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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yeo-Sun Yoon L.M. Kaplan J.H. McClellan Center for Signal and Image Processing Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta GA USA Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA CTSPS Dept. of Engineering Clark Atlanta University Atlanta GA

signal subspace methods in DOA estimation for wideband sources require preprocessing to find initial values which are close enough to the true values or to convert sensor outputs into desired forms. The preprocessing procedure should be carefully done lest it introduce some distortion. Failure to find proper initial values may prevent convergence of the estimator or cause biases in the estimator. The proposed method detects uncorrelated wideband sources using the signal subspace and the noise subspace of decomposed wideband signals. It does not require any initial values. The only preprocessing is narrowband decomposition of the sensor output which is very common in other wideband methods. computer simulation showed that the proposed method has less bias than and comparable variance to CSSM with small focusing errors.

关键词： Wideband Direction of arrival estimation Narrowband Frequency estimation Array signal processing Multiple signal classification Collaboration Government signal processing image processing

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Sonar resolution-based environment mapping 15

Sonar resolution-based environment mapping

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15th IEEE International Conference on Robotics and Automation, ICRA 1998

作者： Cahut, Leyla Vlavanis, Kimon P. Delic, Hakan Robotics and Automation Laboratory Center for and Advanced Computer Studies University of Southwestern Louisiana LafayetteLA70504 United States Signal and Image Processing Laboratory Department of Electrical Engineering BogaziCi University Bebek 80815 Istanbul Turkey

ISBN: (纸本)078034300X

Ultrasonic range sensors are used to obtain the information required for collision-free navigation of a mobile robot in a semi-structured or unstructured environment. A set of range readings from a ring of sonars are correlated to acquire a 2D map of the robot's environment. The map is continuously enhanced via novel matching and update algorithms as new data are collected while the robot is in motion. The algorithm utilizes confidence measures that are directly obtained from the sonar's resolution or accuracy. There are no additional modeling assumptions and the approach is robust. The algorithms are experimentally tested on the Nomad 200 mobile robot and Nomad's Cognos Software Development Package. © 1998 IEEE.

关键词： Software testing

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Automatic detection of Hyperreflective Foci in optical coherence tomography B-scans using Morphological Component Analysis 39

Automatic detection of Hyperreflective Foci in optical coher...

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39th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2017

作者： Mokhtari, Marzieh Ghasemi Kamasi, Zeinab Rabbani, Hossein Department of Bioelectric and Biomedical Engineering School of Advanced Technologies in Medicine Isfahan University of Medical Sciences *** Iran Lane Department of Computer Science and Electrical Engineering West Virginia University 26506 Iran Medical Image and Signal Processing Research Center Isfahan University of Medical Sciences *** Iran

ISBN: (纸本)9781509028092

Hyperreflective Foci (HF) is one of the most common complications distributed in cross-sectional images of patients with Diabetic Macular Edema (DME). Scanning Laser Ophthalmoscope (SLO) images usually consists of several B-scans that represent a cross-sectional reconstruction of a plane through the anterior or posterior regions of retina. In each B-scan, HFs are geometrically distinct constituents in different retinal layers. Since the intensity levels of HFs and many other subjects in B-scans are the same, in this paper we try to separate HFs from other objects by detection of the point and curve singularities in each B-scan. The decomposition algorithm presented in this paper is based on sparse image representation of B-scans using Morphological Component Analysis (MCA) technique. By using curvelet transform and Daubechies wavelet basis, two different over-complete dictionaries are constructed which represent two various aspects of B-scans. The HFs are more distinguished in reconstructed image with wavelet dictionary and other objects are mostly detectable by curvelet dictionary. So, HFs can be detected by applying an optimum threshold criterion on reconstructed image by wavelet atoms. Finally, the false positive points are reduced by removing the candidate points in RNFL and RPE layers, which are automatically segmented based on ridgelet transform. Our simulation results on 1924 HFs show that sensitivity and specificity for HF detection is 91.0% and 100%, respectively. © 2017 IEEE.

关键词： Dictionaries Retina Hafnium image reconstruction Wavelet transforms Diseases

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Component analysis-based unsupervised linear spectral mixture analysis for hyperspectral imagery

Component analysis-based unsupervised linear spectral mixtur...

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WHISPERS '09 - 1st Workshop on Hyperspectral image and signal processing: Evolution in Remote Sensing

作者： Jiao, Xiaoli Du, Yingzi Chang, Chein-I. Remote Sensing Signal and Image Processing Laboratry Department of Computer Science and Electrical Engineering University of Maryland Baltimore County MD 21228 United States Department of Electrical and Computer Engineering Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN 46202 United States Department of Electrical Engineering National Chung Hsing University Taichung Taiwan

ISBN: (纸本)9781424446872

One of the most challenging issues in unsupervised linear spectral mixture analysis (LSMA) is how to obtain unknown knowledge of target signatures referred to as virtual endmembers (VEs) directly from the data to be processed. This issue has never arisen in supervised LSMA where the VEs are either assumed to be known a priori or can be provided by visual inspection. With the recent advent of hyperspectral sensor technology many unknown and subtle signal sources can be uncovered and revealed without prior knowledge. This paper addresses this issue and develops a component analysis-based unsupervised LSMA where the desired VEs can be extracted by component analysis-based transforms directly from the data to be processed without appealing for prior knowledge. In order to substantiate the utility of the proposed approach extensive experiments are conducted for demonstration. © 2009 IEEE.

