensitivity of 90.24%. Also, the Kappa coefficient was approximately 0.85, which proved the suitable reliability of the system performance. Conclusion: The exact positioning of the cancerous tumors allows the radiologist to determine the stage of disease progression and suggest an appropriate treatment in accordance with the tumor growth. The low PPV and high NPV of the system is a warranty of the system and both clinical specialists and patients can trust its output. PMID:25505753

Optimizing Retransmission Threshold in Wireless Sensor Networks

PubMed Central

Bi, Ran; Li, Yingshu; Tan, Guozhen; Sun, Liang

2016-01-01

The retransmission threshold in wireless sensor networks is critical to the latency of data delivery in the networks. However, existing works on data transmission in sensor networks did not consider the optimization of the retransmission threshold, and they simply set the same retransmission threshold for all sensor nodes in advance. The method did not take link quality and delay requirement into account, which decreases the probability of a packet passing its delivery path within a given deadline. This paper investigates the problem of finding optimal retransmission thresholds for relay nodes along a delivery path in a sensor network. The object of optimizing retransmission thresholds is to maximize the summation of the probability of the packet being successfully delivered to the next relay node or destination node in time. A dynamic programming-based distributed algorithm for finding optimal retransmission thresholds for relay nodes along a delivery path in the sensor network is proposed. The time complexity is OnΔ·max1≤i≤n{ui}, where ui is the given upper bound of the retransmission threshold of sensor node i in a given delivery path, n is the length of the delivery path and Δ is the given upper bound of the transmission delay of the delivery path. If Δ is greater than the polynomial, to reduce the time complexity, a linear programming-based (1+pmin)-approximation algorithm is proposed. Furthermore, when the ranges of the upper and lower bounds of retransmission thresholds are big enough, a Lagrange multiplier-based distributed O(1)-approximation algorithm with time complexity O(1) is proposed. Experimental results show that the proposed algorithms have better performance. PMID:27171092

Adaptive control in the presence of unmodeled dynamics. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Rohrs, C. E.

1982-01-01

Stability and robustness properties of a wide class of adaptive control algorithms in the presence of unmodeled dynamics and output disturbances were investigated. The class of adaptive algorithms considered are those commonly referred to as model reference adaptive control algorithms, self-tuning controllers, and dead beat adaptive controllers, developed for both continuous-time systems and discrete-time systems. A unified analytical approach was developed to examine the class of existing adaptive algorithms. It was discovered that all existing algorithms contain an infinite gain operator in the dynamic system that defines command reference errors and parameter errors; it is argued that such an infinite gain operator appears to be generic to all adaptive algorithms, whether they exhibit explicit or implicit parameter identification. It is concluded that none of the adaptive algorithms considered can be used with confidence in a practical control system design, because instability will set in with a high probability.

Accurate motor mapping in awake common marmosets using micro-electrocorticographical stimulation and stochastic threshold estimation

NASA Astrophysics Data System (ADS)

Kosugi, Akito; Takemi, Mitsuaki; Tia, Banty; Castagnola, Elisa; Ansaldo, Alberto; Sato, Kenta; Awiszus, Friedemann; Seki, Kazuhiko; Ricci, Davide; Fadiga, Luciano; Iriki, Atsushi; Ushiba, Junichi

2018-06-01

Objective. Motor map has been widely used as an indicator of motor skills and learning, cortical injury, plasticity, and functional recovery. Cortical stimulation mapping using epidural electrodes is recently adopted for animal studies. However, several technical limitations still remain. Test-retest reliability of epidural cortical stimulation (ECS) mapping has not been examined in detail. Many previous studies defined evoked movements and motor thresholds by visual inspection, and thus, lacked quantitative measurements. A reliable and quantitative motor map is important to elucidate the mechanisms of motor cortical reorganization. The objective of the current study was to perform reliable ECS mapping of motor representations based on the motor thresholds, which were stochastically estimated by motor evoked potentials and chronically implanted micro-electrocorticographical (µECoG) electrode arrays, in common marmosets. Approach. ECS was applied using the implanted µECoG electrode arrays in three adult common marmosets under awake conditions. Motor evoked potentials were recorded through electromyographical electrodes implanted in upper limb muscles. The motor threshold was calculated through a modified maximum likelihood threshold-hunting algorithm fitted with the recorded data from marmosets. Further, a computer simulation confirmed reliability of the algorithm. Main results. Computer simulation suggested that the modified maximum likelihood threshold-hunting algorithm enabled to estimate motor threshold with acceptable precision. In vivo ECS mapping showed high test-retest reliability with respect to the excitability and location of the cortical forelimb motor representations. Significance. Using implanted µECoG electrode arrays and a modified motor threshold-hunting algorithm, we were able to achieve reliable motor mapping in common marmosets with the ECS system.

Accurate motor mapping in awake common marmosets using micro-electrocorticographical stimulation and stochastic threshold estimation.

PubMed

Kosugi, Akito; Takemi, Mitsuaki; Tia, Banty; Castagnola, Elisa; Ansaldo, Alberto; Sato, Kenta; Awiszus, Friedemann; Seki, Kazuhiko; Ricci, Davide; Fadiga, Luciano; Iriki, Atsushi; Ushiba, Junichi

2018-06-01

Motor map has been widely used as an indicator of motor skills and learning, cortical injury, plasticity, and functional recovery. Cortical stimulation mapping using epidural electrodes is recently adopted for animal studies. However, several technical limitations still remain. Test-retest reliability of epidural cortical stimulation (ECS) mapping has not been examined in detail. Many previous studies defined evoked movements and motor thresholds by visual inspection, and thus, lacked quantitative measurements. A reliable and quantitative motor map is important to elucidate the mechanisms of motor cortical reorganization. The objective of the current study was to perform reliable ECS mapping of motor representations based on the motor thresholds, which were stochastically estimated by motor evoked potentials and chronically implanted micro-electrocorticographical (µECoG) electrode arrays, in common marmosets. ECS was applied using the implanted µECoG electrode arrays in three adult common marmosets under awake conditions. Motor evoked potentials were recorded through electromyographical electrodes implanted in upper limb muscles. The motor threshold was calculated through a modified maximum likelihood threshold-hunting algorithm fitted with the recorded data from marmosets. Further, a computer simulation confirmed reliability of the algorithm. Computer simulation suggested that the modified maximum likelihood threshold-hunting algorithm enabled to estimate motor threshold with acceptable precision. In vivo ECS mapping showed high test-retest reliability with respect to the excitability and location of the cortical forelimb motor representations. Using implanted µECoG electrode arrays and a modified motor threshold-hunting algorithm, we were able to achieve reliable motor mapping in common marmosets with the ECS system.

A floor-map-aided WiFi/pseudo-odometry integration algorithm for an indoor positioning system.

PubMed

Wang, Jian; Hu, Andong; Liu, Chunyan; Li, Xin

2015-03-24

This paper proposes a scheme for indoor positioning by fusing floor map, WiFi and smartphone sensor data to provide meter-level positioning without additional infrastructure. A topology-constrained K nearest neighbor (KNN) algorithm based on a floor map layout provides the coordinates required to integrate WiFi data with pseudo-odometry (P-O) measurements simulated using a pedestrian dead reckoning (PDR) approach. One method of further improving the positioning accuracy is to use a more effective multi-threshold step detection algorithm, as proposed by the authors. The "go and back" phenomenon caused by incorrect matching of the reference points (RPs) of a WiFi algorithm is eliminated using an adaptive fading-factor-based extended Kalman filter (EKF), taking WiFi positioning coordinates, P-O measurements and fused heading angles as observations. The "cross-wall" problem is solved based on the development of a floor-map-aided particle filter algorithm by weighting the particles, thereby also eliminating the gross-error effects originating from WiFi or P-O measurements. The performance observed in a field experiment performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building on the China University of Mining and Technology (CUMT) campus confirms that the proposed scheme can reliably achieve meter-level positioning.

Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding

PubMed Central

Sun, Lijuan; Guo, Jian; Xu, Bin; Li, Shujing

2017-01-01

The computation of image segmentation has become more complicated with the increasing number of thresholds, and the option and application of the thresholds in image thresholding fields have become an NP problem at the same time. The paper puts forward the modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights. Taking Kapur's entropy as the optimized function and based on the discreteness of threshold in image segmentation, the paper firstly discretizes the grey wolf optimizer (GWO) and then proposes a new attack strategy by using the weight coefficient to replace the search formula for optimal solution used in the original algorithm. The experimental results show that MDGWO can search out the optimal thresholds efficiently and precisely, which are very close to the result examined by exhaustive searches. In comparison with the electromagnetism optimization (EMO), the differential evolution (DE), the Artifical Bee Colony (ABC), and the classical GWO, it is concluded that MDGWO has advantages over the latter four in terms of image segmentation quality and objective function values and their stability. PMID:28127305

Defect Detection of Steel Surfaces with Global Adaptive Percentile Thresholding of Gradient Image

NASA Astrophysics Data System (ADS)

Neogi, Nirbhar; Mohanta, Dusmanta K.; Dutta, Pranab K.

2017-12-01

Steel strips are used extensively for white goods, auto bodies and other purposes where surface defects are not acceptable. On-line surface inspection systems can effectively detect and classify defects and help in taking corrective actions. For detection of defects use of gradients is very popular in highlighting and subsequently segmenting areas of interest in a surface inspection system. Most of the time, segmentation by a fixed value threshold leads to unsatisfactory results. As defects can be both very small and large in size, segmentation of a gradient image based on percentile thresholding can lead to inadequate or excessive segmentation of defective regions. A global adaptive percentile thresholding of gradient image has been formulated for blister defect and water-deposit (a pseudo defect) in steel strips. The developed method adaptively changes the percentile value used for thresholding depending on the number of pixels above some specific values of gray level of the gradient image. The method is able to segment defective regions selectively preserving the characteristics of defects irrespective of the size of the defects. The developed method performs better than Otsu method of thresholding and an adaptive thresholding method based on local properties.

Rejection of the maternal electrocardiogram in the electrohysterogram signal.

PubMed

Leman, H; Marque, C

2000-08-01

The electrohysterogram (EHG) signal is mainly corrupted by the mother's electrocardiogram (ECG), which remains present despite analog filtering during acquisition. Wavelets are a powerful denoising tool and have already proved their efficiency on the EHG. In this paper, we propose a new method that employs the redundant wavelet packet transform. We first study wavelet packet coefficient histograms and propose an algorithm to automatically detect the histogram mode number. Using a new criterion, we compute a best basis adapted to the denoising. After EHG wavelet packet coefficient thresholding in the selected basis, the inverse transform is applied. The ECG seems to be very efficiently removed.

Compressed sensing system considerations for ECG and EMG wireless biosensors.

PubMed

Dixon, Anna M R; Allstot, Emily G; Gangopadhyay, Daibashish; Allstot, David J

2012-04-01

Compressed sensing (CS) is an emerging signal processing paradigm that enables sub-Nyquist processing of sparse signals such as electrocardiogram (ECG) and electromyogram (EMG) biosignals. Consequently, it can be applied to biosignal acquisition systems to reduce the data rate to realize ultra-low-power performance. CS is compared to conventional and adaptive sampling techniques and several system-level design considerations are presented for CS acquisition systems including sparsity and compression limits, thresholding techniques, encoder bit-precision requirements, and signal recovery algorithms. Simulation studies show that compression factors greater than 16X are achievable for ECG and EMG signals with signal-to-quantization noise ratios greater than 60 dB.

AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques.

PubMed

Khan, Arif Ul Maula; Torelli, Angelo; Wolf, Ivo; Gretz, Norbert

2018-05-08

In biological assays, automated cell/colony segmentation and counting is imperative owing to huge image sets. Problems occurring due to drifting image acquisition conditions, background noise and high variation in colony features in experiments demand a user-friendly, adaptive and robust image processing/analysis method. We present AutoCellSeg (based on MATLAB) that implements a supervised automatic and robust image segmentation method. AutoCellSeg utilizes multi-thresholding aided by a feedback-based watershed algorithm taking segmentation plausibility criteria into account. It is usable in different operation modes and intuitively enables the user to select object features interactively for supervised image segmentation method. It allows the user to correct results with a graphical interface. This publicly available tool outperforms tools like OpenCFU and CellProfiler in terms of accuracy and provides many additional useful features for end-users.

Closed-loop adaptation of neurofeedback based on mental effort facilitates reinforcement learning of brain self-regulation.

PubMed

Bauer, Robert; Fels, Meike; Royter, Vladislav; Raco, Valerio; Gharabaghi, Alireza

2016-09-01

Considering self-rated mental effort during neurofeedback may improve training of brain self-regulation. Twenty-one healthy, right-handed subjects performed kinesthetic motor imagery of opening their left hand, while threshold-based classification of beta-band desynchronization resulted in proprioceptive robotic feedback. The experiment consisted of two blocks in a cross-over design. The participants rated their perceived mental effort nine times per block. In the adaptive block, the threshold was adjusted on the basis of these ratings whereas adjustments were carried out at random in the other block. Electroencephalography was used to examine the cortical activation patterns during the training sessions. The perceived mental effort was correlated with the difficulty threshold of neurofeedback training. Adaptive threshold-setting reduced mental effort and increased the classification accuracy and positive predictive value. This was paralleled by an inter-hemispheric cortical activation pattern in low frequency bands connecting the right frontal and left parietal areas. Optimal balance of mental effort was achieved at thresholds significantly higher than maximum classification accuracy. Rating of mental effort is a feasible approach for effective threshold-adaptation during neurofeedback training. Closed-loop adaptation of the neurofeedback difficulty level facilitates reinforcement learning of brain self-regulation. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Algorithms for accelerated convergence of adaptive PCA.

PubMed

Chatterjee, C; Kang, Z; Roychowdhury, V P

2000-01-01

We derive and discuss new adaptive algorithms for principal component analysis (PCA) that are shown to converge faster than the traditional PCA algorithms due to Oja, Sanger, and Xu. It is well known that traditional PCA algorithms that are derived by using gradient descent on an objective function are slow to converge. Furthermore, the convergence of these algorithms depends on appropriate choices of the gain sequences. Since online applications demand faster convergence and an automatic selection of gains, we present new adaptive algorithms to solve these problems. We first present an unconstrained objective function, which can be minimized to obtain the principal components. We derive adaptive algorithms from this objective function by using: 1) gradient descent; 2) steepest descent; 3) conjugate direction; and 4) Newton-Raphson methods. Although gradient descent produces Xu's LMSER algorithm, the steepest descent, conjugate direction, and Newton-Raphson methods produce new adaptive algorithms for PCA. We also provide a discussion on the landscape of the objective function, and present a global convergence proof of the adaptive gradient descent PCA algorithm using stochastic approximation theory. Extensive experiments with stationary and nonstationary multidimensional Gaussian sequences show faster convergence of the new algorithms over the traditional gradient descent methods.We also compare the steepest descent adaptive algorithm with state-of-the-art methods on stationary and nonstationary sequences.

3D GGO candidate extraction in lung CT images using multilevel thresholding on supervoxels

NASA Astrophysics Data System (ADS)

Huang, Shan; Liu, Xiabi; Han, Guanghui; Zhao, Xinming; Zhao, Yanfeng; Zhou, Chunwu

2018-02-01

The earlier detection of ground glass opacity (GGO) is of great importance since GGOs are more likely to be malignant than solid nodules. However, the detection of GGO is a difficult task in lung cancer screening. This paper proposes a novel GGO candidate extraction method, which performs multilevel thresholding on supervoxels in 3D lung CT images. Firstly, we segment the lung parenchyma based on Otsu algorithm. Secondly, the voxels which are adjacent in 3D discrete space and sharing similar grayscale are clustered into supervoxels. This procedure is used to enhance GGOs and reduce computational complexity. Thirdly, Hessian matrix is used to emphasize focal GGO candidates. Lastly, an improved adaptive multilevel thresholding method is applied on segmented clusters to extract GGO candidates. The proposed method was evaluated on a set of 19 lung CT scans containing 166 GGO lesions from the Lung CT Imaging Signs (LISS) database. The experimental results show that our proposed GGO candidate extraction method is effective, with a sensitivity of 100% and 26.3 of false positives per scan (665 GGO candidates, 499 non-GGO regions and 166 GGO regions). It can handle both focal GGOs and diffuse GGOs.

On-Board Event-Based State Estimation for Trajectory Approaching and Tracking of a Vehicle

PubMed Central

Martínez-Rey, Miguel; Espinosa, Felipe; Gardel, Alfredo; Santos, Carlos

2015-01-01

For the problem of pose estimation of an autonomous vehicle using networked external sensors, the processing capacity and battery consumption of these sensors, as well as the communication channel load should be optimized. Here, we report an event-based state estimator (EBSE) consisting of an unscented Kalman filter that uses a triggering mechanism based on the estimation error covariance matrix to request measurements from the external sensors. This EBSE generates the events of the estimator module on-board the vehicle and, thus, allows the sensors to remain in stand-by mode until an event is generated. The proposed algorithm requests a measurement every time the estimation distance root mean squared error (DRMS) value, obtained from the estimator's covariance matrix, exceeds a threshold value. This triggering threshold can be adapted to the vehicle's working conditions rendering the estimator even more efficient. An example of the use of the proposed EBSE is given, where the autonomous vehicle must approach and follow a reference trajectory. By making the threshold a function of the distance to the reference location, the estimator can halve the use of the sensors with a negligible deterioration in the performance of the approaching maneuver. PMID:26102489

AutoNR: an automated system that measures ECAP thresholds with the Nucleus Freedom cochlear implant via machine intelligence.

PubMed

Botros, Andrew; van Dijk, Bas; Killian, Matthijs

2007-05-01

AutoNRT is an automated system that measures electrically evoked compound action potential (ECAP) thresholds from the auditory nerve with the Nucleus Freedom cochlear implant. ECAP thresholds along the electrode array are useful in objectively fitting cochlear implant systems for individual use. This paper provides the first detailed description of the AutoNRT algorithm and its expert systems, and reports the clinical success of AutoNRT to date. AutoNRT determines thresholds by visual detection, using two decision tree expert systems that automatically recognise ECAPs. The expert systems are guided by a dataset of 5393 neural response measurements. The algorithm approaches threshold from lower stimulus levels, ensuring recipient safety during postoperative measurements. Intraoperative measurements use the same algorithm but proceed faster by beginning at stimulus levels much closer to threshold. When searching for ECAPs, AutoNRT uses a highly specific expert system (specificity of 99% during training, 96% during testing; sensitivity of 91% during training, 89% during testing). Once ECAPs are established, AutoNRT uses an unbiased expert system to determine an accurate threshold. Throughout the execution of the algorithm, recording parameters (such as implant amplifier gain) are automatically optimised when needed. In a study that included 29 intraoperative and 29 postoperative subjects (a total of 418 electrodes), AutoNRT determined a threshold in 93% of cases where a human expert also determined a threshold. When compared to the median threshold of multiple human observers on 77 randomly selected electrodes, AutoNRT performed as accurately as the 'average' clinician. AutoNRT has demonstrated a high success rate and a level of performance that is comparable with human experts. It has been used in many clinics worldwide throughout the clinical trial and commercial launch of Nucleus Custom Sound Suite, significantly streamlining the clinical procedures associated with cochlear implant use.

Developing an Enhanced Lightning Jump Algorithm for Operational Use

NASA Technical Reports Server (NTRS)

Schultz, Christopher J.; Petersen, Walter A.; Carey, Lawrence D.

2009-01-01

Overall Goals: 1. Build on the lightning jump framework set through previous studies. 2. Understand what typically occurs in nonsevere convection with respect to increases in lightning. 3. Ultimately develop a lightning jump algorithm for use on the Geostationary Lightning Mapper (GLM). 4 Lightning jump algorithm configurations were developed (2(sigma), 3(sigma), Threshold 10 and Threshold 8). 5 algorithms were tested on a population of 47 nonsevere and 38 severe thunderstorms. Results indicate that the 2(sigma) algorithm performed best over the entire thunderstorm sample set with a POD of 87%, a far of 35%, a CSI of 59% and a HSS of 75%.

Appropriate threshold levels of cardiac beat-to-beat variation in semi-automatic analysis of equine ECG recordings.

PubMed

Flethøj, Mette; Kanters, Jørgen K; Pedersen, Philip J; Haugaard, Maria M; Carstensen, Helena; Olsen, Lisbeth H; Buhl, Rikke

2016-11-28

Although premature beats are a matter of concern in horses, the interpretation of equine ECG recordings is complicated by a lack of standardized analysis criteria and a limited knowledge of the normal beat-to-beat variation of equine cardiac rhythm. The purpose of this study was to determine the appropriate threshold levels of maximum acceptable deviation of RR intervals in equine ECG analysis, and to evaluate a novel two-step timing algorithm by quantifying the frequency of arrhythmias in a cohort of healthy adult endurance horses. Beat-to-beat variation differed considerably with heart rate (HR), and an adaptable model consisting of three different HR ranges with separate threshold levels of maximum acceptable RR deviation was consequently defined. For resting HRs <60 beats/min (bpm) the threshold level of RR deviation was set at 20%, for HRs in the intermediate range between 60 and 100 bpm the threshold was 10%, and for exercising HRs >100 bpm, the threshold level was 4%. Supraventricular premature beats represented the most prevalent arrhythmia category with varying frequencies in seven horses at rest (median 7, range 2-86) and six horses during exercise (median 2, range 1-24). Beat-to-beat variation of equine cardiac rhythm varies according to HR, and threshold levels in equine ECG analysis should be adjusted accordingly. Standardization of the analysis criteria will enable comparisons of studies and follow-up examinations of patients. A small number of supraventricular premature beats appears to be a normal finding in endurance horses. Further studies are required to validate the findings and determine the clinical significance of premature beats in horses.

A novel gene network inference algorithm using predictive minimum description length approach.

PubMed

Chaitankar, Vijender; Ghosh, Preetam; Perkins, Edward J; Gong, Ping; Deng, Youping; Zhang, Chaoyang

2010-05-28

Reverse engineering of gene regulatory networks using information theory models has received much attention due to its simplicity, low computational cost, and capability of inferring large networks. One of the major problems with information theory models is to determine the threshold which defines the regulatory relationships between genes. The minimum description length (MDL) principle has been implemented to overcome this problem. The description length of the MDL principle is the sum of model length and data encoding length. A user-specified fine tuning parameter is used as control mechanism between model and data encoding, but it is difficult to find the optimal parameter. In this work, we proposed a new inference algorithm which incorporated mutual information (MI), conditional mutual information (CMI) and predictive minimum description length (PMDL) principle to infer gene regulatory networks from DNA microarray data. In this algorithm, the information theoretic quantities MI and CMI determine the regulatory relationships between genes and the PMDL principle method attempts to determine the best MI threshold without the need of a user-specified fine tuning parameter. The performance of the proposed algorithm was evaluated using both synthetic time series data sets and a biological time series data set for the yeast Saccharomyces cerevisiae. The benchmark quantities precision and recall were used as performance measures. The results show that the proposed algorithm produced less false edges and significantly improved the precision, as compared to the existing algorithm. For further analysis the performance of the algorithms was observed over different sizes of data. We have proposed a new algorithm that implements the PMDL principle for inferring gene regulatory networks from time series DNA microarray data that eliminates the need of a fine tuning parameter. The evaluation results obtained from both synthetic and actual biological data sets show that the PMDL principle is effective in determining the MI threshold and the developed algorithm improves precision of gene regulatory network inference. Based on the sensitivity analysis of all tested cases, an optimal CMI threshold value has been identified. Finally it was observed that the performance of the algorithms saturates at a certain threshold of data size.

ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform.

PubMed

El B'charri, Oussama; Latif, Rachid; Elmansouri, Khalifa; Abenaou, Abdenbi; Jenkal, Wissam

2017-02-07

Since the electrocardiogram (ECG) signal has a low frequency and a weak amplitude, it is sensitive to miscellaneous mixed noises, which may reduce the diagnostic accuracy and hinder the physician's correct decision on patients. The dual tree wavelet transform (DT-WT) is one of the most recent enhanced versions of discrete wavelet transform. However, threshold tuning on this method for noise removal from ECG signal has not been investigated yet. In this work, we shall provide a comprehensive study on the impact of the choice of threshold algorithm, threshold value, and the appropriate wavelet decomposition level to evaluate the ECG signal de-noising performance. A set of simulations is performed on both synthetic and real ECG signals to achieve the promised results. First, the synthetic ECG signal is used to observe the algorithm response. The evaluation results of synthetic ECG signal corrupted by various types of noise has showed that the modified unified threshold and wavelet hyperbolic threshold de-noising method is better in realistic and colored noises. The tuned threshold is then used on real ECG signals from the MIT-BIH database. The results has shown that the proposed method achieves higher performance than the ordinary dual tree wavelet transform into all kinds of noise removal from ECG signal. The simulation results indicate that the algorithm is robust for all kinds of noises with varying degrees of input noise, providing a high quality clean signal. Moreover, the algorithm is quite simple and can be used in real time ECG monitoring.

A high speed implementation of the random decrement algorithm

NASA Technical Reports Server (NTRS)

Kiraly, L. J.

1982-01-01

The algorithm is useful for measuring net system damping levels in stochastic processes and for the development of equivalent linearized system response models. The algorithm works by summing together all subrecords which occur after predefined threshold level is crossed. The random decrement signature is normally developed by scanning stored data and adding subrecords together. The high speed implementation of the random decrement algorithm exploits the digital character of sampled data and uses fixed record lengths of 2(n) samples to greatly speed up the process. The contributions to the random decrement signature of each data point was calculated only once and in the same sequence as the data were taken. A hardware implementation of the algorithm using random logic is diagrammed and the process is shown to be limited only by the record size and the threshold crossing frequency of the sampled data. With a hardware cycle time of 200 ns and 1024 point signature, a threshold crossing frequency of 5000 Hertz can be processed and a stably averaged signature presented in real time.

DARK ADAPTATION IN DINEUTES

PubMed Central

Clark, Leonard B.

1938-01-01

The level of dark adaptation of the whirligig beetle can be measured in terms of the threshold intensity calling forth a response. The course of dark adaptation was determined at levels of light adaptation of 6.5, 91.6, and 6100 foot-candles. All data can be fitted by the same curve. This indicates that dark adaptation follows parts of the same course irrespective of the level of light adaptation. The intensity of the adapting light determines the level at which dark adaptation will begin. The relation between log aI 0 (instantaneous threshold) and log of adapting light intensity is linear over the range studied. PMID:19873056

From the SAIN,LIM system to the SENS algorithm: a review of a French approach of nutrient profiling.

PubMed

Tharrey, Marion; Maillot, Matthieu; Azaïs-Braesco, Véronique; Darmon, Nicole

2017-08-01

Nutrient profiling aims to classify or rank foods according to their nutritional composition to assist policies aimed at improving the nutritional quality of foods and diets. The present paper reviews a French approach of nutrient profiling by describing the SAIN,LIM system and its evolution from its early draft to the simplified nutrition labelling system (SENS) algorithm. Considered in 2010 by WHO as the 'French model' of nutrient profiling, SAIN,LIM classifies foods into four classes based on two scores: a nutrient density score (NDS) called SAIN and a score of nutrients to limit called LIM, and one threshold on each score. The system was first developed by the French Food Standard Agency in 2008 in response to the European regulation on nutrition and health claims (European Commission (EC) 1924/2006) to determine foods that may be eligible for bearing claims. Recently, the European regulation (EC 1169/2011) on the provision of food information to consumers allowed simplified nutrition labelling to facilitate consumer information and help them make fully informed choices. In that context, the SAIN,LIM was adapted to obtain the SENS algorithm, a system able to rank foods for simplified nutrition labelling. The implementation of the algorithm followed a step-by-step, systematic, transparent and logical process where shortcomings of the SAIN,LIM were addressed by integrating specificities of food categories in the SENS, reducing the number of nutrients, ordering the four classes and introducing European reference intakes. Through the French example, this review shows how an existing nutrient profiling system can be specifically adapted to support public health nutrition policies.

Cognitive Nonlinear Radar

DTIC Science & Technology

2013-01-01

intelligently selecting waveform parameters using adaptive algorithms. The adaptive algorithms optimize the waveform parameters based on (1) the EM...the environment. 15. SUBJECT TERMS cognitive radar, adaptive sensing, spectrum sensing, multi-objective optimization, genetic algorithms, machine...detection and classification block diagram. .........................................................6 Figure 5. Genetic algorithm block diagram

Improve threshold segmentation using features extraction to automatic lung delimitation.

PubMed

França, Cleunio; Vasconcelos, Germano; Diniz, Paula; Melo, Pedro; Diniz, Jéssica; Novaes, Magdala

2013-01-01

With the consolidation of PACS and RIS systems, the development of algorithms for tissue segmentation and diseases detection have intensely evolved in recent years. These algorithms have advanced to improve its accuracy and specificity, however, there is still some way until these algorithms achieved satisfactory error rates and reduced processing time to be used in daily diagnosis. The objective of this study is to propose a algorithm for lung segmentation in x-ray computed tomography images using features extraction, as Centroid and orientation measures, to improve the basic threshold segmentation. As result we found a accuracy of 85.5%.

Onboard Nonlinear Engine Sensor and Component Fault Diagnosis and Isolation Scheme

NASA Technical Reports Server (NTRS)

Tang, Liang; DeCastro, Jonathan A.; Zhang, Xiaodong

2011-01-01

A method detects and isolates in-flight sensor, actuator, and component faults for advanced propulsion systems. In sharp contrast to many conventional methods, which deal with either sensor fault or component fault, but not both, this method considers sensor fault, actuator fault, and component fault under one systemic and unified framework. The proposed solution consists of two main components: a bank of real-time, nonlinear adaptive fault diagnostic estimators for residual generation, and a residual evaluation module that includes adaptive thresholds and a Transferable Belief Model (TBM)-based residual evaluation scheme. By employing a nonlinear adaptive learning architecture, the developed approach is capable of directly dealing with nonlinear engine models and nonlinear faults without the need of linearization. Software modules have been developed and evaluated with the NASA C-MAPSS engine model. Several typical engine-fault modes, including a subset of sensor/actuator/components faults, were tested with a mild transient operation scenario. The simulation results demonstrated that the algorithm was able to successfully detect and isolate all simulated faults as long as the fault magnitudes were larger than the minimum detectable/isolable sizes, and no misdiagnosis occurred

An adaptive threshold detector and channel parameter estimator for deep space optical communications

NASA Technical Reports Server (NTRS)

Arabshahi, P.; Mukai, R.; Yan, T. -Y.

2001-01-01

This paper presents a method for optimal adaptive setting of ulse-position-modulation pulse detection thresholds, which minimizes the total probability of error for the dynamically fading optical fee space channel.

Unipolar Terminal-Attractor Based Neural Associative Memory with Adaptive Threshold

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Inventor); Barhen, Jacob (Inventor); Farhat, Nabil H. (Inventor); Wu, Chwan-Hwa (Inventor)

1996-01-01

A unipolar terminal-attractor based neural associative memory (TABAM) system with adaptive threshold for perfect convergence is presented. By adaptively setting the threshold values for the dynamic iteration for the unipolar binary neuron states with terminal-attractors for the purpose of reducing the spurious states in a Hopfield neural network for associative memory and using the inner-product approach, perfect convergence and correct retrieval is achieved. Simulation is completed with a small number of stored states (M) and a small number of neurons (N) but a large M/N ratio. An experiment with optical exclusive-OR logic operation using LCTV SLMs shows the feasibility of optoelectronic implementation of the models. A complete inner-product TABAM is implemented using a PC for calculation of adaptive threshold values to achieve a unipolar TABAM (UIT) in the case where there is no crosstalk, and a crosstalk model (CRIT) in the case where crosstalk corrupts the desired state.

Unipolar terminal-attractor based neural associative memory with adaptive threshold

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Inventor); Barhen, Jacob (Inventor); Farhat, Nabil H. (Inventor); Wu, Chwan-Hwa (Inventor)

1993-01-01

A unipolar terminal-attractor based neural associative memory (TABAM) system with adaptive threshold for perfect convergence is presented. By adaptively setting the threshold values for the dynamic iteration for the unipolar binary neuron states with terminal-attractors for the purpose of reducing the spurious states in a Hopfield neural network for associative memory and using the inner product approach, perfect convergence and correct retrieval is achieved. Simulation is completed with a small number of stored states (M) and a small number of neurons (N) but a large M/N ratio. An experiment with optical exclusive-OR logic operation using LCTV SLMs shows the feasibility of optoelectronic implementation of the models. A complete inner-product TABAM is implemented using a PC for calculation of adaptive threshold values to achieve a unipolar TABAM (UIT) in the case where there is no crosstalk, and a crosstalk model (CRIT) in the case where crosstalk corrupts the desired state.

Adaptive spline autoregression threshold method in forecasting Mitsubishi car sales volume at PT Srikandi Diamond Motors

NASA Astrophysics Data System (ADS)

Susanti, D.; Hartini, E.; Permana, A.

2017-01-01

Sale and purchase of the growing competition between companies in Indonesian, make every company should have a proper planning in order to win the competition with other companies. One of the things that can be done to design the plan is to make car sales forecast for the next few periods, it’s required that the amount of inventory of cars that will be sold in proportion to the number of cars needed. While to get the correct forecasting, on of the methods that can be used is the method of Adaptive Spline Threshold Autoregression (ASTAR). Therefore, this time the discussion will focus on the use of Adaptive Spline Threshold Autoregression (ASTAR) method in forecasting the volume of car sales in PT.Srikandi Diamond Motors using time series data.In the discussion of this research, forecasting using the method of forecasting value Adaptive Spline Threshold Autoregression (ASTAR) produce approximately correct.

Controlling false-negative errors in microarray differential expression analysis: a PRIM approach.

PubMed

Cole, Steve W; Galic, Zoran; Zack, Jerome A

2003-09-22

Theoretical considerations suggest that current microarray screening algorithms may fail to detect many true differences in gene expression (Type II analytic errors). We assessed 'false negative' error rates in differential expression analyses by conventional linear statistical models (e.g. t-test), microarray-adapted variants (e.g. SAM, Cyber-T), and a novel strategy based on hold-out cross-validation. The latter approach employs the machine-learning algorithm Patient Rule Induction Method (PRIM) to infer minimum thresholds for reliable change in gene expression from Boolean conjunctions of fold-induction and raw fluorescence measurements. Monte Carlo analyses based on four empirical data sets show that conventional statistical models and their microarray-adapted variants overlook more than 50% of genes showing significant up-regulation. Conjoint PRIM prediction rules recover approximately twice as many differentially expressed transcripts while maintaining strong control over false-positive (Type I) errors. As a result, experimental replication rates increase and total analytic error rates decline. RT-PCR studies confirm that gene inductions detected by PRIM but overlooked by other methods represent true changes in mRNA levels. PRIM-based conjoint inference rules thus represent an improved strategy for high-sensitivity screening of DNA microarrays. Freestanding JAVA application at http://microarray.crump.ucla.edu/focus

Automatic Boosted Flood Mapping from Satellite Data

NASA Technical Reports Server (NTRS)

Coltin, Brian; McMichael, Scott; Smith, Trey; Fong, Terrence

2016-01-01

Numerous algorithms have been proposed to map floods from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. However, most require human input to succeed, either to specify a threshold value or to manually annotate training data. We introduce a new algorithm based on Adaboost which effectively maps floods without any human input, allowing for a truly rapid and automatic response. The Adaboost algorithm combines multiple thresholds to achieve results comparable to state-of-the-art algorithms which do require human input. We evaluate Adaboost, as well as numerous previously proposed flood mapping algorithms, on multiple MODIS flood images, as well as on hundreds of non-flood MODIS lake images, demonstrating its effectiveness across a wide variety of conditions.

Simulation for noise cancellation using LMS adaptive filter

NASA Astrophysics Data System (ADS)

Lee, Jia-Haw; Ooi, Lu-Ean; Ko, Ying-Hao; Teoh, Choe-Yung

2017-06-01

In this paper, the fundamental algorithm of noise cancellation, Least Mean Square (LMS) algorithm is studied and enhanced with adaptive filter. The simulation of the noise cancellation using LMS adaptive filter algorithm is developed. The noise corrupted speech signal and the engine noise signal are used as inputs for LMS adaptive filter algorithm. The filtered signal is compared to the original noise-free speech signal in order to highlight the level of attenuation of the noise signal. The result shows that the noise signal is successfully canceled by the developed adaptive filter. The difference of the noise-free speech signal and filtered signal are calculated and the outcome implies that the filtered signal is approaching the noise-free speech signal upon the adaptive filtering. The frequency range of the successfully canceled noise by the LMS adaptive filter algorithm is determined by performing Fast Fourier Transform (FFT) on the signals. The LMS adaptive filter algorithm shows significant noise cancellation at lower frequency range.

Reduction in Dynamic Visual Acuity Reveals Gaze Control Changes Following Spaceflight

NASA Technical Reports Server (NTRS)

Peters, Brian T.; Brady, Rachel A.; Miller, Chris; Lawrence, Emily L.; Mulavara Ajitkumar P.; Bloomberg, Jacob J.

2010-01-01

INTRODUCTION: Exposure to microgravity causes adaptive changes in eye-head coordination that can lead to altered gaze control. This could affect postflight visual acuity during head and body motion. The goal of this study was to characterize changes in dynamic visual acuity after long-duration spaceflight. METHODS: Dynamic Visual Acuity (DVA) data from 14 astro/cosmonauts were collected after long-duration (6 months) spaceflight. The difference in acuity between seated and walking conditions provided a metric of change in the subjects ability to maintain gaze fixation during self-motion. In each condition, a psychophysical threshold detection algorithm was used to display Landolt ring optotypes at a size that was near each subject s acuity threshold. Verbal responses regarding the orientation of the gap were recorded as the optotypes appeared sequentially on a computer display 4 meters away. During the walking trials, subjects walked at 6.4 km/h on a motorized treadmill. RESULTS: A decrement in mean postflight DVA was found, with mean values returning to baseline within 1 week. The population mean showed a consistent improvement in DVA performance, but it was accompanied by high variability. A closer examination of the individual subject s recovery curves revealed that many did not follow a pattern of continuous improvement with each passing day. When adjusted on the basis of previous long-duration flight experience, the population mean shows a "bounce" in the re-adaptation curve. CONCLUSION: Gaze control during self-motion is altered following long-duration spaceflight and changes in postflight DVA performance indicate that vestibular re-adaptation may be more complex than a gradual return to normal.

Lower-upper-threshold correlation for underwater range-gated imaging self-adaptive enhancement.

PubMed

Sun, Liang; Wang, Xinwei; Liu, Xiaoquan; Ren, Pengdao; Lei, Pingshun; He, Jun; Fan, Songtao; Zhou, Yan; Liu, Yuliang

2016-10-10

In underwater range-gated imaging (URGI), enhancement of low-brightness and low-contrast images is critical for human observation. Traditional histogram equalizations over-enhance images, with the result of details being lost. To compress over-enhancement, a lower-upper-threshold correlation method is proposed for underwater range-gated imaging self-adaptive enhancement based on double-plateau histogram equalization. The lower threshold determines image details and compresses over-enhancement. It is correlated with the upper threshold. First, the upper threshold is updated by searching for the local maximum in real time, and then the lower threshold is calculated by the upper threshold and the number of nonzero units selected from a filtered histogram. With this method, the backgrounds of underwater images are constrained with enhanced details. Finally, the proof experiments are performed. Peak signal-to-noise-ratio, variance, contrast, and human visual properties are used to evaluate the objective quality of the global and regions of interest images. The evaluation results demonstrate that the proposed method adaptively selects the proper upper and lower thresholds under different conditions. The proposed method contributes to URGI with effective image enhancement for human eyes.

