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Relevant bibliographies by topics / Sparsity level detection

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Journal articles

Academic literature on the topic 'Sparsity level detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Sparsity level detection"

1

Najjar, L. "Sparsity level-aware threshold-based channel structure detection in OFDM systems." Electronics Letters 48, no. 9 (2012): 495. http://dx.doi.org/10.1049/el.2012.0287.

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Liu, Zhoufeng, Lei Yan, Chunlei Li, Yan Dong, and Guangshuai Gao. "Fabric defect detection based on sparse representation of main local binary pattern." International Journal of Clothing Science and Technology 29, no. 3 (2017): 282–93. http://dx.doi.org/10.1108/ijcst-04-2016-0040.

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Purpose The purpose of this paper is to find an efficient fabric defect detection algorithm by means of exploring the sparsity characteristics of main local binary pattern (MLBP) extracted from the original fabric texture. Design/methodology/approach In the proposed algorithm, original LBP features are extracted from the fabric texture to be detected, and MLBP are selected by occurrence probability. Second, a dictionary is established with MLBP atoms which can sparsely represent all the LBP. Then, the value of the gray-scale difference between gray level of neighborhood pixels and the central

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Lin, Huiping, Hang Chen, Hongmiao Wang, Junjun Yin, and Jian Yang. "Ship Detection for PolSAR Images via Task-Driven Discriminative Dictionary Learning." Remote Sensing 11, no. 7 (2019): 769. http://dx.doi.org/10.3390/rs11070769.

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Ship detection with polarimetric synthetic aperture radar (PolSAR) has received increasing attention for its wide usage in maritime applications. However, extracting discriminative features to implement ship detection is still a challenging problem. In this paper, we propose a novel ship detection method for PolSAR images via task-driven discriminative dictionary learning (TDDDL). An assumption that ship and clutter information are sparsely coded under two separate dictionaries is made. Contextual information is considered by imposing superpixel-level joint sparsity constraints. In order to am

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Guo, Peifang, Alan Evans, and Prabir Bhattacharya. "Nuclei Segmentation for Quantification of Brain Tumors in Digital Pathology Images." International Journal of Software Science and Computational Intelligence 10, no. 2 (2018): 36–49. http://dx.doi.org/10.4018/ijssci.2018040103.

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In this article, based on image transformation of HSV (Hue, Saturation, Value), the authors propose a method for cancer nuclei segmentation when such conflicts of cancer nuclei involve ‘omics' indicative of brain tumors pathologically. To constrain the problem space in the region of color information, i.e. cancer nuclei, they convert the images into the V component of HSV first, and then apply the threshold level-set segmentation and the sparsity technique (VTLS-ST) in segmentation. The combined technique of the proposed VTLS-ST is implemented using the real-time CBTC dataset in the validation

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Somasundaram, K., and P. Alli Rajendran. "Diagnosing and Ranking Retinopathy Disease Level Using Diabetic Fundus Image Recuperation Approach." Scientific World Journal 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/534045.

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Retinal fundus images are widely used in diagnosing different types of eye diseases. The existing methods such as Feature Based Macular Edema Detection (FMED) and Optimally Adjusted Morphological Operator (OAMO) effectively detected the presence of exudation in fundus images and identified the true positive ratio of exudates detection, respectively. These mechanically detected exudates did not include more detailed feature selection technique to the system for detection of diabetic retinopathy. To categorize the exudates, Diabetic Fundus Image Recuperation (DFIR) method based on sliding window

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Qu, Lele, Shimiao An, and Yanpeng Sun. "Cross Validation Based Distributed Greedy Sparse Recovery for Multiview Through-the-Wall Radar Imaging." International Journal of Antennas and Propagation 2019 (April 9, 2019): 1–9. http://dx.doi.org/10.1155/2019/5651602.

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Multiview through-the-wall radar imaging (TWRI) can improve the imaging quality and target detection by exploiting the measurement data acquired from various views. Based on the established joint sparsity signal model for multiview TWRI, a cross validation (CV) based distributed greedy sparse recovery algorithm which combines the strengths of the CV technique and censored simultaneous orthogonal matching pursuit algorithm (CSOMP) is proposed in this paper. The developed imaging algorithm named by CV-CSOMP which separates the total measurements into reconstruction measurements and CV measuremen

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7

Huan, Dai, Luo, and Ai. "Bayesian Compress Sensing Based Countermeasure Scheme Against the Interrupted Sampling Repeater Jamming." Sensors 19, no. 15 (2019): 3279. http://dx.doi.org/10.3390/s19153279.

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The interrupted sampling repeater jamming (ISRJ) is considered an efficient deception method of jamming for coherent radar detection. However, current countermeasure methods against ISRJ interference may fail in detecting weak echoes, particularly when the transmitting power of the jammer is relatively high. In this paper, we propose a novel countermeasure scheme against ISRJ based on Bayesian compress sensing (BCS), where stable target signal can be reconstructed over a relatively large range of signal-to-noise ratio (SNR) for both single target and multi-target scenarios. By deriving the ISR

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Gargouri, Yosra, Hervé Petit, Patrick Loumeau, Baptiste Cecconi, and Patricia Desgreys. "Compressive Sampling for Efficient Astrophysical Signals Digitizing: From Compressibility Study to Data Recovery." Journal of Astronomical Instrumentation 05, no. 04 (2016): 1641020. http://dx.doi.org/10.1142/s2251171716410208.

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The design of a new digital radio receiver for radio astronomical observations in outer space is challenged with energy and bandwidth constraints. This paper proposes a new solution to reduce the number of samples acquired under the Shannon–Nyquist limit while retaining the relevant information of the signal. For this, it proposes to exploit the sparsity of the signal by using a compressive sampling process (also called Compressed Sensing (CS)) at the Analog-to-Digital Converter (ADC) to reduce the amount of data acquired and the energy consumption. As an example of an astrophysical signal, we

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G., Madhu Chandra, and Sreerama Reddy G. M. "Framework for Contextual Outlier Identification using Multivariate Analysis approach and Unsupervised Learning." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 2 (2018): 1092. http://dx.doi.org/10.11591/ijece.v8i2.pp1092-1101.

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Majority of the existing commercial application for video surveillance system only captures the event frames where the accuracy level of captures is too poor. We reviewed the existing system to find that at present there is no such research technique that offers contextual-based scene identification of outliers. Therefore, we presented a framework that uses unsupervised learning approach to perform precise identification of outliers for a given video frames concerning the contextual information of the scene. The proposed system uses matrix decomposition method using multivariate analysis to ma

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Rawassizadeh, Reza, Chelsea Dobbins, Mohammad Akbari, and Michael Pazzani. "Indexing Multivariate Mobile Data through Spatio-Temporal Event Detection and Clustering." Sensors 19, no. 3 (2019): 448. http://dx.doi.org/10.3390/s19030448.

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Mobile and wearable devices are capable of quantifying user behaviors based on their contextual sensor data. However, few indexing and annotation mechanisms are available, due to difficulties inherent in raw multivariate data types and the relative sparsity of sensor data. These issues have slowed the development of higher level human-centric searching and querying mechanisms. Here, we propose a pipeline of three algorithms. First, we introduce a spatio-temporal event detection algorithm. Then, we introduce a clustering algorithm based on mobile contextual data. Our spatio-temporal clustering

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