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Journal articles on the topic 'Information spectrum'

Author: Grafiati

Published: 1 February 2025

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1

Vazquez-Vilar, Gonzalo, Roberto Lopez-Valcarce, and Josep Sala. "Multiantenna Spectrum Sensing Exploiting Spectral a priori Information." IEEE Transactions on Wireless Communications 10, no. 12 (December 2011): 4345–55. http://dx.doi.org/10.1109/twc.2011.101211.110665.

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2

Zhang, Lian Shun, and Ai Juan Shi. "Classification of Biological Spectrum Based on Principal Component Cluster Analysis." Advanced Materials Research 605-607 (December 2012): 2245–48. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.2245.

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Spectrums of 17 biological tissue phantoms were measured using the fiber-optic spectrometer. Then, the spectrum was preprocessed by multiplicative scatter correction method to devoice the spectrum. Afterwards the features of the spectrum were extracted via principal component analysis. Ultimately, we applied cluster analysis for the spectral features. The results showed that the accumulated credibility of the first 12 spectral principal components was 99.86% for the spectrum after preprocessing; indicating that this spectrum feature extraction might be done in the case of losing no key information. And the results showed that the 17 biological tissue phantoms can be divided into four main categories according their optical features.

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3

Shahabinejad, Hadi, Davorin Sudac, Miltiadis Alamaniotis, Karlo Nad, and Jasmina Obhodas. "Precise gamma-ray spectrum stabilization using full spectral information." Radiation Physics and Chemistry 215 (February 2024): 111337. http://dx.doi.org/10.1016/j.radphyschem.2023.111337.

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4

Lingli, Jiang, Li Shuhui, Li Xuejun, Lei Jiale, and Yang Dalian. "Fault diagnosis of a planetary gearbox based on a local bi-spectrum and a convolutional neural network." Measurement Science and Technology 33, no. 4 (January 21, 2022): 045008. http://dx.doi.org/10.1088/1361-6501/ac471a.

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Abstract The transmission paths of vibration signals in the planetary gearboxes are complex. The signals have the characteristics of strong background noise, instability and non-Gaussian. Bi-spectrums can suppress Gaussian colored noise and are suitable for vibration signal processing of planetary gearboxes. In the traditional fault diagnosis methods based on bi-spectrums, the amplitudes of fault characteristic frequency, or the other further quantitative calculations values, are generally used as the basis of fault diagnosis processes. It has been found that bi-spectrum images can directly characterize the faults of the planetary gearboxes. Convolutional neural networks (CNNs) have been used in mechanical fault diagnoses in recent years. One-dimensional original signals are converted into two-dimensional images as CNN input, which is an effective method for mechanical fault diagnoses. At the present time, there has not been any relevant research conducted using bi-spectral images as CNN input. In this study, a fault diagnosis method based on local bi-spectrum and CNN was proposed. A bi-spectral analysis of the vibration signals of the planetary gearbox was first carried out in order to reveal the fault information while retaining the non-Gaussian information. Then, according to the bi-spectrum symmetry, local images containing the main information were taken as the input of the CNN, which reduced the redundancy of the fault information. Then, in order to improve the diagnostic accuracy of the CNN, the key parameters of CNN architecture were optimized. Finally, a CNN diagnosis model was built to realize the classification diagnoses of different fault positions and different fault degrees of planetary gearboxes. This study’s comparison of the diagnosis results of the full bi-spectrum + CNN, original vibration signal + CNN, local bi-spectrum + (support vector machines), and local bi-spectrum + (stacked auto-encoder) showed that the proposed method in this study had achieved both accuracy and rapidity in the fault diagnoses of planetary gearboxes.

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Mochizuki, Ryo, Tadashi Okubo, and Tetsunori Kobayashi. "Spectrum conversion using prosodic information." Systems and Computers in Japan 38, no. 10 (2007): 12–20. http://dx.doi.org/10.1002/scj.20667.

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6

Romero, Daniel, and Geert Leus. "Wideband Spectrum Sensing From Compressed Measurements Using Spectral Prior Information." IEEE Transactions on Signal Processing 61, no. 24 (December 2013): 6232–46. http://dx.doi.org/10.1109/tsp.2013.2283473.

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Dong, W. P., and K. J. Stout. "Two-Dimensional Fast Fourier Transform and Power Spectrum for Surface Roughness in three Dimensions." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 209, no. 5 (October 1995): 381–91. http://dx.doi.org/10.1243/pime_proc_1995_209_097_02.

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Two-dimensional power spectrums of engineering surfaces contain plenty of information that is important and valuable for surface characterization. However, the characteristics of the two-dimensional spectrums are largely unknown and the algorithm to implement them is not familiar to many engineers or researchers. This paper describes a detailed procedure to implement the two-dimensional fast Fourier transform and power spectrum for surface roughness in three dimensions. Methods used to extract information from the spectrums are introduced. In order to perform two-dimensional spectral analysis and to have a comprehensive understanding of the characteristics of engineering surfaces, an atlas of the two-dimensional spectrums of representative engineering surfaces are presented. The properties of the spectrums are discussed in conjunction with theoretical analysis and visual characterization of the presented spectrums.