Comparative Analysis of Daytime Fire Detection Algorithms, Using AVHRR Data for the 1995 Fire Season in Canda: Perspective for MODIS

NASA Technical Reports Server (NTRS)

Ichoku, Charles; Kaufman, Y. J.; Fraser, R. H.; Jin, J.-Z.; Park, W. M.; Lau, William K. M. (Technical Monitor)

2001-01-01

Two fixed-threshold Canada Centre for Remote Sensing and European Space Agency (CCRS and ESA) and three contextual GIGLIO, International Geosphere and Biosphere Project, and Moderate Resolution Imaging Spectroradiometer (GIGLIO, IGBP, and MODIS) algorithms were used for fire detection with Advanced Very High Resolution Radiometer (AVHRR) data acquired over Canada during the 1995 fire season. The CCRS algorithm was developed for the boreal ecosystem, while the other four are for global application. The MODIS algorithm, although developed specifically for use with the MODIS sensor data, was applied to AVHRR in this study for comparative purposes. Fire detection accuracy assessment for the algorithms was based on comparisons with available 1995 burned area ground survey maps covering five Canadian provinces. Overall accuracy estimations in terms of omission (CCRS=46%, ESA=81%, GIGLIO=75%, IGBP=51%, MODIS=81%) and commission (CCRS=0.35%, ESA=0.08%, GIGLIO=0.56%, IGBP=0.75%, MODIS=0.08%) errors over forested areas revealed large differences in performance between the algorithms, with no relevance to type (fixed-threshold or contextual). CCRS performed best in detecting real forest fires, with the least omission error, while ESA and MODIS produced the highest omission error, probably because of their relatively high threshold values designed for global application. The commission error values appear small because the area of pixels falsely identified by each algorithm was expressed as a ratio of the vast unburned forest area. More detailed study shows that most commission errors in all the algorithms were incurred in nonforest agricultural areas, especially on days with very high surface temperatures. The advantage of the high thresholds in ESA and MODIS was that they incurred the least commission errors.

Subsurface characterization with localized ensemble Kalman filter employing adaptive thresholding

NASA Astrophysics Data System (ADS)

Delijani, Ebrahim Biniaz; Pishvaie, Mahmoud Reza; Boozarjomehry, Ramin Bozorgmehry

2014-07-01

Ensemble Kalman filter, EnKF, as a Monte Carlo sequential data assimilation method has emerged promisingly for subsurface media characterization during past decade. Due to high computational cost of large ensemble size, EnKF is limited to small ensemble set in practice. This results in appearance of spurious correlation in covariance structure leading to incorrect or probable divergence of updated realizations. In this paper, a universal/adaptive thresholding method is presented to remove and/or mitigate spurious correlation problem in the forecast covariance matrix. This method is, then, extended to regularize Kalman gain directly. Four different thresholding functions have been considered to threshold forecast covariance and gain matrices. These include hard, soft, lasso and Smoothly Clipped Absolute Deviation (SCAD) functions. Three benchmarks are used to evaluate the performances of these methods. These benchmarks include a small 1D linear model and two 2D water flooding (in petroleum reservoirs) cases whose levels of heterogeneity/nonlinearity are different. It should be noted that beside the adaptive thresholding, the standard distance dependant localization and bootstrap Kalman gain are also implemented for comparison purposes. We assessed each setup with different ensemble sets to investigate the sensitivity of each method on ensemble size. The results indicate that thresholding of forecast covariance yields more reliable performance than Kalman gain. Among thresholding function, SCAD is more robust for both covariance and gain estimation. Our analyses emphasize that not all assimilation cycles do require thresholding and it should be performed wisely during the early assimilation cycles. The proposed scheme of adaptive thresholding outperforms other methods for subsurface characterization of underlying benchmarks.

Prediction of cardiovascular risk in rheumatoid arthritis: performance of original and adapted SCORE algorithms.

PubMed

Arts, E E A; Popa, C D; Den Broeder, A A; Donders, R; Sandoo, A; Toms, T; Rollefstad, S; Ikdahl, E; Semb, A G; Kitas, G D; Van Riel, P L C M; Fransen, J

2016-04-01

Predictive performance of cardiovascular disease (CVD) risk calculators appears suboptimal in rheumatoid arthritis (RA). A disease-specific CVD risk algorithm may improve CVD risk prediction in RA. The objectives of this study are to adapt the Systematic COronary Risk Evaluation (SCORE) algorithm with determinants of CVD risk in RA and to assess the accuracy of CVD risk prediction calculated with the adapted SCORE algorithm. Data from the Nijmegen early RA inception cohort were used. The primary outcome was first CVD events. The SCORE algorithm was recalibrated by reweighing included traditional CVD risk factors and adapted by adding other potential predictors of CVD. Predictive performance of the recalibrated and adapted SCORE algorithms was assessed and the adapted SCORE was externally validated. Of the 1016 included patients with RA, 103 patients experienced a CVD event. Discriminatory ability was comparable across the original, recalibrated and adapted SCORE algorithms. The Hosmer-Lemeshow test results indicated that all three algorithms provided poor model fit (p<0.05) for the Nijmegen and external validation cohort. The adapted SCORE algorithm mainly improves CVD risk estimation in non-event cases and does not show a clear advantage in reclassifying patients with RA who develop CVD (event cases) into more appropriate risk groups. This study demonstrates for the first time that adaptations of the SCORE algorithm do not provide sufficient improvement in risk prediction of future CVD in RA to serve as an appropriate alternative to the original SCORE. Risk assessment using the original SCORE algorithm may underestimate CVD risk in patients with RA. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Lowering threshold energy for femtosecond laser pulse photodisruption through turbid media using adaptive optics

NASA Astrophysics Data System (ADS)

Hansen, A.; Ripken, Tammo; Krueger, Ronald R.; Lubatschowski, Holger

2011-03-01

Focussed femtosecond laser pulses are applied in ophthalmic tissues to create an optical breakdown and therefore a tissue dissection through photodisruption. The threshold irradiance for the optical breakdown depends on the photon density in the focal volume which can be influenced by the pulse energy, the size of the irradiated area (focus), and the irradiation time. For an application in the posterior eye segment the aberrations of the anterior eye elements cause a distortion of the wavefront and therefore an increased focal volume which reduces the photon density and thus raises the required energy for surpassing the threshold irradiance. The influence of adaptive optics on lowering the pulse energy required for photodisruption by refining a distorted focus was investigated. A reduction of the threshold energy can be shown when using adaptive optics. The spatial confinement with adaptive optics furthermore raises the irradiance at constant pulse energy. The lowered threshold energy allows for tissue dissection with reduced peripheral damage. This offers the possibility for moving femtosecond laser surgery from corneal or lental applications in the anterior eye to vitreal or retinal applications in the posterior eye.

Guaranteeing Failsafe Operation of Extended-Scene Shack-Hartmann Wavefront Sensor Algorithm

NASA Technical Reports Server (NTRS)

Sidick, Erikin

2009-01-01

A Shack-Hartmann sensor (SHS) is an optical instrument consisting of a lenslet array and a camera. It is widely used for wavefront sensing in optical testing and astronomical adaptive optics. The camera is placed at the focal point of the lenslet array and points at a star or any other point source. The image captured is an array of spot images. When the wavefront error at the lenslet array changes, the position of each spot measurably shifts from its original position. Determining the shifts of the spot images from their reference points shows the extent of the wavefront error. An adaptive cross-correlation (ACC) algorithm has been developed to use scenes as well as point sources for wavefront error detection. Qualifying an extended scene image is often not an easy task due to changing conditions in scene content, illumination level, background, Poisson noise, read-out noise, dark current, sampling format, and field of view. The proposed new technique based on ACC algorithm analyzes the effects of these conditions on the performance of the ACC algorithm and determines the viability of an extended scene image. If it is viable, then it can be used for error correction; if it is not, the image fails and will not be further processed. By potentially testing for a wide variety of conditions, the algorithm s accuracy can be virtually guaranteed. In a typical application, the ACC algorithm finds image shifts of more than 500 Shack-Hartmann camera sub-images relative to a reference sub -image or cell when performing one wavefront sensing iteration. In the proposed new technique, a pair of test and reference cells is selected from the same frame, preferably from two well-separated locations. The test cell is shifted by an integer number of pixels, say, for example, from m= -5 to 5 along the x-direction by choosing a different area on the same sub-image, and the shifts are estimated using the ACC algorithm. The same is done in the y-direction. If the resulting shift estimate errors are less than a pre-determined threshold (e.g., 0.03 pixel), the image is accepted. Otherwise, it is rejected.

C-band Joint Active/Passive Dual Polarization Sea Ice Detection

NASA Astrophysics Data System (ADS)

Keller, M. R.; Gifford, C. M.; Winstead, N. S.; Walton, W. C.; Dietz, J. E.

2017-12-01

A technique for synergistically-combining high-resolution SAR returns with like-frequency passive microwave emissions to detect thin (<30 cm) ice under the difficult conditions of late melt and freeze-up is presented. As the Arctic sea ice cover thins and shrinks, the algorithm offers an approach to adapting existing sensors monitoring thicker ice to provide continuing coverage. Lower resolution (10-26 km) ice detections with spaceborne radiometers and scatterometers are challenged by rapidly changing thin ice. Synthetic Aperture Radar (SAR) is high resolution (5-100m) but because of cross section ambiguities automated algorithms have had difficulty separating thin ice types from water. The radiometric emissivity of thin ice versus water at microwave frequencies is generally unambiguous in the early stages of ice growth. The method, developed using RADARSAT-2 and AMSR-E data, uses higher-ordered statistics. For the SAR, the COV (coefficient of variation, ratio of standard deviation to mean) has fewer ambiguities between ice and water than cross sections, but breaking waves still produce ice-like signatures for both polarizations. For the radiometer, the PRIC (polarization ratio ice concentration) identifies areas that are unambiguously water. Applying cumulative statistics to co-located COV levels adaptively determines an ice/water threshold. Outcomes from extensive testing with Sentinel and AMSR-2 data are shown in the results. The detection algorithm was applied to the freeze-up in the Beaufort, Chukchi, Barents, and East Siberian Seas in 2015 and 2016, spanning mid-September to early November of both years. At the end of the melt, 6 GHz PRIC values are 5-10% greater than those reported by radiometric algorithms at 19 and 37 GHz. During freeze-up, COV separates grease ice (<5 cm thick) from water. As the ice thickens, the COV is less reliable, but adding a mask based on either the PRIC or the cross-pol/co-pol SAR ratio corrects for COV deficiencies. In general, the dual-sensor detection algorithm reports 10-15% higher total ice concentrations than operational scatterometer or radiometer algorithms, mostly from ice edge and coastal areas. In conclusion, the algorithm presented combines high-resolution SAR returns with passive microwave emissions for automated ice detection at SAR resolutions.

Adaptive cockroach swarm algorithm

NASA Astrophysics Data System (ADS)

Obagbuwa, Ibidun C.; Abidoye, Ademola P.

2017-07-01

An adaptive cockroach swarm optimization (ACSO) algorithm is proposed in this paper to strengthen the existing cockroach swarm optimization (CSO) algorithm. The ruthless component of CSO algorithm is modified by the employment of blend crossover predator-prey evolution method which helps algorithm prevent any possible population collapse, maintain population diversity and create adaptive search in each iteration. The performance of the proposed algorithm on 16 global optimization benchmark function problems was evaluated and compared with the existing CSO, cuckoo search, differential evolution, particle swarm optimization and artificial bee colony algorithms.

Comparing the ISO-recommended and the cumulative data-reduction algorithms in S-on-1 laser damage test by a reverse approach method

NASA Astrophysics Data System (ADS)

Zorila, Alexandru; Stratan, Aurel; Nemes, George

2018-01-01

We compare the ISO-recommended (the standard) data-reduction algorithm used to determine the surface laser-induced damage threshold of optical materials by the S-on-1 test with two newly suggested algorithms, both named "cumulative" algorithms/methods, a regular one and a limit-case one, intended to perform in some respects better than the standard one. To avoid additional errors due to real experiments, a simulated test is performed, named the reverse approach. This approach simulates the real damage experiments, by generating artificial test-data of damaged and non-damaged sites, based on an assumed, known damage threshold fluence of the target and on a given probability distribution function to induce the damage. In this work, a database of 12 sets of test-data containing both damaged and non-damaged sites was generated by using four different reverse techniques and by assuming three specific damage probability distribution functions. The same value for the threshold fluence was assumed, and a Gaussian fluence distribution on each irradiated site was considered, as usual for the S-on-1 test. Each of the test-data was independently processed by the standard and by the two cumulative data-reduction algorithms, the resulting fitted probability distributions were compared with the initially assumed probability distribution functions, and the quantities used to compare these algorithms were determined. These quantities characterize the accuracy and the precision in determining the damage threshold and the goodness of fit of the damage probability curves. The results indicate that the accuracy in determining the absolute damage threshold is best for the ISO-recommended method, the precision is best for the limit-case of the cumulative method, and the goodness of fit estimator (adjusted R-squared) is almost the same for all three algorithms.

A Floor-Map-Aided WiFi/Pseudo-Odometry Integration Algorithm for an Indoor Positioning System

PubMed Central

Wang, Jian; Hu, Andong; Liu, Chunyan; Li, Xin

2015-01-01

This paper proposes a scheme for indoor positioning by fusing floor map, WiFi and smartphone sensor data to provide meter-level positioning without additional infrastructure. A topology-constrained K nearest neighbor (KNN) algorithm based on a floor map layout provides the coordinates required to integrate WiFi data with pseudo-odometry (P-O) measurements simulated using a pedestrian dead reckoning (PDR) approach. One method of further improving the positioning accuracy is to use a more effective multi-threshold step detection algorithm, as proposed by the authors. The “go and back” phenomenon caused by incorrect matching of the reference points (RPs) of a WiFi algorithm is eliminated using an adaptive fading-factor-based extended Kalman filter (EKF), taking WiFi positioning coordinates, P-O measurements and fused heading angles as observations. The “cross-wall” problem is solved based on the development of a floor-map-aided particle filter algorithm by weighting the particles, thereby also eliminating the gross-error effects originating from WiFi or P-O measurements. The performance observed in a field experiment performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI) building on the China University of Mining and Technology (CUMT) campus confirms that the proposed scheme can reliably achieve meter-level positioning. PMID:25811224

Lidar detection algorithm for time and range anomalies.

PubMed

Ben-David, Avishai; Davidson, Charles E; Vanderbeek, Richard G

2007-10-10

A new detection algorithm for lidar applications has been developed. The detection is based on hyperspectral anomaly detection that is implemented for time anomaly where the question "is a target (aerosol cloud) present at range R within time t(1) to t(2)" is addressed, and for range anomaly where the question "is a target present at time t within ranges R(1) and R(2)" is addressed. A detection score significantly different in magnitude from the detection scores for background measurements suggests that an anomaly (interpreted as the presence of a target signal in space/time) exists. The algorithm employs an option for a preprocessing stage where undesired oscillations and artifacts are filtered out with a low-rank orthogonal projection technique. The filtering technique adaptively removes the one over range-squared dependence of the background contribution of the lidar signal and also aids visualization of features in the data when the signal-to-noise ratio is low. A Gaussian-mixture probability model for two hypotheses (anomaly present or absent) is computed with an expectation-maximization algorithm to produce a detection threshold and probabilities of detection and false alarm. Results of the algorithm for CO(2) lidar measurements of bioaerosol clouds Bacillus atrophaeus (formerly known as Bacillus subtilis niger, BG) and Pantoea agglomerans, Pa (formerly known as Erwinia herbicola, Eh) are shown and discussed.

Aiding the Detection of QRS Complex in ECG Signals by Detecting S Peaks Independently.

PubMed

Sabherwal, Pooja; Singh, Latika; Agrawal, Monika

2018-03-30

In this paper, a novel algorithm for the accurate detection of QRS complex by combining the independent detection of R and S peaks, using fusion algorithm is proposed. R peak detection has been extensively studied and is being used to detect the QRS complex. Whereas, S peaks, which is also part of QRS complex can be independently detected to aid the detection of QRS complex. In this paper, we suggest a method to first estimate S peak from raw ECG signal and then use them to aid the detection of QRS complex. The amplitude of S peak in ECG signal is relatively weak than corresponding R peak, which is traditionally used for the detection of QRS complex, therefore, an appropriate digital filter is designed to enhance the S peaks. These enhanced S peaks are then detected by adaptive thresholding. The algorithm is validated on all the signals of MIT-BIH arrhythmia database and noise stress database taken from physionet.org. The algorithm performs reasonably well even for the signals highly corrupted by noise. The algorithm performance is confirmed by sensitivity and positive predictivity of 99.99% and the detection accuracy of 99.98% for QRS complex detection. The number of false positives and false negatives resulted while analysis has been drastically reduced to 80 and 42 against the 98 and 84 the best results reported so far.

An adaptive replacement algorithm for paged-memory computer systems.

NASA Technical Reports Server (NTRS)

Thorington, J. M., Jr.; Irwin, J. D.

1972-01-01

A general class of adaptive replacement schemes for use in paged memories is developed. One such algorithm, called SIM, is simulated using a probability model that generates memory traces, and the results of the simulation of this adaptive scheme are compared with those obtained using the best nonlookahead algorithms. A technique for implementing this type of adaptive replacement algorithm with state of the art digital hardware is also presented.

A New Model for Solving Time-Cost-Quality Trade-Off Problems in Construction

PubMed Central

Fu, Fang; Zhang, Tao

2016-01-01

A poor quality affects project makespan and its total costs negatively, but it can be recovered by repair works during construction. We construct a new non-linear programming model based on the classic multi-mode resource constrained project scheduling problem considering repair works. In order to obtain satisfactory quality without a high increase of project cost, the objective is to minimize total quality cost which consists of the prevention cost and failure cost according to Quality-Cost Analysis. A binary dependent normal distribution function is adopted to describe the activity quality; Cumulative quality is defined to determine whether to initiate repair works, according to the different relationships among activity qualities, namely, the coordinative and precedence relationship. Furthermore, a shuffled frog-leaping algorithm is developed to solve this discrete trade-off problem based on an adaptive serial schedule generation scheme and adjusted activity list. In the program of the algorithm, the frog-leaping progress combines the crossover operator of genetic algorithm and a permutation-based local search. Finally, an example of a construction project for a framed railway overpass is provided to examine the algorithm performance, and it assist in decision making to search for the appropriate makespan and quality threshold with minimal cost. PMID:27911939

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation

PubMed Central

Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

2016-01-01

The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms. PMID:27999361

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation.

PubMed

Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

2016-12-19

The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms.

Error minimization algorithm for comparative quantitative PCR analysis: Q-Anal.

PubMed

OConnor, William; Runquist, Elizabeth A

2008-07-01

Current methods for comparative quantitative polymerase chain reaction (qPCR) analysis, the threshold and extrapolation methods, either make assumptions about PCR efficiency that require an arbitrary threshold selection process or extrapolate to estimate relative levels of messenger RNA (mRNA) transcripts. Here we describe an algorithm, Q-Anal, that blends elements from current methods to by-pass assumptions regarding PCR efficiency and improve the threshold selection process to minimize error in comparative qPCR analysis. This algorithm uses iterative linear regression to identify the exponential phase for both target and reference amplicons and then selects, by minimizing linear regression error, a fluorescence threshold where efficiencies for both amplicons have been defined. From this defined fluorescence threshold, cycle time (Ct) and the error for both amplicons are calculated and used to determine the expression ratio. Ratios in complementary DNA (cDNA) dilution assays from qPCR data were analyzed by the Q-Anal method and compared with the threshold method and an extrapolation method. Dilution ratios determined by the Q-Anal and threshold methods were 86 to 118% of the expected cDNA ratios, but relative errors for the Q-Anal method were 4 to 10% in comparison with 4 to 34% for the threshold method. In contrast, ratios determined by an extrapolation method were 32 to 242% of the expected cDNA ratios, with relative errors of 67 to 193%. Q-Anal will be a valuable and quick method for minimizing error in comparative qPCR analysis.

A Self-Organizing Spatial Clustering Approach to Support Large-Scale Network RTK Systems.

PubMed

Shen, Lili; Guo, Jiming; Wang, Lei

2018-06-06

The network real-time kinematic (RTK) technique can provide centimeter-level real time positioning solutions and play a key role in geo-spatial infrastructure. With ever-increasing popularity, network RTK systems will face issues in the support of large numbers of concurrent users. In the past, high-precision positioning services were oriented towards professionals and only supported a few concurrent users. Currently, precise positioning provides a spatial foundation for artificial intelligence (AI), and countless smart devices (autonomous cars, unmanned aerial-vehicles (UAVs), robotic equipment, etc.) require precise positioning services. Therefore, the development of approaches to support large-scale network RTK systems is urgent. In this study, we proposed a self-organizing spatial clustering (SOSC) approach which automatically clusters online users to reduce the computational load on the network RTK system server side. The experimental results indicate that both the SOSC algorithm and the grid algorithm can reduce the computational load efficiently, while the SOSC algorithm gives a more elastic and adaptive clustering solution with different datasets. The SOSC algorithm determines the cluster number and the mean distance to cluster center (MDTCC) according to the data set, while the grid approaches are all predefined. The side-effects of clustering algorithms on the user side are analyzed with real global navigation satellite system (GNSS) data sets. The experimental results indicate that 10 km can be safely used as the cluster radius threshold for the SOSC algorithm without significantly reducing the positioning precision and reliability on the user side.

An algorithm for simulating fracture of cohesive-frictional materials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nukala, Phani K; Sampath, Rahul S; Barai, Pallab

Fracture of disordered frictional granular materials is dominated by interfacial failure response that is characterized by de-cohesion followed by frictional sliding response. To capture such an interfacial failure response, we introduce a cohesive-friction random fuse model (CFRFM), wherein the cohesive response of the interface is represented by a linear stress-strain response until a failure threshold, which is then followed by a constant response at a threshold lower than the initial failure threshold to represent the interfacial frictional sliding mechanism. This paper presents an efficient algorithm for simulating fracture of such disordered frictional granular materials using the CFRFM. We note that,more » when applied to perfectly plastic disordered materials, our algorithm is both theoretically and numerically equivalent to the traditional tangent algorithm (Roux and Hansen 1992 J. Physique II 2 1007) used for such simulations. However, the algorithm is general and is capable of modeling discontinuous interfacial response. Our numerical simulations using the algorithm indicate that the local and global roughness exponents ({zeta}{sub loc} and {zeta}, respectively) of the fracture surface are equal to each other, and the two-dimensional crack roughness exponent is estimated to be {zeta}{sub loc} = {zeta} = 0.69 {+-} 0.03.« less

A Purkinje shift in the spectral sensitivity of grey squirrels

PubMed Central

Silver, Priscilla H.

1966-01-01

1. The light-adapted spectral sensitivity of the grey squirrel has been determined by an automated training method at a level about 6 log units above the squirrel's absolute threshold. 2. The maximum sensitivity is near 555 nm, under light-adapted conditions, compared with the dark-adapted maximum near 500 nm found by a similar method. 3. Neither the light-adapted nor the dark-adapted behavioural threshold agrees with electrophysiological findings using single flash techniques, but there is agreement with e.r.g. results obtained with sinusoidal stimuli. PMID:5972118

Hybrid Artificial Root Foraging Optimizer Based Multilevel Threshold for Image Segmentation

PubMed Central

Liu, Yang; Liu, Junfei

2016-01-01

This paper proposes a new plant-inspired optimization algorithm for multilevel threshold image segmentation, namely, hybrid artificial root foraging optimizer (HARFO), which essentially mimics the iterative root foraging behaviors. In this algorithm the new growth operators of branching, regrowing, and shrinkage are initially designed to optimize continuous space search by combining root-to-root communication and coevolution mechanism. With the auxin-regulated scheme, various root growth operators are guided systematically. With root-to-root communication, individuals exchange information in different efficient topologies, which essentially improve the exploration ability. With coevolution mechanism, the hierarchical spatial population driven by evolutionary pressure of multiple subpopulations is structured, which ensure that the diversity of root population is well maintained. The comparative results on a suit of benchmarks show the superiority of the proposed algorithm. Finally, the proposed HARFO algorithm is applied to handle the complex image segmentation problem based on multilevel threshold. Computational results of this approach on a set of tested images show the outperformance of the proposed algorithm in terms of optimization accuracy computation efficiency. PMID:27725826

Hybrid Artificial Root Foraging Optimizer Based Multilevel Threshold for Image Segmentation.

PubMed

Liu, Yang; Liu, Junfei; Tian, Liwei; Ma, Lianbo

2016-01-01

This paper proposes a new plant-inspired optimization algorithm for multilevel threshold image segmentation, namely, hybrid artificial root foraging optimizer (HARFO), which essentially mimics the iterative root foraging behaviors. In this algorithm the new growth operators of branching, regrowing, and shrinkage are initially designed to optimize continuous space search by combining root-to-root communication and coevolution mechanism. With the auxin-regulated scheme, various root growth operators are guided systematically. With root-to-root communication, individuals exchange information in different efficient topologies, which essentially improve the exploration ability. With coevolution mechanism, the hierarchical spatial population driven by evolutionary pressure of multiple subpopulations is structured, which ensure that the diversity of root population is well maintained. The comparative results on a suit of benchmarks show the superiority of the proposed algorithm. Finally, the proposed HARFO algorithm is applied to handle the complex image segmentation problem based on multilevel threshold. Computational results of this approach on a set of tested images show the outperformance of the proposed algorithm in terms of optimization accuracy computation efficiency.

QPSO-Based Adaptive DNA Computing Algorithm

PubMed Central

Karakose, Mehmet; Cigdem, Ugur

2013-01-01

DNA (deoxyribonucleic acid) computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and effectiveness. In this paper, a new approach for improvement of DNA computing is proposed. This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). Some contributions provided by the proposed QPSO based on adaptive DNA computing algorithm are as follows: (1) parameters of population size, crossover rate, maximum number of operations, enzyme and virus mutation rate, and fitness function of DNA computing algorithm are simultaneously tuned for adaptive process, (2) adaptive algorithm is performed using QPSO algorithm for goal-driven progress, faster operation, and flexibility in data, and (3) numerical realization of DNA computing algorithm with proposed approach is implemented in system identification. Two experiments with different systems were carried out to evaluate the performance of the proposed approach with comparative results. Experimental results obtained with Matlab and FPGA demonstrate ability to provide effective optimization, considerable convergence speed, and high accuracy according to DNA computing algorithm. PMID:23935409

3D kinematic measurement of human movement using low cost fish-eye cameras

NASA Astrophysics Data System (ADS)

Islam, Atiqul; Asikuzzaman, Md.; Garratt, Matthew A.; Pickering, Mark R.

2017-02-01

3D motion capture is difficult when the capturing is performed in an outdoor environment without controlled surroundings. In this paper, we propose a new approach of using two ordinary cameras arranged in a special stereoscopic configuration and passive markers on a subject's body to reconstruct the motion of the subject. Firstly for each frame of the video, an adaptive thresholding algorithm is applied for extracting the markers on the subject's body. Once the markers are extracted, an algorithm for matching corresponding markers in each frame is applied. Zhang's planar calibration method is used to calibrate the two cameras. As the cameras use the fisheye lens, they cannot be well estimated using a pinhole camera model which makes it difficult to estimate the depth information. In this work, to restore the 3D coordinates we use a unique calibration method for fisheye lenses. The accuracy of the 3D coordinate reconstruction is evaluated by comparing with results from a commercially available Vicon motion capture system.

Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology

PubMed Central

Roy, Mohendra; Seo, Dongmin; Oh, Sangwoo; Chae, Yeonghun; Nam, Myung-Hyun; Seo, Sungkyu

2016-01-01

Lens-free imaging technology has been extensively used recently for microparticle and biological cell analysis because of its high throughput, low cost, and simple and compact arrangement. However, this technology still lacks a dedicated and automated detection system. In this paper, we describe a custom-developed automated micro-object detection method for a lens-free imaging system. In our previous work (Roy et al.), we developed a lens-free imaging system using low-cost components. This system was used to generate and capture the diffraction patterns of micro-objects and a global threshold was used to locate the diffraction patterns. In this work we used the same setup to develop an improved automated detection and analysis algorithm based on adaptive threshold and clustering of signals. For this purpose images from the lens-free system were then used to understand the features and characteristics of the diffraction patterns of several types of samples. On the basis of this information, we custom-developed an automated algorithm for the lens-free imaging system. Next, all the lens-free images were processed using this custom-developed automated algorithm. The performance of this approach was evaluated by comparing the counting results with standard optical microscope results. We evaluated the counting results for polystyrene microbeads, red blood cells, HepG2, HeLa, and MCF7 cells lines. The comparison shows good agreement between the systems, with a correlation coefficient of 0.91 and linearity slope of 0.877. We also evaluated the automated size profiles of the microparticle samples. This Wi-Fi-enabled lens-free imaging system, along with the dedicated software, possesses great potential for telemedicine applications in resource-limited settings. PMID:27164146

An Automatic Cloud Mask Algorithm Based on Time Series of MODIS Measurements

NASA Technical Reports Server (NTRS)

Lyapustin, Alexei; Wang, Yujie; Frey, R.

2008-01-01

Quality of aerosol retrievals and atmospheric correction depends strongly on accuracy of the cloud mask (CM) algorithm. The heritage CM algorithms developed for AVHRR and MODIS use the latest sensor measurements of spectral reflectance and brightness temperature and perform processing at the pixel level. The algorithms are threshold-based and empirically tuned. They don't explicitly address the classical problem of cloud search, wherein the baseline clear-skies scene is defined for comparison. Here, we report on a new CM algorithm which explicitly builds and maintains a reference clear-skies image of the surface (refcm) using a time series of MODIS measurements. The new algorithm, developed as part of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm for MODIS, relies on fact that clear-skies images of the same surface area have a common textural pattern, defined by the surface topography, boundaries of rivers and lakes, distribution of soils and vegetation etc. This pattern changes slowly given the daily rate of global Earth observations, whereas clouds introduce high-frequency random disturbances. Under clear skies, consecutive gridded images of the same surface area have a high covariance, whereas in presence of clouds covariance is usually low. This idea is central to initialization of refcm which is used to derive cloud mask in combination with spectral and brightness temperature tests. The refcm is continuously updated with the latest clear-skies MODIS measurements, thus adapting to seasonal and rapid surface changes. The algorithm is enhanced by an internal dynamic land-water-snow classification coupled with a surface change mask. An initial comparison shows that the new algorithm offers the potential to perform better than the MODIS MOD35 cloud mask in situations where the land surface is changing rapidly, and over Earth regions covered by snow and ice.

Automatic cardiac cycle determination directly from EEG-fMRI data by multi-scale peak detection method.

PubMed

Wong, Chung-Ki; Luo, Qingfei; Zotev, Vadim; Phillips, Raquel; Chan, Kam Wai Clifford; Bodurka, Jerzy

2018-03-31

In simultaneous EEG-fMRI, identification of the period of cardioballistic artifact (BCG) in EEG is required for the artifact removal. Recording the electrocardiogram (ECG) waveform during fMRI is difficult, often causing inaccurate period detection. Since the waveform of the BCG extracted by independent component analysis (ICA) is relatively invariable compared to the ECG waveform, we propose a multiple-scale peak-detection algorithm to determine the BCG cycle directly from the EEG data. The algorithm first extracts the high contrast BCG component from the EEG data by ICA. The BCG cycle is then estimated by band-pass filtering the component around the fundamental frequency identified from its energy spectral density, and the peak of BCG artifact occurrence is selected from each of the estimated cycle. The algorithm is shown to achieve a high accuracy on a large EEG-fMRI dataset. It is also adaptive to various heart rates without the needs of adjusting the threshold parameters. The cycle detection remains accurate with the scan duration reduced to half a minute. Additionally, the algorithm gives a figure of merit to evaluate the reliability of the detection accuracy. The algorithm is shown to give a higher detection accuracy than the commonly used cycle detection algorithm fmrib_qrsdetect implemented in EEGLAB. The achieved high cycle detection accuracy of our algorithm without using the ECG waveforms makes possible to create and automate pipelines for processing large EEG-fMRI datasets, and virtually eliminates the need for ECG recordings for BCG artifact removal. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

A neural algorithm for the non-uniform and adaptive sampling of biomedical data.

PubMed

Mesin, Luca

2016-04-01

Body sensors are finding increasing applications in the self-monitoring for health-care and in the remote surveillance of sensitive people. The physiological data to be sampled can be non-stationary, with bursts of high amplitude and frequency content providing most information. Such data could be sampled efficiently with a non-uniform schedule that increases the sampling rate only during activity bursts. A real time and adaptive algorithm is proposed to select the sampling rate, in order to reduce the number of measured samples, but still recording the main information. The algorithm is based on a neural network which predicts the subsequent samples and their uncertainties, requiring a measurement only when the risk of the prediction is larger than a selectable threshold. Four examples of application to biomedical data are discussed: electromyogram, electrocardiogram, electroencephalogram, and body acceleration. Sampling rates are reduced under the Nyquist limit, still preserving an accurate representation of the data and of their power spectral densities (PSD). For example, sampling at 60% of the Nyquist frequency, the percentage average rectified errors in estimating the signals are on the order of 10% and the PSD is fairly represented, until the highest frequencies. The method outperforms both uniform sampling and compressive sensing applied to the same data. The discussed method allows to go beyond Nyquist limit, still preserving the information content of non-stationary biomedical signals. It could find applications in body sensor networks to lower the number of wireless communications (saving sensor power) and to reduce the occupation of memory. Copyright © 2016 Elsevier Ltd. All rights reserved.

Detection and recognition of uneaten fish food pellets in aquaculture using image processing

NASA Astrophysics Data System (ADS)

Liu, Huanyu; Xu, Lihong; Li, Dawei

2015-03-01

The waste of fish food has always been a serious problem in aquaculture. On one hand, the leftover fish food spawns a big waste in the aquaculture industry because fish food accounts for a large proportion of the investment. On the other hand, the left over fish food may pollute the water and make fishes sick. In general, the reason for fish food waste is that there is no feedback about the consumption of delivered fish food after feeding. So it is extremely difficult for fish farmers to determine the amount of feedstuff that should be delivered each time and the feeding intervals. In this paper, we propose an effective method using image processing techniques to solve this problem. During feeding events, we use an underwater camera with supplementary LED lights to obtain images of uneaten fish food pellets on the tank bottom. An algorithm is then developed to figure out the number of left pellets using adaptive Otsu thresholding and a linear-time component labeling algorithm. This proposed algorithm proves to be effective in handling the non-uniform lighting and very accurate number of pellets are counted in experiments.

A generalized time-frequency subtraction method for robust speech enhancement based on wavelet filter banks modeling of human auditory system.

PubMed

Shao, Yu; Chang, Chip-Hong

2007-08-01

We present a new speech enhancement scheme for a single-microphone system to meet the demand for quality noise reduction algorithms capable of operating at a very low signal-to-noise ratio. A psychoacoustic model is incorporated into the generalized perceptual wavelet denoising method to reduce the residual noise and improve the intelligibility of speech. The proposed method is a generalized time-frequency subtraction algorithm, which advantageously exploits the wavelet multirate signal representation to preserve the critical transient information. Simultaneous masking and temporal masking of the human auditory system are modeled by the perceptual wavelet packet transform via the frequency and temporal localization of speech components. The wavelet coefficients are used to calculate the Bark spreading energy and temporal spreading energy, from which a time-frequency masking threshold is deduced to adaptively adjust the subtraction parameters of the proposed method. An unvoiced speech enhancement algorithm is also integrated into the system to improve the intelligibility of speech. Through rigorous objective and subjective evaluations, it is shown that the proposed speech enhancement system is capable of reducing noise with little speech degradation in adverse noise environments and the overall performance is superior to several competitive methods.

THRESHOLD LOGIC.

DTIC Science & Technology

synthesis procedures; a ’best’ method is definitely established. (2) ’Symmetry Types for Threshold Logic’ is a tutorial expositon including a careful...development of the Goto-Takahasi self-dual type ideas. (3) ’Best Threshold Gate Decisions’ reports a comparison, on the 2470 7-argument threshold ...interpretation is shown best. (4) ’ Threshold Gate Networks’ reviews the previously discussed 2-algorithm in geometric terms, describes our FORTRAN

Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images.

PubMed

Karim, Rashed; Bhagirath, Pranav; Claus, Piet; James Housden, R; Chen, Zhong; Karimaghaloo, Zahra; Sohn, Hyon-Mok; Lara Rodríguez, Laura; Vera, Sergio; Albà, Xènia; Hennemuth, Anja; Peitgen, Heinz-Otto; Arbel, Tal; Gonzàlez Ballester, Miguel A; Frangi, Alejandro F; Götte, Marco; Razavi, Reza; Schaeffter, Tobias; Rhode, Kawal

2016-05-01

Studies have demonstrated the feasibility of late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging for guiding the management of patients with sequelae to myocardial infarction, such as ventricular tachycardia and heart failure. Clinical implementation of these developments necessitates a reproducible and reliable segmentation of the infarcted regions. It is challenging to compare new algorithms for infarct segmentation in the left ventricle (LV) with existing algorithms. Benchmarking datasets with evaluation strategies are much needed to facilitate comparison. This manuscript presents a benchmarking evaluation framework for future algorithms that segment infarct from LGE CMR of the LV. The image database consists of 30 LGE CMR images of both humans and pigs that were acquired from two separate imaging centres. A consensus ground truth was obtained for all data using maximum likelihood estimation. Six widely-used fixed-thresholding methods and five recently developed algorithms are tested on the benchmarking framework. Results demonstrate that the algorithms have better overlap with the consensus ground truth than most of the n-SD fixed-thresholding methods, with the exception of the Full-Width-at-Half-Maximum (FWHM) fixed-thresholding method. Some of the pitfalls of fixed thresholding methods are demonstrated in this work. The benchmarking evaluation framework, which is a contribution of this work, can be used to test and benchmark future algorithms that detect and quantify infarct in LGE CMR images of the LV. The datasets, ground truth and evaluation code have been made publicly available through the website: https://www.cardiacatlas.org/web/guest/challenges. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Predicting coral bleaching hotspots: the role of regional variability in thermal stress and potential adaptation rates

NASA Astrophysics Data System (ADS)

Teneva, Lida; Karnauskas, Mandy; Logan, Cheryl A.; Bianucci, Laura; Currie, Jock C.; Kleypas, Joan A.

2012-03-01

Sea surface temperature fields (1870-2100) forced by CO2-induced climate change under the IPCC SRES A1B CO2 scenario, from three World Climate Research Programme Coupled Model Intercomparison Project Phase 3 (WCRP CMIP3) models (CCSM3, CSIRO MK 3.5, and GFDL CM 2.1), were used to examine how coral sensitivity to thermal stress and rates of adaption affect global projections of coral-reef bleaching. The focus of this study was two-fold, to: (1) assess how the impact of Degree-Heating-Month (DHM) thermal stress threshold choice affects potential bleaching predictions and (2) examine the effect of hypothetical adaptation rates of corals to rising temperature. DHM values were estimated using a conventional threshold of 1°C and a variability-based threshold of 2σ above the climatological maximum Coral adaptation rates were simulated as a function of historical 100-year exposure to maximum annual SSTs with a dynamic rather than static climatological maximum based on the previous 100 years, for a given reef cell. Within CCSM3 simulations, the 1°C threshold predicted later onset of mild bleaching every 5 years for the fraction of reef grid cells where 1°C > 2σ of the climatology time series of annual SST maxima (1961-1990). Alternatively, DHM values using both thresholds, with CSIRO MK 3.5 and GFDL CM 2.1 SSTs, did not produce drastically different onset timing for bleaching every 5 years. Across models, DHMs based on 1°C thermal stress threshold show the most threatened reefs by 2100 could be in the Central and Western Equatorial Pacific, whereas use of the variability-based threshold for DHMs yields the Coral Triangle and parts of Micronesia and Melanesia as bleaching hotspots. Simulations that allow corals to adapt to increases in maximum SST drastically reduce the rates of bleaching. These findings highlight the importance of considering the thermal stress threshold in DHM estimates as well as potential adaptation models in future coral bleaching projections.