关键词： Mixtures

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A statistical method for binary classification of images

A statistical method for binary classification of images

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2005 ACM Symposium on Document engineering

作者： Simske, Steven J. Li, Dalong Aronoff, Jason S. Hewlett-Packard Labs. Mailstop 85 3404 E. Harmony Road Fort Collins CO 80528 United States Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 United States

ISBN: (纸本)1595932402

The classification of documents with sparse text, and video analysis, relies on accurate image classification. We herein present a method for binary classification that accommodates any number of individual classifiers. Each individual classifier is defined by the critical point between its two means, and its relative weighting is inversely proportional to its expected error rate. Using 10 simple image analysis metrics, we distinguish a set of "natural" and "city" scenes, providing a "semantically meaningful" classification. The optimal combination of 5 of these 10 classifiers provides 85.8% accuracy on a small (120 image) feasibility corpus. When this feasibility corpus is then split into half training and half testing images, the mean accuracy of the optimum set of classifiers was 81.7%. Accuracy as high as 90% was obtained for the test set when training percentage was increased. These results demonstrate that an accurate classifier can be constructed from a large pool of simple classifiers through the use of the statistical ("Normal") classification method described herein. Copyright 2005 ACM.

关键词： Classification (of information)

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A comparison of the octave-band directional filter bank and gabor filters for texture classification

A comparison of the octave-band directional filter bank and ...

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2004 International Conference on image processing, ICIP 2004

作者： Hong, Paul S. Kaplan, Lance M. Smith, Mark J. T. Center for Signal and Image Processing Georgia Institute of Technology Atlanta GA 30332 United States Center for Theoretical Studies of Physical Systems Clark Atlanta University Atlanta GA 30314 United States School of Electrical and Computer Engineering Purdue University West Lafayette IN 47907 United States

ISBN: (纸本)0780385543

This paper investigates the similarities between Gabor filters and the octave-band directional filter bank (OBDFB) with respect to texture classification. Both decompositions can discriminate angularly and radially in the 2-D frequency domain;however, the OBDFB has a computationally efficient implementation that makes it an attractive alternative to Gabor filters. An analysis of local energy features from Gabor filters and the OBDFB is presented showing similarities between their texture features. Texture classification experiments using both decompositions show comparable classification accuracy. © 2004 IEEE.

关键词： Textures

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FOCAL: A Cost-Aware Video Dataset for Active Learning

FOCAL: A Cost-Aware Video Dataset for Active Learning

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Kokilepersaud, Kiran Logan, Yash-Yee Benkert, Ryan Zhou, Chen Prabhushankar, Mohit Alregib, Ghassan Corona, Enrique Singh, Kunjan Parchami, Mostafa Georgia Institute of Technology Olives at the Center for Signal and Information Processing Csip School of Electrical and Computer Engineering AtlantaGA United States Ford Motor Company DearbornMI United States

ISBN: (纸本)9798350324457

In this paper, we introduce the FOCAL (Ford-OLIVES Collaboration on Active Learning) dataset which enables the study of the impact of annotation-cost within a video active learning setting. Annotation-cost refers to the time it takes an annotator to label and quality-assure a given video sequence. A practical motivation for active learning research is to minimize annotation-cost by selectively labeling informative samples that will maximize performance within a given budget constraint. However, previous work in video active learning lacks real-time annotation labels for accurately assessing cost minimization and instead operates under the assumption that annotation-cost scales linearly with the amount of data to annotate. This assumption does not take into account a variety of real-world confounding factors that contribute to a nonlinear cost such as the effect of an assistive labeling tool and the variety of interactions within a scene such as occluded objects, weather, and motion of objects. FOChL addresses this discrepancy by providing real annotation-cost labels for 126 video sequences across 69 unique city scenes with a variety of weather, lighting, and seasonal conditions. These videos have a wide range of interactions that are at the intersection of infrastructure-assisted autonomy and autonomous vehicle communities. We show through a statistical analysis of the FOChL dataset that cost is more correlated with a variety of factors beyond just the length of a video sequence. We also introduce a set of conformal active learning algorithms that take advantage of the sequential structure of video data in order to achieve a better trade-off between annotation-cost and performance while also reducing floating point operations (FLOPS) overhead by at least 77.67%. We show how these approaches better reflect how annotations on videos are done in practice through a sequence selection framework. We further demonstrate the advantage of these approaches by introducing two