Accuracy evaluation of a new real-time continuous glucose monitoring algorithm in hypoglycemia.

PubMed

Mahmoudi, Zeinab; Jensen, Morten Hasselstrøm; Dencker Johansen, Mette; Christensen, Toke Folke; Tarnow, Lise; Christiansen, Jens Sandahl; Hejlesen, Ole

2014-10-01

The purpose of this study was to evaluate the performance of a new continuous glucose monitoring (CGM) calibration algorithm and to compare it with the Guardian(®) REAL-Time (RT) (Medtronic Diabetes, Northridge, CA) calibration algorithm in hypoglycemia. CGM data were obtained from 10 type 1 diabetes patients undergoing insulin-induced hypoglycemia. Data were obtained in two separate sessions using the Guardian RT CGM device. Data from the same CGM sensor were calibrated by two different algorithms: the Guardian RT algorithm and a new calibration algorithm. The accuracy of the two algorithms was compared using four performance metrics. The median (mean) of absolute relative deviation in the whole range of plasma glucose was 20.2% (32.1%) for the Guardian RT calibration and 17.4% (25.9%) for the new calibration algorithm. The mean (SD) sample-based sensitivity for the hypoglycemic threshold of 70 mg/dL was 31% (33%) for the Guardian RT algorithm and 70% (33%) for the new algorithm. The mean (SD) sample-based specificity at the same hypoglycemic threshold was 95% (8%) for the Guardian RT algorithm and 90% (16%) for the new calibration algorithm. The sensitivity of the event-based hypoglycemia detection for the hypoglycemic threshold of 70 mg/dL was 61% for the Guardian RT calibration and 89% for the new calibration algorithm. Application of the new calibration caused one false-positive instance for the event-based hypoglycemia detection, whereas the Guardian RT caused no false-positive instances. The overestimation of plasma glucose by CGM was corrected from 33.2 mg/dL in the Guardian RT algorithm to 21.9 mg/dL in the new calibration algorithm. The results suggest that the new algorithm may reduce the inaccuracy of Guardian RT CGM system within the hypoglycemic range; however, data from a larger number of patients are required to compare the clinical reliability of the two algorithms.

Developing Bayesian adaptive methods for estimating sensitivity thresholds (d′) in Yes-No and forced-choice tasks

PubMed Central

Lesmes, Luis A.; Lu, Zhong-Lin; Baek, Jongsoo; Tran, Nina; Dosher, Barbara A.; Albright, Thomas D.

2015-01-01

Motivated by Signal Detection Theory (SDT), we developed a family of novel adaptive methods that estimate the sensitivity threshold—the signal intensity corresponding to a pre-defined sensitivity level (d′ = 1)—in Yes-No (YN) and Forced-Choice (FC) detection tasks. Rather than focus stimulus sampling to estimate a single level of %Yes or %Correct, the current methods sample psychometric functions more broadly, to concurrently estimate sensitivity and decision factors, and thereby estimate thresholds that are independent of decision confounds. Developed for four tasks—(1) simple YN detection, (2) cued YN detection, which cues the observer's response state before each trial, (3) rated YN detection, which incorporates a Not Sure response, and (4) FC detection—the qYN and qFC methods yield sensitivity thresholds that are independent of the task's decision structure (YN or FC) and/or the observer's subjective response state. Results from simulation and psychophysics suggest that 25 trials (and sometimes less) are sufficient to estimate YN thresholds with reasonable precision (s.d. = 0.10–0.15 decimal log units), but more trials are needed for FC thresholds. When the same subjects were tested across tasks of simple, cued, rated, and FC detection, adaptive threshold estimates exhibited excellent agreement with the method of constant stimuli (MCS), and with each other. These YN adaptive methods deliver criterion-free thresholds that have previously been exclusive to FC methods. PMID:26300798

Multi-element array signal reconstruction with adaptive least-squares algorithms

NASA Technical Reports Server (NTRS)

Kumar, R.

1992-01-01

Two versions of the adaptive least-squares algorithm are presented for combining signals from multiple feeds placed in the focal plane of a mechanical antenna whose reflector surface is distorted due to various deformations. Coherent signal combining techniques based on the adaptive least-squares algorithm are examined for nearly optimally and adaptively combining the outputs of the feeds. The performance of the two versions is evaluated by simulations. It is demonstrated for the example considered that both of the adaptive least-squares algorithms are capable of offsetting most of the loss in the antenna gain incurred due to reflector surface deformations.

Threshold-based segmentation of fluorescent and chromogenic images of microglia, astrocytes and oligodendrocytes in FIJI.

PubMed

Healy, Sinead; McMahon, Jill; Owens, Peter; Dockery, Peter; FitzGerald, Una

2018-02-01

Image segmentation is often imperfect, particularly in complex image sets such z-stack micrographs of slice cultures and there is a need for sufficient details of parameters used in quantitative image analysis to allow independent repeatability and appraisal. For the first time, we have critically evaluated, quantified and validated the performance of different segmentation methodologies using z-stack images of ex vivo glial cells. The BioVoxxel toolbox plugin, available in FIJI, was used to measure the relative quality, accuracy, specificity and sensitivity of 16 global and 9 local threshold automatic thresholding algorithms. Automatic thresholding yields improved binary representation of glial cells compared with the conventional user-chosen single threshold approach for confocal z-stacks acquired from ex vivo slice cultures. The performance of threshold algorithms varies considerably in quality, specificity, accuracy and sensitivity with entropy-based thresholds scoring highest for fluorescent staining. We have used the BioVoxxel toolbox to correctly and consistently select the best automated threshold algorithm to segment z-projected images of ex vivo glial cells for downstream digital image analysis and to define segmentation quality. The automated OLIG2 cell count was validated using stereology. As image segmentation and feature extraction can quite critically affect the performance of successive steps in the image analysis workflow, it is becoming increasingly necessary to consider the quality of digital segmenting methodologies. Here, we have applied, validated and extended an existing performance-check methodology in the BioVoxxel toolbox to z-projected images of ex vivo glia cells. Copyright © 2017 Elsevier B.V. All rights reserved.

A wavelet and least square filter based spatial-spectral denoising approach of hyperspectral imagery

NASA Astrophysics Data System (ADS)

Li, Ting; Chen, Xiao-Mei; Chen, Gang; Xue, Bo; Ni, Guo-Qiang

2009-11-01

Noise reduction is a crucial step in hyperspectral imagery pre-processing. Based on sensor characteristics, the noise of hyperspectral imagery represents in both spatial and spectral domain. However, most prevailing denosing techniques process the imagery in only one specific domain, which have not utilized multi-domain nature of hyperspectral imagery. In this paper, a new spatial-spectral noise reduction algorithm is proposed, which is based on wavelet analysis and least squares filtering techniques. First, in the spatial domain, a new stationary wavelet shrinking algorithm with improved threshold function is utilized to adjust the noise level band-by-band. This new algorithm uses BayesShrink for threshold estimation, and amends the traditional soft-threshold function by adding shape tuning parameters. Comparing with soft or hard threshold function, the improved one, which is first-order derivable and has a smooth transitional region between noise and signal, could save more details of image edge and weaken Pseudo-Gibbs. Then, in the spectral domain, cubic Savitzky-Golay filter based on least squares method is used to remove spectral noise and artificial noise that may have been introduced in during the spatial denoising. Appropriately selecting the filter window width according to prior knowledge, this algorithm has effective performance in smoothing the spectral curve. The performance of the new algorithm is experimented on a set of Hyperion imageries acquired in 2007. The result shows that the new spatial-spectral denoising algorithm provides more significant signal-to-noise-ratio improvement than traditional spatial or spectral method, while saves the local spectral absorption features better.

Adaptive region-growing with maximum curvature strategy for tumor segmentation in 18F-FDG PET

NASA Astrophysics Data System (ADS)

Tan, Shan; Li, Laquan; Choi, Wookjin; Kang, Min Kyu; D'Souza, Warren D.; Lu, Wei

2017-07-01

Accurate tumor segmentation in PET is crucial in many oncology applications. We developed an adaptive region-growing (ARG) algorithm with a maximum curvature strategy (ARG_MC) for tumor segmentation in PET. The ARG_MC repeatedly applied a confidence connected region-growing algorithm with increasing relaxing factor f. The optimal relaxing factor (ORF) was then determined at the transition point on the f-volume curve, where the volume just grew from the tumor into the surrounding normal tissues. The ARG_MC along with five widely used algorithms were tested on a phantom with 6 spheres at different signal to background ratios and on two clinic datasets including 20 patients with esophageal cancer and 11 patients with non-Hodgkin lymphoma (NHL). The ARG_MC did not require any phantom calibration or any a priori knowledge of the tumor or PET scanner. The identified ORF varied with tumor types (mean ORF  =  9.61, 3.78 and 2.55 respectively for the phantom, esophageal cancer, and NHL datasets), and varied from one tumor to another. For the phantom, the ARG_MC ranked the second in segmentation accuracy with an average Dice similarity index (DSI) of 0.86, only slightly worse than Daisne’s adaptive thresholding method (DSI  =  0.87), which required phantom calibration. For both the esophageal cancer dataset and the NHL dataset, the ARG_MC had the highest accuracy with an average DSI of 0.87 and 0.84, respectively. The ARG_MC was robust to parameter settings and region of interest selection, and it did not depend on scanners, imaging protocols, or tumor types. Furthermore, the ARG_MC made no assumption about the tumor size or tumor uptake distribution, making it suitable for segmenting tumors with heterogeneous FDG uptake. In conclusion, the ARG_MC was accurate, robust and easy to use, it provides a highly potential tool for PET tumor segmentation in clinic.

Adaptive Region-Growing with Maximum Curvature Strategy for Tumor Segmentation in 18F-FDG PET

PubMed Central

Tan, Shan; Li, Laquan; Choi, Wookjin; Kang, Min Kyu; D’Souza, Warren D.; Lu, Wei

2017-01-01

Accurate tumor segmentation in PET is crucial in many oncology applications. We developed an adaptive region-growing (ARG) algorithm with a maximum curvature strategy (ARG_MC) for tumor segmentation in PET. The ARG_MC repeatedly applied a confidence connected region-growing (CCRG) algorithm with increasing relaxing factor f. The optimal relaxing factor (ORF) was then determined at the transition point on the f-volume curve, where the volume just grew from the tumor into the surrounding normal tissues. The ARG_MC along with five widely used algorithms were tested on a phantom with 6 spheres at different signal to background ratios and on two clinic datasets including 20 patients with esophageal cancer and 11 patients with non-Hodgkin lymphoma (NHL). The ARG_MC did not require any phantom calibration or any a priori knowledge of the tumor or PET scanner. The identified ORF varied with tumor types (mean ORF = 9.61, 3.78 and 2.55 respectively for the phantom, esophageal cancer, and NHL datasets), and varied from one tumor to another. For the phantom, the ARG_MC ranked the second in segmentation accuracy with an average Dice similarity index (DSI) of 0.86, only slightly worse than Daisne’s adaptive thresholding method (DSI=0.87), which required phantom calibration. For both the esophageal cancer dataset and the NHL dataset, the ARG_MC had the highest accuracy with an average DSI of 0.87 and 0.84, respectively. The ARG_MC was robust to parameter settings and region of interest selection, and it did not depend on scanners, imaging protocols, or tumor types. Furthermore, the ARG_MC made no assumption about the tumor size or tumor uptake distribution, making it suitable for segmenting tumors with heterogeneous FDG uptake. In conclusion, the ARG_MC was accurate, robust and easy to use, it provides a highly potential tool for PET tumor segmentation in clinic. PMID:28604372

Stochastic analysis of epidemics on adaptive time varying networks

NASA Astrophysics Data System (ADS)

Kotnis, Bhushan; Kuri, Joy

2013-06-01

Many studies investigating the effect of human social connectivity structures (networks) and human behavioral adaptations on the spread of infectious diseases have assumed either a static connectivity structure or a network which adapts itself in response to the epidemic (adaptive networks). However, human social connections are inherently dynamic or time varying. Furthermore, the spread of many infectious diseases occur on a time scale comparable to the time scale of the evolving network structure. Here we aim to quantify the effect of human behavioral adaptations on the spread of asymptomatic infectious diseases on time varying networks. We perform a full stochastic analysis using a continuous time Markov chain approach for calculating the outbreak probability, mean epidemic duration, epidemic reemergence probability, etc. Additionally, we use mean-field theory for calculating epidemic thresholds. Theoretical predictions are verified using extensive simulations. Our studies have uncovered the existence of an “adaptive threshold,” i.e., when the ratio of susceptibility (or infectivity) rate to recovery rate is below the threshold value, adaptive behavior can prevent the epidemic. However, if it is above the threshold, no amount of behavioral adaptations can prevent the epidemic. Our analyses suggest that the interaction patterns of the infected population play a major role in sustaining the epidemic. Our results have implications on epidemic containment policies, as awareness campaigns and human behavioral responses can be effective only if the interaction levels of the infected populace are kept in check.

Computational Investigation of Environment-Noise Interaction in Single-Cell Organisms: The Merit of Expression Stochasticity Depends on the Quality of Environmental Fluctuations.

PubMed

Lück, Anja; Klimmasch, Lukas; Großmann, Peter; Germerodt, Sebastian; Kaleta, Christoph

2018-01-10

Organisms need to adapt to changing environments and they do so by using a broad spectrum of strategies. These strategies include finding the right balance between expressing genes before or when they are needed, and adjusting the degree of noise inherent in gene expression. We investigated the interplay between different nutritional environments and the inhabiting organisms' metabolic and genetic adaptations by applying an evolutionary algorithm to an agent-based model of a concise bacterial metabolism. Our results show that constant environments and rapidly fluctuating environments produce similar adaptations in the organisms, making the predictability of the environment a major factor in determining optimal adaptation. We show that exploitation of expression noise occurs only in some types of fluctuating environment and is strongly dependent on the quality and availability of nutrients: stochasticity is generally detrimental in fluctuating environments and beneficial only at equal periods of nutrient availability and above a threshold environmental richness. Moreover, depending on the availability and nutritional value of nutrients, nutrient-dependent and stochastic expression are both strategies used to deal with environmental changes. Overall, we comprehensively characterize the interplay between the quality and periodicity of an environment and the resulting optimal deterministic and stochastic regulation strategies of nutrient-catabolizing pathways.

A multigrid method for steady Euler equations on unstructured adaptive grids

NASA Technical Reports Server (NTRS)

Riemslagh, Kris; Dick, Erik

1993-01-01

A flux-difference splitting type algorithm is formulated for the steady Euler equations on unstructured grids. The polynomial flux-difference splitting technique is used. A vertex-centered finite volume method is employed on a triangular mesh. The multigrid method is in defect-correction form. A relaxation procedure with a first order accurate inner iteration and a second-order correction performed only on the finest grid, is used. A multi-stage Jacobi relaxation method is employed as a smoother. Since the grid is unstructured a Jacobi type is chosen. The multi-staging is necessary to provide sufficient smoothing properties. The domain is discretized using a Delaunay triangular mesh generator. Three grids with more or less uniform distribution of nodes but with different resolution are generated by successive refinement of the coarsest grid. Nodes of coarser grids appear in the finer grids. The multigrid method is started on these grids. As soon as the residual drops below a threshold value, an adaptive refinement is started. The solution on the adaptively refined grid is accelerated by a multigrid procedure. The coarser multigrid grids are generated by successive coarsening through point removement. The adaption cycle is repeated a few times. Results are given for the transonic flow over a NACA-0012 airfoil.

Shape Adaptive, Robust Iris Feature Extraction from Noisy Iris Images

PubMed Central

Ghodrati, Hamed; Dehghani, Mohammad Javad; Danyali, Habibolah

2013-01-01

In the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. Whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. To the best of our knowledge, the effect of noise factors on feature extraction has not been considered in the previous works. This paper investigates the effect of shape adaptive wavelet transform and shape adaptive Gabor-wavelet for feature extraction on the iris recognition performance. In addition, an effective noise-removing approach is proposed in this paper. The contribution is to detect eyelashes and reflections by calculating appropriate thresholds by a procedure called statistical decision making. The eyelids are segmented by parabolic Hough transform in normalized iris image to decrease computational burden through omitting rotation term. The iris is localized by an accurate and fast algorithm based on coarse-to-fine strategy. The principle of mask code generation is to assign the noisy bits in an iris code in order to exclude them in matching step is presented in details. An experimental result shows that by using the shape adaptive Gabor-wavelet technique there is an improvement on the accuracy of recognition rate. PMID:24696801

Shape adaptive, robust iris feature extraction from noisy iris images.

PubMed

Ghodrati, Hamed; Dehghani, Mohammad Javad; Danyali, Habibolah

2013-10-01

In the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. Whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. To the best of our knowledge, the effect of noise factors on feature extraction has not been considered in the previous works. This paper investigates the effect of shape adaptive wavelet transform and shape adaptive Gabor-wavelet for feature extraction on the iris recognition performance. In addition, an effective noise-removing approach is proposed in this paper. The contribution is to detect eyelashes and reflections by calculating appropriate thresholds by a procedure called statistical decision making. The eyelids are segmented by parabolic Hough transform in normalized iris image to decrease computational burden through omitting rotation term. The iris is localized by an accurate and fast algorithm based on coarse-to-fine strategy. The principle of mask code generation is to assign the noisy bits in an iris code in order to exclude them in matching step is presented in details. An experimental result shows that by using the shape adaptive Gabor-wavelet technique there is an improvement on the accuracy of recognition rate.

[A cloud detection algorithm for MODIS images combining Kmeans clustering and multi-spectral threshold method].

PubMed

Wang, Wei; Song, Wei-Guo; Liu, Shi-Xing; Zhang, Yong-Ming; Zheng, Hong-Yang; Tian, Wei

2011-04-01

An improved method for detecting cloud combining Kmeans clustering and the multi-spectral threshold approach is described. On the basis of landmark spectrum analysis, MODIS data is categorized into two major types initially by Kmeans method. The first class includes clouds, smoke and snow, and the second class includes vegetation, water and land. Then a multi-spectral threshold detection is applied to eliminate interference such as smoke and snow for the first class. The method is tested with MODIS data at different time under different underlying surface conditions. By visual method to test the performance of the algorithm, it was found that the algorithm can effectively detect smaller area of cloud pixels and exclude the interference of underlying surface, which provides a good foundation for the next fire detection approach.

Characterization of Rod Function Phenotypes Across a Range of Age-Related Macular Degeneration Severities and Subretinal Drusenoid Deposits

PubMed Central

Flynn, Oliver J.; Cukras, Catherine A.; Jeffrey, Brett G.

2018-01-01

Purpose To examine spatial changes in rod-mediated function in relationship to local structural changes across the central retina in eyes with a spectrum of age-related macular degeneration (AMD) disease severity. Methods Participants were categorized into five AMD severity groups based on fundus features. Scotopic thresholds were measured at 14 loci spanning ±18° along the vertical meridian from one eye of each of 42 participants (mean = 71.7 ± 9.9 years). Following a 30% bleach, dark adaptation was measured at eight loci (±12°). Rod intercept time (RIT) was defined from the time to detect a −3.1 log cd/m2 stimulus. RITslope was defined from the linear fit of RIT with decreasing retinal eccentricity. The presence of subretinal drusenoid deposits (SDD), ellipsoid (EZ) band disruption, and drusen at the test loci was evaluated using optical coherence tomography. Results Scotopic thresholds indicated greater rod function loss in the macula, which correlated with increasing AMD group severity. RITslope, which captures the spatial change in the rate of dark adaptation, increased with AMD severity (P < 0.0001). Three rod function phenotypes emerged: RF1, normal rod function; RF2, normal scotopic thresholds but slowed dark adaptation; and RF3, elevated scotopic thresholds with slowed dark adaptation. Dark adaptation was slowed at all loci with SDD or EZ band disruption, and at 32% of loci with no local structural changes. Conclusions Three rod function phenotypes were defined from combined measurement of scotopic threshold and dark adaptation. Spatial changes in dark adaptation across the macula were captured with RITslope, which may be a useful outcome measure for functional studies of AMD. PMID:29847647

Characterization of Rod Function Phenotypes Across a Range of Age-Related Macular Degeneration Severities and Subretinal Drusenoid Deposits.

PubMed

Flynn, Oliver J; Cukras, Catherine A; Jeffrey, Brett G

2018-05-01

To examine spatial changes in rod-mediated function in relationship to local structural changes across the central retina in eyes with a spectrum of age-related macular degeneration (AMD) disease severity. Participants were categorized into five AMD severity groups based on fundus features. Scotopic thresholds were measured at 14 loci spanning ±18° along the vertical meridian from one eye of each of 42 participants (mean = 71.7 ± 9.9 years). Following a 30% bleach, dark adaptation was measured at eight loci (±12°). Rod intercept time (RIT) was defined from the time to detect a -3.1 log cd/m2 stimulus. RITslope was defined from the linear fit of RIT with decreasing retinal eccentricity. The presence of subretinal drusenoid deposits (SDD), ellipsoid (EZ) band disruption, and drusen at the test loci was evaluated using optical coherence tomography. Scotopic thresholds indicated greater rod function loss in the macula, which correlated with increasing AMD group severity. RITslope, which captures the spatial change in the rate of dark adaptation, increased with AMD severity (P < 0.0001). Three rod function phenotypes emerged: RF1, normal rod function; RF2, normal scotopic thresholds but slowed dark adaptation; and RF3, elevated scotopic thresholds with slowed dark adaptation. Dark adaptation was slowed at all loci with SDD or EZ band disruption, and at 32% of loci with no local structural changes. Three rod function phenotypes were defined from combined measurement of scotopic threshold and dark adaptation. Spatial changes in dark adaptation across the macula were captured with RITslope, which may be a useful outcome measure for functional studies of AMD.

Adaptive Algorithms for Automated Processing of Document Images

DTIC Science & Technology

2011-01-01

ABSTRACT Title of dissertation: ADAPTIVE ALGORITHMS FOR AUTOMATED PROCESSING OF DOCUMENT IMAGES Mudit Agrawal, Doctor of Philosophy, 2011...2011 4. TITLE AND SUBTITLE Adaptive Algorithms for Automated Processing of Document Images 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM...ALGORITHMS FOR AUTOMATED PROCESSING OF DOCUMENT IMAGES by Mudit Agrawal Dissertation submitted to the Faculty of the Graduate School of the University

Motion Cueing Algorithm Development: Initial Investigation and Redesign of the Algorithms

NASA Technical Reports Server (NTRS)

Telban, Robert J.; Wu, Weimin; Cardullo, Frank M.; Houck, Jacob A. (Technical Monitor)

2000-01-01

In this project four motion cueing algorithms were initially investigated. The classical algorithm generated results with large distortion and delay and low magnitude. The NASA adaptive algorithm proved to be well tuned with satisfactory performance, while the UTIAS adaptive algorithm produced less desirable results. Modifications were made to the adaptive algorithms to reduce the magnitude of undesirable spikes. The optimal algorithm was found to have the potential for improved performance with further redesign. The center of simulator rotation was redefined. More terms were added to the cost function to enable more tuning flexibility. A new design approach using a Fortran/Matlab/Simulink setup was employed. A new semicircular canals model was incorporated in the algorithm. With these changes results show the optimal algorithm has some advantages over the NASA adaptive algorithm. Two general problems observed in the initial investigation required solutions. A nonlinear gain algorithm was developed that scales the aircraft inputs by a third-order polynomial, maximizing the motion cues while remaining within the operational limits of the motion system. A braking algorithm was developed to bring the simulator to a full stop at its motion limit and later release the brake to follow the cueing algorithm output.

Pulmonary airways tree segmentation from CT examinations using adaptive volume of interest

NASA Astrophysics Data System (ADS)

Park, Sang Cheol; Kim, Won Pil; Zheng, Bin; Leader, Joseph K.; Pu, Jiantao; Tan, Jun; Gur, David

2009-02-01

Airways tree segmentation is an important step in quantitatively assessing the severity of and changes in several lung diseases such as chronic obstructive pulmonary disease (COPD), asthma, and cystic fibrosis. It can also be used in guiding bronchoscopy. The purpose of this study is to develop an automated scheme for segmenting the airways tree structure depicted on chest CT examinations. After lung volume segmentation, the scheme defines the first cylinder-like volume of interest (VOI) using a series of images depicting the trachea. The scheme then iteratively defines and adds subsequent VOIs using a region growing algorithm combined with adaptively determined thresholds in order to trace possible sections of airways located inside the combined VOI in question. The airway tree segmentation process is automatically terminated after the scheme assesses all defined VOIs in the iteratively assembled VOI list. In this preliminary study, ten CT examinations with 1.25mm section thickness and two different CT image reconstruction kernels ("bone" and "standard") were selected and used to test the proposed airways tree segmentation scheme. The experiment results showed that (1) adopting this approach affectively prevented the scheme from infiltrating into the parenchyma, (2) the proposed method reasonably accurately segmented the airways trees with lower false positive identification rate as compared with other previously reported schemes that are based on 2-D image segmentation and data analyses, and (3) the proposed adaptive, iterative threshold selection method for the region growing step in each identified VOI enables the scheme to segment the airways trees reliably to the 4th generation in this limited dataset with successful segmentation up to the 5th generation in a fraction of the airways tree branches.

Estimating Position of Mobile Robots From Omnidirectional Vision Using an Adaptive Algorithm.

PubMed

Li, Luyang; Liu, Yun-Hui; Wang, Kai; Fang, Mu

2015-08-01

This paper presents a novel and simple adaptive algorithm for estimating the position of a mobile robot with high accuracy in an unknown and unstructured environment by fusing images of an omnidirectional vision system with measurements of odometry and inertial sensors. Based on a new derivation where the omnidirectional projection can be linearly parameterized by the positions of the robot and natural feature points, we propose a novel adaptive algorithm, which is similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the robot's position by using the tracked feature points in image sequence, the robot's velocity, and orientation angles measured by odometry and inertial sensors. It is proved that the adaptive algorithm leads to global exponential convergence of the position estimation errors to zero. Simulations and real-world experiments are performed to demonstrate the performance of the proposed algorithm.

Automated tumour boundary delineation on 18F-FDG PET images using active contour coupled with shifted-optimal thresholding method

NASA Astrophysics Data System (ADS)

Khamwan, Kitiwat; Krisanachinda, Anchali; Pluempitiwiriyawej, Charnchai

2012-10-01

This study presents an automatic method to trace the boundary of the tumour in positron emission tomography (PET) images. It has been discovered that Otsu's threshold value is biased when the within-class variances between the object and the background are significantly different. To solve the problem, a double-stage threshold search that minimizes the energy between the first Otsu's threshold and the maximum intensity value is introduced. Such shifted-optimal thresholding is embedded into a region-based active contour so that both algorithms are performed consecutively. The efficiency of the method is validated using six sphere inserts (0.52-26.53 cc volume) of the IEC/2001 torso phantom. Both spheres and phantom were filled with 18F solution with four source-to-background ratio (SBR) measurements of PET images. The results illustrate that the tumour volumes segmented by combined algorithm are of higher accuracy than the traditional active contour. The method had been clinically implemented in ten oesophageal cancer patients. The results are evaluated and compared with the manual tracing by an experienced radiation oncologist. The advantage of the algorithm is the reduced erroneous delineation that improves the precision and accuracy of PET tumour contouring. Moreover, the combined method is robust, independent of the SBR threshold-volume curves, and it does not require prior lesion size measurement.

a Threshold-Free Filtering Algorithm for Airborne LIDAR Point Clouds Based on Expectation-Maximization

NASA Astrophysics Data System (ADS)

Hui, Z.; Cheng, P.; Ziggah, Y. Y.; Nie, Y.

2018-04-01

Filtering is a key step for most applications of airborne LiDAR point clouds. Although lots of filtering algorithms have been put forward in recent years, most of them suffer from parameters setting or thresholds adjusting, which will be time-consuming and reduce the degree of automation of the algorithm. To overcome this problem, this paper proposed a threshold-free filtering algorithm based on expectation-maximization. The proposed algorithm is developed based on an assumption that point clouds are seen as a mixture of Gaussian models. The separation of ground points and non-ground points from point clouds can be replaced as a separation of a mixed Gaussian model. Expectation-maximization (EM) is applied for realizing the separation. EM is used to calculate maximum likelihood estimates of the mixture parameters. Using the estimated parameters, the likelihoods of each point belonging to ground or object can be computed. After several iterations, point clouds can be labelled as the component with a larger likelihood. Furthermore, intensity information was also utilized to optimize the filtering results acquired using the EM method. The proposed algorithm was tested using two different datasets used in practice. Experimental results showed that the proposed method can filter non-ground points effectively. To quantitatively evaluate the proposed method, this paper adopted the dataset provided by the ISPRS for the test. The proposed algorithm can obtain a 4.48 % total error which is much lower than most of the eight classical filtering algorithms reported by the ISPRS.

Adaptive Reception for Underwater Communications

DTIC Science & Technology

2011-06-01

Experimental results prove the effectiveness of the receiver. 14. SUBJECT TERMS Underwater acoustic communications, adaptive algorithms , Kalman filter...the update algorithm design and the value of the spatial diversity are addressed. In this research, an adaptive multichannel equalizer made up of a...for the time-varying nature of the channel is to use an Adaptive Decision Feedback Equalizer based on either the RLS or LMS algorithm . Although this

Kmeans-ICA based automatic method for ocular artifacts removal in a motorimagery classification.

PubMed

Bou Assi, Elie; Rihana, Sandy; Sawan, Mohamad

2014-01-01

Electroencephalogram (EEG) recordings aroused as inputs of a motor imagery based BCI system. Eye blinks contaminate the spectral frequency of the EEG signals. Independent Component Analysis (ICA) has been already proved for removing these artifacts whose frequency band overlap with the EEG of interest. However, already ICA developed methods, use a reference lead such as the ElectroOculoGram (EOG) to identify the ocular artifact components. In this study, artifactual components were identified using an adaptive thresholding by means of Kmeans clustering. The denoised EEG signals have been fed into a feature extraction algorithm extracting the band power, the coherence and the phase locking value and inserted into a linear discriminant analysis classifier for a motor imagery classification.

a Method of Generating dem from Dsm Based on Airborne Insar Data

NASA Astrophysics Data System (ADS)

Lu, W.; Zhang, J.; Xue, G.; Wang, C.

2018-04-01

Traditional methods of terrestrial survey to acquire DEM cannot meet the requirement of acquiring large quantities of data in real time, but the DSM can be quickly obtained by using the dual antenna synthetic aperture radar interferometry and the DEM generated by the DSM is more fast and accurate. Therefore it is most important to acquire DEM from DSM based on airborne InSAR data. This paper aims to the method that generate DEM from DSM accurately. Two steps in this paper are applied to acquire accurate DEM. First of all, when the DSM is generated by interferometry, unavoidable factors such as overlay and shadow will produce gross errors to affect the data accuracy, so the adaptive threshold segmentation method is adopted to remove the gross errors and the threshold is selected according to the coherence of the interferometry. Secondly DEM will be generated by the progressive triangulated irregular network densification filtering algorithm. Finally, experimental results are compared with the existing high-precision DEM results. The results show that this method can effectively filter out buildings, vegetation and other objects to obtain the high-precision DEM.

Development of a thresholding algorithm for calcium classification at multiple CT energies

NASA Astrophysics Data System (ADS)

Ng, LY.; Alssabbagh, M.; Tajuddin, A. A.; Shuaib, I. L.; Zainon, R.

2017-05-01

The objective of this study was to develop a thresholding method for calcium classification with different concentration using single-energy computed tomography. Five different concentrations of calcium chloride were filled in PMMA tubes and placed inside a water-filled PMMA phantom (diameter 10 cm). The phantom was scanned at 70, 80, 100, 120 and 140 kV using a SECT. CARE DOSE 4D was used and the slice thickness was set to 1 mm for all energies. ImageJ software inspired by the National Institute of Health (NIH) was used to measure the CT numbers for each calcium concentration from the CT images. The results were compared with a developed algorithm for verification. The percentage differences between the measured CT numbers obtained from the developed algorithm and the ImageJ show similar results. The multi-thresholding algorithm was found to be able to distinguish different concentrations of calcium chloride. However, it was unable to detect low concentrations of calcium chloride and iron (III) nitrate with CT numbers between 25 HU and 65 HU. The developed thresholding method used in this study may help to differentiate between calcium plaques and other types of plaques in blood vessels as it is proven to have a good ability to detect the high concentration of the calcium chloride. However, the algorithm needs to be improved to solve the limitations of detecting calcium chloride solution which has a similar CT number with iron (III) nitrate solution.

A novel segmentation method for uneven lighting image with noise injection based on non-local spatial information and intuitionistic fuzzy entropy

NASA Astrophysics Data System (ADS)

Yu, Haiyan; Fan, Jiulun

2017-12-01

Local thresholding methods for uneven lighting image segmentation always have the limitations that they are very sensitive to noise injection and that the performance relies largely upon the choice of the initial window size. This paper proposes a novel algorithm for segmenting uneven lighting images with strong noise injection based on non-local spatial information and intuitionistic fuzzy theory. We regard an image as a gray wave in three-dimensional space, which is composed of many peaks and troughs, and these peaks and troughs can divide the image into many local sub-regions in different directions. Our algorithm computes the relative characteristic of each pixel located in the corresponding sub-region based on fuzzy membership function and uses it to replace its absolute characteristic (its gray level) to reduce the influence of uneven light on image segmentation. At the same time, the non-local adaptive spatial constraints of pixels are introduced to avoid noise interference with the search of local sub-regions and the computation of local characteristics. Moreover, edge information is also taken into account to avoid false peak and trough labeling. Finally, a global method based on intuitionistic fuzzy entropy is employed on the wave transformation image to obtain the segmented result. Experiments on several test images show that the proposed method has excellent capability of decreasing the influence of uneven illumination on images and noise injection and behaves more robustly than several classical global and local thresholding methods.

An automatic segmentation method of a parameter-adaptive PCNN for medical images.

PubMed

Lian, Jing; Shi, Bin; Li, Mingcong; Nan, Ziwei; Ma, Yide

2017-09-01

Since pre-processing and initial segmentation steps in medical images directly affect the final segmentation results of the regions of interesting, an automatic segmentation method of a parameter-adaptive pulse-coupled neural network is proposed to integrate the above-mentioned two segmentation steps into one. This method has a low computational complexity for different kinds of medical images and has a high segmentation precision. The method comprises four steps. Firstly, an optimal histogram threshold is used to determine the parameter [Formula: see text] for different kinds of images. Secondly, we acquire the parameter [Formula: see text] according to a simplified pulse-coupled neural network (SPCNN). Thirdly, we redefine the parameter V of the SPCNN model by sub-intensity distribution range of firing pixels. Fourthly, we add an offset [Formula: see text] to improve initial segmentation precision. Compared with the state-of-the-art algorithms, the new method achieves a comparable performance by the experimental results from ultrasound images of the gallbladder and gallstones, magnetic resonance images of the left ventricle, and mammogram images of the left and the right breast, presenting the overall metric UM of 0.9845, CM of 0.8142, TM of 0.0726. The algorithm has a great potential to achieve the pre-processing and initial segmentation steps in various medical images. This is a premise for assisting physicians to detect and diagnose clinical cases.

Electrocardiogram signal denoising based on a new improved wavelet thresholding

NASA Astrophysics Data System (ADS)

Han, Guoqiang; Xu, Zhijun

2016-08-01

Good quality electrocardiogram (ECG) is utilized by physicians for the interpretation and identification of physiological and pathological phenomena. In general, ECG signals may mix various noises such as baseline wander, power line interference, and electromagnetic interference in gathering and recording process. As ECG signals are non-stationary physiological signals, wavelet transform is investigated to be an effective tool to discard noises from corrupted signals. A new compromising threshold function called sigmoid function-based thresholding scheme is adopted in processing ECG signals. Compared with other methods such as hard/soft thresholding or other existing thresholding functions, the new algorithm has many advantages in the noise reduction of ECG signals. It perfectly overcomes the discontinuity at ±T of hard thresholding and reduces the fixed deviation of soft thresholding. The improved wavelet thresholding denoising can be proved to be more efficient than existing algorithms in ECG signal denoising. The signal to noise ratio, mean square error, and percent root mean square difference are calculated to verify the denoising performance as quantitative tools. The experimental results reveal that the waves including P, Q, R, and S waves of ECG signals after denoising coincide with the original ECG signals by employing the new proposed method.

Star adaptation for two-algorithms used on serial computers

NASA Technical Reports Server (NTRS)

Howser, L. M.; Lambiotte, J. J., Jr.

1974-01-01

Two representative algorithms used on a serial computer and presently executed on the Control Data Corporation 6000 computer were adapted to execute efficiently on the Control Data STAR-100 computer. Gaussian elimination for the solution of simultaneous linear equations and the Gauss-Legendre quadrature formula for the approximation of an integral are the two algorithms discussed. A description is given of how the programs were adapted for STAR and why these adaptations were necessary to obtain an efficient STAR program. Some points to consider when adapting an algorithm for STAR are discussed. Program listings of the 6000 version coded in 6000 FORTRAN, the adapted STAR version coded in 6000 FORTRAN, and the STAR version coded in STAR FORTRAN are presented in the appendices.

SMI adaptive antenna arrays for weak interfering signals

NASA Technical Reports Server (NTRS)

Gupta, I. J.

1987-01-01

The performance of adaptive antenna arrays is studied when a sample matrix inversion (SMI) algorithm is used to control array weights. It is shown that conventional SMI adaptive antennas, like other adaptive antennas, are unable to suppress weak interfering signals (below thermal noise) encountered in broadcasting satellite communication systems. To overcome this problem, the SMI algorithm is modified. In the modified algorithm, the covariance matrix is modified such that the effect of thermal noise on the weights of the adaptive array is reduced. Thus, the weights are dictated by relatively weak coherent signals. It is shown that the modified algorithm provides the desired interference protection. The use of defocused feeds as auxiliary elements of an SMI adaptive array is also discussed.

Classification of adaptive memetic algorithms: a comparative study.

PubMed

Ong, Yew-Soon; Lim, Meng-Hiot; Zhu, Ning; Wong, Kok-Wai

2006-02-01

Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive memetic algorithms (MAs). One unique feature of adaptive MAs is the choice of local search methods or memes and recent studies have shown that this choice significantly affects the performances of problem searches. In this paper, we present a classification of memes adaptation in adaptive MAs on the basis of the mechanism used and the level of historical knowledge on the memes employed. Then the asymptotic convergence properties of the adaptive MAs considered are analyzed according to the classification. Subsequently, empirical studies on representatives of adaptive MAs for different type-level meme adaptations using continuous benchmark problems indicate that global-level adaptive MAs exhibit better search performances. Finally we conclude with some promising research directions in the area.

Cloud Detection of Optical Satellite Images Using Support Vector Machine

NASA Astrophysics Data System (ADS)

Lee, Kuan-Yi; Lin, Chao-Hung

2016-06-01

Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atmosphere effects, calculations of vegetation induces, land cover classification, and land cover change detection. In previous studies, thresholding is a common and useful method in cloud detection. However, a selected threshold is usually suitable for certain cases or local study areas, and it may be failed in other cases. In other words, thresholding-based methods are data-sensitive. Besides, there are many exceptions to control, and the environment is changed dynamically. Using the same threshold value on various data is not effective. In this study, a threshold-free method based on Support Vector Machine (SVM) is proposed, which can avoid the abovementioned problems. A statistical model is adopted to detect clouds instead of a subjective thresholding-based method, which is the main idea of this study. The features used in a classifier is the key to a successful classification. As a result, Automatic Cloud Cover Assessment (ACCA) algorithm, which is based on physical characteristics of clouds, is used to distinguish the clouds and other objects. In the same way, the algorithm called Fmask (Zhu et al., 2012) uses a lot of thresholds and criteria to screen clouds, cloud shadows, and snow. Therefore, the algorithm of feature extraction is based on the ACCA algorithm and Fmask. Spatial and temporal information are also important for satellite images. Consequently, co-occurrence matrix and temporal variance with uniformity of the major principal axis are used in proposed method. We aim to classify images into three groups: cloud, non-cloud and the others. In experiments, images acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and images containing the landscapes of agriculture, snow area, and island are tested. Experiment results demonstrate the detection accuracy of the proposed method is better than related methods.

Influence of Injury Risk Thresholds on the Performance of an Algorithm to Predict Crashes with Serious Injuries

PubMed Central

Bahouth, George; Digges, Kennerly; Schulman, Carl

2012-01-01

This paper presents methods to estimate crash injury risk based on crash characteristics captured by some passenger vehicles equipped with Advanced Automatic Crash Notification technology. The resulting injury risk estimates could be used within an algorithm to optimize rescue care. Regression analysis was applied to the National Automotive Sampling System / Crashworthiness Data System (NASS/CDS) to determine how variations in a specific injury risk threshold would influence the accuracy of predicting crashes with serious injuries. The recommended thresholds for classifying crashes with severe injuries are 0.10 for frontal crashes and 0.05 for side crashes. The regression analysis of NASS/CDS indicates that these thresholds will provide sensitivity above 0.67 while maintaining a positive predictive value in the range of 0.20. PMID:23169132

Bayesian microsaccade detection

PubMed Central

Mihali, Andra; van Opheusden, Bas; Ma, Wei Ji

2017-01-01

Microsaccades are high-velocity fixational eye movements, with special roles in perception and cognition. The default microsaccade detection method is to determine when the smoothed eye velocity exceeds a threshold. We have developed a new method, Bayesian microsaccade detection (BMD), which performs inference based on a simple statistical model of eye positions. In this model, a hidden state variable changes between drift and microsaccade states at random times. The eye position is a biased random walk with different velocity distributions for each state. BMD generates samples from the posterior probability distribution over the eye state time series given the eye position time series. Applied to simulated data, BMD recovers the “true” microsaccades with fewer errors than alternative algorithms, especially at high noise. Applied to EyeLink eye tracker data, BMD detects almost all the microsaccades detected by the default method, but also apparent microsaccades embedded in high noise—although these can also be interpreted as false positives. Next we apply the algorithms to data collected with a Dual Purkinje Image eye tracker, whose higher precision justifies defining the inferred microsaccades as ground truth. When we add artificial measurement noise, the inferences of all algorithms degrade; however, at noise levels comparable to EyeLink data, BMD recovers the “true” microsaccades with 54% fewer errors than the default algorithm. Though unsuitable for online detection, BMD has other advantages: It returns probabilities rather than binary judgments, and it can be straightforwardly adapted as the generative model is refined. We make our algorithm available as a software package. PMID:28114483

Real-time detection of small and dim moving objects in IR video sequences using a robust background estimator and a noise-adaptive double thresholding

NASA Astrophysics Data System (ADS)

Zingoni, Andrea; Diani, Marco; Corsini, Giovanni

2016-10-01

We developed an algorithm for automatically detecting small and poorly contrasted (dim) moving objects in real-time, within video sequences acquired through a steady infrared camera. The algorithm is suitable for different situations since it is independent of the background characteristics and of changes in illumination. Unlike other solutions, small objects of any size (up to single-pixel), either hotter or colder than the background, can be successfully detected. The algorithm is based on accurately estimating the background at the pixel level and then rejecting it. A novel approach permits background estimation to be robust to changes in the scene illumination and to noise, and not to be biased by the transit of moving objects. Care was taken in avoiding computationally costly procedures, in order to ensure the real-time performance even using low-cost hardware. The algorithm was tested on a dataset of 12 video sequences acquired in different conditions, providing promising results in terms of detection rate and false alarm rate, independently of background and objects characteristics. In addition, the detection map was produced frame by frame in real-time, using cheap commercial hardware. The algorithm is particularly suitable for applications in the fields of video-surveillance and computer vision. Its reliability and speed permit it to be used also in critical situations, like in search and rescue, defence and disaster monitoring.

Thresher: an improved algorithm for peak height thresholding of microbial community profiles.

PubMed

Starke, Verena; Steele, Andrew

2014-11-15

This article presents Thresher, an improved technique for finding peak height thresholds for automated rRNA intergenic spacer analysis (ARISA) profiles. We argue that thresholds must be sample dependent, taking community richness into account. In most previous fragment analyses, a common threshold is applied to all samples simultaneously, ignoring richness variations among samples and thereby compromising cross-sample comparison. Our technique solves this problem, and at the same time provides a robust method for outlier rejection, selecting for removal any replicate pairs that are not valid replicates. Thresholds are calculated individually for each replicate in a pair, and separately for each sample. The thresholds are selected to be the ones that minimize the dissimilarity between the replicates after thresholding. If a choice of threshold results in the two replicates in a pair failing a quantitative test of similarity, either that threshold or that sample must be rejected. We compare thresholded ARISA results with sequencing results, and demonstrate that the Thresher algorithm outperforms conventional thresholding techniques. The software is implemented in R, and the code is available at http://verenastarke.wordpress.com or by contacting the author. vstarke@ciw.edu or http://verenastarke.wordpress.com Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Optical communication system performance with tracking error induced signal fading.

NASA Technical Reports Server (NTRS)

Tycz, M.; Fitzmaurice, M. W.; Premo, D. A.

1973-01-01

System performance is determined for an optical communication system using noncoherent detection in the presence of tracking error induced signal fading assuming (1) binary on-off modulation (OOK) with both fixed and adaptive threshold receivers, and (2) binary polarization modulation (BPM). BPM is shown to maintain its inherent 2- to 3-dB advantage over OOK when adaptive thresholding is used, and to have a substantially greater advantage when the OOK system is restricted to a fixed decision threshold.

The Topo-trigger: a new concept of stereo trigger system for imaging atmospheric Cherenkov telescopes

NASA Astrophysics Data System (ADS)

López-Coto, R.; Mazin, D.; Paoletti, R.; Blanch Bigas, O.; Cortina, J.

2016-04-01

Imaging atmospheric Cherenkov telescopes (IACTs) such as the Major Atmospheric Gamma-ray Imaging Cherenkov (MAGIC) telescopes endeavor to reach the lowest possible energy threshold. In doing so the trigger system is a key element. Reducing the trigger threshold is hampered by the rapid increase of accidental triggers generated by ambient light (the so-called Night Sky Background NSB). In this paper we present a topological trigger, dubbed Topo-trigger, which rejects events on the basis of their relative orientation in the telescope cameras. We have simulated and tested the trigger selection algorithm in the MAGIC telescopes. The algorithm was tested using MonteCarlo simulations and shows a rejection of 85% of the accidental stereo triggers while preserving 99% of the gamma rays. A full implementation of this trigger system would achieve an increase in collection area between 10 and 20% at the energy threshold. The analysis energy threshold of the instrument is expected to decrease by ~ 8%. The selection algorithm was tested on real MAGIC data taken with the current trigger configuration and no γ-like events were found to be lost.

Automatic characterization and segmentation of human skin using three-dimensional optical coherence tomography

NASA Astrophysics Data System (ADS)

Hori, Yasuaki; Yasuno, Yoshiaki; Sakai, Shingo; Matsumoto, Masayuki; Sugawara, Tomoko; Madjarova, Violeta; Yamanari, Masahiro; Makita, Shuichi; Yasui, Takeshi; Araki, Tsutomu; Itoh, Masahide; Yatagai, Toyohiko

2006-03-01

A set of fully automated algorithms that is specialized for analyzing a three-dimensional optical coherence tomography (OCT) volume of human skin is reported. The algorithm set first determines the skin surface of the OCT volume, and a depth-oriented algorithm provides the mean epidermal thickness, distribution map of the epidermis, and a segmented volume of the epidermis. Subsequently, an en face shadowgram is produced by an algorithm to visualize the infundibula in the skin with high contrast. The population and occupation ratio of the infundibula are provided by a histogram-based thresholding algorithm and a distance mapping algorithm. En face OCT slices at constant depths from the sample surface are extracted, and the histogram-based thresholding algorithm is again applied to these slices, yielding a three-dimensional segmented volume of the infundibula. The dermal attenuation coefficient is also calculated from the OCT volume in order to evaluate the skin texture. The algorithm set examines swept-source OCT volumes of the skins of several volunteers, and the results show the high stability, portability and reproducibility of the algorithm.

Real time algorithms for sharp wave ripple detection.

PubMed

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.

SART-Type Half-Threshold Filtering Approach for CT Reconstruction

PubMed Central

YU, HENGYONG; WANG, GE

2014-01-01

The ℓ1 regularization problem has been widely used to solve the sparsity constrained problems. To enhance the sparsity constraint for better imaging performance, a promising direction is to use the ℓp norm (0 < p < 1) and solve the ℓp minimization problem. Very recently, Xu et al. developed an analytic solution for the ℓ1∕2 regularization via an iterative thresholding operation, which is also referred to as half-threshold filtering. In this paper, we design a simultaneous algebraic reconstruction technique (SART)-type half-threshold filtering framework to solve the computed tomography (CT) reconstruction problem. In the medical imaging filed, the discrete gradient transform (DGT) is widely used to define the sparsity. However, the DGT is noninvertible and it cannot be applied to half-threshold filtering for CT reconstruction. To demonstrate the utility of the proposed SART-type half-threshold filtering framework, an emphasis of this paper is to construct a pseudoinverse transforms for DGT. The proposed algorithms are evaluated with numerical and physical phantom data sets. Our results show that the SART-type half-threshold filtering algorithms have great potential to improve the reconstructed image quality from few and noisy projections. They are complementary to the counterparts of the state-of-the-art soft-threshold filtering and hard-threshold filtering. PMID:25530928

SART-Type Half-Threshold Filtering Approach for CT Reconstruction.

PubMed

Yu, Hengyong; Wang, Ge

2014-01-01

The [Formula: see text] regularization problem has been widely used to solve the sparsity constrained problems. To enhance the sparsity constraint for better imaging performance, a promising direction is to use the [Formula: see text] norm (0 < p < 1) and solve the [Formula: see text] minimization problem. Very recently, Xu et al. developed an analytic solution for the [Formula: see text] regularization via an iterative thresholding operation, which is also referred to as half-threshold filtering. In this paper, we design a simultaneous algebraic reconstruction technique (SART)-type half-threshold filtering framework to solve the computed tomography (CT) reconstruction problem. In the medical imaging filed, the discrete gradient transform (DGT) is widely used to define the sparsity. However, the DGT is noninvertible and it cannot be applied to half-threshold filtering for CT reconstruction. To demonstrate the utility of the proposed SART-type half-threshold filtering framework, an emphasis of this paper is to construct a pseudoinverse transforms for DGT. The proposed algorithms are evaluated with numerical and physical phantom data sets. Our results show that the SART-type half-threshold filtering algorithms have great potential to improve the reconstructed image quality from few and noisy projections. They are complementary to the counterparts of the state-of-the-art soft-threshold filtering and hard-threshold filtering.

Robust visual tracking based on deep convolutional neural networks and kernelized correlation filters

NASA Astrophysics Data System (ADS)

Yang, Hua; Zhong, Donghong; Liu, Chenyi; Song, Kaiyou; Yin, Zhouping

2018-03-01

Object tracking is still a challenging problem in computer vision, as it entails learning an effective model to account for appearance changes caused by occlusion, out of view, plane rotation, scale change, and background clutter. This paper proposes a robust visual tracking algorithm called deep convolutional neural network (DCNNCT) to simultaneously address these challenges. The proposed DCNNCT algorithm utilizes a DCNN to extract the image feature of a tracked target, and the full range of information regarding each convolutional layer is used to express the image feature. Subsequently, the kernelized correlation filters (CF) in each convolutional layer are adaptively learned, the correlation response maps of that are combined to estimate the location of the tracked target. To avoid the case of tracking failure, an online random ferns classifier is employed to redetect the tracked target, and a dual-threshold scheme is used to obtain the final target location by comparing the tracking result with the detection result. Finally, the change in scale of the target is determined by building scale pyramids and training a CF. Extensive experiments demonstrate that the proposed algorithm is effective at tracking, especially when evaluated using an index called the overlap rate. The DCNNCT algorithm is also highly competitive in terms of robustness with respect to state-of-the-art trackers in various challenging scenarios.

Novel density-based and hierarchical density-based clustering algorithms for uncertain data.

PubMed

Zhang, Xianchao; Liu, Han; Zhang, Xiaotong

2017-09-01

Uncertain data has posed a great challenge to traditional clustering algorithms. Recently, several algorithms have been proposed for clustering uncertain data, and among them density-based techniques seem promising for handling data uncertainty. However, some issues like losing uncertain information, high time complexity and nonadaptive threshold have not been addressed well in the previous density-based algorithm FDBSCAN and hierarchical density-based algorithm FOPTICS. In this paper, we firstly propose a novel density-based algorithm PDBSCAN, which improves the previous FDBSCAN from the following aspects: (1) it employs a more accurate method to compute the probability that the distance between two uncertain objects is less than or equal to a boundary value, instead of the sampling-based method in FDBSCAN; (2) it introduces new definitions of probability neighborhood, support degree, core object probability, direct reachability probability, thus reducing the complexity and solving the issue of nonadaptive threshold (for core object judgement) in FDBSCAN. Then, we modify the algorithm PDBSCAN to an improved version (PDBSCANi), by using a better cluster assignment strategy to ensure that every object will be assigned to the most appropriate cluster, thus solving the issue of nonadaptive threshold (for direct density reachability judgement) in FDBSCAN. Furthermore, as PDBSCAN and PDBSCANi have difficulties for clustering uncertain data with non-uniform cluster density, we propose a novel hierarchical density-based algorithm POPTICS by extending the definitions of PDBSCAN, adding new definitions of fuzzy core distance and fuzzy reachability distance, and employing a new clustering framework. POPTICS can reveal the cluster structures of the datasets with different local densities in different regions better than PDBSCAN and PDBSCANi, and it addresses the issues in FOPTICS. Experimental results demonstrate the superiority of our proposed algorithms over the existing algorithms in accuracy and efficiency. Copyright © 2017 Elsevier Ltd. All rights reserved.

An Algorithm to Automate Yeast Segmentation and Tracking

PubMed Central

Doncic, Andreas; Eser, Umut; Atay, Oguzhan; Skotheim, Jan M.

2013-01-01

Our understanding of dynamic cellular processes has been greatly enhanced by rapid advances in quantitative fluorescence microscopy. Imaging single cells has emphasized the prevalence of phenomena that can be difficult to infer from population measurements, such as all-or-none cellular decisions, cell-to-cell variability, and oscillations. Examination of these phenomena requires segmenting and tracking individual cells over long periods of time. However, accurate segmentation and tracking of cells is difficult and is often the rate-limiting step in an experimental pipeline. Here, we present an algorithm that accomplishes fully automated segmentation and tracking of budding yeast cells within growing colonies. The algorithm incorporates prior information of yeast-specific traits, such as immobility and growth rate, to segment an image using a set of threshold values rather than one specific optimized threshold. Results from the entire set of thresholds are then used to perform a robust final segmentation. PMID:23520484

Accelerated Path-following Iterative Shrinkage Thresholding Algorithm with Application to Semiparametric Graph Estimation

PubMed Central

Zhao, Tuo; Liu, Han

2016-01-01

We propose an accelerated path-following iterative shrinkage thresholding algorithm (APISTA) for solving high dimensional sparse nonconvex learning problems. The main difference between APISTA and the path-following iterative shrinkage thresholding algorithm (PISTA) is that APISTA exploits an additional coordinate descent subroutine to boost the computational performance. Such a modification, though simple, has profound impact: APISTA not only enjoys the same theoretical guarantee as that of PISTA, i.e., APISTA attains a linear rate of convergence to a unique sparse local optimum with good statistical properties, but also significantly outperforms PISTA in empirical benchmarks. As an application, we apply APISTA to solve a family of nonconvex optimization problems motivated by estimating sparse semiparametric graphical models. APISTA allows us to obtain new statistical recovery results which do not exist in the existing literature. Thorough numerical results are provided to back up our theory. PMID:28133430

G/SPLINES: A hybrid of Friedman's Multivariate Adaptive Regression Splines (MARS) algorithm with Holland's genetic algorithm

NASA Technical Reports Server (NTRS)

Rogers, David

1991-01-01

G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.

A family of variable step-size affine projection adaptive filter algorithms using statistics of channel impulse response

NASA Astrophysics Data System (ADS)

Shams Esfand Abadi, Mohammad; AbbasZadeh Arani, Seyed Ali Asghar

2011-12-01

This paper extends the recently introduced variable step-size (VSS) approach to the family of adaptive filter algorithms. This method uses prior knowledge of the channel impulse response statistic. Accordingly, optimal step-size vector is obtained by minimizing the mean-square deviation (MSD). The presented algorithms are the VSS affine projection algorithm (VSS-APA), the VSS selective partial update NLMS (VSS-SPU-NLMS), the VSS-SPU-APA, and the VSS selective regressor APA (VSS-SR-APA). In VSS-SPU adaptive algorithms the filter coefficients are partially updated which reduce the computational complexity. In VSS-SR-APA, the optimal selection of input regressors is performed during the adaptation. The presented algorithms have good convergence speed, low steady state mean square error (MSE), and low computational complexity features. We demonstrate the good performance of the proposed algorithms through several simulations in system identification scenario.

Thresholds for Coral Bleaching: Are Synergistic Factors and Shifting Thresholds Changing the Landscape for Management? (Invited)

NASA Astrophysics Data System (ADS)

Eakin, C.; Donner, S. D.; Logan, C. A.; Gledhill, D. K.; Liu, G.; Heron, S. F.; Christensen, T.; Rauenzahn, J.; Morgan, J.; Parker, B. A.; Hoegh-Guldberg, O.; Skirving, W. J.; Strong, A. E.

2010-12-01

As carbon dioxide rises in the atmosphere, climate change and ocean acidification are modifying important physical and chemical parameters in the oceans with resulting impacts on coral reef ecosystems. Rising CO2 is warming the world’s oceans and causing corals to bleach, with both alarming frequency and severity. The frequent return of stressful temperatures has already resulted in major damage to many of the world’s coral reefs and is expected to continue in the foreseeable future. Warmer oceans also have contributed to a rise in coral infectious diseases. Both bleaching and infectious disease can result in coral mortality and threaten one of the most diverse ecosystems on Earth and the important ecosystem services they provide. Additionally, ocean acidification from rising CO2 is reducing the availability of carbonate ions needed by corals to build their skeletons and perhaps depressing the threshold for bleaching. While thresholds vary among species and locations, it is clear that corals around the world are already experiencing anomalous temperatures that are too high, too often, and that warming is exceeding the rate at which corals can adapt. This is despite a complex adaptive capacity that involves both the coral host and the zooxanthellae, including changes in the relative abundance of the latter in their coral hosts. The safe upper limit for atmospheric CO2 is probably somewhere below 350ppm, a level we passed decades ago, and for temperature is a sustained global temperature increase of less than 1.5°C above pre-industrial levels. How much can corals acclimate and/or adapt to the unprecedented fast changing environmental conditions? Any change in the threshold for coral bleaching as the result of acclimation and/or adaption may help corals to survive in the future but adaptation to one stress may be maladaptive to another. There also is evidence that ocean acidification and nutrient enrichment modify this threshold. What do shifting thresholds mean for identifying limits and taking management actions to adapt to climate change?

A Controlled Study of the Effectiveness of an Adaptive Closed-Loop Algorithm to Minimize Corticosteroid-Induced Stress Hyperglycemia in Type 1 Diabetes

PubMed Central

Youssef, Joseph El; Castle, Jessica R; Branigan, Deborah L; Massoud, Ryan G; Breen, Matthew E; Jacobs, Peter G; Bequette, B Wayne; Ward, W Kenneth

2011-01-01

To be effective in type 1 diabetes, algorithms must be able to limit hyperglycemic excursions resulting from medical and emotional stress. We tested an algorithm that estimates insulin sensitivity at regular intervals and continually adjusts gain factors of a fading memory proportional-derivative (FMPD) algorithm. In order to assess whether the algorithm could appropriately adapt and limit the degree of hyperglycemia, we administered oral hydrocortisone repeatedly to create insulin resistance. We compared this indirect adaptive proportional-derivative (APD) algorithm to the FMPD algorithm, which used fixed gain parameters. Each subject with type 1 diabetes (n = 14) was studied on two occasions, each for 33 h. The APD algorithm consistently identified a fall in insulin sensitivity after hydrocortisone. The gain factors and insulin infusion rates were appropriately increased, leading to satisfactory glycemic control after adaptation (premeal glucose on day 2, 148 ± 6 mg/dl). After sufficient time was allowed for adaptation, the late postprandial glucose increment was significantly lower than when measured shortly after the onset of the steroid effect. In addition, during the controlled comparison, glycemia was significantly lower with the APD algorithm than with the FMPD algorithm. No increase in hypoglycemic frequency was found in the APD-only arm. An afferent system of duplicate amperometric sensors demonstrated a high degree of accuracy; the mean absolute relative difference of the sensor used to control the algorithm was 9.6 ± 0.5%. We conclude that an adaptive algorithm that frequently estimates insulin sensitivity and adjusts gain factors is capable of minimizing corticosteroid-induced stress hyperglycemia. PMID:22226248

A fast image matching algorithm based on key points

NASA Astrophysics Data System (ADS)

Wang, Huilin; Wang, Ying; An, Ru; Yan, Peng

2014-05-01

Image matching is a very important technique in image processing. It has been widely used for object recognition and tracking, image retrieval, three-dimensional vision, change detection, aircraft position estimation, and multi-image registration. Based on the requirements of matching algorithm for craft navigation, such as speed, accuracy and adaptability, a fast key point image matching method is investigated and developed. The main research tasks includes: (1) Developing an improved celerity key point detection approach using self-adapting threshold of Features from Accelerated Segment Test (FAST). A method of calculating self-adapting threshold was introduced for images with different contrast. Hessian matrix was adopted to eliminate insecure edge points in order to obtain key points with higher stability. This approach in detecting key points has characteristics of small amount of computation, high positioning accuracy and strong anti-noise ability; (2) PCA-SIFT is utilized to describe key point. 128 dimensional vector are formed based on the SIFT method for the key points extracted. A low dimensional feature space was established by eigenvectors of all the key points, and each eigenvector was projected onto the feature space to form a low dimensional eigenvector. These key points were re-described by dimension-reduced eigenvectors. After reducing the dimension by the PCA, the descriptor was reduced to 20 dimensions from the original 128. This method can reduce dimensions of searching approximately near neighbors thereby increasing overall speed; (3) Distance ratio between the nearest neighbour and second nearest neighbour searching is regarded as the measurement criterion for initial matching points from which the original point pairs matched are obtained. Based on the analysis of the common methods (e.g. RANSAC (random sample consensus) and Hough transform cluster) used for elimination false matching point pairs, a heuristic local geometric restriction strategy is adopted to discard false matched point pairs further; and (4) Affine transformation model is introduced to correct coordinate difference between real-time image and reference image. This resulted in the matching of the two images. SPOT5 Remote sensing images captured at different date and airborne images captured with different flight attitude were used to test the performance of the method from matching accuracy, operation time and ability to overcome rotation. Results show the effectiveness of the approach.

TU-AB-303-11: Predict Parotids Deformation Applying SIS Epidemiological Model in H&N Adaptive RT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maffei, N; Guidi, G; University of Bologna, Bologna, Bologna

2015-06-15

Purpose: The aim is to investigate the use of epidemiological models to predict morphological variations in patients undergoing radiation therapy (RT). The susceptible-infected-susceptible (SIS) deterministic model was applied to simulate warping within a focused region of interest (ROI). Hypothesis is to consider each voxel like a single subject of the whole sample and to treat displacement vector fields like an infection. Methods: Using Raystation hybrid deformation algorithms and automatic re-contouring based on mesh grid, we post-processed 360 MVCT images of 12 H&N patients treated with Tomotherapy. Study focused on parotid glands, identified by literature and previous analysis, as ROI moremore » susceptible to warping in H&N region. Susceptible (S) and infectious (I) cases were identified in voxels with inter-fraction movement respectively under and over a set threshold. IronPython scripting allowed to export positions and displacement data of surface voxels for every fraction. A MATLAB homemade toolbox was developed to model the SIS. Results: SIS model was validated simulating organ motion on QUASAR phantom. Applying model in patients, within a [0–1cm] range, a single voxel movement of 0.4cm was selected as displacement threshold. SIS indexes were evaluated by MATLAB simulations. Dynamic time warping algorithm was used to assess matching between model and parotids behavior days of treatments. The best fit of the model was obtained with contact rate of 7.89±0.94 and recovery rate of 2.36±0.21. Conclusion: SIS model can follow daily structures evolutions, making possible to compare warping conditions and highlighting challenges due to abnormal variation and set-up errors. By epidemiology approach, organ motion could be assessed and predicted not in terms of average of the whole ROI, but in a voxel-by-voxel deterministic trend. Identifying anatomical region subjected to variations, would be possible to focus clinic controls within a cohort of pre-selected patients eligible for adaptive RT. The research is partially co-funded by the Italian Research Grant: Dose warping methods for IGRT and Adaptive RT: dose accumulation based on organ motion and anatomical variations of the patients during radiation therapy treatments,MoH (GR-2010-2318757) and Tecnologie Avanzate S.r.l.(Italy)« less

Feasibility and performance of an adaptive contrast-oriented FDG PET/CT quantification technique for global disease assessment of malignant pleural mesothelioma and a brief review of the literature.

PubMed

Marin-Oyaga, Victor A; Salavati, Ali; Houshmand, Sina; Pasha, Ahmed Khurshid; Gharavi, Mohammad; Saboury, Babak; Basu, Sandip; Torigian, Drew A; Alavi, Abass

2015-01-01

Treatment of malignant pleural mesothelioma (MPM) remains very challenging. Assessment of response to treatment is necessary for modifying treatment and using new drugs. Global disease assessment (GDA) by implementing image processing methods to extract more information out of positron emission tomography (PET) images may provide reliable information. In this study we show the feasibility of this method of semi-quantification in patients with mesothelioma, and compare it with the conventional methods. We also present a review of the literature about this topic. Nineteen subjects with histologically proven MPM who had undergone fluoride-18-fluorodeoxyglucose PET/computed tomography ((18)F-FDG PET/CT) before and after treatment were included in this study. An adaptive contrast-oriented thresholding algorithm was used for the image analysis and semi-quantification. Metabolic tumor volume (MTV), maximum and mean standardized uptake volume (SUVmax, SUVmean) and total lesion glycolysis (TLG) were calculated for each region of interest. The global tumor glycolysis (GTG) was obtained by summing up all TLG. Treatment response was assessed by the European Organisation for Research and Treatment of Cancer (EORTC) criteria and the changes of GTG. Agreement between global disease assessment and conventional method was also determined. In patients with progressive disease based on EORTC criteria, GTG showed an increase of 150.7 but in patients with stable or partial response, GTG showed a decrease of 433.1. The SUVmax of patients before treatment was 5.95 (SD: 2.93) and after the treatment it increased to 6.38 (SD: 3.19). Overall concordance of conventional method with GDA method was 57%. Concordance of progression of disease based on conventional method was 44%, stable disease was 85% and partial response was 33%. Discordance was 55%, 14% and 66%. Adaptive contrast-oriented thresholding algorithm is a promising method to quantify the whole tumor glycolysis in patients with mesothelioma. We are able to assess the total metabolic lesion volume, lesion glycolysis, SUVmax, tumor SUVmean and GTG for this particular tumor. Also we were able to demonstrate the potential use of this technique in the monitoring of treatment response. More studies comparing this technique with conventional and other global disease assessment methods are needed in order to clarify its role in the assessment of treatment response and prognosis of these patients.

Downscaling Land Surface Temperature in Complex Regions by Using Multiple Scale Factors with Adaptive Thresholds

PubMed Central

Yang, Yingbao; Li, Xiaolong; Pan, Xin; Zhang, Yong; Cao, Chen

2017-01-01

Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in urban areas with several mixed surface types. In this study, LST was downscaled by a multiple linear regression model between LST and multiple scale factors in mixed areas with three or four surface types. The correlation coefficients (CCs) between LST and the scale factors were used to assess the importance of the scale factors within a moving window. CC thresholds determined which factors participated in the fitting of the regression equation. The proposed downscaling approach, which involves an adaptive selection of the scale factors, was evaluated using the LST derived from four Landsat 8 thermal imageries of Nanjing City in different seasons. Results of the visual and quantitative analyses show that the proposed approach achieves relatively satisfactory downscaling results on 11 August, with coefficient of determination and root-mean-square error of 0.87 and 1.13 °C, respectively. Relative to other approaches, our approach shows the similar accuracy and the availability in all seasons. The best (worst) availability occurred in the region of vegetation (water). Thus, the approach is an efficient and reliable LST downscaling method. Future tasks include reliable LST downscaling in challenging regions and the application of our model in middle and low spatial resolutions. PMID:28368301

SMI adaptive antenna arrays for weak interfering signals. [Sample Matrix Inversion

NASA Technical Reports Server (NTRS)

Gupta, Inder J.

1986-01-01

The performance of adaptive antenna arrays in the presence of weak interfering signals (below thermal noise) is studied. It is shown that a conventional adaptive antenna array sample matrix inversion (SMI) algorithm is unable to suppress such interfering signals. To overcome this problem, the SMI algorithm is modified. In the modified algorithm, the covariance matrix is redefined such that the effect of thermal noise on the weights of adaptive arrays is reduced. Thus, the weights are dictated by relatively weak signals. It is shown that the modified algorithm provides the desired interference protection.

Intermediate view reconstruction using adaptive disparity search algorithm for real-time 3D processing

NASA Astrophysics Data System (ADS)

Bae, Kyung-hoon; Park, Changhan; Kim, Eun-soo

2008-03-01

In this paper, intermediate view reconstruction (IVR) using adaptive disparity search algorithm (ASDA) is for realtime 3-dimensional (3D) processing proposed. The proposed algorithm can reduce processing time of disparity estimation by selecting adaptive disparity search range. Also, the proposed algorithm can increase the quality of the 3D imaging. That is, by adaptively predicting the mutual correlation between stereo images pair using the proposed algorithm, the bandwidth of stereo input images pair can be compressed to the level of a conventional 2D image and a predicted image also can be effectively reconstructed using a reference image and disparity vectors. From some experiments, stereo sequences of 'Pot Plant' and 'IVO', it is shown that the proposed algorithm improves the PSNRs of a reconstructed image to about 4.8 dB by comparing with that of conventional algorithms, and reduces the Synthesizing time of a reconstructed image to about 7.02 sec by comparing with that of conventional algorithms.

Statistical efficiency of adaptive algorithms.

PubMed

Widrow, Bernard; Kamenetsky, Max

2003-01-01

The statistical efficiency of a learning algorithm applied to the adaptation of a given set of variable weights is defined as the ratio of the quality of the converged solution to the amount of data used in training the weights. Statistical efficiency is computed by averaging over an ensemble of learning experiences. A high quality solution is very close to optimal, while a low quality solution corresponds to noisy weights and less than optimal performance. In this work, two gradient descent adaptive algorithms are compared, the LMS algorithm and the LMS/Newton algorithm. LMS is simple and practical, and is used in many applications worldwide. LMS/Newton is based on Newton's method and the LMS algorithm. LMS/Newton is optimal in the least squares sense. It maximizes the quality of its adaptive solution while minimizing the use of training data. Many least squares adaptive algorithms have been devised over the years, but no other least squares algorithm can give better performance, on average, than LMS/Newton. LMS is easily implemented, but LMS/Newton, although of great mathematical interest, cannot be implemented in most practical applications. Because of its optimality, LMS/Newton serves as a benchmark for all least squares adaptive algorithms. The performances of LMS and LMS/Newton are compared, and it is found that under many circumstances, both algorithms provide equal performance. For example, when both algorithms are tested with statistically nonstationary input signals, their average performances are equal. When adapting with stationary input signals and with random initial conditions, their respective learning times are on average equal. However, under worst-case initial conditions, the learning time of LMS can be much greater than that of LMS/Newton, and this is the principal disadvantage of the LMS algorithm. But the strong points of LMS are ease of implementation and optimal performance under important practical conditions. For these reasons, the LMS algorithm has enjoyed very widespread application. It is used in almost every modem for channel equalization and echo cancelling. Furthermore, it is related to the famous backpropagation algorithm used for training neural networks.

An adaptive embedded mesh procedure for leading-edge vortex flows

NASA Technical Reports Server (NTRS)

Powell, Kenneth G.; Beer, Michael A.; Law, Glenn W.

1989-01-01

A procedure for solving the conical Euler equations on an adaptively refined mesh is presented, along with a method for determining which cells to refine. The solution procedure is a central-difference cell-vertex scheme. The adaptation procedure is made up of a parameter on which the refinement decision is based, and a method for choosing a threshold value of the parameter. The refinement parameter is a measure of mesh-convergence, constructed by comparison of locally coarse- and fine-grid solutions. The threshold for the refinement parameter is based on the curvature of the curve relating the number of cells flagged for refinement to the value of the refinement threshold. Results for three test cases are presented. The test problem is that of a delta wing at angle of attack in a supersonic free-stream. The resulting vortices and shocks are captured efficiently by the adaptive code.

Using Information From Prior Satellite Scans to Improve Cloud Detection Near the Day-Night Terminator

NASA Technical Reports Server (NTRS)

Yost, Christopher R.; Minnis, Patrick; Trepte, Qing Z.; Palikonda, Rabindra; Ayers, Jeffrey K.; Spangenberg, Doulas A.

2012-01-01

With geostationary satellite data it is possible to have a continuous record of diurnal cycles of cloud properties for a large portion of the globe. Daytime cloud property retrieval algorithms are typically superior to nighttime algorithms because daytime methods utilize measurements of reflected solar radiation. However, reflected solar radiation is difficult to accurately model for high solar zenith angles where the amount of incident radiation is small. Clear and cloudy scenes can exhibit very small differences in reflected radiation and threshold-based cloud detection methods have more difficulty setting the proper thresholds for accurate cloud detection. Because top-of-atmosphere radiances are typically more accurately modeled outside the terminator region, information from previous scans can help guide cloud detection near the terminator. This paper presents an algorithm that uses cloud fraction and clear and cloudy infrared brightness temperatures from previous satellite scan times to improve the performance of a threshold-based cloud mask near the terminator. Comparisons of daytime, nighttime, and terminator cloud fraction derived from Geostationary Operational Environmental Satellite (GOES) radiance measurements show that the algorithm greatly reduces the number of false cloud detections and smoothes the transition from the daytime to the nighttime clod detection algorithm. Comparisons with the Geoscience Laser Altimeter System (GLAS) data show that using this algorithm decreases the number of false detections by approximately 20 percentage points.

Adaptive optics image restoration algorithm based on wavefront reconstruction and adaptive total variation method

NASA Astrophysics Data System (ADS)

Li, Dongming; Zhang, Lijuan; Wang, Ting; Liu, Huan; Yang, Jinhua; Chen, Guifen

2016-11-01

To improve the adaptive optics (AO) image's quality, we study the AO image restoration algorithm based on wavefront reconstruction technology and adaptive total variation (TV) method in this paper. Firstly, the wavefront reconstruction using Zernike polynomial is used for initial estimated for the point spread function (PSF). Then, we develop our proposed iterative solutions for AO images restoration, addressing the joint deconvolution issue. The image restoration experiments are performed to verify the image restoration effect of our proposed algorithm. The experimental results show that, compared with the RL-IBD algorithm and Wiener-IBD algorithm, we can see that GMG measures (for real AO image) from our algorithm are increased by 36.92%, and 27.44% respectively, and the computation time are decreased by 7.2%, and 3.4% respectively, and its estimation accuracy is significantly improved.

Designing adaptive operating rules for a large multi-purpose reservoir

NASA Astrophysics Data System (ADS)

Geressu, Robel; Rougé, Charles; Harou, Julien

2017-04-01

Reservoirs whose live storage capacity is large compared with annual inflow have "memory", i.e., their storage levels contain information about past inflows and reservoir operations. Such "long-memory" reservoirs can be found in basins in dry regions such as the Nile River Basin in Africa, the Colorado River Basin in the US, or river basins in Western and Central Asia. There the effects of a dry year have the potential to impact reservoir levels and downstream releases for several subsequent years, prompting tensions in transboundary basins. Yet, current reservoir operation rules in those reservoirs do not reflect this by integrating past climate history and release decisions among the factors that influence operating decisions. This work proposes and demonstrates an adaptive reservoir operating rule that explicitly accounts for the recent history of release decisions, and not only current storage level and near-term inflow forecasts. This implies adding long-term (e.g., multiyear) objectives to the existing short-term (e.g., annual) ones. We apply these operating rules to the Grand Ethiopian Renaissance Dam, a large reservoir under construction on the Blue Nile River. Energy generation has to be balanced with the imperative of releasing enough water in low flow years (e.g., the minimum 1, 2 or 3 year cumulative flow) to avoid tensions with downstream countries, Sudan and Egypt. Maximizing the minimum multi-year releases could be of interest for the Nile problem to minimize the impact on performance of the large High Aswan Dam in Egypt. Objectives include maximizing the average and minimum annual energy generation and maximizing the minimum annual, two year and three year cumulative releases. The system model is tested using 30 stochastically generated streamflow series. One can then derive adaptive release rules depending on the value of one- and two-year total releases with respect to thresholds. Then, there are 3 sets of release rules for the reservoir depending on whether one or both thresholds are not met, vs. only one with a non-adaptive rule. Multi-objective evolutionary algorithms (MOEAs) are used to obtain the Pareto front, i.e., non-dominated adaptive and non-adaptive operating rule sets. Implementing adaptive rules is found to improve the trade-offs between energy generation criteria and minimum release targets. Compared with non-adaptive operations, an adaptive operating policy shows an increase of around 3 and 10 Billion cubic meters in the minimum 1 and 3-year cumulative releases for a given value of the same average annual energy generation.

Highly Scalable Matching Pursuit Signal Decomposition Algorithm

NASA Technical Reports Server (NTRS)

Christensen, Daniel; Das, Santanu; Srivastava, Ashok N.