关键词： active learning labeling cost sequential video

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EEG based stress classification by using difference plots of variational modes and machine learning

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16347-16360页

作者： Salankar, Nilima Qaisar, Saeed Mian School of Computer Science University of Petroleum and Energy Studies Dehradun India Persistent System Limited IT Park Maharashtra Nagpur India Department of Electrical and Computer Engineering EFfat University Jeddah22332 Saudi Arabia Communication and Signal Processing Lab Energy and Technology Research Center Effat University Jeddah22332 Saudi Arabia

The recent trend in healthcare is to use the automated biomedical signals processing for an augmented and precise diagnosis. In this context, an original approach is presented for categorization of stress and non-stress classes by processing the multichannel Electroencephalogram (EEG) signals. The EEG signals are decomposed by using the "Empirical Mode Decomposition" (EMD) and "Variational Mode Decomposition" (VMD), respectively render the "Intrinsic Mode Functions" (IMFs) and Modes. The Second Order Difference Plots (SODPs) are traced for each IMF and Mode. Shapes of SODPs are used to discriminate the two intended categories. The feature space includes areas, Central Tendency Measures (CTMs) and means of SODPs. It is derived from first 7 IMFs and Modes. The identification of stress and non-stress categories is performed by using the robust machine learning algorithms namely, the Support Vector Machine (SVM), Boosting with Random Forest (RF) and Multilayer Perceptron Neural Network (MLPNN). The applicability is tested by using a publicly available EEG signals dataset, recorded during mental arithmetic test. A detailed evaluation is carried out at the subset, individual channel and lobes wise. Noteworthy highest accuracies of subset wise 99.89%, channel wise 98.89% and lobe wise 99.99% are obtained respectively. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Empirical mode decomposition

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Forward-backward sequential Monte Carlo smoothing for joint target detection and tracking

Forward-backward sequential Monte Carlo smoothing for joint ...

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2009 12th International Conference on Information Fusion, FUSION 2009

作者： Clark, Daniel E. Ba, Tuong Vo Ba, Ngu Vo Joint Research Institute in Signal and Image Processing School of Engineering and Physical Sciences Heriot-Watt University United Kingdom School of Electrical Electronic and Computer Engineering University of Western Australia 35 Stirling Hwy Crawley WA 6009 Australia Department of Electrical and Electronic Engineering University of Melbourne Parkville VIC 3052 Australia

ISBN: (纸本)9780982443804

The problem of jointly detecting whether a target is present in a scene and, if there is, estimating its state can be viewed as a multi-object estimation problem where there is a maximum of one target. This joint detection and estimation problem can be solved using a special case of the multi-object Bayes filter. In this paper we investigate the joint target detection and estimation problem with forward-backward smoothing and propose a sequential Monte Carlo implementation. Finite Set Statistics not only facilitates the development of appropriate joint detection and estimation filters, but also the direct extension of these filtering solutions to their related smoothing counterparts. Preliminary results indicate that using the smoothing has two distinct advantages over just using filtering: Firstly, we are able to more accurately identify the appearance and disappearance of a target in the scene and secondly, we can provide improved state estimates when the target exists. ©2009 ISIF.

关键词： Monte Carlo methods

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Enhanced fault diagnosis of wind energy conversion systems using ensemble learning based on sine cosine algorithm

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Journal of engineering and Applied Science 2023年第1期70卷 56页

作者： Attouri, Khadija Dhibi, Khaled Mansouri, Majdi Hajji, Mansour Bouzrara, Kais Nounou, Hazem Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine1200 Tunisia Chemical Engineering Program Texas A&M University at Qatar 23874 Doha Qatar Electrical and Computer Engineering Program Texas A&M University at Qatar 23874 Doha Qatar Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir Monastir5019 Tunisia

This paper investigates the problem of incipient fault detection and diagnosis (FDD) in wind energy conversion systems (WECS) using an innovative and effective approach called the ensemble learning-sine cosine optimization algorithm (EL-SCOA). The evolved strategy involves two primary steps: first, a sine-cosine algorithm is used to extract and optimize features in order to only select the most descriptive ones. Second, to further improve the capability, thereby providing the highest accuracy performance, the newly gathered dataset is introduced as input to an ensemble learning paradigm, which merges the benefits of boosting and bagging techniques with an artificial neural network classifier. The essential goal of the developed proposal is to discriminate between the diverse operating conditions (one healthy and six faulty conditions). Three potential and frequent types of faults that can affect the system behaviors including short-circuit, open-circuit, and wear-out are considered and thereby injected at diverse locations and sides (grid and generator sides) in order to evaluate the availability and performance of the proposed technique when compared to the conventional FDD methods. The diagnosis performance is analyzed in terms of accuracy, recall, precision, and computation time. The acquired outcomes demonstrate the efficiency of the suggested diagnostic paradigm compared to conventional FDD techniques (accuracy rate has been successfully achieved 98.35%). © 2023, This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply.

关键词： Failure analysis

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