2009-01-01

Matching Pursuit Decomposition (MPD) is a powerful iterative algorithm for signal decomposition and feature extraction. MPD decomposes any signal into linear combinations of its dictionary elements or atoms . A best fit atom from an arbitrarily defined dictionary is determined through cross-correlation. The selected atom is subtracted from the signal and this procedure is repeated on the residual in the subsequent iterations until a stopping criterion is met. The reconstructed signal reveals the waveform structure of the original signal. However, a sufficiently large dictionary is required for an accurate reconstruction; this in return increases the computational burden of the algorithm, thus limiting its applicability and level of adoption. The purpose of this research is to improve the scalability and performance of the classical MPD algorithm. Correlation thresholds were defined to prune insignificant atoms from the dictionary. The Coarse-Fine Grids and Multiple Atom Extraction techniques were proposed to decrease the computational burden of the algorithm. The Coarse-Fine Grids method enabled the approximation and refinement of the parameters for the best fit atom. The ability to extract multiple atoms within a single iteration enhanced the effectiveness and efficiency of each iteration. These improvements were implemented to produce an improved Matching Pursuit Decomposition algorithm entitled MPD++. Disparate signal decomposition applications may require a particular emphasis of accuracy or computational efficiency. The prominence of the key signal features required for the proper signal classification dictates the level of accuracy necessary in the decomposition. The MPD++ algorithm may be easily adapted to accommodate the imposed requirements. Certain feature extraction applications may require rapid signal decomposition. The full potential of MPD++ may be utilized to produce incredible performance gains while extracting only slightly less energy than the standard algorithm. When the utmost accuracy must be achieved, the modified algorithm extracts atoms more conservatively but still exhibits computational gains over classical MPD. The MPD++ algorithm was demonstrated using an over-complete dictionary on real life data. Computational times were reduced by factors of 1.9 and 44 for the emphases of accuracy and performance, respectively. The modified algorithm extracted similar amounts of energy compared to classical MPD. The degree of the improvement in computational time depends on the complexity of the data, the initialization parameters, and the breadth of the dictionary. The results of the research confirm that the three modifications successfully improved the scalability and computational efficiency of the MPD algorithm. Correlation Thresholding decreased the time complexity by reducing the dictionary size. Multiple Atom Extraction also reduced the time complexity by decreasing the number of iterations required for a stopping criterion to be reached. The Course-Fine Grids technique enabled complicated atoms with numerous variable parameters to be effectively represented in the dictionary. Due to the nature of the three proposed modifications, they are capable of being stacked and have cumulative effects on the reduction of the time complexity.

Olfactory Detection Thresholds and Adaptation in Adults with Autism Spectrum Condition

ERIC Educational Resources Information Center

Tavassoli, T.; Baron-Cohen, S.

2012-01-01

Sensory issues have been widely reported in Autism Spectrum Conditions (ASC). Since olfaction is one of the least investigated senses in ASC, the current studies explore olfactory detection thresholds and adaptation to olfactory stimuli in adults with ASC. 80 participants took part, 38 (18 females, 20 males) with ASC and 42 control participants…

Microscopy mineral image enhancement based on improved adaptive threshold in nonsubsampled shearlet transform domain

NASA Astrophysics Data System (ADS)

Li, Liangliang; Si, Yujuan; Jia, Zhenhong

2018-03-01

In this paper, a novel microscopy mineral image enhancement method based on adaptive threshold in non-subsampled shearlet transform (NSST) domain is proposed. First, the image is decomposed into one low-frequency sub-band and several high-frequency sub-bands. Second, the gamma correction is applied to process the low-frequency sub-band coefficients, and the improved adaptive threshold is adopted to suppress the noise of the high-frequency sub-bands coefficients. Third, the processed coefficients are reconstructed with the inverse NSST. Finally, the unsharp filter is used to enhance the details of the reconstructed image. Experimental results on various microscopy mineral images demonstrated that the proposed approach has a better enhancement effect in terms of objective metric and subjective metric.

Low complexity adaptive equalizers for underwater acoustic communications

NASA Astrophysics Data System (ADS)

Soflaei, Masoumeh; Azmi, Paeiz

2014-08-01

Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggested ways is to use adaptive equalizers. Convergence rate and misadjustment error in adaptive algorithms play important roles in adaptive equalizer performance. In this paper, affine projection algorithm (APA), selective regressor APA(SR-APA), family of selective partial update (SPU) algorithms, family of set-membership (SM) algorithms and selective partial update selective regressor APA (SPU-SR-APA) are compared with conventional algorithms such as the least mean square (LMS) in underwater acoustic communications. We apply experimental data from the Strait of Hormuz for demonstrating the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error (MSE) of SR-APA, SPU-APA, SPU-normalized least mean square (SPU-NLMS), SPU-SR-APA, SM-APA and SM-NLMS algorithms decrease in comparison with the LMS algorithm. Also these algorithms have better convergence rates than LMS type algorithm.

A robustness test of the braided device foreshortening algorithm

NASA Astrophysics Data System (ADS)

Moyano, Raquel Kale; Fernandez, Hector; Macho, Juan M.; Blasco, Jordi; San Roman, Luis; Narata, Ana Paula; Larrabide, Ignacio

2017-11-01

Different computational methods have been recently proposed to simulate the virtual deployment of a braided stent inside a patient vasculature. Those methods are primarily based on the segmentation of the region of interest to obtain the local vessel morphology descriptors. The goal of this work is to evaluate the influence of the segmentation quality on the method named "Braided Device Foreshortening" (BDF). METHODS: We used the 3DRA images of 10 aneurysmatic patients (cases). The cases were segmented by applying a marching cubes algorithm with a broad range of thresholds in order to generate 10 surface models each. We selected a braided device to apply the BDF algorithm to each surface model. The range of the computed flow diverter lengths for each case was obtained to calculate the variability of the method against the threshold segmentation values. RESULTS: An evaluation study over 10 clinical cases indicates that the final length of the deployed flow diverter in each vessel model is stable, shielding maximum difference of 11.19% in vessel diameter and maximum of 9.14% in the simulated stent length for the threshold values. The average coefficient of variation was found to be 4.08 %. CONCLUSION: A study evaluating how the threshold segmentation affects the simulated length of the deployed FD, was presented. The segmentation algorithm used to segment intracranial aneurysm 3D angiography images presents small variation in the resulting stent simulation.

Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks

NASA Astrophysics Data System (ADS)

Ghasemian, Amir; Zhang, Pan; Clauset, Aaron; Moore, Cristopher; Peel, Leto

2016-07-01

The detection of communities within a dynamic network is a common means for obtaining a coarse-grained view of a complex system and for investigating its underlying processes. While a number of methods have been proposed in the machine learning and physics literature, we lack a theoretical analysis of their strengths and weaknesses, or of the ultimate limits on when communities can be detected. Here, we study the fundamental limits of detecting community structure in dynamic networks. Specifically, we analyze the limits of detectability for a dynamic stochastic block model where nodes change their community memberships over time, but where edges are generated independently at each time step. Using the cavity method, we derive a precise detectability threshold as a function of the rate of change and the strength of the communities. Below this sharp threshold, we claim that no efficient algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this threshold. The first uses belief propagation, which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the belief propagation equations. These results extend our understanding of the limits of community detection in an important direction, and introduce new mathematical tools for similar extensions to networks with other types of auxiliary information.

Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sheng, Zheng, E-mail: 19994035@sina.com; Wang, Jun; Zhou, Bihua

2014-03-15

This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented tomore » tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.« less

Automatic macroscopic characterization of diesel sprays by means of a new image processing algorithm

NASA Astrophysics Data System (ADS)

Rubio-Gómez, Guillermo; Martínez-Martínez, S.; Rua-Mojica, Luis F.; Gómez-Gordo, Pablo; de la Garza, Oscar A.

2018-05-01

A novel algorithm is proposed for the automatic segmentation of diesel spray images and the calculation of their macroscopic parameters. The algorithm automatically detects each spray present in an image, and therefore it is able to work with diesel injectors with a different number of nozzle holes without any modification. The main characteristic of the algorithm is that it splits each spray into three different regions and then segments each one with an individually calculated binarization threshold. Each threshold level is calculated from the analysis of a representative luminosity profile of each region. This approach makes it robust to irregular light distribution along a single spray and between different sprays of an image. Once the sprays are segmented, the macroscopic parameters of each one are calculated. The algorithm is tested with two sets of diesel spray images taken under normal and irregular illumination setups.

Adaptive Control Allocation in the Presence of Actuator Failures

NASA Technical Reports Server (NTRS)

Liu, Yu; Crespo, Luis G.

2010-01-01

In this paper, a novel adaptive control allocation framework is proposed. In the adaptive control allocation structure, cooperative actuators are grouped and treated as an equivalent control effector. A state feedback adaptive control signal is designed for the equivalent effector and allocated to the member actuators adaptively. Two adaptive control allocation algorithms are proposed, which guarantee closed-loop stability and asymptotic state tracking in the presence of uncertain loss of effectiveness and constant-magnitude actuator failures. The proposed algorithms can be shown to reduce the controller complexity with proper grouping of the actuators. The proposed adaptive control allocation schemes are applied to two linearized aircraft models, and the simulation results demonstrate the performance of the proposed algorithms.

Dual-threshold segmentation using Arimoto entropy based on chaotic bee colony optimization

NASA Astrophysics Data System (ADS)

Li, Li

2018-03-01

In order to extract target from complex background more quickly and accurately, and to further improve the detection effect of defects, a method of dual-threshold segmentation using Arimoto entropy based on chaotic bee colony optimization was proposed. Firstly, the method of single-threshold selection based on Arimoto entropy was extended to dual-threshold selection in order to separate the target from the background more accurately. Then intermediate variables in formulae of Arimoto entropy dual-threshold selection was calculated by recursion to eliminate redundant computation effectively and to reduce the amount of calculation. Finally, the local search phase of artificial bee colony algorithm was improved by chaotic sequence based on tent mapping. The fast search for two optimal thresholds was achieved using the improved bee colony optimization algorithm, thus the search could be accelerated obviously. A large number of experimental results show that, compared with the existing segmentation methods such as multi-threshold segmentation method using maximum Shannon entropy, two-dimensional Shannon entropy segmentation method, two-dimensional Tsallis gray entropy segmentation method and multi-threshold segmentation method using reciprocal gray entropy, the proposed method can segment target more quickly and accurately with superior segmentation effect. It proves to be an instant and effective method for image segmentation.

SACFIR: SDN-Based Application-Aware Centralized Adaptive Flow Iterative Reconfiguring Routing Protocol for WSNs.

PubMed

Aslam, Muhammad; Hu, Xiaopeng; Wang, Fan

2017-12-13

Smart reconfiguration of a dynamic networking environment is offered by the central control of Software-Defined Networking (SDN). Centralized SDN-based management architectures are capable of retrieving global topology intelligence and decoupling the forwarding plane from the control plane. Routing protocols developed for conventional Wireless Sensor Networks (WSNs) utilize limited iterative reconfiguration methods to optimize environmental reporting. However, the challenging networking scenarios of WSNs involve a performance overhead due to constant periodic iterative reconfigurations. In this paper, we propose the SDN-based Application-aware Centralized adaptive Flow Iterative Reconfiguring (SACFIR) routing protocol with the centralized SDN iterative solver controller to maintain the load-balancing between flow reconfigurations and flow allocation cost. The proposed SACFIR's routing protocol offers a unique iterative path-selection algorithm, which initially computes suitable clustering based on residual resources at the control layer and then implements application-aware threshold-based multi-hop report transmissions on the forwarding plane. The operation of the SACFIR algorithm is centrally supervised by the SDN controller residing at the Base Station (BS). This paper extends SACFIR to SDN-based Application-aware Main-value Centralized adaptive Flow Iterative Reconfiguring (SAMCFIR) to establish both proactive and reactive reporting. The SAMCFIR transmission phase enables sensor nodes to trigger direct transmissions for main-value reports, while in the case of SACFIR, all reports follow computed routes. Our SDN-enabled proposed models adjust the reconfiguration period according to the traffic burden on sensor nodes, which results in heterogeneity awareness, load-balancing and application-specific reconfigurations of WSNs. Extensive experimental simulation-based results show that SACFIR and SAMCFIR yield the maximum scalability, network lifetime and stability period when compared to existing routing protocols.

SACFIR: SDN-Based Application-Aware Centralized Adaptive Flow Iterative Reconfiguring Routing Protocol for WSNs

PubMed Central

Hu, Xiaopeng; Wang, Fan

2017-01-01

Smart reconfiguration of a dynamic networking environment is offered by the central control of Software-Defined Networking (SDN). Centralized SDN-based management architectures are capable of retrieving global topology intelligence and decoupling the forwarding plane from the control plane. Routing protocols developed for conventional Wireless Sensor Networks (WSNs) utilize limited iterative reconfiguration methods to optimize environmental reporting. However, the challenging networking scenarios of WSNs involve a performance overhead due to constant periodic iterative reconfigurations. In this paper, we propose the SDN-based Application-aware Centralized adaptive Flow Iterative Reconfiguring (SACFIR) routing protocol with the centralized SDN iterative solver controller to maintain the load-balancing between flow reconfigurations and flow allocation cost. The proposed SACFIR’s routing protocol offers a unique iterative path-selection algorithm, which initially computes suitable clustering based on residual resources at the control layer and then implements application-aware threshold-based multi-hop report transmissions on the forwarding plane. The operation of the SACFIR algorithm is centrally supervised by the SDN controller residing at the Base Station (BS). This paper extends SACFIR to SDN-based Application-aware Main-value Centralized adaptive Flow Iterative Reconfiguring (SAMCFIR) to establish both proactive and reactive reporting. The SAMCFIR transmission phase enables sensor nodes to trigger direct transmissions for main-value reports, while in the case of SACFIR, all reports follow computed routes. Our SDN-enabled proposed models adjust the reconfiguration period according to the traffic burden on sensor nodes, which results in heterogeneity awareness, load-balancing and application-specific reconfigurations of WSNs. Extensive experimental simulation-based results show that SACFIR and SAMCFIR yield the maximum scalability, network lifetime and stability period when compared to existing routing protocols. PMID:29236031

When do Indians feel hot? Internet searches indicate seasonality suppresses adaptation to heat

NASA Astrophysics Data System (ADS)

Singh, Tanya; Siderius, Christian; Van der Velde, Ype

2018-05-01

In a warming world an increasing number of people are being exposed to heat, making a comfortable thermal environment an important need. This study explores the potential of using Regional Internet Search Frequencies (RISF) for air conditioning devices as an indicator for thermal discomfort (i.e. dissatisfaction with the thermal environment) with the aim to quantify the adaptation potential of individuals living across different climate zones and at the high end of the temperature range, in India, where access to health data is limited. We related RISF for the years 2011–2015 to daily daytime outdoor temperature in 17 states and determined at which temperature RISF for air conditioning starts to peak, i.e. crosses a ‘heat threshold’, in each state. Using the spatial variation in heat thresholds, we explored whether people continuously exposed to higher temperatures show a lower response to heat extremes through adaptation (e.g. physiological, behavioural or psychological). State-level heat thresholds ranged from 25.9 °C in Madhya Pradesh to 31.0 °C in Orissa. Local adaptation was found to occur at state level: the higher the average temperature in a state, the higher the heat threshold; and the higher the intra-annual temperature range (warmest minus coldest month) the lower the heat threshold. These results indicate there is potential within India to adapt to warmer temperatures, but that a large intra-annual temperature variability attenuates this potential to adapt to extreme heat. This winter ‘reset’ mechanism should be taken into account when assessing the impact of global warming, with changes in minimum temperatures being an important factor in addition to the change in maximum temperatures itself. Our findings contribute to a better understanding of local heat thresholds and people’s adaptive capacity, which can support the design of local thermal comfort standards and early heat warning systems.

[Research on Control System of an Exoskeleton Upper-limb Rehabilitation Robot].

PubMed

Wang, Lulu; Hu, Xin; Hu, Jie; Fang, Youfang; He, Rongrong; Yu, Hongliu

2016-12-01

In order to help the patients with upper-limb disfunction go on rehabilitation training,this paper proposed an upper-limb exoskeleton rehabilitation robot with four degrees of freedom(DOF),and realized two control schemes,i.e.,voice control and electromyography control.The hardware and software design of the voice control system was completed based on RSC-4128 chips,which realized the speech recognition technology of a specific person.Besides,this study adapted self-made surface eletromyogram(sEMG)signal extraction electrodes to collect sEMG signals and realized pattern recognition by conducting sEMG signals processing,extracting time domain features and fixed threshold algorithm.In addition,the pulse-width modulation(PWM)algorithm was used to realize the speed adjustment of the system.Voice control and electromyography control experiments were then carried out,and the results showed that the mean recognition rate of the voice control and electromyography control reached 93.1%and 90.9%,respectively.The results proved the feasibility of the control system.This study is expected to lay a theoretical foundation for the further improvement of the control system of the upper-limb rehabilitation robot.

Dynamic Self-adaptive Remote Health Monitoring System for Diabetics

PubMed Central

Suh, Myung-kyung; Moin, Tannaz; Woodbridge, Jonathan; Lan, Mars; Ghasemzadeh, Hassan; Bui, Alex; Ahmadi, Sheila; Sarrafzadeh, Majid

2016-01-01

Diabetes is the seventh leading cause of death in the United States. In 2010, about 1.9 million new cases of diabetes were diagnosed in people aged 20 years or older. Remote health monitoring systems can help diabetics and their healthcare professionals monitor health-related measurements by providing real-time feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the remote health monitoring. This paper presents a task optimization technique used in WANDA (Weight and Activity with Blood Pressure and Other Vital Signs); a wireless health project that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. WANDA applies data analytics in real-time to improving the quality of care. The developed algorithm minimizes the number of daily tasks required by diabetic patients using association rules that satisfies a minimum support threshold. Each of these tasks maximizes information gain, thereby improving the overall level of care. Experimental results show that the developed algorithm can reduce the number of tasks up to 28.6% with minimum support 0.95, minimum confidence 0.97 and high efficiency. PMID:23366365

The energy ratio mapping algorithm: a tool to improve the energy-based detection of odontocete echolocation clicks.

PubMed

Klinck, Holger; Mellinger, David K

2011-04-01

The energy ratio mapping algorithm (ERMA) was developed to improve the performance of energy-based detection of odontocete echolocation clicks, especially for application in environments with limited computational power and energy such as acoustic gliders. ERMA systematically evaluates many frequency bands for energy ratio-based detection of echolocation clicks produced by a target species in the presence of the species mix in a given geographic area. To evaluate the performance of ERMA, a Teager-Kaiser energy operator was applied to the series of energy ratios as derived by ERMA. A noise-adaptive threshold was then applied to the Teager-Kaiser function to identify clicks in data sets. The method was tested for detecting clicks of Blainville's beaked whales while rejecting echolocation clicks of Risso's dolphins and pilot whales. Results showed that the ERMA-based detector correctly identified 81.6% of the beaked whale clicks in an extended evaluation data set. Average false-positive detection rate was 6.3% (3.4% for Risso's dolphins and 2.9% for pilot whales).

Multivariate statistical analysis strategy for multiple misfire detection in internal combustion engines

NASA Astrophysics Data System (ADS)

Hu, Chongqing; Li, Aihua; Zhao, Xingyang

2011-02-01

This paper proposes a multivariate statistical analysis approach to processing the instantaneous engine speed signal for the purpose of locating multiple misfire events in internal combustion engines. The state of each cylinder is described with a characteristic vector extracted from the instantaneous engine speed signal following a three-step procedure. These characteristic vectors are considered as the values of various procedure parameters of an engine cycle. Therefore, determination of occurrence of misfire events and identification of misfiring cylinders can be accomplished by a principal component analysis (PCA) based pattern recognition methodology. The proposed algorithm can be implemented easily in practice because the threshold can be defined adaptively without the information of operating conditions. Besides, the effect of torsional vibration on the engine speed waveform is interpreted as the presence of super powerful cylinder, which is also isolated by the algorithm. The misfiring cylinder and the super powerful cylinder are often adjacent in the firing sequence, thus missing detections and false alarms can be avoided effectively by checking the relationship between the cylinders.

Synchronous Firefly Algorithm for Cluster Head Selection in WSN.

PubMed

Baskaran, Madhusudhanan; Sadagopan, Chitra

2015-01-01

Wireless Sensor Network (WSN) consists of small low-cost, low-power multifunctional nodes interconnected to efficiently aggregate and transmit data to sink. Cluster-based approaches use some nodes as Cluster Heads (CHs) and organize WSNs efficiently for aggregation of data and energy saving. A CH conveys information gathered by cluster nodes and aggregates/compresses data before transmitting it to a sink. However, this additional responsibility of the node results in a higher energy drain leading to uneven network degradation. Low Energy Adaptive Clustering Hierarchy (LEACH) offsets this by probabilistically rotating cluster heads role among nodes with energy above a set threshold. CH selection in WSN is NP-Hard as optimal data aggregation with efficient energy savings cannot be solved in polynomial time. In this work, a modified firefly heuristic, synchronous firefly algorithm, is proposed to improve the network performance. Extensive simulation shows the proposed technique to perform well compared to LEACH and energy-efficient hierarchical clustering. Simulations show the effectiveness of the proposed method in decreasing the packet loss ratio by an average of 9.63% and improving the energy efficiency of the network when compared to LEACH and EEHC.

The crack detection algorithm of pavement image based on edge information

NASA Astrophysics Data System (ADS)

Yang, Chunde; Geng, Mingyue

2018-05-01

As the images of pavement cracks are affected by a large amount of complicated noises, such as uneven illumination and water stains, the detected cracks are discontinuous and the main body information at the edge of the cracks is easily lost. In order to solve the problem, a crack detection algorithm in pavement image based on edge information is proposed. Firstly, the image is pre-processed by the nonlinear gray-scale transform function and reconstruction filter to enhance the linear characteristic of the crack. At the same time, an adaptive thresholding method is designed to coarsely extract the cracks edge according to the gray-scale gradient feature and obtain the crack gradient information map. Secondly, the candidate edge points are obtained according to the gradient information, and the edge is detected based on the single pixel percolation processing, which is improved by using the local difference between pixels in the fixed region. Finally, complete crack is obtained by filling the crack edge. Experimental results show that the proposed method can accurately detect pavement cracks and preserve edge information.

A real-time tracking system of infrared dim and small target based on FPGA and DSP

NASA Astrophysics Data System (ADS)

Rong, Sheng-hui; Zhou, Hui-xin; Qin, Han-lin; Wang, Bing-jian; Qian, Kun

2014-11-01

A core technology in the infrared warning system is the detection tracking of dim and small targets with complicated background. Consequently, running the detection algorithm on the hardware platform has highly practical value in the military field. In this paper, a real-time detection tracking system of infrared dim and small target which is used FPGA (Field Programmable Gate Array) and DSP (Digital Signal Processor) as the core was designed and the corresponding detection tracking algorithm and the signal flow is elaborated. At the first stage, the FPGA obtain the infrared image sequence from the sensor, then it suppresses background clutter by mathematical morphology method and enhances the target intensity by Laplacian of Gaussian operator. At the second stage, the DSP obtain both the original image and the filtered image form the FPGA via the video port. Then it segments the target from the filtered image by an adaptive threshold segmentation method and gets rid of false target by pipeline filter. Experimental results show that our system can achieve higher detection rate and lower false alarm rate.

Resource Efficient Hardware Architecture for Fast Computation of Running Max/Min Filters

PubMed Central

Torres-Huitzil, Cesar

2013-01-01

Running max/min filters on rectangular kernels are widely used in many digital signal and image processing applications. Filtering with a k × k kernel requires of k 2 − 1 comparisons per sample for a direct implementation; thus, performance scales expensively with the kernel size k. Faster computations can be achieved by kernel decomposition and using constant time one-dimensional algorithms on custom hardware. This paper presents a hardware architecture for real-time computation of running max/min filters based on the van Herk/Gil-Werman (HGW) algorithm. The proposed architecture design uses less computation and memory resources than previously reported architectures when targeted to Field Programmable Gate Array (FPGA) devices. Implementation results show that the architecture is able to compute max/min filters, on 1024 × 1024 images with up to 255 × 255 kernels, in around 8.4 milliseconds, 120 frames per second, at a clock frequency of 250 MHz. The implementation is highly scalable for the kernel size with good performance/area tradeoff suitable for embedded applications. The applicability of the architecture is shown for local adaptive image thresholding. PMID:24288456

Adaptive control of nonlinear system using online error minimum neural networks.

PubMed

Jia, Chao; Li, Xiaoli; Wang, Kang; Ding, Dawei

2016-11-01

In this paper, a new learning algorithm named OEM-ELM (Online Error Minimized-ELM) is proposed based on ELM (Extreme Learning Machine) neural network algorithm and the spreading of its main structure. The core idea of this OEM-ELM algorithm is: online learning, evaluation of network performance, and increasing of the number of hidden nodes. It combines the advantages of OS-ELM and EM-ELM, which can improve the capability of identification and avoid the redundancy of networks. The adaptive control based on the proposed algorithm OEM-ELM is set up which has stronger adaptive capability to the change of environment. The adaptive control of chemical process Continuous Stirred Tank Reactor (CSTR) is also given for application. The simulation results show that the proposed algorithm with respect to the traditional ELM algorithm can avoid network redundancy and improve the control performance greatly. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Psychophysical Measurement of Rod and Cone Thresholds in Stargardt Disease with Full-Field Stimuli

PubMed Central

Collison, Frederick T.; Fishman, Gerald A.; McAnany, J. Jason; Zernant, Jana; Allikmets, Rando

2014-01-01

Purpose To investigate psychophysical thresholds in Stargardt disease with the full-field stimulus test (FST). Methods Visual acuity (VA), spectral-domain optical coherence tomography (SD-OCT), full-field electroretinogram (ERG), and FST measurements were made in one eye of 24 patients with Stargardt disease. Dark-adapted rod FST thresholds were measured with short-wavelength stimuli, and cone FST thresholds were obtained from the cone plateau phase of dark adaptation using long-wavelength stimuli. Correlation coefficients were calculated for FST thresholds versus macular thickness, VA and ERG amplitudes. Results Stargardt patient FST cone thresholds correlated significantly with VA, macular thickness, and ERG cone-response amplitudes (all P<0.01). The patients’ FST rod thresholds correlated with ERG rod-response amplitudes (P<0.01), but not macular thickness (P=0.05). All Stargardt disease patients with flecks confined to the macula and most of the patients with flecks extending outside of the macula had normal FST thresholds. All patients with extramacular atrophic changes had elevated FST cone thresholds and most had elevated FST rod thresholds. Conclusion FST rod and cone threshold elevation in Stargardt disease patients correlated well with measures of structure and function, as well as ophthalmoscopic retinal appearance. FST appears to be a useful tool for assessing rod and cone function in Stargardt disease. PMID:24695063

Recursive least squares background prediction of univariate syndromic surveillance data

PubMed Central

2009-01-01

Background Surveillance of univariate syndromic data as a means of potential indicator of developing public health conditions has been used extensively. This paper aims to improve the performance of detecting outbreaks by using a background forecasting algorithm based on the adaptive recursive least squares method combined with a novel treatment of the Day of the Week effect. Methods Previous work by the first author has suggested that univariate recursive least squares analysis of syndromic data can be used to characterize the background upon which a prediction and detection component of a biosurvellance system may be built. An adaptive implementation is used to deal with data non-stationarity. In this paper we develop and implement the RLS method for background estimation of univariate data. The distinctly dissimilar distribution of data for different days of the week, however, can affect filter implementations adversely, and so a novel procedure based on linear transformations of the sorted values of the daily counts is introduced. Seven-days ahead daily predicted counts are used as background estimates. A signal injection procedure is used to examine the integrated algorithm's ability to detect synthetic anomalies in real syndromic time series. We compare the method to a baseline CDC forecasting algorithm known as the W2 method. Results We present detection results in the form of Receiver Operating Characteristic curve values for four different injected signal to noise ratios using 16 sets of syndromic data. We find improvements in the false alarm probabilities when compared to the baseline W2 background forecasts. Conclusion The current paper introduces a prediction approach for city-level biosurveillance data streams such as time series of outpatient clinic visits and sales of over-the-counter remedies. This approach uses RLS filters modified by a correction for the weekly patterns often seen in these data series, and a threshold detection algorithm from the residuals of the RLS forecasts. We compare the detection performance of this algorithm to the W2 method recently implemented at CDC. The modified RLS method gives consistently better sensitivity at multiple background alert rates, and we recommend that it should be considered for routine application in bio-surveillance systems. PMID:19149886

Recursive least squares background prediction of univariate syndromic surveillance data.

PubMed

Najmi, Amir-Homayoon; Burkom, Howard

2009-01-16

Surveillance of univariate syndromic data as a means of potential indicator of developing public health conditions has been used extensively. This paper aims to improve the performance of detecting outbreaks by using a background forecasting algorithm based on the adaptive recursive least squares method combined with a novel treatment of the Day of the Week effect. Previous work by the first author has suggested that univariate recursive least squares analysis of syndromic data can be used to characterize the background upon which a prediction and detection component of a biosurvellance system may be built. An adaptive implementation is used to deal with data non-stationarity. In this paper we develop and implement the RLS method for background estimation of univariate data. The distinctly dissimilar distribution of data for different days of the week, however, can affect filter implementations adversely, and so a novel procedure based on linear transformations of the sorted values of the daily counts is introduced. Seven-days ahead daily predicted counts are used as background estimates. A signal injection procedure is used to examine the integrated algorithm's ability to detect synthetic anomalies in real syndromic time series. We compare the method to a baseline CDC forecasting algorithm known as the W2 method. We present detection results in the form of Receiver Operating Characteristic curve values for four different injected signal to noise ratios using 16 sets of syndromic data. We find improvements in the false alarm probabilities when compared to the baseline W2 background forecasts. The current paper introduces a prediction approach for city-level biosurveillance data streams such as time series of outpatient clinic visits and sales of over-the-counter remedies. This approach uses RLS filters modified by a correction for the weekly patterns often seen in these data series, and a threshold detection algorithm from the residuals of the RLS forecasts. We compare the detection performance of this algorithm to the W2 method recently implemented at CDC. The modified RLS method gives consistently better sensitivity at multiple background alert rates, and we recommend that it should be considered for routine application in bio-surveillance systems.

Automated measurements of metabolic tumor volume and metabolic parameters in lung PET/CT imaging

NASA Astrophysics Data System (ADS)

Orologas, F.; Saitis, P.; Kallergi, M.

2017-11-01

Patients with lung tumors or inflammatory lung disease could greatly benefit in terms of treatment and follow-up by PET/CT quantitative imaging, namely measurements of metabolic tumor volume (MTV), standardized uptake values (SUVs) and total lesion glycolysis (TLG). The purpose of this study was the development of an unsupervised or partially supervised algorithm using standard image processing tools for measuring MTV, SUV, and TLG from lung PET/CT scans. Automated metabolic lesion volume and metabolic parameter measurements were achieved through a 5 step algorithm: (i) The segmentation of the lung areas on the CT slices, (ii) the registration of the CT segmented lung regions on the PET images to define the anatomical boundaries of the lungs on the functional data, (iii) the segmentation of the regions of interest (ROIs) on the PET images based on adaptive thresholding and clinical criteria, (iv) the estimation of the number of pixels and pixel intensities in the PET slices of the segmented ROIs, (v) the estimation of MTV, SUVs, and TLG from the previous step and DICOM header data. Whole body PET/CT scans of patients with sarcoidosis were used for training and testing the algorithm. Lung area segmentation on the CT slices was better achieved with semi-supervised techniques that reduced false positive detections significantly. Lung segmentation results agreed with the lung volumes published in the literature while the agreement between experts and algorithm in the segmentation of the lesions was around 88%. Segmentation results depended on the image resolution selected for processing. The clinical parameters, SUV (either mean or max or peak) and TLG estimated by the segmented ROIs and DICOM header data provided a way to correlate imaging data to clinical and demographic data. In conclusion, automated MTV, SUV, and TLG measurements offer powerful analysis tools in PET/CT imaging of the lungs. Custom-made algorithms are often a better approach than the manufacturer’s general analysis software at much lower cost. Relatively simple processing techniques could lead to customized, unsupervised or partially supervised methods that can successfully perform the desirable analysis and adapt to the specific disease requirements.

Optimal Design of Passive Power Filters Based on Pseudo-parallel Genetic Algorithm

NASA Astrophysics Data System (ADS)

Li, Pei; Li, Hongbo; Gao, Nannan; Niu, Lin; Guo, Liangfeng; Pei, Ying; Zhang, Yanyan; Xu, Minmin; Chen, Kerui

2017-05-01

The economic costs together with filter efficiency are taken as targets to optimize the parameter of passive filter. Furthermore, the method of combining pseudo-parallel genetic algorithm with adaptive genetic algorithm is adopted in this paper. In the early stages pseudo-parallel genetic algorithm is introduced to increase the population diversity, and adaptive genetic algorithm is used in the late stages to reduce the workload. At the same time, the migration rate of pseudo-parallel genetic algorithm is improved to change with population diversity adaptively. Simulation results show that the filter designed by the proposed method has better filtering effect with lower economic cost, and can be used in engineering.

A Self Adaptive Differential Evolution Algorithm for Global Optimization

NASA Astrophysics Data System (ADS)

Kumar, Pravesh; Pant, Millie

This paper presents a new Differential Evolution algorithm based on hybridization of adaptive control parameters and trigonometric mutation. First we propose a self adaptive DE named ADE where choice of control parameter F and Cr is not fixed at some constant value but is taken iteratively. The proposed algorithm is further modified by applying trigonometric mutation in it and the corresponding algorithm is named as ATDE. The performance of ATDE is evaluated on the set of 8 benchmark functions and the results are compared with the classical DE algorithm in terms of average fitness function value, number of function evaluations, convergence time and success rate. The numerical result shows the competence of the proposed algorithm.

SU-F-J-66: Anatomy Deformation Based Comparison Between One-Step and Two-Step Optimization for Online ART

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feng, Z; Yu, G; Qin, S

Purpose: This study investigated that how the quality of adapted plan was affected by inter-fractional anatomy deformation by using one-step and two-step optimization for on line adaptive radiotherapy (ART) procedure. Methods: 10 lung carcinoma patients were chosen randomly to produce IMRT plan by one-step and two-step algorithms respectively, and the prescribed dose was set as 60 Gy on the planning target volume (PTV) for all patients. To simulate inter-fractional target deformation, four specific cases were created by systematic anatomy variation; including target superior shift 0.5 cm, 0.3cm contraction, 0.3 cm expansion and 45-degree rotation. Based on these four anatomy deformation,more » adapted plan, regenerated plan and non-adapted plan were created to evaluate quality of adaptation. Adapted plans were generated automatically by using one-step and two-step algorithms respectively to optimize original plans, and regenerated plans were manually created by experience physicists. Non-adapted plans were produced by recalculating the dose distribution based on corresponding original plans. The deviations among these three plans were statistically analyzed by paired T-test. Results: In PTV superior shift case, adapted plans had significantly better PTV coverage by using two-step algorithm compared with one-step one, and meanwhile there was a significant difference of V95 by comparison with adapted and non-adapted plans (p=0.0025). In target contraction deformation, with almost same PTV coverage, the total lung received lower dose using one-step algorithm than two-step algorithm (p=0.0143,0.0126 for V20, Dmean respectively). In other two deformation cases, there were no significant differences observed by both two optimized algorithms. Conclusion: In geometry deformation such as target contraction, with comparable PTV coverage, one-step algorithm gave better OAR sparing than two-step algorithm. Reversely, the adaptation by using two-step algorithm had higher efficiency and accuracy as target occurred position displacement. We want to thank Dr. Lei Xing and Dr. Yong Yang in the Stanford University School of Medicine for this work. This work was jointly supported by NSFC (61471226), Natural Science Foundation for Distinguished Young Scholars of Shandong Province (JQ201516), and China Postdoctoral Science Foundation (2015T80739, 2014M551949).« less

Adaptive optics compensation of orbital angular momentum beams with a modified Gerchberg-Saxton-based phase retrieval algorithm

NASA Astrophysics Data System (ADS)

Chang, Huan; Yin, Xiao-li; Cui, Xiao-zhou; Zhang, Zhi-chao; Ma, Jian-xin; Wu, Guo-hua; Zhang, Li-jia; Xin, Xiang-jun

2017-12-01

Practical orbital angular momentum (OAM)-based free-space optical (FSO) communications commonly experience serious performance degradation and crosstalk due to atmospheric turbulence. In this paper, we propose a wave-front sensorless adaptive optics (WSAO) system with a modified Gerchberg-Saxton (GS)-based phase retrieval algorithm to correct distorted OAM beams. We use the spatial phase perturbation (SPP) GS algorithm with a distorted probe Gaussian beam as the only input. The principle and parameter selections of the algorithm are analyzed, and the performance of the algorithm is discussed. The simulation results show that the proposed adaptive optics (AO) system can significantly compensate for distorted OAM beams in single-channel or multiplexed OAM systems, which provides new insights into adaptive correction systems using OAM beams.

A Physical Model of Human Skin and Its Application for Search and Rescue

DTIC Science & Technology

2009-12-01

allow iv for the development of skin detection algorithms with a high probability of detection (PD) and a low probability of false alarm ( PFA ). The...various skin colors were collected. The skin detection algorithm developed in this work had a PD as high as 0.95 with a PFA of 0.006. Skin...threshold 0 < γ < 1. . . . . . . . . . . . . 103 63. Top: Detection Image with NDSI threshold γ = 0.314 with PD = 0.95 and corresponding PFA = 0.0156

Improved adaptive genetic algorithm with sparsity constraint applied to thermal neutron CT reconstruction of two-phase flow

NASA Astrophysics Data System (ADS)

Yan, Mingfei; Hu, Huasi; Otake, Yoshie; Taketani, Atsushi; Wakabayashi, Yasuo; Yanagimachi, Shinzo; Wang, Sheng; Pan, Ziheng; Hu, Guang

2018-05-01

Thermal neutron computer tomography (CT) is a useful tool for visualizing two-phase flow due to its high imaging contrast and strong penetrability of neutrons for tube walls constructed with metallic material. A novel approach for two-phase flow CT reconstruction based on an improved adaptive genetic algorithm with sparsity constraint (IAGA-SC) is proposed in this paper. In the algorithm, the neighborhood mutation operator is used to ensure the continuity of the reconstructed object. The adaptive crossover probability P c and mutation probability P m are improved to help the adaptive genetic algorithm (AGA) achieve the global optimum. The reconstructed results for projection data, obtained from Monte Carlo simulation, indicate that the comprehensive performance of the IAGA-SC algorithm exceeds the adaptive steepest descent-projection onto convex sets (ASD-POCS) algorithm in restoring typical and complex flow regimes. It especially shows great advantages in restoring the simply connected flow regimes and the shape of object. In addition, the CT experiment for two-phase flow phantoms was conducted on the accelerator-driven neutron source to verify the performance of the developed IAGA-SC algorithm.

Real-Time Feedback Control of Flow-Induced Cavity Tones. Part 2; Adaptive Control

NASA Technical Reports Server (NTRS)

Kegerise, M. A.; Cabell, R. H.; Cattafesta, L. N., III

2006-01-01

An adaptive generalized predictive control (GPC) algorithm was formulated and applied to the cavity flow-tone problem. The algorithm employs gradient descent to update the GPC coefficients at each time step. Past input-output data and an estimate of the open-loop pulse response sequence are all that is needed to implement the algorithm for application at fixed Mach numbers. Transient measurements made during controller adaptation revealed that the controller coefficients converged to a steady state in the mean, and this implies that adaptation can be turned off at some point with no degradation in control performance. When converged, the control algorithm demonstrated multiple Rossiter mode suppression at fixed Mach numbers ranging from 0.275 to 0.38. However, as in the case of fixed-gain GPC, the adaptive GPC performance was limited by spillover in sidebands around the suppressed Rossiter modes. The algorithm was also able to maintain suppression of multiple cavity tones as the freestream Mach number was varied over a modest range (0.275 to 0.29). Beyond this range, stable operation of the control algorithm was not possible due to the fixed plant model in the algorithm.

Lowered threshold energy for femtosecond laser induced optical breakdown in a water based eye model by aberration correction with adaptive optics.

PubMed

Hansen, Anja; Géneaux, Romain; Günther, Axel; Krüger, Alexander; Ripken, Tammo

2013-06-01

In femtosecond laser ophthalmic surgery tissue dissection is achieved by photodisruption based on laser induced optical breakdown. In order to minimize collateral damage to the eye laser surgery systems should be optimized towards the lowest possible energy threshold for photodisruption. However, optical aberrations of the eye and the laser system distort the irradiance distribution from an ideal profile which causes a rise in breakdown threshold energy even if great care is taken to minimize the aberrations of the system during design and alignment. In this study we used a water chamber with an achromatic focusing lens and a scattering sample as eye model and determined breakdown threshold in single pulse plasma transmission loss measurements. Due to aberrations, the precise lower limit for breakdown threshold irradiance in water is still unknown. Here we show that the threshold energy can be substantially reduced when using adaptive optics to improve the irradiance distribution by spatial beam shaping. We found that for initial aberrations with a root-mean-square wave front error of only one third of the wavelength the threshold energy can still be reduced by a factor of three if the aberrations are corrected to the diffraction limit by adaptive optics. The transmitted pulse energy is reduced by 17% at twice the threshold. Furthermore, the gas bubble motions after breakdown for pulse trains at 5 kilohertz repetition rate show a more transverse direction in the corrected case compared to the more spherical distribution without correction. Our results demonstrate how both applied and transmitted pulse energy could be reduced during ophthalmic surgery when correcting for aberrations. As a consequence, the risk of retinal damage by transmitted energy and the extent of collateral damage to the focal volume could be minimized accordingly when using adaptive optics in fs-laser surgery.

Lowered threshold energy for femtosecond laser induced optical breakdown in a water based eye model by aberration correction with adaptive optics

PubMed Central

Hansen, Anja; Géneaux, Romain; Günther, Axel; Krüger, Alexander; Ripken, Tammo

2013-01-01

In femtosecond laser ophthalmic surgery tissue dissection is achieved by photodisruption based on laser induced optical breakdown. In order to minimize collateral damage to the eye laser surgery systems should be optimized towards the lowest possible energy threshold for photodisruption. However, optical aberrations of the eye and the laser system distort the irradiance distribution from an ideal profile which causes a rise in breakdown threshold energy even if great care is taken to minimize the aberrations of the system during design and alignment. In this study we used a water chamber with an achromatic focusing lens and a scattering sample as eye model and determined breakdown threshold in single pulse plasma transmission loss measurements. Due to aberrations, the precise lower limit for breakdown threshold irradiance in water is still unknown. Here we show that the threshold energy can be substantially reduced when using adaptive optics to improve the irradiance distribution by spatial beam shaping. We found that for initial aberrations with a root-mean-square wave front error of only one third of the wavelength the threshold energy can still be reduced by a factor of three if the aberrations are corrected to the diffraction limit by adaptive optics. The transmitted pulse energy is reduced by 17% at twice the threshold. Furthermore, the gas bubble motions after breakdown for pulse trains at 5 kilohertz repetition rate show a more transverse direction in the corrected case compared to the more spherical distribution without correction. Our results demonstrate how both applied and transmitted pulse energy could be reduced during ophthalmic surgery when correcting for aberrations. As a consequence, the risk of retinal damage by transmitted energy and the extent of collateral damage to the focal volume could be minimized accordingly when using adaptive optics in fs-laser surgery. PMID:23761849

Fuzzy entropy thresholding and multi-scale morphological approach for microscopic image enhancement

NASA Astrophysics Data System (ADS)

Zhou, Jiancan; Li, Yuexiang; Shen, Linlin

2017-07-01

Microscopic images provide lots of useful information for modern diagnosis and biological research. However, due to the unstable lighting condition during image capturing, two main problems, i.e., high-level noises and low image contrast, occurred in the generated cell images. In this paper, a simple but efficient enhancement framework is proposed to address the problems. The framework removes image noises using a hybrid method based on wavelet transform and fuzzy-entropy, and enhances the image contrast with an adaptive morphological approach. Experiments on real cell dataset were made to assess the performance of proposed framework. The experimental results demonstrate that our proposed enhancement framework increases the cell tracking accuracy to an average of 74.49%, which outperforms the benchmark algorithm, i.e., 46.18%.

Locally adaptive vector quantization: Data compression with feature preservation

NASA Technical Reports Server (NTRS)

Cheung, K. M.; Sayano, M.

1992-01-01

A study of a locally adaptive vector quantization (LAVQ) algorithm for data compression is presented. This algorithm provides high-speed one-pass compression and is fully adaptable to any data source and does not require a priori knowledge of the source statistics. Therefore, LAVQ is a universal data compression algorithm. The basic algorithm and several modifications to improve performance are discussed. These modifications are nonlinear quantization, coarse quantization of the codebook, and lossless compression of the output. Performance of LAVQ on various images using irreversible (lossy) coding is comparable to that of the Linde-Buzo-Gray algorithm, but LAVQ has a much higher speed; thus this algorithm has potential for real-time video compression. Unlike most other image compression algorithms, LAVQ preserves fine detail in images. LAVQ's performance as a lossless data compression algorithm is comparable to that of Lempel-Ziv-based algorithms, but LAVQ uses far less memory during the coding process.

Automated and Adaptable Quantification of Cellular Alignment from Microscopic Images for Tissue Engineering Applications

PubMed Central

Xu, Feng; Beyazoglu, Turker; Hefner, Evan; Gurkan, Umut Atakan

2011-01-01

Cellular alignment plays a critical role in functional, physical, and biological characteristics of many tissue types, such as muscle, tendon, nerve, and cornea. Current efforts toward regeneration of these tissues include replicating the cellular microenvironment by developing biomaterials that facilitate cellular alignment. To assess the functional effectiveness of the engineered microenvironments, one essential criterion is quantification of cellular alignment. Therefore, there is a need for rapid, accurate, and adaptable methodologies to quantify cellular alignment for tissue engineering applications. To address this need, we developed an automated method, binarization-based extraction of alignment score (BEAS), to determine cell orientation distribution in a wide variety of microscopic images. This method combines a sequenced application of median and band-pass filters, locally adaptive thresholding approaches and image processing techniques. Cellular alignment score is obtained by applying a robust scoring algorithm to the orientation distribution. We validated the BEAS method by comparing the results with the existing approaches reported in literature (i.e., manual, radial fast Fourier transform-radial sum, and gradient based approaches). Validation results indicated that the BEAS method resulted in statistically comparable alignment scores with the manual method (coefficient of determination R2=0.92). Therefore, the BEAS method introduced in this study could enable accurate, convenient, and adaptable evaluation of engineered tissue constructs and biomaterials in terms of cellular alignment and organization. PMID:21370940

Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control.

PubMed

Luo, Biao; Liu, Derong; Wu, Huai-Ning; Wang, Ding; Lewis, Frank L

2017-10-01

The model-free optimal control problem of general discrete-time nonlinear systems is considered in this paper, and a data-based policy gradient adaptive dynamic programming (PGADP) algorithm is developed to design an adaptive optimal controller method. By using offline and online data rather than the mathematical system model, the PGADP algorithm improves control policy with a gradient descent scheme. The convergence of the PGADP algorithm is proved by demonstrating that the constructed Q -function sequence converges to the optimal Q -function. Based on the PGADP algorithm, the adaptive control method is developed with an actor-critic structure and the method of weighted residuals. Its convergence properties are analyzed, where the approximate Q -function converges to its optimum. Computer simulation results demonstrate the effectiveness of the PGADP-based adaptive control method.

Threshold secret sharing scheme based on phase-shifting interferometry.

PubMed

Deng, Xiaopeng; Shi, Zhengang; Wen, Wei

2016-11-01

We propose a new method for secret image sharing with the (3,N) threshold scheme based on phase-shifting interferometry. The secret image, which is multiplied with an encryption key in advance, is first encrypted by using Fourier transformation. Then, the encoded image is shared into N shadow images based on the recording principle of phase-shifting interferometry. Based on the reconstruction principle of phase-shifting interferometry, any three or more shadow images can retrieve the secret image, while any two or fewer shadow images cannot obtain any information of the secret image. Thus, a (3,N) threshold secret sharing scheme can be implemented. Compared with our previously reported method, the algorithm of this paper is suited for not only a binary image but also a gray-scale image. Moreover, the proposed algorithm can obtain a larger threshold value t. Simulation results are presented to demonstrate the feasibility of the proposed method.

A Stochastic Total Least Squares Solution of Adaptive Filtering Problem

PubMed Central

Ahmad, Noor Atinah

2014-01-01

An efficient and computationally linear algorithm is derived for total least squares solution of adaptive filtering problem, when both input and output signals are contaminated by noise. The proposed total least mean squares (TLMS) algorithm is designed by recursively computing an optimal solution of adaptive TLS problem by minimizing instantaneous value of weighted cost function. Convergence analysis of the algorithm is given to show the global convergence of the proposed algorithm, provided that the stepsize parameter is appropriately chosen. The TLMS algorithm is computationally simpler than the other TLS algorithms and demonstrates a better performance as compared with the least mean square (LMS) and normalized least mean square (NLMS) algorithms. It provides minimum mean square deviation by exhibiting better convergence in misalignment for unknown system identification under noisy inputs. PMID:24688412

Properties of perimetric threshold estimates from Full Threshold, SITA Standard, and SITA Fast strategies.

PubMed

Artes, Paul H; Iwase, Aiko; Ohno, Yuko; Kitazawa, Yoshiaki; Chauhan, Balwantray C

2002-08-01

To investigate the distributions of threshold estimates with the Swedish Interactive Threshold Algorithms (SITA) Standard, SITA Fast, and the Full Threshold algorithm (Humphrey Field Analyzer; Zeiss-Humphrey Instruments, Dublin, CA) and to compare the pointwise test-retest variability of these strategies. One eye of 49 patients (mean age, 61.6 years; range, 22-81) with glaucoma (Mean Deviation mean, -7.13 dB; range, +1.8 to -23.9 dB) was examined four times with each of the three strategies. The mean and median SITA Standard and SITA Fast threshold estimates were compared with a "best available" estimate of sensitivity (mean results of three Full Threshold tests). Pointwise 90% retest limits (5th and 95th percentiles of retest thresholds) were derived to assess the reproducibility of individual threshold estimates. The differences between the threshold estimates of the SITA and Full Threshold strategies were largest ( approximately 3 dB) for midrange sensitivities ( approximately 15 dB). The threshold distributions of SITA were considerably different from those of the Full Threshold strategy. The differences remained of similar magnitude when the analysis was repeated on a subset of 20 locations that are examined early during the course of a Full Threshold examination. With sensitivities above 25 dB, both SITA strategies exhibited lower test-retest variability than the Full Threshold strategy. Below 25 dB, the retest intervals of SITA Standard were slightly smaller than those of the Full Threshold strategy, whereas those of SITA Fast were larger. SITA Standard may be superior to the Full Threshold strategy for monitoring patients with visual field loss. The greater test-retest variability of SITA Fast in areas of low sensitivity is likely to offset the benefit of even shorter test durations with this strategy. The sensitivity differences between the SITA and Full Threshold strategies may relate to factors other than reduced fatigue. They are, however, small in comparison to the test-retest variability.

Tracks detection from high-orbit space objects

NASA Astrophysics Data System (ADS)

Shumilov, Yu. P.; Vygon, V. G.; Grishin, E. A.; Konoplev, A. O.; Semichev, O. P.; Shargorodskii, V. D.

2017-05-01

The paper presents studies results of a complex algorithm for the detection of highly orbital space objects. Before the implementation of the algorithm, a series of frames with weak tracks of space objects, which can be discrete, is recorded. The algorithm includes pre-processing, classical for astronomy, consistent filtering of each frame and its threshold processing, shear transformation, median filtering of the transformed series of frames, repeated threshold processing and detection decision making. Modeling of space objects weak tracks on of the night starry sky real frames obtained in the regime of a stationary telescope was carried out. It is shown that the permeability of an optoelectronic device has increased by almost 2m.

ANOTHER LOOK AT THE FAST ITERATIVE SHRINKAGE/THRESHOLDING ALGORITHM (FISTA)*

PubMed Central

Kim, Donghwan; Fessler, Jeffrey A.

2017-01-01

This paper provides a new way of developing the “Fast Iterative Shrinkage/Thresholding Algorithm (FISTA)” [3] that is widely used for minimizing composite convex functions with a nonsmooth term such as the ℓ1 regularizer. In particular, this paper shows that FISTA corresponds to an optimized approach to accelerating the proximal gradient method with respect to a worst-case bound of the cost function. This paper then proposes a new algorithm that is derived by instead optimizing the step coefficients of the proximal gradient method with respect to a worst-case bound of the composite gradient mapping. The proof is based on the worst-case analysis called Performance Estimation Problem in [11]. PMID:29805242

Centrifugal unbalance detection system

DOEpatents

Cordaro, Joseph V.; Reeves, George; Mets, Michael

2002-01-01

A system consisting of an accelerometer sensor attached to a centrifuge enclosure for sensing vibrations and outputting a signal in the form of a sine wave with an amplitude and frequency that is passed through a pre-amp to convert it to a voltage signal, a low pass filter for removing extraneous noise, an A/D converter and a processor and algorithm for operating on the signal, whereby the algorithm interprets the amplitude and frequency associated with the signal and once an amplitude threshold has been exceeded the algorithm begins to count cycles during a predetermined time period and if a given number of complete cycles exceeds the frequency threshold during the predetermined time period, the system shuts down the centrifuge.

3D-segmentation of the 18F-choline PET signal for target volume definition in radiation therapy of the prostate.

PubMed

Ciernik, I Frank; Brown, Derek W; Schmid, Daniel; Hany, Thomas; Egli, Peter; Davis, J Bernard

2007-02-01

Volumetric assessment of PET signals becomes increasingly relevant for radiotherapy (RT) planning. Here, we investigate the utility of 18F-choline PET signals to serve as a structure for semi-automatic segmentation for forward treatment planning of prostate cancer. 18F-choline PET and CT scans of ten patients with histologically proven prostate cancer without extracapsular growth were acquired using a combined PET/CT scanner. Target volumes were manually delineated on CT images using standard software. Volumes were also obtained from 18F-choline PET images using an asymmetrical segmentation algorithm. PTVs were derived from CT 18F-choline PET based clinical target volumes (CTVs) by automatic expansion and comparative planning was performed. As a read-out for dose given to non-target structures, dose to the rectal wall was assessed. Planning target volumes (PTVs) derived from CT and 18F-choline PET yielded comparable results. Optimal matching of CT and 18F-choline PET derived volumes in the lateral and cranial-caudal directions was obtained using a background-subtracted signal thresholds of 23.0+/-2.6%. In antero-posterior direction, where adaptation compensating for rectal signal overflow was required, optimal matching was achieved with a threshold of 49.5+/-4.6%. 3D-conformal planning with CT or 18F-choline PET resulted in comparable doses to the rectal wall. Choline PET signals of the prostate provide adequate spatial information amendable to standardized asymmetrical region growing algorithms for PET-based target volume definition for external beam RT.

18F-fluorocholine PET-guided target volume delineation techniques for partial prostate re-irradiation in local recurrent prostate cancer.

PubMed

Wang, Hui; Vees, Hansjörg; Miralbell, Raymond; Wissmeyer, Michael; Steiner, Charles; Ratib, Osman; Senthamizhchelvan, Srinivasan; Zaidi, Habib

2009-11-01

We evaluate the contribution of (18)F-choline PET/CT in the delineation of gross tumour volume (GTV) in local recurrent prostate cancer after initial irradiation using various PET image segmentation techniques. Seventeen patients with local-only recurrent prostate cancer (median=5.7 years) after initial irradiation were included in the study. Rebiopsies were performed in 10 patients that confirmed the local recurrence. Following injection of 300 MBq of (18)F-fluorocholine, dynamic PET frames (3 min each) were reconstructed from the list-mode acquisition. Five PET image segmentation techniques were used to delineate the (18)F-choline-based GTVs. These included manual delineation of contours (GTV(man)) by two teams consisting of a radiation oncologist and a nuclear medicine physician each, a fixed threshold of 40% and 50% of the maximum signal intensity (GTV(40%) and GTV(50%)), signal-to-background ratio-based adaptive thresholding (GTV(SBR)), and a region growing (GTV(RG)) algorithm. Geographic mismatches between the GTVs were also assessed using overlap analysis. Inter-observer variability for manual delineation of GTVs was high but not statistically significant (p=0.459). In addition, the volumes and shapes of GTVs delineated using semi-automated techniques were significantly higher than those of GTVs defined manually. Semi-automated segmentation techniques for (18)F-choline PET-guided GTV delineation resulted in substantially higher GTVs compared to manual delineation and might replace the latter for determination of recurrent prostate cancer for partial prostate re-irradiation. The selection of the most appropriate segmentation algorithm still needs to be determined.

A General and Efficient Method for Incorporating Precise Spike Times in Globally Time-Driven Simulations

PubMed Central

Hanuschkin, Alexander; Kunkel, Susanne; Helias, Moritz; Morrison, Abigail; Diesmann, Markus

2010-01-01

Traditionally, event-driven simulations have been limited to the very restricted class of neuronal models for which the timing of future spikes can be expressed in closed form. Recently, the class of models that is amenable to event-driven simulation has been extended by the development of techniques to accurately calculate firing times for some integrate-and-fire neuron models that do not enable the prediction of future spikes in closed form. The motivation of this development is the general perception that time-driven simulations are imprecise. Here, we demonstrate that a globally time-driven scheme can calculate firing times that cannot be discriminated from those calculated by an event-driven implementation of the same model; moreover, the time-driven scheme incurs lower computational costs. The key insight is that time-driven methods are based on identifying a threshold crossing in the recent past, which can be implemented by a much simpler algorithm than the techniques for predicting future threshold crossings that are necessary for event-driven approaches. As run time is dominated by the cost of the operations performed at each incoming spike, which includes spike prediction in the case of event-driven simulation and retrospective detection in the case of time-driven simulation, the simple time-driven algorithm outperforms the event-driven approaches. Additionally, our method is generally applicable to all commonly used integrate-and-fire neuronal models; we show that a non-linear model employing a standard adaptive solver can reproduce a reference spike train with a high degree of precision. PMID:21031031

Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons.

PubMed

Mensi, Skander; Hagens, Olivier; Gerstner, Wulfram; Pozzorini, Christian

2016-02-01

The way in which single neurons transform input into output spike trains has fundamental consequences for network coding. Theories and modeling studies based on standard Integrate-and-Fire models implicitly assume that, in response to increasingly strong inputs, neurons modify their coding strategy by progressively reducing their selective sensitivity to rapid input fluctuations. Combining mathematical modeling with in vitro experiments, we demonstrate that, in L5 pyramidal neurons, the firing threshold dynamics adaptively adjust the effective timescale of somatic integration in order to preserve sensitivity to rapid signals over a broad range of input statistics. For that, a new Generalized Integrate-and-Fire model featuring nonlinear firing threshold dynamics and conductance-based adaptation is introduced that outperforms state-of-the-art neuron models in predicting the spiking activity of neurons responding to a variety of in vivo-like fluctuating currents. Our model allows for efficient parameter extraction and can be analytically mapped to a Generalized Linear Model in which both the input filter--describing somatic integration--and the spike-history filter--accounting for spike-frequency adaptation--dynamically adapt to the input statistics, as experimentally observed. Overall, our results provide new insights on the computational role of different biophysical processes known to underlie adaptive coding in single neurons and support previous theoretical findings indicating that the nonlinear dynamics of the firing threshold due to Na+-channel inactivation regulate the sensitivity to rapid input fluctuations.

Deriving flow directions for coarse-resolution (1-4 km) gridded hydrologic modeling

NASA Astrophysics Data System (ADS)

Reed, Seann M.

2003-09-01

The National Weather Service Hydrology Laboratory (NWS-HL) is currently testing a grid-based distributed hydrologic model at a resolution (4 km) commensurate with operational, radar-based precipitation products. To implement distributed routing algorithms in this framework, a flow direction must be assigned to each model cell. A new algorithm, referred to as cell outlet tracing with an area threshold (COTAT) has been developed to automatically, accurately, and efficiently assign flow directions to any coarse-resolution grid cells using information from any higher-resolution digital elevation model. Although similar to previously published algorithms, this approach offers some advantages. Use of an area threshold allows more control over the tendency for producing diagonal flow directions. Analyses of results at different output resolutions ranging from 300 m to 4000 m indicate that it is possible to choose an area threshold that will produce minimal differences in average network flow lengths across this range of scales. Flow direction grids at a 4 km resolution have been produced for the conterminous United States.

Pitch-Learning Algorithm For Speech Encoders

NASA Technical Reports Server (NTRS)

Bhaskar, B. R. Udaya

1988-01-01

Adaptive algorithm detects and corrects errors in sequence of estimates of pitch period of speech. Algorithm operates in conjunction with techniques used to estimate pitch period. Used in such parametric and hybrid speech coders as linear predictive coders and adaptive predictive coders.

Temporal resolution in children.

PubMed

Wightman, F; Allen, P; Dolan, T; Kistler, D; Jamieson, D

1989-06-01

The auditory temporal resolving power of young children was measured using an adaptive forced-choice psychophysical paradigm that was disguised as a video game. 20 children between 3 and 7 years of age and 5 adults were asked to detect the presence of a temporal gap in a burst of half-octave-band noise at band center frequencies of 400 and 2,000 Hz. The minimum detectable gap (gap threshold) was estimated adaptively in 20-trial runs. The mean gap thresholds in the 400-Hz condition were higher for the younger children than for the adults, with the 3-year-old children producing the highest thresholds. Gap thresholds in the 2,000-Hz condition were generally lower than in the 400-Hz condition and showed a similar age effect. All the individual adaptive runs were "adult-like," suggesting that the children were generally attentive to the task during each run. However, the variability of threshold estimates from run to run was substantial, especially in the 3-5-year-old children. Computer simulations suggested that this large within-subjects variability could have resulted from frequent, momentary lapses of attention, which would lead to "guessing" on a substantial portion of the trials.

Method for Assessing Contrast Performance under Lighting Conditions such as Entering a Tunnel on Sunny Day.

PubMed

Huang, Y; Menozzi, M

2015-04-01

Clinical assessment of dark adaptation is time consuming and requires a specialised instrumentation such as a nyktometer. It is therefore not surprising that dark adaptation is rarely tested in practice. As for the case of testing fitness of a driver, demands on adaptation in daily driving tasks mostly depart from settings in a nyktometer. In daily driving, adaptation is stressed by high and fast transitions of light levels, and the period of time which is relevant to safe driving starts right after a transition and ends several seconds later. In the nyktometer dark adaptation is tested after completion of the adaptation process. RESULTS of a nyktometer test may therefore deliver little information about adaptation shortly after light transitions. In an attempt to develop a clinical test aiming to fulfill both a short measurement time and offering test conditions comparable to conditions in driving, we conducted a preliminary study in which contrast sensitivity thresholds were recorded for light transitions as found in daily driving tasks and for various times after transition onsets. Contrast sensitivity performance is compared to dark adaptation performance as assessed by a myktometer. Contrast sensitivity thresholds were recorded in 17 participants by means of a twin projection apparatus. The apparatus enabled the projection of an adapting field and of a Landolt ring both with a variable luminance. Five different stepwise transitions in levels of adapting luminance were tested. All transitions occurred from bright to dark. The Landolt ring was flashed 100 or 500 ms after the transition had occurred. Participants were instructed to report the orientation of the Landolt ring. A Rodenstock Nyktometer, Plate 501, was used to record dark adaptation threshold. Experimental data from the proposed test revealed a noticeably increasing contrast detection threshold measured in dark adaptation in the stronger transition from 14 000 to 8 cd/m2 than in the weaker transition from 2000 to 8 cd/m2. By raising the dark adaption luminance level from 8 to 60 cd/m2 in the stronger transition case, the contrast detection threshold was then improved by a factor of four. Another main finding showed that for the adaptation process from strong glare stimuli to the dark adaptation, a peak deterioration in contrast sensitivity occurred at the light adaptation level of 6000 cd/m2. Comparing the contrast performance assessed by the proposed test with that of the nyktometer test, there was no clear correlation between the two methods. Our suggested method to assess dark adaptation performance proved to be practical in use and, since the patient does not have to spend a long time to attain complete dark adaptation, the method required a short time for measurement. Our negative experience in the use of the myktometer was in agreement with reported experience in the literature. Georg Thieme Verlag KG Stuttgart · New York.

Adaptive track scheduling to optimize concurrency and vectorization in GeantV

DOE PAGES

Apostolakis, J.; Bandieramonte, M.; Bitzes, G.; ...

2015-05-22

The GeantV project is focused on the R&D of new particle transport techniques to maximize parallelism on multiple levels, profiting from the use of both SIMD instructions and co-processors for the CPU-intensive calculations specific to this type of applications. In our approach, vectors of tracks belonging to multiple events and matching different locality criteria must be gathered and dispatched to algorithms having vector signatures. While the transport propagates tracks and changes their individual states, data locality becomes harder to maintain. The scheduling policy has to be changed to maintain efficient vectors while keeping an optimal level of concurrency. The modelmore » has complex dynamics requiring tuning the thresholds to switch between the normal regime and special modes, i.e. prioritizing events to allow flushing memory, adding new events in the transport pipeline to boost locality, dynamically adjusting the particle vector size or switching between vector to single track mode when vectorization causes only overhead. Lastly, this work requires a comprehensive study for optimizing these parameters to make the behaviour of the scheduler self-adapting, presenting here its initial results.« less

Rotation and scale invariant shape context registration for remote sensing images with background variations

NASA Astrophysics Data System (ADS)

Jiang, Jie; Zhang, Shumei; Cao, Shixiang

2015-01-01

Multitemporal remote sensing images generally suffer from background variations, which significantly disrupt traditional region feature and descriptor abstracts, especially between pre and postdisasters, making registration by local features unreliable. Because shapes hold relatively stable information, a rotation and scale invariant shape context based on multiscale edge features is proposed. A multiscale morphological operator is adapted to detect edges of shapes, and an equivalent difference of Gaussian scale space is built to detect local scale invariant feature points along the detected edges. Then, a rotation invariant shape context with improved distance discrimination serves as a feature descriptor. For a distance shape context, a self-adaptive threshold (SAT) distance division coordinate system is proposed, which improves the discriminative property of the feature descriptor in mid-long pixel distances from the central point while maintaining it in shorter ones. To achieve rotation invariance, the magnitude of Fourier transform in one-dimension is applied to calculate angle shape context. Finally, the residual error is evaluated after obtaining thin-plate spline transformation between reference and sensed images. Experimental results demonstrate the robustness, efficiency, and accuracy of this automatic algorithm.

Parametric adaptive filtering and data validation in the bar GW detector AURIGA

NASA Astrophysics Data System (ADS)

Ortolan, A.; Baggio, L.; Cerdonio, M.; Prodi, G. A.; Vedovato, G.; Vitale, S.

2002-04-01

We report on our experience gained in the signal processing of the resonant GW detector AURIGA. Signal amplitude and arrival time are estimated by means of a matched-adaptive Wiener filter. The detector noise, entering in the filter set-up, is modelled as a parametric ARMA process; to account for slow non-stationarity of the noise, the ARMA parameters are estimated on an hourly basis. A requirement of the set-up of an unbiased Wiener filter is the separation of time spans with 'almost Gaussian' noise from non-Gaussian and/or strongly non-stationary time spans. The separation algorithm consists basically of a variance estimate with the Chauvenet convergence method and a threshold on the Curtosis index. The subsequent validation of data is strictly connected with the separation procedure: in fact, by injecting a large number of artificial GW signals into the 'almost Gaussian' part of the AURIGA data stream, we have demonstrated that the effective probability distributions of the signal-to-noise ratio χ2 and the time of arrival are those that are expected.

A randomized trial of the effect of automated ventricular capture on device longevity and threshold measurement in pacemaker patients.

PubMed

Koplan, Bruce A; Gilligan, David M; Nguyen, Luc S; Lau, Theodore K; Thackeray, Lisa M; Berg, Kellie Chase

2008-11-01

An automatic capture (AC) algorithm adjusts ventricular pacing output to capture the ventricle while optimizing output to 0.5 V above threshold. AC maintains this output and confirms capture on a beat-to-beat basis in bipolar and unipolar pacing and sensing. To assess the AC algorithm and its impact on device longevity. Patients implanted with a pacemaker were randomized 1:1 to have the AC feature on or off for 12 months. Two threshold tests were conducted at each visit- automatic threshold and manual threshold. Average ventricular voltage output and projected device longevity were compared between AC on and off using nonparametric tests. Nine hundred ten patients were enrolled and underwent device implantation. Average ventricular voltage output was 1.6 V for the AC on arm (n = 444) and 3.1 V for the AC off arm (n = 446) (P < 0.001). Projected device longevity was 10.3 years for AC on and 8.9 years for AC off (P < 0.0001), or a 16% increase in longevity for AC on. The proportion of patients in whom there was a difference between automatic threshold and manual threshold of

Effectiveness of a Predictive Algorithm in the Prevention of Exercise-Induced Hypoglycemia in Type 1 Diabetes.

PubMed

Abraham, Mary B; Davey, Raymond; O'Grady, Michael J; Ly, Trang T; Paramalingam, Nirubasini; Fournier, Paul A; Roy, Anirban; Grosman, Benyamin; Kurtz, Natalie; Fairchild, Janice M; King, Bruce R; Ambler, Geoffrey R; Cameron, Fergus; Jones, Timothy W; Davis, Elizabeth A

2016-09-01

Sensor-augmented pump therapy (SAPT) with a predictive algorithm to suspend insulin delivery has the potential to reduce hypoglycemia, a known obstacle in improving physical activity in patients with type 1 diabetes. The predictive low glucose management (PLGM) system employs a predictive algorithm that suspends basal insulin when hypoglycemia is predicted. The aim of this study was to determine the efficacy of this algorithm in the prevention of exercise-induced hypoglycemia under in-clinic conditions. This was a randomized, controlled cross-over study in which 25 participants performed 2 consecutive sessions of 30 min of moderate-intensity exercise while on basal continuous subcutaneous insulin infusion on 2 study days: a control day with SAPT alone and an intervention day with SAPT and PLGM. The predictive algorithm suspended basal insulin when sensor glucose was predicted to be below the preset hypoglycemic threshold in 30 min. We tested preset hypoglycemic thresholds of 70 and 80 mg/dL. The primary outcome was the requirement for hypoglycemia treatment (symptomatic hypoglycemia with plasma glucose <63 mg/dL or plasma glucose <50 mg/dL) and was compared in both control and intervention arms. Results were analyzed in 19 participants. In the intervention arm with both thresholds, only 6 participants (32%) required treatment for hypoglycemia compared with 17 participants (89%) in the control arm (P = 0.003). In participants with a 2-h pump suspension on intervention days, the plasma glucose was 84 ± 12 and 99 ± 24 mg/dL at thresholds of 70 and 80 mg/dL, respectively. SAPT with PLGM reduced the need for hypoglycemia treatment after moderate-intensity exercise in an in-clinic setting.

An Adaptive Buddy Check for Observational Quality Control

NASA Technical Reports Server (NTRS)

Dee, Dick P.; Rukhovets, Leonid; Todling, Ricardo; DaSilva, Arlindo M.; Larson, Jay W.; Einaudi, Franco (Technical Monitor)

2000-01-01

An adaptive buddy check algorithm is presented that adjusts tolerances for outlier observations based on the variability of surrounding data. The algorithm derives from a statistical hypothesis test combined with maximum-likelihood covariance estimation. Its stability is shown to depend on the initial identification of outliers by a simple background check. The adaptive feature ensures that the final quality control decisions are not very sensitive to prescribed statistics of first-guess and observation errors, nor on other approximations introduced into the algorithm. The implementation of the algorithm in a global atmospheric data assimilation is described. Its performance is contrasted with that of a non-adaptive buddy check, for the surface analysis of an extreme storm that took place in Europe on 27 December 1999. The adaptive algorithm allowed the inclusion of many important observations that differed greatly from the first guess and that would have been excluded on the basis of prescribed statistics. The analysis of the storm development was much improved as a result of these additional observations.

Adaptive time-sequential binary sensing for high dynamic range imaging

NASA Astrophysics Data System (ADS)

Hu, Chenhui; Lu, Yue M.

2012-06-01

We present a novel image sensor for high dynamic range imaging. The sensor performs an adaptive one-bit quantization at each pixel, with the pixel output switched from 0 to 1 only if the number of photons reaching that pixel is greater than or equal to a quantization threshold. With an oracle knowledge of the incident light intensity, one can pick an optimal threshold (for that light intensity) and the corresponding Fisher information contained in the output sequence follows closely that of an ideal unquantized sensor over a wide range of intensity values. This observation suggests the potential gains one may achieve by adaptively updating the quantization thresholds. As the main contribution of this work, we propose a time-sequential threshold-updating rule that asymptotically approaches the performance of the oracle scheme. With every threshold mapped to a number of ordered states, the dynamics of the proposed scheme can be modeled as a parametric Markov chain. We show that the frequencies of different thresholds converge to a steady-state distribution that is concentrated around the optimal choice. Moreover, numerical experiments show that the theoretical performance measures (Fisher information and Craḿer-Rao bounds) can be achieved by a maximum likelihood estimator, which is guaranteed to find globally optimal solution due to the concavity of the log-likelihood functions. Compared with conventional image sensors and the strategy that utilizes a constant single-photon threshold considered in previous work, the proposed scheme attains orders of magnitude improvement in terms of sensor dynamic ranges.

A new strategy for snow-cover mapping using remote sensing data and ensemble based systems techniques

NASA Astrophysics Data System (ADS)

Roberge, S.; Chokmani, K.; De Sève, D.

2012-04-01

The snow cover plays an important role in the hydrological cycle of Quebec (Eastern Canada). Consequently, evaluating its spatial extent interests the authorities responsible for the management of water resources, especially hydropower companies. The main objective of this study is the development of a snow-cover mapping strategy using remote sensing data and ensemble based systems techniques. Planned to be tested in a near real-time operational mode, this snow-cover mapping strategy has the advantage to provide the probability of a pixel to be snow covered and its uncertainty. Ensemble systems are made of two key components. First, a method is needed to build an ensemble of classifiers that is diverse as much as possible. Second, an approach is required to combine the outputs of individual classifiers that make up the ensemble in such a way that correct decisions are amplified, and incorrect ones are cancelled out. In this study, we demonstrate the potential of ensemble systems to snow-cover mapping using remote sensing data. The chosen classifier is a sequential thresholds algorithm using NOAA-AVHRR data adapted to conditions over Eastern Canada. Its special feature is the use of a combination of six sequential thresholds varying according to the day in the winter season. Two versions of the snow-cover mapping algorithm have been developed: one is specific for autumn (from October 1st to December 31st) and the other for spring (from March 16th to May 31st). In order to build the ensemble based system, different versions of the algorithm are created by varying randomly its parameters. One hundred of the versions are included in the ensemble. The probability of a pixel to be snow, no-snow or cloud covered corresponds to the amount of votes the pixel has been classified as such by all classifiers. The overall performance of ensemble based mapping is compared to the overall performance of the chosen classifier, and also with ground observations at meteorological stations.

Adaptive Two Dimensional RLS (Recursive Least Squares) Algorithms

DTIC Science & Technology

1989-03-01

in Monterey wonderful. IX I. INTRODUCTION Adaptive algorithms have been used successfully for many years in a wide range of digital signal...SIMULATION RESULTS The 2-D FRLS algorithm was tested both on computer-generated data and on digitized images. For a baseline reference the 2-D L:rv1S...Alexander, S. T. Adaptivt Signal Processing: Theory and Applications. Springer- Verlag, New York. 1986. 7. Bellanger, Maurice G. Adaptive Digital

Multi-objective Optimization Design of Gear Reducer Based on Adaptive Genetic Algorithms

NASA Astrophysics Data System (ADS)

Li, Rui; Chang, Tian; Wang, Jianwei; Wei, Xiaopeng; Wang, Jinming

2008-11-01

An adaptive Genetic Algorithm (GA) is introduced to solve the multi-objective optimized design of the reducer. Firstly, according to the structure, strength, etc. in a reducer, a multi-objective optimized model of the helical gear reducer is established. And then an adaptive GA based on a fuzzy controller is introduced, aiming at the characteristics of multi-objective, multi-parameter, multi-constraint conditions. Finally, a numerical example is illustrated to show the advantages of this approach and the effectiveness of an adaptive genetic algorithm used in optimized design of a reducer.

Formulation and implementation of nonstationary adaptive estimation algorithm with applications to air-data reconstruction

NASA Technical Reports Server (NTRS)

Whitmore, S. A.

1985-01-01

The dynamics model and data sources used to perform air-data reconstruction are discussed, as well as the Kalman filter. The need for adaptive determination of the noise statistics of the process is indicated. The filter innovations are presented as a means of developing the adaptive criterion, which is based on the true mean and covariance of the filter innovations. A method for the numerical approximation of the mean and covariance of the filter innovations is presented. The algorithm as developed is applied to air-data reconstruction for the space shuttle, and data obtained from the third landing are presented. To verify the performance of the adaptive algorithm, the reconstruction is also performed using a constant covariance Kalman filter. The results of the reconstructions are compared, and the adaptive algorithm exhibits better performance.

Lossless medical image compression using geometry-adaptive partitioning and least square-based prediction.

PubMed

Song, Xiaoying; Huang, Qijun; Chang, Sheng; He, Jin; Wang, Hao

2018-06-01

To improve the compression rates for lossless compression of medical images, an efficient algorithm, based on irregular segmentation and region-based prediction, is proposed in this paper. Considering that the first step of a region-based compression algorithm is segmentation, this paper proposes a hybrid method by combining geometry-adaptive partitioning and quadtree partitioning to achieve adaptive irregular segmentation for medical images. Then, least square (LS)-based predictors are adaptively designed for each region (regular subblock or irregular subregion). The proposed adaptive algorithm not only exploits spatial correlation between pixels but it utilizes local structure similarity, resulting in efficient compression performance. Experimental results show that the average compression performance of the proposed algorithm is 10.48, 4.86, 3.58, and 0.10% better than that of JPEG 2000, CALIC, EDP, and JPEG-LS, respectively. Graphical abstract ᅟ.

Combination of Adaptive Feedback Cancellation and Binaural Adaptive Filtering in Hearing Aids

NASA Astrophysics Data System (ADS)

Lombard, Anthony; Reindl, Klaus; Kellermann, Walter

2009-12-01

We study a system combining adaptive feedback cancellation and adaptive filtering connecting inputs from both ears for signal enhancement in hearing aids. For the first time, such a binaural system is analyzed in terms of system stability, convergence of the algorithms, and possible interaction effects. As major outcomes of this study, a new stability condition adapted to the considered binaural scenario is presented, some already existing and commonly used feedback cancellation performance measures for the unilateral case are adapted to the binaural case, and possible interaction effects between the algorithms are identified. For illustration purposes, a blind source separation algorithm has been chosen as an example for adaptive binaural spatial filtering. Experimental results for binaural hearing aids confirm the theoretical findings and the validity of the new measures.

Improvement of retinal blood vessel detection using morphological component analysis.

PubMed

Imani, Elaheh; Javidi, Malihe; Pourreza, Hamid-Reza

2015-03-01

Detection and quantitative measurement of variations in the retinal blood vessels can help diagnose several diseases including diabetic retinopathy. Intrinsic characteristics of abnormal retinal images make blood vessel detection difficult. The major problem with traditional vessel segmentation algorithms is producing false positive vessels in the presence of diabetic retinopathy lesions. To overcome this problem, a novel scheme for extracting retinal blood vessels based on morphological component analysis (MCA) algorithm is presented in this paper. MCA was developed based on sparse representation of signals. This algorithm assumes that each signal is a linear combination of several morphologically distinct components. In the proposed method, the MCA algorithm with appropriate transforms is adopted to separate vessels and lesions from each other. Afterwards, the Morlet Wavelet Transform is applied to enhance the retinal vessels. The final vessel map is obtained by adaptive thresholding. The performance of the proposed method is measured on the publicly available DRIVE and STARE datasets and compared with several state-of-the-art methods. An accuracy of 0.9523 and 0.9590 has been respectively achieved on the DRIVE and STARE datasets, which are not only greater than most methods, but are also superior to the second human observer's performance. The results show that the proposed method can achieve improved detection in abnormal retinal images and decrease false positive vessels in pathological regions compared to other methods. Also, the robustness of the method in the presence of noise is shown via experimental result. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2015-09-01

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

Efficient Solar Scene Wavefront Estimation with Reduced Systematic and RMS Errors: Summary

NASA Astrophysics Data System (ADS)

Anugu, N.; Garcia, P.

2016-04-01

Wave front sensing for solar telescopes is commonly implemented with the Shack-Hartmann sensors. Correlation algorithms are usually used to estimate the extended scene Shack-Hartmann sub-aperture image shifts or slopes. The image shift is computed by correlating a reference sub-aperture image with the target distorted sub-aperture image. The pixel position where the maximum correlation is located gives the image shift in integer pixel coordinates. Sub-pixel precision image shifts are computed by applying a peak-finding algorithm to the correlation peak Poyneer (2003); Löfdahl (2010). However, the peak-finding algorithm results are usually biased towards the integer pixels, these errors are called as systematic bias errors Sjödahl (1994). These errors are caused due to the low pixel sampling of the images. The amplitude of these errors depends on the type of correlation algorithm and the type of peak-finding algorithm being used. To study the systematic errors in detail, solar sub-aperture synthetic images are constructed by using a Swedish Solar Telescope solar granulation image1. The performance of cross-correlation algorithm in combination with different peak-finding algorithms is investigated. The studied peak-finding algorithms are: parabola Poyneer (2003); quadratic polynomial Löfdahl (2010); threshold center of gravity Bailey (2003); Gaussian Nobach & Honkanen (2005) and Pyramid Bailey (2003). The systematic error study reveals that that the pyramid fit is the most robust to pixel locking effects. The RMS error analysis study reveals that the threshold centre of gravity behaves better in low SNR, although the systematic errors in the measurement are large. It is found that no algorithm is best for both the systematic and the RMS error reduction. To overcome the above problem, a new solution is proposed. In this solution, the image sampling is increased prior to the actual correlation matching. The method is realized in two steps to improve its computational efficiency. In the first step, the cross-correlation is implemented at the original image spatial resolution grid (1 pixel). In the second step, the cross-correlation is performed using a sub-pixel level grid by limiting the field of search to 4 × 4 pixels centered at the first step delivered initial position. The generation of these sub-pixel grid based region of interest images is achieved with the bi-cubic interpolation. The correlation matching with sub-pixel grid technique was previously reported in electronic speckle photography Sjö'dahl (1994). This technique is applied here for the solar wavefront sensing. A large dynamic range and a better accuracy in the measurements are achieved with the combination of the original pixel grid based correlation matching in a large field of view and a sub-pixel interpolated image grid based correlation matching within a small field of view. The results revealed that the proposed method outperforms all the different peak-finding algorithms studied in the first approach. It reduces both the systematic error and the RMS error by a factor of 5 (i.e., 75% systematic error reduction), when 5 times improved image sampling was used. This measurement is achieved at the expense of twice the computational cost. With the 5 times improved image sampling, the wave front accuracy is increased by a factor of 5. The proposed solution is strongly recommended for wave front sensing in the solar telescopes, particularly, for measuring large dynamic image shifts involved open loop adaptive optics. Also, by choosing an appropriate increment of image sampling in trade-off between the computational speed limitation and the aimed sub-pixel image shift accuracy, it can be employed in closed loop adaptive optics. The study is extended to three other class of sub-aperture images (a point source; a laser guide star; a Galactic Center extended scene). The results are planned to submit for the Optical Express journal.

Automated video-based detection of nocturnal convulsive seizures in a residential care setting.

PubMed

Geertsema, Evelien E; Thijs, Roland D; Gutter, Therese; Vledder, Ben; Arends, Johan B; Leijten, Frans S; Visser, Gerhard H; Kalitzin, Stiliyan N

2018-06-01

People with epilepsy need assistance and are at risk of sudden death when having convulsive seizures (CS). Automated real-time seizure detection systems can help alert caregivers, but wearable sensors are not always tolerated. We determined algorithm settings and investigated detection performance of a video algorithm to detect CS in a residential care setting. The algorithm calculates power in the 2-6 Hz range relative to 0.5-12.5 Hz range in group velocity signals derived from video-sequence optical flow. A detection threshold was found using a training set consisting of video-electroencephalogaphy (EEG) recordings of 72 CS. A test set consisting of 24 full nights of 12 new subjects in residential care and additional recordings of 50 CS selected randomly was used to estimate performance. All data were analyzed retrospectively. The start and end of CS (generalized clonic and tonic-clonic seizures) and other seizures considered desirable to detect (long generalized tonic, hyperkinetic, and other major seizures) were annotated. The detection threshold was set to the value that obtained 97% sensitivity in the training set. Sensitivity, latency, and false detection rate (FDR) per night were calculated in the test set. A seizure was detected when the algorithm output exceeded the threshold continuously for 2 seconds. With the detection threshold determined in the training set, all CS were detected in the test set (100% sensitivity). Latency was ≤10 seconds in 78% of detections. Three/five hyperkinetic and 6/9 other major seizures were detected. Median FDR was 0.78 per night and no false detections occurred in 9/24 nights. Our algorithm could improve safety unobtrusively by automated real-time detection of CS in video registrations, with an acceptable latency and FDR. The algorithm can also detect some other motor seizures requiring assistance. © 2018 The Authors. Epilepsia published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy.

Retained energy-based coding for EEG signals.

PubMed

Bazán-Prieto, Carlos; Blanco-Velasco, Manuel; Cárdenas-Barrera, Julián; Cruz-Roldán, Fernando

2012-09-01

The recent use of long-term records in electroencephalography is becoming more frequent due to its diagnostic potential and the growth of novel signal processing methods that deal with these types of recordings. In these cases, the considerable volume of data to be managed makes compression necessary to reduce the bit rate for transmission and storage applications. In this paper, a new compression algorithm specifically designed to encode electroencephalographic (EEG) signals is proposed. Cosine modulated filter banks are used to decompose the EEG signal into a set of subbands well adapted to the frequency bands characteristic of the EEG. Given that no regular pattern may be easily extracted from the signal in time domain, a thresholding-based method is applied for quantizing samples. The method of retained energy is designed for efficiently computing the threshold in the decomposition domain which, at the same time, allows the quality of the reconstructed EEG to be controlled. The experiments are conducted over a large set of signals taken from two public databases available at Physionet and the results show that the compression scheme yields better compression than other reported methods. Copyright © 2011 IPEM. Published by Elsevier Ltd. All rights reserved.

Face recognition: database acquisition, hybrid algorithms, and human studies

NASA Astrophysics Data System (ADS)

Gutta, Srinivas; Huang, Jeffrey R.; Singh, Dig; Wechsler, Harry

1997-02-01

One of the most important technologies absent in traditional and emerging frontiers of computing is the management of visual information. Faces are accessible `windows' into the mechanisms that govern our emotional and social lives. The corresponding face recognition tasks considered herein include: (1) Surveillance, (2) CBIR, and (3) CBIR subject to correct ID (`match') displaying specific facial landmarks such as wearing glasses. We developed robust matching (`classification') and retrieval schemes based on hybrid classifiers and showed their feasibility using the FERET database. The hybrid classifier architecture consist of an ensemble of connectionist networks--radial basis functions-- and decision trees. The specific characteristics of our hybrid architecture include (a) query by consensus as provided by ensembles of networks for coping with the inherent variability of the image formation and data acquisition process, and (b) flexible and adaptive thresholds as opposed to ad hoc and hard thresholds. Experimental results, proving the feasibility of our approach, yield (i) 96% accuracy, using cross validation (CV), for surveillance on a data base consisting of 904 images (ii) 97% accuracy for CBIR tasks, on a database of 1084 images, and (iii) 93% accuracy, using CV, for CBIR subject to correct ID match tasks on a data base of 200 images.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marathe, Aniruddha P.; Harris, Rachel A.; Lowenthal, David K.

The use of clouds to execute high-performance computing (HPC) applications has greatly increased recently. Clouds provide several potential advantages over traditional supercomputers and in-house clusters. The most popular cloud is currently Amazon EC2, which provides fixed-cost and variable-cost, auction-based options. The auction market trades lower cost for potential interruptions that necessitate checkpointing; if the market price exceeds the bid price, a node is taken away from the user without warning. We explore techniques to maximize performance per dollar given a time constraint within which an application must complete. Specifically, we design and implement multiple techniques to reduce expected cost bymore » exploiting redundancy in the EC2 auction market. We then design an adaptive algorithm that selects a scheduling algorithm and determines the bid price. We show that our adaptive algorithm executes programs up to seven times cheaper than using the on-demand market and up to 44 percent cheaper than the best non-redundant, auction-market algorithm. We extend our adaptive algorithm to incorporate application scalability characteristics for further cost savings. In conclusion, we show that the adaptive algorithm informed with scalability characteristics of applications achieves up to 56 percent cost savings compared to the expected cost for the base adaptive algorithm run at a fixed, user-defined scale.« less

Evidence Accumulator or Decision Threshold – Which Cortical Mechanism are We Observing?

PubMed Central

Simen, Patrick

2012-01-01

Most psychological models of perceptual decision making are of the accumulation-to-threshold variety. The neural basis of accumulation in parietal and prefrontal cortex is therefore a topic of great interest in neuroscience. In contrast, threshold mechanisms have received less attention, and their neural basis has usually been sought in subcortical structures. Here I analyze a model of a decision threshold that can be implemented in the same cortical areas as evidence accumulators, and whose behavior bears on two open questions in decision neuroscience: (1) When ramping activity is observed in a brain region during decision making, does it reflect evidence accumulation? (2) Are changes in speed-accuracy tradeoffs and response biases more likely to be achieved by changes in thresholds, or in accumulation rates and starting points? The analysis suggests that task-modulated ramping activity, by itself, is weak evidence that a brain area mediates evidence accumulation as opposed to threshold readout; and that signs of modulated accumulation are as likely to indicate threshold adaptation as adaptation of starting points and accumulation rates. These conclusions imply that how thresholds are modeled can dramatically impact accumulator-based interpretations of this data. PMID:22737136

A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials.

PubMed

Aziz, Omar; Musngi, Magnus; Park, Edward J; Mori, Greg; Robinovitch, Stephen N

2017-01-01

Falls are the leading cause of injury-related morbidity and mortality among older adults. Over 90 % of hip and wrist fractures and 60 % of traumatic brain injuries in older adults are due to falls. Another serious consequence of falls among older adults is the 'long lie' experienced by individuals who are unable to get up and remain on the ground for an extended period of time after a fall. Considerable research has been conducted over the past decade on the design of wearable sensor systems that can automatically detect falls and send an alert to care providers to reduce the frequency and severity of long lies. While most systems described to date incorporate threshold-based algorithms, machine learning algorithms may offer increased accuracy in detecting falls. In the current study, we compared the accuracy of these two approaches in detecting falls by conducting a comprehensive set of falling experiments with 10 young participants. Participants wore waist-mounted tri-axial accelerometers and simulated the most common causes of falls observed in older adults, along with near-falls and activities of daily living. The overall performance of five machine learning algorithms was greater than the performance of five threshold-based algorithms described in the literature, with support vector machines providing the highest combination of sensitivity and specificity.

Comparison between iterative wavefront control algorithm and direct gradient wavefront control algorithm for adaptive optics system

NASA Astrophysics Data System (ADS)

Cheng, Sheng-Yi; Liu, Wen-Jin; Chen, Shan-Qiu; Dong, Li-Zhi; Yang, Ping; Xu, Bing

2015-08-01

Among all kinds of wavefront control algorithms in adaptive optics systems, the direct gradient wavefront control algorithm is the most widespread and common method. This control algorithm obtains the actuator voltages directly from wavefront slopes through pre-measuring the relational matrix between deformable mirror actuators and Hartmann wavefront sensor with perfect real-time characteristic and stability. However, with increasing the number of sub-apertures in wavefront sensor and deformable mirror actuators of adaptive optics systems, the matrix operation in direct gradient algorithm takes too much time, which becomes a major factor influencing control effect of adaptive optics systems. In this paper we apply an iterative wavefront control algorithm to high-resolution adaptive optics systems, in which the voltages of each actuator are obtained through iteration arithmetic, which gains great advantage in calculation and storage. For AO system with thousands of actuators, the computational complexity estimate is about O(n2) ˜ O(n3) in direct gradient wavefront control algorithm, while the computational complexity estimate in iterative wavefront control algorithm is about O(n) ˜ (O(n)3/2), in which n is the number of actuators of AO system. And the more the numbers of sub-apertures and deformable mirror actuators, the more significant advantage the iterative wavefront control algorithm exhibits. Project supported by the National Key Scientific and Research Equipment Development Project of China (Grant No. ZDYZ2013-2), the National Natural Science Foundation of China (Grant No. 11173008), and the Sichuan Provincial Outstanding Youth Academic Technology Leaders Program, China (Grant No. 2012JQ0012).

Self-adaptive multi-objective harmony search for optimal design of water distribution networks

NASA Astrophysics Data System (ADS)

Choi, Young Hwan; Lee, Ho Min; Yoo, Do Guen; Kim, Joong Hoon

2017-11-01

In multi-objective optimization computing, it is important to assign suitable parameters to each optimization problem to obtain better solutions. In this study, a self-adaptive multi-objective harmony search (SaMOHS) algorithm is developed to apply the parameter-setting-free technique, which is an example of a self-adaptive methodology. The SaMOHS algorithm attempts to remove some of the inconvenience from parameter setting and selects the most adaptive parameters during the iterative solution search process. To verify the proposed algorithm, an optimal least cost water distribution network design problem is applied to three different target networks. The results are compared with other well-known algorithms such as multi-objective harmony search and the non-dominated sorting genetic algorithm-II. The efficiency of the proposed algorithm is quantified by suitable performance indices. The results indicate that SaMOHS can be efficiently applied to the search for Pareto-optimal solutions in a multi-objective solution space.

Shape anomaly detection under strong measurement noise: An analytical approach to adaptive thresholding

NASA Astrophysics Data System (ADS)

Krasichkov, Alexander S.; Grigoriev, Eugene B.; Bogachev, Mikhail I.; Nifontov, Eugene M.

2015-10-01

We suggest an analytical approach to the adaptive thresholding in a shape anomaly detection problem. We find an analytical expression for the distribution of the cosine similarity score between a reference shape and an observational shape hindered by strong measurement noise that depends solely on the noise level and is independent of the particular shape analyzed. The analytical treatment is also confirmed by computer simulations and shows nearly perfect agreement. Using this analytical solution, we suggest an improved shape anomaly detection approach based on adaptive thresholding. We validate the noise robustness of our approach using typical shapes of normal and pathological electrocardiogram cycles hindered by additive white noise. We show explicitly that under high noise levels our approach considerably outperforms the conventional tactic that does not take into account variations in the noise level.

An environment-adaptive management algorithm for hearing-support devices incorporating listening situation and noise type classifiers.

PubMed

Yook, Sunhyun; Nam, Kyoung Won; Kim, Heepyung; Hong, Sung Hwa; Jang, Dong Pyo; Kim, In Young

2015-04-01

In order to provide more consistent sound intelligibility for the hearing-impaired person, regardless of environment, it is necessary to adjust the setting of the hearing-support (HS) device to accommodate various environmental circumstances. In this study, a fully automatic HS device management algorithm that can adapt to various environmental situations is proposed; it is composed of a listening-situation classifier, a noise-type classifier, an adaptive noise-reduction algorithm, and a management algorithm that can selectively turn on/off one or more of the three basic algorithms-beamforming, noise-reduction, and feedback cancellation-and can also adjust internal gains and parameters of the wide-dynamic-range compression (WDRC) and noise-reduction (NR) algorithms in accordance with variations in environmental situations. Experimental results demonstrated that the implemented algorithms can classify both listening situation and ambient noise type situations with high accuracies (92.8-96.4% and 90.9-99.4%, respectively), and the gains and parameters of the WDRC and NR algorithms were successfully adjusted according to variations in environmental situation. The average values of signal-to-noise ratio (SNR), frequency-weighted segmental SNR, Perceptual Evaluation of Speech Quality, and mean opinion test scores of 10 normal-hearing volunteers of the adaptive multiband spectral subtraction (MBSS) algorithm were improved by 1.74 dB, 2.11 dB, 0.49, and 0.68, respectively, compared to the conventional fixed-parameter MBSS algorithm. These results indicate that the proposed environment-adaptive management algorithm can be applied to HS devices to improve sound intelligibility for hearing-impaired individuals in various acoustic environments. Copyright © 2014 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Synthesis of the adaptive continuous system for the multi-axle wheeled vehicle body oscillation damping

NASA Astrophysics Data System (ADS)

Zhileykin, M. M.; Kotiev, G. O.; Nagatsev, M. V.

2018-02-01

In order to meet the growing mobility requirements for the wheeled vehicles on all types of terrain the engineers have to develop a large number of specialized control algorithms for the multi-axle wheeled vehicle (MWV) suspension improving such qualities as ride comfort, handling and stability. The authors have developed an adaptive algorithm of the dynamic damping of the MVW body oscillations. The algorithm provides high ride comfort and high mobility of the vehicle. The article discloses a method for synthesis of an adaptive dynamic continuous algorithm of the MVW body oscillation damping and provides simulation results proving high efficiency of the developed control algorithm.

Properties of an adaptive feedback equalization algorithm.

PubMed

Engebretson, A M; French-St George, M

1993-01-01

This paper describes a new approach to feedback equalization for hearing aids. The method involves the use of an adaptive algorithm that estimates and tracks the characteristic of the hearing aid feedback path. The algorithm is described and the results of simulation studies and bench testing are presented.

SIMULATION OF DISPERSION OF A POWER PLANT PLUME USING AN ADAPTIVE GRID ALGORITHM

EPA Science Inventory

A new dynamic adaptive grid algorithm has been developed for use in air quality modeling. This algorithm uses a higher order numerical scheme?the piecewise parabolic method (PPM)?for computing advective solution fields; a weight function capable of promoting grid node clustering ...

SIMULATION OF A REACTING POLLUTANT PUFF USING AN ADAPTIVE GRID ALGORITHM

EPA Science Inventory

A new dynamic solution adaptive grid algorithm DSAGA-PPM, has been developed for use in air quality modeling. In this paper, this algorithm is described and evaluated with a test problem. Cone-shaped distributions of various chemical species undergoing chemical reactions are rota...

Continuous Adaptive Population Reduction (CAPR) for Differential Evolution Optimization.

PubMed

Wong, Ieong; Liu, Wenjia; Ho, Chih-Ming; Ding, Xianting

2017-06-01

Differential evolution (DE) has been applied extensively in drug combination optimization studies in the past decade. It allows for identification of desired drug combinations with minimal experimental effort. This article proposes an adaptive population-sizing method for the DE algorithm. Our new method presents improvements in terms of efficiency and convergence over the original DE algorithm and constant stepwise population reduction-based DE algorithm, which would lead to a reduced number of cells and animals required to identify an optimal drug combination. The method continuously adjusts the reduction of the population size in accordance with the stage of the optimization process. Our adaptive scheme limits the population reduction to occur only at the exploitation stage. We believe that continuously adjusting for a more effective population size during the evolutionary process is the major reason for the significant improvement in the convergence speed of the DE algorithm. The performance of the method is evaluated through a set of unimodal and multimodal benchmark functions. In combining with self-adaptive schemes for mutation and crossover constants, this adaptive population reduction method can help shed light on the future direction of a completely parameter tune-free self-adaptive DE algorithm.

An adaptive inverse kinematics algorithm for robot manipulators

NASA Technical Reports Server (NTRS)

Colbaugh, R.; Glass, K.; Seraji, H.

1990-01-01

An adaptive algorithm for solving the inverse kinematics problem for robot manipulators is presented. The algorithm is derived using model reference adaptive control (MRAC) theory and is computationally efficient for online applications. The scheme requires no a priori knowledge of the kinematics of the robot if Cartesian end-effector sensing is available, and it requires knowledge of only the forward kinematics if joint position sensing is used. Computer simulation results are given for the redundant seven-DOF robotics research arm, demonstrating that the proposed algorithm yields accurate joint angle trajectories for a given end-effector position/orientation trajectory.

Adaptive Identification and Control of Flow-Induced Cavity Oscillations

NASA Technical Reports Server (NTRS)

Kegerise, M. A.; Cattafesta, L. N.; Ha, C.

2002-01-01

Progress towards an adaptive self-tuning regulator (STR) for the cavity tone problem is discussed in this paper. Adaptive system identification algorithms were applied to an experimental cavity-flow tested as a prerequisite to control. In addition, a simple digital controller and a piezoelectric bimorph actuator were used to demonstrate multiple tone suppression. The control tests at Mach numbers of 0.275, 0.40, and 0.60 indicated approx. = 7dB tone reductions at multiple frequencies. Several different adaptive system identification algorithms were applied at a single freestream Mach number of 0.275. Adaptive finite-impulse response (FIR) filters of orders up to N = 100 were found to be unsuitable for modeling the cavity flow dynamics. Adaptive infinite-impulse response (IIR) filters of comparable order better captured the system dynamics. Two recursive algorithms, the least-mean square (LMS) and the recursive-least square (RLS), were utilized to update the adaptive filter coefficients. Given the sample-time requirements imposed by the cavity flow dynamics, the computational simplicity of the least mean squares (LMS) algorithm is advantageous for real-time control.

Adaptive Interventions in Drug Court: A Pilot Experiment

PubMed Central

Marlowe, Douglas B.; Festinger, David S.; Arabia, Patricia L.; Dugosh, Karen L.; Benasutti, Kathleen M.; Croft, Jason R.; McKay, James R.

2009-01-01

This pilot study (N = 30) experimentally examined the effects of an adaptive intervention in an adult misdemeanor drug court. The adaptive algorithm adjusted the frequency of judicial status hearings and clinical case-management sessions according to pre-specified criteria in response to participants' ongoing performance in the program. Results revealed the adaptive algorithm was acceptable to both clients and staff, feasible to implement with greater than 85% fidelity, and showed promise for eliciting clinically meaningful improvements in drug abstinence and graduation rates. Estimated effect sizes ranged from 0.40 to 0.60 across various dependent measures. Compared to drug court as-usual, participants in the adaptive condition were more likely to receive responses from the drug court team for inadequate performance in the program and received those responses after a substantially shorter period of time. This suggests the adaptive algorithm may have more readily focused the drug court team's attention on poorly-performing individuals, thus allowing the team to “nip problems in the bud” before they developed too fully. These preliminary data justify additional research evaluating the effects of the adaptive algorithm in a fully powered experimental trial. PMID:19724664

Algorithm for measuring the internal quantum efficiency of individual injection lasers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sommers, H.S. Jr.

1978-05-01

A new algorithm permits determination of the internal quantum efficiency eta/sub i/ of individual lasers. Above threshold, the current is partitioned into a ''coherent'' component driving the lasing modes and the ''noncoherent'' remainder. Below threshold the current is known to grow as exp(qV/n/sub 0/KT); the algorithm proposes that extrapolation of this equation into the lasing region measures the noncoherent remainder, enabling deduction of the coherent component and of its current derivative eta/sub i/. Measurements on five (AlGa)As double-heterojunction lasers cut from one wafer demonstrate the power of the new method. Comparison with band calculations of Stern shows that n/sub 0/more » originates in carrier degeneracy.« less

A novel decoding algorithm based on the hierarchical reliable strategy for SCG-LDPC codes in optical communications

NASA Astrophysics Data System (ADS)

Yuan, Jian-guo; Tong, Qing-zhen; Huang, Sheng; Wang, Yong

2013-11-01

An effective hierarchical reliable belief propagation (HRBP) decoding algorithm is proposed according to the structural characteristics of systematically constructed Gallager low-density parity-check (SCG-LDPC) codes. The novel decoding algorithm combines the layered iteration with the reliability judgment, and can greatly reduce the number of the variable nodes involved in the subsequent iteration process and accelerate the convergence rate. The result of simulation for SCG-LDPC(3969,3720) code shows that the novel HRBP decoding algorithm can greatly reduce the computing amount at the condition of ensuring the performance compared with the traditional belief propagation (BP) algorithm. The bit error rate (BER) of the HRBP algorithm is considerable at the threshold value of 15, but in the subsequent iteration process, the number of the variable nodes for the HRBP algorithm can be reduced by about 70% at the high signal-to-noise ratio (SNR) compared with the BP algorithm. When the threshold value is further increased, the HRBP algorithm will gradually degenerate into the layered-BP algorithm, but at the BER of 10-7 and the maximal iteration number of 30, the net coding gain (NCG) of the HRBP algorithm is 0.2 dB more than that of the BP algorithm, and the average iteration times can be reduced by about 40% at the high SNR. Therefore, the novel HRBP decoding algorithm is more suitable for optical communication systems.

Application of Avco data analysis and prediction techniques (ADAPT) to prediction of sunspot activity

NASA Technical Reports Server (NTRS)

Hunter, H. E.; Amato, R. A.

1972-01-01

The results are presented of the application of Avco Data Analysis and Prediction Techniques (ADAPT) to derivation of new algorithms for the prediction of future sunspot activity. The ADAPT derived algorithms show a factor of 2 to 3 reduction in the expected 2-sigma errors in the estimates of the 81-day running average of the Zurich sunspot numbers. The report presents: (1) the best estimates for sunspot cycles 20 and 21, (2) a comparison of the ADAPT performance with conventional techniques, and (3) specific approaches to further reduction in the errors of estimated sunspot activity and to recovery of earlier sunspot historical data. The ADAPT programs are used both to derive regression algorithm for prediction of the entire 11-year sunspot cycle from the preceding two cycles and to derive extrapolation algorithms for extrapolating a given sunspot cycle based on any available portion of the cycle.

Entropy-aware projected Landweber reconstruction for quantized block compressive sensing of aerial imagery

NASA Astrophysics Data System (ADS)

Liu, Hao; Li, Kangda; Wang, Bing; Tang, Hainie; Gong, Xiaohui

2017-01-01

A quantized block compressive sensing (QBCS) framework, which incorporates the universal measurement, quantization/inverse quantization, entropy coder/decoder, and iterative projected Landweber reconstruction, is summarized. Under the QBCS framework, this paper presents an improved reconstruction algorithm for aerial imagery, QBCS, with entropy-aware projected Landweber (QBCS-EPL), which leverages the full-image sparse transform without Wiener filter and an entropy-aware thresholding model for wavelet-domain image denoising. Through analyzing the functional relation between the soft-thresholding factors and entropy-based bitrates for different quantization methods, the proposed model can effectively remove wavelet-domain noise of bivariate shrinkage and achieve better image reconstruction quality. For the overall performance of QBCS reconstruction, experimental results demonstrate that the proposed QBCS-EPL algorithm significantly outperforms several existing algorithms. With the experiment-driven methodology, the QBCS-EPL algorithm can obtain better reconstruction quality at a relatively moderate computational cost, which makes it more desirable for aerial imagery applications.

A Cross-Layer User Centric Vertical Handover Decision Approach Based on MIH Local Triggers

NASA Astrophysics Data System (ADS)

Rehan, Maaz; Yousaf, Muhammad; Qayyum, Amir; Malik, Shahzad

Vertical handover decision algorithm that is based on user preferences and coupled with Media Independent Handover (MIH) local triggers have not been explored much in the literature. We have developed a comprehensive cross-layer solution, called Vertical Handover Decision (VHOD) approach, which consists of three parts viz. mechanism for collecting and storing user preferences, Vertical Handover Decision (VHOD) algorithm and the MIH Function (MIHF). MIHF triggers the VHOD algorithm which operates on user preferences to issue handover commands to mobility management protocol. VHOD algorithm is an MIH User and therefore needs to subscribe events and configure thresholds for receiving triggers from MIHF. In this regard, we have performed experiments in WLAN to suggest thresholds for Link Going Down trigger. We have also critically evaluated the handover decision process, proposed Just-in-time interface activation technique, compared our proposed approach with prominent user centric approaches and analyzed our approach from different aspects.

Development and validation of a dual sensing scheme to improve accuracy of bradycardia and pause detection in an insertable cardiac monitor.

PubMed

Passman, Rod S; Rogers, John D; Sarkar, Shantanu; Reiland, Jerry; Reisfeld, Erin; Koehler, Jodi; Mittal, Suneet

2017-07-01

Undersensing of premature ventricular beats and low-amplitude R waves are primary causes for inappropriate bradycardia and pause detections in insertable cardiac monitors (ICMs). The purpose of this study was to develop and validate an enhanced algorithm to reduce inappropriately detected bradycardia and pause episodes. Independent data sets to develop and validate the enhanced algorithm were derived from a database of ICM-detected bradycardia and pause episodes in de-identified patients monitored for unexplained syncope. The original algorithm uses an auto-adjusting sensitivity threshold for R-wave sensing to detect tachycardia and avoid T-wave oversensing. In the enhanced algorithm, a second sensing threshold is used with a long blanking and fixed lower sensitivity threshold, looking for evidence of undersensed signals. Data reported includes percent change in appropriate and inappropriate bradycardia and pause detections as well as changes in episode detection sensitivity and positive predictive value with the enhanced algorithm. The validation data set, from 663 consecutive patients, consisted of 4904 (161 patients) bradycardia and 2582 (133 patients) pause episodes, of which 2976 (61%) and 996 (39%) were appropriately detected bradycardia and pause episodes. The enhanced algorithm reduced inappropriate bradycardia and pause episodes by 95% and 47%, respectively, with 1.7% and 0.6% reduction in appropriate episodes, respectively. The average episode positive predictive value improved by 62% (P < .001) for bradycardia detection and by 26% (P < .001) for pause detection, with an average relative sensitivity of 95% (P < .001) and 99% (P = .5), respectively. The enhanced dual sense algorithm for bradycardia and pause detection in ICMs substantially reduced inappropriate episode detection with a minimal reduction in true episode detection. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Inversion for Refractivity Parameters Using a Dynamic Adaptive Cuckoo Search with Crossover Operator Algorithm

PubMed Central

Zhang, Zhihua; Sheng, Zheng; Shi, Hanqing; Fan, Zhiqiang

2016-01-01

Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter. PMID:27212938

A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks

PubMed Central

Figueiredo, Carlos M. S.; Nakamura, Eduardo F.; Loureiro, Antonio A. F.

2009-01-01

Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption. PMID:22423207

A hybrid adaptive routing algorithm for event-driven wireless sensor networks.

PubMed

Figueiredo, Carlos M S; Nakamura, Eduardo F; Loureiro, Antonio A F

2009-01-01

Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption.

Exploration of a physiologically-inspired hearing-aid algorithm using a computer model mimicking impaired hearing.

PubMed

Jürgens, Tim; Clark, Nicholas R; Lecluyse, Wendy; Meddis, Ray

2016-01-01

To use a computer model of impaired hearing to explore the effects of a physiologically-inspired hearing-aid algorithm on a range of psychoacoustic measures. A computer model of a hypothetical impaired listener's hearing was constructed by adjusting parameters of a computer model of normal hearing. Absolute thresholds, estimates of compression, and frequency selectivity (summarized to a hearing profile) were assessed using this model with and without pre-processing the stimuli by a hearing-aid algorithm. The influence of different settings of the algorithm on the impaired profile was investigated. To validate the model predictions, the effect of the algorithm on hearing profiles of human impaired listeners was measured. A computer model simulating impaired hearing (total absence of basilar membrane compression) was used, and three hearing-impaired listeners participated. The hearing profiles of the model and the listeners showed substantial changes when the test stimuli were pre-processed by the hearing-aid algorithm. These changes consisted of lower absolute thresholds, steeper temporal masking curves, and sharper psychophysical tuning curves. The hearing-aid algorithm affected the impaired hearing profile of the model to approximate a normal hearing profile. Qualitatively similar results were found with the impaired listeners' hearing profiles.

A Unified Nonlinear Adaptive Approach for Detection and Isolation of Engine Faults

NASA Technical Reports Server (NTRS)

Tang, Liang; DeCastro, Jonathan A.; Zhang, Xiaodong; Farfan-Ramos, Luis; Simon, Donald L.

2010-01-01

A challenging problem in aircraft engine health management (EHM) system development is to detect and isolate faults in system components (i.e., compressor, turbine), actuators, and sensors. Existing nonlinear EHM methods often deal with component faults, actuator faults, and sensor faults separately, which may potentially lead to incorrect diagnostic decisions and unnecessary maintenance. Therefore, it would be ideal to address sensor faults, actuator faults, and component faults under one unified framework. This paper presents a systematic and unified nonlinear adaptive framework for detecting and isolating sensor faults, actuator faults, and component faults for aircraft engines. The fault detection and isolation (FDI) architecture consists of a parallel bank of nonlinear adaptive estimators. Adaptive thresholds are appropriately designed such that, in the presence of a particular fault, all components of the residual generated by the adaptive estimator corresponding to the actual fault type remain below their thresholds. If the faults are sufficiently different, then at least one component of the residual generated by each remaining adaptive estimator should exceed its threshold. Therefore, based on the specific response of the residuals, sensor faults, actuator faults, and component faults can be isolated. The effectiveness of the approach was evaluated using the NASA C-MAPSS turbofan engine model, and simulation results are presented.

Parallel Algorithm Solves Coupled Differential Equations

NASA Technical Reports Server (NTRS)

Hayashi, A.

1987-01-01

Numerical methods adapted to concurrent processing. Algorithm solves set of coupled partial differential equations by numerical integration. Adapted to run on hypercube computer, algorithm separates problem into smaller problems solved concurrently. Increase in computing speed with concurrent processing over that achievable with conventional sequential processing appreciable, especially for large problems.

SIMULATION OF DISPERSION OF A POWER PLANT PLUME USING AN ADAPTIVE GRID ALGORITHM. (R827028)

EPA Science Inventory

A new dynamic adaptive grid algorithm has been developed for use in air quality modeling. This algorithm uses a higher order numerical scheme––the piecewise parabolic method (PPM)––for computing advective solution fields; a weight function capable o...

A directed matched filtering algorithm (DMF) for discriminating hydrothermal alteration zones using the ASTER remote sensing data

NASA Astrophysics Data System (ADS)

Fereydooni, H.; Mojeddifar, S.

2017-09-01

This study introduced a different procedure to implement matched filtering algorithm (MF) on the ASTER images to obtain the distribution map of alteration minerals in the northwestern part of the Kerman Cenozoic Magmatic Arc (KCMA). This region contains many areas with porphyry copper mineralization such as Meiduk, Abdar, Kader, Godekolvari, Iju, Serenu, Chahfiroozeh and Parkam. Also argillization, sericitization and propylitization are the most common types of hydrothermal alteration in the area. Matched filtering results were provided for alteration minerals with a matched filtering score, called MF image. To identify the pixels which contain only one material (endmember), an appropriate threshold value should be used to the MF image. The chosen threshold classifies a MF image into background and target pixels. This article argues that the current thresholding process (the choice of a threshold) shows misclassification for MF image. To address the issue, this paper introduced the directed matched filtering (DMF) algorithm in which a spectral signature-based filter (SSF) was used instead of the thresholding process. SSF is a user-defined rule package which contains numeral descriptions about the spectral reflectance of alteration minerals. On the other hand, the spectral bands are defined by an upper and lower limit in SSF filter for each alteration minerals. SSF was developed for chlorite, kaolinite, alunite, and muscovite minerals to map alteration zones. The validation proved that, at first: selecting a contiguous range of MF values could not identify desirable results, second: unexpectedly, considerable frequency of pure pixels was observed in the MF scores less than threshold value. Also, the comparison between DMF results and field studies showed an accuracy of 88.51%.

Quick fuzzy backpropagation algorithm.

PubMed

Nikov, A; Stoeva, S

2001-03-01

A modification of the fuzzy backpropagation (FBP) algorithm called QuickFBP algorithm is proposed, where the computation of the net function is significantly quicker. It is proved that the FBP algorithm is of exponential time complexity, while the QuickFBP algorithm is of polynomial time complexity. Convergence conditions of the QuickFBP, resp. the FBP algorithm are defined and proved for: (1) single output neural networks in case of training patterns with different targets; and (2) multiple output neural networks in case of training patterns with equivalued target vector. They support the automation of the weights training process (quasi-unsupervised learning) establishing the target value(s) depending on the network's input values. In these cases the simulation results confirm the convergence of both algorithms. An example with a large-sized neural network illustrates the significantly greater training speed of the QuickFBP rather than the FBP algorithm. The adaptation of an interactive web system to users on the basis of the QuickFBP algorithm is presented. Since the QuickFBP algorithm ensures quasi-unsupervised learning, this implies its broad applicability in areas of adaptive and adaptable interactive systems, data mining, etc. applications.

An Adaptive Tradeoff Algorithm for Multi-issue SLA Negotiation

NASA Astrophysics Data System (ADS)

Son, Seokho; Sim, Kwang Mong

Since participants in a Cloud may be independent bodies, mechanisms are necessary for resolving different preferences in leasing Cloud services. Whereas there are currently mechanisms that support service-level agreement negotiation, there is little or no negotiation support for concurrent price and timeslot for Cloud service reservations. For the concurrent price and timeslot negotiation, a tradeoff algorithm to generate and evaluate a proposal which consists of price and timeslot proposal is necessary. The contribution of this work is thus to design an adaptive tradeoff algorithm for multi-issue negotiation mechanism. The tradeoff algorithm referred to as "adaptive burst mode" is especially designed to increase negotiation speed and total utility and to reduce computational load by adaptively generating concurrent set of proposals. The empirical results obtained from simulations carried out using a testbed suggest that due to the concurrent price and timeslot negotiation mechanism with adaptive tradeoff algorithm: 1) both agents achieve the best performance in terms of negotiation speed and utility; 2) the number of evaluations of each proposal is comparatively lower than previous scheme (burst-N).

Thermal Detection Thresholds of Aδ- and C-Fibre Afferents Activated by Brief CO2 Laser Pulses Applied onto the Human Hairy Skin

PubMed Central

Churyukanov, Maxim; Plaghki, Léon; Legrain, Valéry; Mouraux, André

2012-01-01

Brief high-power laser pulses applied onto the hairy skin of the distal end of a limb generate a double sensation related to the activation of Aδ- and C-fibres, referred to as first and second pain. However, neurophysiological and behavioural responses related to the activation of C-fibres can be studied reliably only if the concomitant activation of Aδ-fibres is avoided. Here, using a novel CO2 laser stimulator able to deliver constant-temperature heat pulses through a feedback regulation of laser power by an online measurement of skin temperature at target site, combined with an adaptive staircase algorithm using reaction-time to distinguish between responses triggered by Aδ- and C-fibre input, we show that it is possible to estimate robustly and independently the thermal detection thresholds of Aδ-fibres (46.9±1.7°C) and C-fibres (39.8±1.7°C). Furthermore, we show that both thresholds are dependent on the skin temperature preceding and/or surrounding the test stimulus, indicating that the Aδ- and C-fibre afferents triggering the behavioural responses to brief laser pulses behave, at least partially, as detectors of a change in skin temperature rather than as pure level detectors. Most importantly, our results show that the difference in threshold between Aδ- and C-fibre afferents activated by brief laser pulses can be exploited to activate C-fibres selectively and reliably, provided that the rise in skin temperature generated by the laser stimulator is well-controlled. Our approach could constitute a tool to explore, in humans, the physiological and pathophysiological mechanisms involved in processing C- and Aδ-fibre input, respectively. PMID:22558230

Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search.

PubMed

Mei, Gang; Xu, Nengxiong; Xu, Liangliang

2016-01-01

This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-nearest neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptively determine the power parameter; and then the desired prediction value of the interpolated point is obtained by weighted interpolating using the power parameter. In this work, we develop a fast kNN search approach based on the space-partitioning data structure, even grid, to improve the previous GPU-accelerated AIDW algorithm. The improved algorithm is composed of the stages of kNN search and weighted interpolating. To evaluate the performance of the improved algorithm, we perform five groups of experimental tests. The experimental results indicate: (1) the improved algorithm can achieve a speedup of up to 1017 over the corresponding serial algorithm; (2) the improved algorithm is at least two times faster than our previous GPU-accelerated AIDW algorithm; and (3) the utilization of fast kNN search can significantly improve the computational efficiency of the entire GPU-accelerated AIDW algorithm.

3D SAPIV particle field reconstruction method based on adaptive threshold.

PubMed

Qu, Xiangju; Song, Yang; Jin, Ying; Li, Zhenhua; Wang, Xuezhen; Guo, ZhenYan; Ji, Yunjing; He, Anzhi

2018-03-01

Particle image velocimetry (PIV) is a necessary flow field diagnostic technique that provides instantaneous velocimetry information non-intrusively. Three-dimensional (3D) PIV methods can supply the full understanding of a 3D structure, the complete stress tensor, and the vorticity vector in the complex flows. In synthetic aperture particle image velocimetry (SAPIV), the flow field can be measured with large particle intensities from the same direction by different cameras. During SAPIV particle reconstruction, particles are commonly reconstructed by manually setting a threshold to filter out unfocused particles in the refocused images. In this paper, the particle intensity distribution in refocused images is analyzed, and a SAPIV particle field reconstruction method based on an adaptive threshold is presented. By using the adaptive threshold to filter the 3D measurement volume integrally, the three-dimensional location information of the focused particles can be reconstructed. The cross correlations between images captured from cameras and images projected by the reconstructed particle field are calculated for different threshold values. The optimal threshold is determined by cubic curve fitting and is defined as the threshold value that causes the correlation coefficient to reach its maximum. The numerical simulation of a 16-camera array and a particle field at two adjacent time events quantitatively evaluates the performance of the proposed method. An experimental system consisting of a camera array of 16 cameras was used to reconstruct the four adjacent frames in a vortex flow field. The results show that the proposed reconstruction method can effectively reconstruct the 3D particle fields.

Epidemic spreading on preferred degree adaptive networks.

PubMed

Jolad, Shivakumar; Liu, Wenjia; Schmittmann, B; Zia, R K P

2012-01-01

We study the standard SIS model of epidemic spreading on networks where individuals have a fluctuating number of connections around a preferred degree κ. Using very simple rules for forming such preferred degree networks, we find some unusual statistical properties not found in familiar Erdös-Rényi or scale free networks. By letting κ depend on the fraction of infected individuals, we model the behavioral changes in response to how the extent of the epidemic is perceived. In our models, the behavioral adaptations can be either 'blind' or 'selective'--depending on whether a node adapts by cutting or adding links to randomly chosen partners or selectively, based on the state of the partner. For a frozen preferred network, we find that the infection threshold follows the heterogeneous mean field result λ(c)/μ = <κ>/<κ2> and the phase diagram matches the predictions of the annealed adjacency matrix (AAM) approach. With 'blind' adaptations, although the epidemic threshold remains unchanged, the infection level is substantially affected, depending on the details of the adaptation. The 'selective' adaptive SIS models are most interesting. Both the threshold and the level of infection changes, controlled not only by how the adaptations are implemented but also how often the nodes cut/add links (compared to the time scales of the epidemic spreading). A simple mean field theory is presented for the selective adaptations which capture the qualitative and some of the quantitative features of the infection phase diagram.

Speckle reduction during all-fiber common-path optical coherence tomography of the cavernous nerves

NASA Astrophysics Data System (ADS)

Chitchian, Shahab; Fiddy, Michael; Fried, Nathaniel M.

2009-02-01

Improvements in identification, imaging, and visualization of the cavernous nerves during prostate cancer surgery, which are responsible for erectile function, may improve nerve preservation and postoperative sexual potency. In this study, we use a rat prostate, ex vivo, to evaluate the feasibility of optical coherence tomography (OCT) as a diagnostic tool for real-time imaging and identification of the cavernous nerves. A novel OCT system based on an all single-mode fiber common-path interferometer-based scanning system is used for this purpose. A wavelet shrinkage denoising technique using Stein's unbiased risk estimator (SURE) algorithm to calculate a data-adaptive threshold is implemented for speckle noise reduction in the OCT image. The signal-to-noise ratio (SNR) was improved by 9 dB and the image quality metrics of the cavernous nerves also improved significantly.

An Artificial Intelligence Approach to Automatic Left Ventricular Border Detection in 2-D Echocardiography

PubMed Central

Buchan, Iris; Covvey, H. Dominic; Rakowski, Harry

1985-01-01

A program has been developed for left ventricular (LV) border tracking on ultrasound images. For each frame, forty border points at equally-spaced angles around the LV center are found gradually during three passes. Pass 1 uses adaptive thresholding to find the most obvious border points. Pass 2 then uses an artificial intelligence technique of finding possible border path segments, associating a score with each, and, from paths with superior scores, obtaining more of the border points. Pass 3 closes any remaining gaps by interpolation. The program tracks the LV border quite well in spite of dropout and interference from intracardiac structures, except during end-systole. Multi-level passes provide a very useful structure for border tracking, with increasingly slow but more sophisticated algorithms possible at higher levels for use when earlier passes recognise failure.

Three-dimensional geoelectric modelling with optimal work/accuracy rate using an adaptive wavelet algorithm

NASA Astrophysics Data System (ADS)

Plattner, A.; Maurer, H. R.; Vorloeper, J.; Dahmen, W.

2010-08-01

Despite the ever-increasing power of modern computers, realistic modelling of complex 3-D earth models is still a challenging task and requires substantial computing resources. The overwhelming majority of current geophysical modelling approaches includes either finite difference or non-adaptive finite element algorithms and variants thereof. These numerical methods usually require the subsurface to be discretized with a fine mesh to accurately capture the behaviour of the physical fields. However, this may result in excessive memory consumption and computing times. A common feature of most of these algorithms is that the modelled data discretizations are independent of the model complexity, which may be wasteful when there are only minor to moderate spatial variations in the subsurface parameters. Recent developments in the theory of adaptive numerical solvers have the potential to overcome this problem. Here, we consider an adaptive wavelet-based approach that is applicable to a large range of problems, also including nonlinear problems. In comparison with earlier applications of adaptive solvers to geophysical problems we employ here a new adaptive scheme whose core ingredients arose from a rigorous analysis of the overall asymptotically optimal computational complexity, including in particular, an optimal work/accuracy rate. Our adaptive wavelet algorithm offers several attractive features: (i) for a given subsurface model, it allows the forward modelling domain to be discretized with a quasi minimal number of degrees of freedom, (ii) sparsity of the associated system matrices is guaranteed, which makes the algorithm memory efficient and (iii) the modelling accuracy scales linearly with computing time. We have implemented the adaptive wavelet algorithm for solving 3-D geoelectric problems. To test its performance, numerical experiments were conducted with a series of conductivity models exhibiting varying degrees of structural complexity. Results were compared with a non-adaptive finite element algorithm, which incorporates an unstructured mesh to best-fitting subsurface boundaries. Such algorithms represent the current state-of-the-art in geoelectric modelling. An analysis of the numerical accuracy as a function of the number of degrees of freedom revealed that the adaptive wavelet algorithm outperforms the finite element solver for simple and moderately complex models, whereas the results become comparable for models with high spatial variability of electrical conductivities. The linear dependence of the modelling error and the computing time proved to be model-independent. This feature will allow very efficient computations using large-scale models as soon as our experimental code is optimized in terms of its implementation.

Flight Tests of a Remaining Flying Time Prediction System for Small Electric Aircraft in the Presence of Faults

NASA Technical Reports Server (NTRS)

Hogge, Edward F.; Kulkarni, Chetan S.; Vazquez, Sixto L.; Smalling, Kyle M.; Strom, Thomas H.; Hill, Boyd L.; Quach, Cuong C.

2017-01-01

This paper addresses the problem of building trust in the online prediction of a battery powered aircraft's remaining flying time. A series of flight tests is described that make use of a small electric powered unmanned aerial vehicle (eUAV) to verify the performance of the remaining flying time prediction algorithm. The estimate of remaining flying time is used to activate an alarm when the predicted remaining time is two minutes. This notifies the pilot to transition to the landing phase of the flight. A second alarm is activated when the battery charge falls below a specified limit threshold. This threshold is the point at which the battery energy reserve would no longer safely support two repeated aborted landing attempts. During the test series, the motor system is operated with the same predefined timed airspeed profile for each test. To test the robustness of the prediction, half of the tests were performed with, and half were performed without, a simulated powertrain fault. The pilot remotely engages a resistor bank at a specified time during the test flight to simulate a partial powertrain fault. The flying time prediction system is agnostic of the pilot's activation of the fault and must adapt to the vehicle's state. The time at which the limit threshold on battery charge is reached is then used to measure the accuracy of the remaining flying time predictions. Accuracy requirements for the alarms are considered and the results discussed.

Multi-limit unsymmetrical MLIBD image restoration algorithm

NASA Astrophysics Data System (ADS)

Yang, Yang; Cheng, Yiping; Chen, Zai-wang; Bo, Chen

2012-11-01

A novel multi-limit unsymmetrical iterative blind deconvolution(MLIBD) algorithm was presented to enhance the performance of adaptive optics image restoration.The algorithm enhances the reliability of iterative blind deconvolution by introducing the bandwidth limit into the frequency domain of point spread(PSF),and adopts the PSF dynamic support region estimation to improve the convergence speed.The unsymmetrical factor is automatically computed to advance its adaptivity.Image deconvolution comparing experiments between Richardson-Lucy IBD and MLIBD were done,and the result indicates that the iteration number is reduced by 22.4% and the peak signal-to-noise ratio is improved by 10.18dB with MLIBD method. The performance of MLIBD algorithm is outstanding in the images restoration the FK5-857 adaptive optics and the double-star adaptive optics.

Hardware Acceleration of Adaptive Neural Algorithms.

DOE Office of Scientific and Technical Information (OSTI.GOV)

James, Conrad D.

As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - worldmore » conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.« less

A chaos wolf optimization algorithm with self-adaptive variable step-size

NASA Astrophysics Data System (ADS)

Zhu, Yong; Jiang, Wanlu; Kong, Xiangdong; Quan, Lingxiao; Zhang, Yongshun

2017-10-01

To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA) with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as "winner-take-all" and the update mechanism as "survival of the fittest" were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.

An Adaptive Numeric Predictor-corrector Guidance Algorithm for Atmospheric Entry Vehicles. M.S. Thesis - MIT, Cambridge

NASA Technical Reports Server (NTRS)

Spratlin, Kenneth Milton

1987-01-01

An adaptive numeric predictor-corrector guidance is developed for atmospheric entry vehicles which utilize lift to achieve maximum footprint capability. Applicability of the guidance design to vehicles with a wide range of performance capabilities is desired so as to reduce the need for algorithm redesign with each new vehicle. Adaptability is desired to minimize mission-specific analysis and planning. The guidance algorithm motivation and design are presented. Performance is assessed for application of the algorithm to the NASA Entry Research Vehicle (ERV). The dispersions the guidance must be designed to handle are presented. The achievable operational footprint for expected worst-case dispersions is presented. The algorithm performs excellently for the expected dispersions and captures most of the achievable footprint.

Postmortem validation of breast density using dual-energy mammography

PubMed Central

Molloi, Sabee; Ducote, Justin L.; Ding, Huanjun; Feig, Stephen A.

2014-01-01

Purpose: Mammographic density has been shown to be an indicator of breast cancer risk and also reduces the sensitivity of screening mammography. Currently, there is no accepted standard for measuring breast density. Dual energy mammography has been proposed as a technique for accurate measurement of breast density. The purpose of this study is to validate its accuracy in postmortem breasts and compare it with other existing techniques. Methods: Forty postmortem breasts were imaged using a dual energy mammography system. Glandular and adipose equivalent phantoms of uniform thickness were used to calibrate a dual energy basis decomposition algorithm. Dual energy decomposition was applied after scatter correction to calculate breast density. Breast density was also estimated using radiologist reader assessment, standard histogram thresholding and a fuzzy C-mean algorithm. Chemical analysis was used as the reference standard to assess the accuracy of different techniques to measure breast composition. Results: Breast density measurements using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm, and dual energy were in good agreement with the measured fibroglandular volume fraction using chemical analysis. The standard error estimates using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean, and dual energy were 9.9%, 8.6%, 7.2%, and 4.7%, respectively. Conclusions: The results indicate that dual energy mammography can be used to accurately measure breast density. The variability in breast density estimation using dual energy mammography was lower than reader assessment rankings, standard histogram thresholding, and fuzzy C-mean algorithm. Improved quantification of breast density is expected to further enhance its utility as a risk factor for breast cancer. PMID:25086548

Postmortem validation of breast density using dual-energy mammography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Molloi, Sabee, E-mail: symolloi@uci.edu; Ducote, Justin L.; Ding, Huanjun

2014-08-15

Purpose: Mammographic density has been shown to be an indicator of breast cancer risk and also reduces the sensitivity of screening mammography. Currently, there is no accepted standard for measuring breast density. Dual energy mammography has been proposed as a technique for accurate measurement of breast density. The purpose of this study is to validate its accuracy in postmortem breasts and compare it with other existing techniques. Methods: Forty postmortem breasts were imaged using a dual energy mammography system. Glandular and adipose equivalent phantoms of uniform thickness were used to calibrate a dual energy basis decomposition algorithm. Dual energy decompositionmore » was applied after scatter correction to calculate breast density. Breast density was also estimated using radiologist reader assessment, standard histogram thresholding and a fuzzy C-mean algorithm. Chemical analysis was used as the reference standard to assess the accuracy of different techniques to measure breast composition. Results: Breast density measurements using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm, and dual energy were in good agreement with the measured fibroglandular volume fraction using chemical analysis. The standard error estimates using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean, and dual energy were 9.9%, 8.6%, 7.2%, and 4.7%, respectively. Conclusions: The results indicate that dual energy mammography can be used to accurately measure breast density. The variability in breast density estimation using dual energy mammography was lower than reader assessment rankings, standard histogram thresholding, and fuzzy C-mean algorithm. Improved quantification of breast density is expected to further enhance its utility as a risk factor for breast cancer.« less

Segmentation of fluorescence microscopy images for quantitative analysis of cell nuclear architecture.

PubMed

Russell, Richard A; Adams, Niall M; Stephens, David A; Batty, Elizabeth; Jensen, Kirsten; Freemont, Paul S

2009-04-22

Considerable advances in microscopy, biophysics, and cell biology have provided a wealth of imaging data describing the functional organization of the cell nucleus. Until recently, cell nuclear architecture has largely been assessed by subjective visual inspection of fluorescently labeled components imaged by the optical microscope. This approach is inadequate to fully quantify spatial associations, especially when the patterns are indistinct, irregular, or highly punctate. Accurate image processing techniques as well as statistical and computational tools are thus necessary to interpret this data if meaningful spatial-function relationships are to be established. Here, we have developed a thresholding algorithm, stable count thresholding (SCT), to segment nuclear compartments in confocal laser scanning microscopy image stacks to facilitate objective and quantitative analysis of the three-dimensional organization of these objects using formal statistical methods. We validate the efficacy and performance of the SCT algorithm using real images of immunofluorescently stained nuclear compartments and fluorescent beads as well as simulated images. In all three cases, the SCT algorithm delivers a segmentation that is far better than standard thresholding methods, and more importantly, is comparable to manual thresholding results. By applying the SCT algorithm and statistical analysis, we quantify the spatial configuration of promyelocytic leukemia nuclear bodies with respect to irregular-shaped SC35 domains. We show that the compartments are closer than expected under a null model for their spatial point distribution, and furthermore that their spatial association varies according to cell state. The methods reported are general and can readily be applied to quantify the spatial interactions of other nuclear compartments.

On the importance of FIB-SEM specific segmentation algorithms for porous media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Salzer, Martin, E-mail: martin.salzer@uni-ulm.de; Thiele, Simon, E-mail: simon.thiele@imtek.uni-freiburg.de; Zengerle, Roland, E-mail: zengerle@imtek.uni-freiburg.de

2014-09-15

A new algorithmic approach to segmentation of highly porous three dimensional image data gained by focused ion beam tomography is described which extends the key-principle of local threshold backpropagation described in Salzer et al. (2012). The technique of focused ion beam tomography has shown to be capable of imaging the microstructure of functional materials. In order to perform a quantitative analysis on the corresponding microstructure a segmentation task needs to be performed. However, algorithmic segmentation of images obtained with focused ion beam tomography is a challenging problem for highly porous materials if filling the pore phase, e.g. with epoxy resin,more » is difficult. The gray intensities of individual voxels are not sufficient to determine the phase represented by them and usual thresholding methods are not applicable. We thus propose a new approach to segmentation that pays respect to the specifics of the imaging process of focused ion beam tomography. As an application of our approach, the segmentation of three dimensional images for a cathode material used in polymer electrolyte membrane fuel cells is discussed. We show that our approach preserves significantly more of the original nanostructure than a thresholding approach. - Highlights: • We describe a new approach to the segmentation of FIB-SEM images of porous media. • The first and last occurrences of structures are detected by analysing the z-profiles. • The algorithm is validated by comparing it to a manual segmentation. • The new approach shows significantly less artifacts than a thresholding approach. • A structural analysis also shows improved results for the obtained microstructure.« less

Segmentation of Fluorescence Microscopy Images for Quantitative Analysis of Cell Nuclear Architecture

PubMed Central

Russell, Richard A.; Adams, Niall M.; Stephens, David A.; Batty, Elizabeth; Jensen, Kirsten; Freemont, Paul S.

2009-01-01

Abstract Considerable advances in microscopy, biophysics, and cell biology have provided a wealth of imaging data describing the functional organization of the cell nucleus. Until recently, cell nuclear architecture has largely been assessed by subjective visual inspection of fluorescently labeled components imaged by the optical microscope. This approach is inadequate to fully quantify spatial associations, especially when the patterns are indistinct, irregular, or highly punctate. Accurate image processing techniques as well as statistical and computational tools are thus necessary to interpret this data if meaningful spatial-function relationships are to be established. Here, we have developed a thresholding algorithm, stable count thresholding (SCT), to segment nuclear compartments in confocal laser scanning microscopy image stacks to facilitate objective and quantitative analysis of the three-dimensional organization of these objects using formal statistical methods. We validate the efficacy and performance of the SCT algorithm using real images of immunofluorescently stained nuclear compartments and fluorescent beads as well as simulated images. In all three cases, the SCT algorithm delivers a segmentation that is far better than standard thresholding methods, and more importantly, is comparable to manual thresholding results. By applying the SCT algorithm and statistical analysis, we quantify the spatial configuration of promyelocytic leukemia nuclear bodies with respect to irregular-shaped SC35 domains. We show that the compartments are closer than expected under a null model for their spatial point distribution, and furthermore that their spatial association varies according to cell state. The methods reported are general and can readily be applied to quantify the spatial interactions of other nuclear compartments. PMID:19383481

Improving the Projections of Vegetation Biogeography by Integrating Climate Envelope Models and Dynamic Global Vegetation Models

NASA Astrophysics Data System (ADS)

Case, M. J.; Kim, J. B.

2015-12-01

Assessing changes in vegetation is increasingly important for conservation planning in the face of climate change. Dynamic global vegetation models (DGVMs) are important tools for assessing such changes. DGVMs have been applied at regional scales to create projections of range expansions and contractions of plant functional types. Many DGVMs use a number of algorithms to determine the biogeography of plant functional types. One such DGVM, MC2, uses a series of decision trees based on bioclimatic thresholds while others, such as LPJ, use constraining emergent properties with a limited set of bioclimatic threshold-based rules. Although both approaches have been used widely, we demonstrate that these biogeography outputs perform poorly at continental scales when compared to existing potential vegetation maps. Specifically, we found that with MC2, the algorithm for determining leaf physiognomy is too simplistic to capture arid and semi-arid vegetation in much of the western U.S., as well as is the algorithm for determining the broadleaf and needleleaf mix in the Southeast. With LPJ, we found that the bioclimatic thresholds used to allow seedling establishment are too broad and fail to capture regional-scale biogeography of the plant functional types. In response, we demonstrate a new approach to determining the biogeography of plant functional types by integrating the climatic thresholds produced for individual tree species by a series of climate envelope models with the biogeography algorithms of MC2 and LPJ. Using this approach, we find that MC2 and LPJ perform considerably better when compared to potential vegetation maps.

Exploiting Redundancy and Application Scalability for Cost-Effective, Time-Constrained Execution of HPC Applications on Amazon EC2

DOE PAGES

Marathe, Aniruddha P.; Harris, Rachel A.; Lowenthal, David K.; ...

2015-12-17

The use of clouds to execute high-performance computing (HPC) applications has greatly increased recently. Clouds provide several potential advantages over traditional supercomputers and in-house clusters. The most popular cloud is currently Amazon EC2, which provides fixed-cost and variable-cost, auction-based options. The auction market trades lower cost for potential interruptions that necessitate checkpointing; if the market price exceeds the bid price, a node is taken away from the user without warning. We explore techniques to maximize performance per dollar given a time constraint within which an application must complete. Specifically, we design and implement multiple techniques to reduce expected cost bymore » exploiting redundancy in the EC2 auction market. We then design an adaptive algorithm that selects a scheduling algorithm and determines the bid price. We show that our adaptive algorithm executes programs up to seven times cheaper than using the on-demand market and up to 44 percent cheaper than the best non-redundant, auction-market algorithm. We extend our adaptive algorithm to incorporate application scalability characteristics for further cost savings. In conclusion, we show that the adaptive algorithm informed with scalability characteristics of applications achieves up to 56 percent cost savings compared to the expected cost for the base adaptive algorithm run at a fixed, user-defined scale.« less

Network Algorithms for Detection of Radiation Sources

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rao, Nageswara S; Brooks, Richard R; Wu, Qishi

In support of national defense, Domestic Nuclear Detection Office s (DNDO) Intelligent Radiation Sensor Systems (IRSS) program supported the development of networks of radiation counters for detecting, localizing and identifying low-level, hazardous radiation sources. Industry teams developed the first generation of such networks with tens of counters, and demonstrated several of their capabilities in indoor and outdoor characterization tests. Subsequently, these test measurements have been used in algorithm replays using various sub-networks of counters. Test measurements combined with algorithm outputs are used to extract Key Measurements and Benchmark (KMB) datasets. We present two selective analyses of these datasets: (a) amore » notional border monitoring scenario that highlights the benefits of a network of counters compared to individual detectors, and (b) new insights into the Sequential Probability Ratio Test (SPRT) detection method, which lead to its adaptations for improved detection. Using KMB datasets from an outdoor test, we construct a notional border monitoring scenario, wherein twelve 2 *2 NaI detectors are deployed on the periphery of 21*21meter square region. A Cs-137 (175 uCi) source is moved across this region, starting several meters from outside and finally moving away. The measurements from individual counters and the network were processed using replays of a particle filter algorithm developed under IRSS program. The algorithm outputs from KMB datasets clearly illustrate the benefits of combining measurements from all networked counters: the source was detected before it entered the region, during its trajectory inside, and until it moved several meters away. When individual counters are used for detection, the source was detected for much shorter durations, and sometimes was missed in the interior region. The application of SPRT for detecting radiation sources requires choosing the detection threshold, which in turn requires a source strength estimate, typically specified as a multiplier of the background radiation level. A judicious selection of this source multiplier is essential to achieve optimal detection probability at a specified false alarm rate. Typically, this threshold is chosen from the Receiver Operating Characteristic (ROC) by varying the source multiplier estimate. ROC is expected to have a monotonically increasing profile between the detection probability and false alarm rate. We derived ROCs for multiple indoor tests using KMB datasets, which revealed an unexpected loop shape: as the multiplier increases, detection probability and false alarm rate both increase until a limit, and then both contract. Consequently, two detection probabilities correspond to the same false alarm rate, and the higher is achieved at a lower multiplier, which is the desired operating point. Using the Chebyshev s inequality we analytically confirm this shape. Then, we present two improved network-SPRT methods by (a) using the threshold off-set as a weighting factor for the binary decisions from individual detectors in a weighted majority voting fusion rule, and (b) applying a composite SPRT derived using measurements from all counters.« less

Estimating the extreme low-temperature event using nonparametric methods

NASA Astrophysics Data System (ADS)

D'Silva, Anisha

This thesis presents a new method of estimating the one-in-N low temperature threshold using a non-parametric statistical method called kernel density estimation applied to daily average wind-adjusted temperatures. We apply our One-in-N Algorithm to local gas distribution companies (LDCs), as they have to forecast the daily natural gas needs of their consumers. In winter, demand for natural gas is high. Extreme low temperature events are not directly related to an LDCs gas demand forecasting, but knowledge of extreme low temperatures is important to ensure that an LDC has enough capacity to meet customer demands when extreme low temperatures are experienced. We present a detailed explanation of our One-in-N Algorithm and compare it to the methods using the generalized extreme value distribution, the normal distribution, and the variance-weighted composite distribution. We show that our One-in-N Algorithm estimates the one-in- N low temperature threshold more accurately than the methods using the generalized extreme value distribution, the normal distribution, and the variance-weighted composite distribution according to root mean square error (RMSE) measure at a 5% level of significance. The One-in- N Algorithm is tested by counting the number of times the daily average wind-adjusted temperature is less than or equal to the one-in- N low temperature threshold.

Gas ultrasonic flow rate measurement through genetic-ant colony optimization based on the ultrasonic pulse received signal model

NASA Astrophysics Data System (ADS)

Hou, Huirang; Zheng, Dandan; Nie, Laixiao

2015-04-01

For gas ultrasonic flowmeters, the signals received by ultrasonic sensors are susceptible to noise interference. If signals are mingled with noise, a large error in flow measurement can be caused by triggering mistakenly using the traditional double-threshold method. To solve this problem, genetic-ant colony optimization (GACO) based on the ultrasonic pulse received signal model is proposed. Furthermore, in consideration of the real-time performance of the flow measurement system, the improvement of processing only the first three cycles of the received signals rather than the whole signal is proposed. Simulation results show that the GACO algorithm has the best estimation accuracy and ant-noise ability compared with the genetic algorithm, ant colony optimization, double-threshold and enveloped zero-crossing. Local convergence doesn’t appear with the GACO algorithm until -10 dB. For the GACO algorithm, the converging accuracy and converging speed and the amount of computation are further improved when using the first three cycles (called GACO-3cycles). Experimental results involving actual received signals show that the accuracy of single-gas ultrasonic flow rate measurement can reach 0.5% with GACO-3 cycles, which is better than with the double-threshold method.

Threshold matrix for digital halftoning by genetic algorithm optimization

NASA Astrophysics Data System (ADS)

Alander, Jarmo T.; Mantere, Timo J.; Pyylampi, Tero

1998-10-01

Digital halftoning is used both in low and high resolution high quality printing technologies. Our method is designed to be mainly used for low resolution ink jet marking machines to produce both gray tone and color images. The main problem with digital halftoning is pink noise caused by the human eye's visual transfer function. To compensate for this the random dot patterns used are optimized to contain more blue than pink noise. Several such dot pattern generator threshold matrices have been created automatically by using genetic algorithm optimization, a non-deterministic global optimization method imitating natural evolution and genetics. A hybrid of genetic algorithm with a search method based on local backtracking was developed together with several fitness functions evaluating dot patterns for rectangular grids. By modifying the fitness function, a family of dot generators results, each with its particular statistical features. Several versions of genetic algorithms, backtracking and fitness functions were tested to find a reasonable combination. The generated threshold matrices have been tested by simulating a set of test images using the Khoros image processing system. Even though the work was focused on developing low resolution marking technology, the resulting family of dot generators can be applied also in other halftoning application areas including high resolution printing technology.

An Auditory-Masking-Threshold-Based Noise Suppression Algorithm GMMSE-AMT[ERB] for Listeners with Sensorineural Hearing Loss

NASA Astrophysics Data System (ADS)

Natarajan, Ajay; Hansen, John H. L.; Arehart, Kathryn Hoberg; Rossi-Katz, Jessica

2005-12-01

This study describes a new noise suppression scheme for hearing aid applications based on the auditory masking threshold (AMT) in conjunction with a modified generalized minimum mean square error estimator (GMMSE) for individual subjects with hearing loss. The representation of cochlear frequency resolution is achieved in terms of auditory filter equivalent rectangular bandwidths (ERBs). Estimation of AMT and spreading functions for masking are implemented in two ways: with normal auditory thresholds and normal auditory filter bandwidths (GMMSE-AMT[ERB]-NH) and with elevated thresholds and broader auditory filters characteristic of cochlear hearing loss (GMMSE-AMT[ERB]-HI). Evaluation is performed using speech corpora with objective quality measures (segmental SNR, Itakura-Saito), along with formal listener evaluations of speech quality rating and intelligibility. While no measurable changes in intelligibility occurred, evaluations showed quality improvement with both algorithm implementations. However, the customized formulation based on individual hearing losses was similar in performance to the formulation based on the normal auditory system.

The impact of different algorithms for ideal body weight on screening for hydroxychloroquine retinopathy in women.

PubMed

Browning, David J; Lee, Chong; Rotberg, David

2014-01-01

To determine how algorithms for ideal body weight (IBW) affect hydroxychloroquine dosing in women. This was a retrospective study of 520 patients screened for hydroxychloroquine retinopathy. Charts were reviewed for sex, height, weight, and daily dose. The outcome measures were ranges of IBW across algorithms; rates of potentially toxic dosing; height thresholds below which 400 mg/d dosing is potentially toxic; and rates for which actual body weight (ABW) was less than IBW. Women made up 474 (91%) of the patients. The IBWs for a height varied from 30-34 pounds (13.6-15.5 kg) across algorithms. The threshold heights below which toxic dosing occurred varied from 62-70 inches (157.5-177.8 cm). Different algorithms placed 16%-98% of women in the toxic dosing range. The proportion for whom dosing should have been based on ABW rather than IBW ranged from 5%-31% across algorithms. Although hydroxychloroquine dosing should be based on the lesser of ABW and IBW, there is no consensus about the definition of IBW. The Michaelides algorithm is associated with the most frequent need to adjust dosing; the Metropolitan Life Insurance, large frame, mean value table with the least frequent need. No evidence indicates that one algorithm is superior to others.

Psychophysical measurements in children: challenges, pitfalls, and considerations.

PubMed

Witton, Caroline; Talcott, Joel B; Henning, G Bruce

2017-01-01

Measuring sensory sensitivity is important in studying development and developmental disorders. However, with children, there is a need to balance reliable but lengthy sensory tasks with the child's ability to maintain motivation and vigilance. We used simulations to explore the problems associated with shortening adaptive psychophysical procedures, and suggest how these problems might be addressed. We quantify how adaptive procedures with too few reversals can over-estimate thresholds, introduce substantial measurement error, and make estimates of individual thresholds less reliable. The associated measurement error also obscures group differences. Adaptive procedures with children should therefore use as many reversals as possible, to reduce the effects of both Type 1 and Type 2 errors. Differences in response consistency, resulting from lapses in attention, further increase the over-estimation of threshold. Comparisons between data from individuals who may differ in lapse rate are therefore problematic, but measures to estimate and account for lapse rates in analyses may mitigate this problem.

Stroboscopic Goggles as a Countermeasure for Dynamic Visual Acuity and Landing Sickness in Crewmembers Returning from Long-Duration Spaceflight

NASA Technical Reports Server (NTRS)

Rosenberg, M. J. F.; Kreutzberg, G. A.; Peters, B. T.; Reschke, M. F.

2017-01-01

Long-term exposure to microgravity causes sensorimotor adaptations that result in functional deficits upon returning to a gravitational environment. At landing, the vestibular system and the central nervous system, responsible for coordinating head and eye movements via the vestibulo-occular reflex (VOR), are adapted to microgravity and must re-adapt to the Earth's gravitational environment. This re-adaptation causes decrements in gaze control and dynamic visual acuity, with astronauts reporting oscillopsia and blurred vision. These effects are caused by retinal slip, or the inability to keep an image focused on their retina, which is thought to drive motion sickness symptoms experienced upon landing. Retinal slip can be estimated by dynamic visual acuity (DVA); visual acuity while in motion. Peters et al. (2011) find that DVA is worsened in astronauts by an average of 0.75 eye-chart lines one day after landing. Previously, the use of stroboscopic goggles has shown to be effective in minimizing motion sickness symptoms due to retinal slip (Reschke et al. 2007). In this study, we simulated the decrement in DVA caused by sensorimotor re-adaptation by using minifying lenses and then testing the efficacy of stroboscopic goggles in preventing retinal slip and improving DVA. Dynamic visual acuity is assessed using an oscillating chair developed in the Neuroscience Laboratory at JSC. This chair is motor-driven and oscillates vertically at 2 Hz with a vertical displacement of +/- 2 cm to simulate the vertical translations that occur while walking. As the subject is being oscillated, they are asked to discern the direction of Landolt-C optotypes of varying sizes and record their direction using a gamepad. The visual acuity thresholds are determined using an algorithm that alters the size of the optotype based on the previous responses of the subject using a forced-choice best parameter estimation that is able to rapidly converge on the threshold value. Visual acuity thresholds were determined both for static (seated) and dynamic (oscillating) conditions. Dynamic visual acuity is defined as the difference between the dynamic and static conditions. We found that healthy subjects (n=20) have a significantly impaired DVA while wearing the minifying lenses, demonstrating that the VOR is in an adaptive state and retinal slip is occurring. When subjects' acuity was tested wearing the stroboscopic goggles with the minifying lenses, there was no significant difference in their DVA compared to their baseline DVA. This suggests that stroboscopic goggles are preventing retinal slip and would function as an efficient countermeasure for VOR adaptations and thus help mitigate landing sickness symptoms experienced by long-duration crewmembers. These goggles might also be used to counter blurred vision (caused by retinal slip) experienced by crewmembers during launch where the vehicle vibrations are greatest. The stroboscopic effect could be built into a section of their head mounted displays on the visor of their helmets to be used in these high vibration situation if a mission critical task is necessary.

A homotopy method based on WENO schemes for solving steady state problems of hyperbolic conservation laws

DTIC Science & Technology

2012-09-03

prac- tice to solve these initial value problems. Additionally, the predictor / corrector methods are combined with adaptive stepsize and adaptive ...for implementing a numerical path tracking algorithm is to decide which predictor / corrector method to employ, how large to take the step ∆t, and what...the endgame algorithm . Output: A steady state solution Set ǫ = 1 while ǫ >= ǫend do set the stepsize ∆ǫ by using adaptive stepsize control algorithm

Sequential Insertion Heuristic with Adaptive Bee Colony Optimisation Algorithm for Vehicle Routing Problem with Time Windows

PubMed Central

Jawarneh, Sana; Abdullah, Salwani

2015-01-01

This paper presents a bee colony optimisation (BCO) algorithm to tackle the vehicle routing problem with time window (VRPTW). The VRPTW involves recovering an ideal set of routes for a fleet of vehicles serving a defined number of customers. The BCO algorithm is a population-based algorithm that mimics the social communication patterns of honeybees in solving problems. The performance of the BCO algorithm is dependent on its parameters, so the online (self-adaptive) parameter tuning strategy is used to improve its effectiveness and robustness. Compared with the basic BCO, the adaptive BCO performs better. Diversification is crucial to the performance of the population-based algorithm, but the initial population in the BCO algorithm is generated using a greedy heuristic, which has insufficient diversification. Therefore the ways in which the sequential insertion heuristic (SIH) for the initial population drives the population toward improved solutions are examined. Experimental comparisons indicate that the proposed adaptive BCO-SIH algorithm works well across all instances and is able to obtain 11 best results in comparison with the best-known results in the literature when tested on Solomon’s 56 VRPTW 100 customer instances. Also, a statistical test shows that there is a significant difference between the results. PMID:26132158

An analysis of neural receptive field plasticity by point process adaptive filtering

PubMed Central

Brown, Emery N.; Nguyen, David P.; Frank, Loren M.; Wilson, Matthew A.; Solo, Victor

2001-01-01

Neural receptive fields are plastic: with experience, neurons in many brain regions change their spiking responses to relevant stimuli. Analysis of receptive field plasticity from experimental measurements is crucial for understanding how neural systems adapt their representations of relevant biological information. Current analysis methods using histogram estimates of spike rate functions in nonoverlapping temporal windows do not track the evolution of receptive field plasticity on a fine time scale. Adaptive signal processing is an established engineering paradigm for estimating time-varying system parameters from experimental measurements. We present an adaptive filter algorithm for tracking neural receptive field plasticity based on point process models of spike train activity. We derive an instantaneous steepest descent algorithm by using as the criterion function the instantaneous log likelihood of a point process spike train model. We apply the point process adaptive filter algorithm in a study of spatial (place) receptive field properties of simulated and actual spike train data from rat CA1 hippocampal neurons. A stability analysis of the algorithm is sketched in the Appendix. The adaptive algorithm can update the place field parameter estimates on a millisecond time scale. It reliably tracked the migration, changes in scale, and changes in maximum firing rate characteristic of hippocampal place fields in a rat running on a linear track. Point process adaptive filtering offers an analytic method for studying the dynamics of neural receptive fields. PMID:11593043

Accelerated Dimension-Independent Adaptive Metropolis

DOE PAGES

Chen, Yuxin; Keyes, David E.; Law, Kody J.; ...

2016-10-27

This work describes improvements from algorithmic and architectural means to black-box Bayesian inference over high-dimensional parameter spaces. The well-known adaptive Metropolis (AM) algorithm [33] is extended herein to scale asymptotically uniformly with respect to the underlying parameter dimension for Gaussian targets, by respecting the variance of the target. The resulting algorithm, referred to as the dimension-independent adaptive Metropolis (DIAM) algorithm, also shows improved performance with respect to adaptive Metropolis on non-Gaussian targets. This algorithm is further improved, and the possibility of probing high-dimensional (with dimension d 1000) targets is enabled, via GPU-accelerated numerical libraries and periodically synchronized concurrent chains (justimore » ed a posteriori). Asymptotically in dimension, this GPU implementation exhibits a factor of four improvement versus a competitive CPU-based Intel MKL parallel version alone. Strong scaling to concurrent chains is exhibited, through a combination of longer time per sample batch (weak scaling) and yet fewer necessary samples to convergence. The algorithm performance is illustrated on several Gaussian and non-Gaussian target examples, in which the dimension may be in excess of one thousand.« less