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Li, Jian, Jinming Guo, Mingxing Ma, Yuan Zeng, Chuankun Li, and Jibin Xu. "A Gunshot Recognition Method Based on Multi-Scale Spectrum Shift Module." Electronics 11, no. 23 (November 23, 2022): 3859. http://dx.doi.org/10.3390/electronics11233859.

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In view of the issues such as the larger network model and lower recognition accuracy of the current gunshot recognition networks, a neural network based on a multi-scale spectrum shift module is proposed in this paper to fully mine the relevant information among the gunshot spectrums. This network employs the architecture of a densely connected convolutional network and uses a multi-scale spectrum shift module on the branch to realize the interaction among spectrum information. This spectrum shift replaces the under-sampling operation among the spectrums, realizes the globalized feature extraction of the spectrum, avoids the loss of information during the under-sampling process, and further improves the quality of the spectrum feature map. Experiments were conducted based on the NIJ Grant 2016-DN-BX-0183 gunshot dataset and YouTube dataset on gunshots that have been open to the public, both of whose classification accuracy reached 83.2% and 95.1%, respectively, with the size of the network model being controlled at around 16 MB. The experimental results indicate that, compared with other existing methods for convolutional neural network, the proposed network can mine globalized time-frequency information better and effectively, and has a higher accuracy of gunshot recognition.

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Shinotsuka, Hiroshi, Hideki Yoshikawa, Ryo Murakami, Kazuki Nakamura, Hiromi Tanaka, and Kazuhiro Yoshihara. "Automated information compression of XPS spectrum using information criteria." Journal of Electron Spectroscopy and Related Phenomena 239 (February 2020): 146903. http://dx.doi.org/10.1016/j.elspec.2019.146903.

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10

Noam, Yair, Amir Leshem, and Hagit Messer. "Competitive Spectrum Management With Incomplete Information." IEEE Transactions on Signal Processing 58, no. 12 (December 2010): 6251–65. http://dx.doi.org/10.1109/tsp.2010.2077286.

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11

Han, Ning, and Hongbin Li. "Cooperative Spectrum Sensing With Location Information." IEEE Transactions on Vehicular Technology 61, no. 7 (September 2012): 3015–24. http://dx.doi.org/10.1109/tvt.2012.2202410.

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Wang, Wei, and Huifu Xu. "Robust Spectral Risk Optimization When Information on Risk Spectrum Is Incomplete." SIAM Journal on Optimization 30, no. 4 (January 2020): 3198–229. http://dx.doi.org/10.1137/19m1284270.

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13

Et.al, Tae-Yun Jung. "Spectrum Sensing Based On Deep Learning To Increase Spectrum Utilization." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 10, 2021): 538–43. http://dx.doi.org/10.17762/turcomat.v12i6.1971.

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This paper proposes a new spectrum sensing technique for cognitive radio systems. To determine vacancy of the spectrum, the proposed method employs the recurrent neural network (RNN), one of the popular deep learning techniques. The proposed technique determines the spectrum occupancy of the primary user (PU) by observing the received signal’s energy and any information on the PU signal characteristic is not used. To this end, the received signal’s spectrum is obtained by fast Fourier transform (FFT). This process is performed on consecutive received signals and the resulting spectrums are stacked. Finally, a 2-dimensional spectrum (or spectrogram) is made. This 2-D spectrum is cut into sensing channel bandwidths and inputted to the deep learning model to decide the channel’s occupancy. While the recently published spectrum sensing technique based on convolutional neural network (CNN) relies on an empty channel, the proposed technique does not require any empty channel. Only the channel signal of interest to sense is needed. Since spectrum sensing results is two (busy or idle), binary classification deep learning model is developed. According to the computer simulation results, the proposed method has similar performance with the conventional CNN-based method while the spectral efficiency of the proposed method is much higher than that of the existing scheme. In addition, the overall learnable parameters of the proposed deep learning model is only 2/3 of the existing method

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Vallev, Anvar. "COMBINED VIBRATION AND STRAIN GAUGE ANALYSIS FOR DIAGNOSTICS OF INDUSTRIAL MACHINES." VOLUME 39, VOLUME 39 (2021): 38. http://dx.doi.org/10.36336/akustika20213938.

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The Paper is devoted to combined vibration and strain gauge analysis for diagnostics of industrial machines. It is suggested to use strain gauge spectrum analysis. For studying it an experimental installation is designed and created. This installation allows getting signal from strain gauge sensors with maximum frequency 12.87 kHz. According to experimental study application of strain gauge spectrum analysis can provide almost the same information as vibration spectrum analysis. Combination of these two spectrums can give more useful information. Particularly it can be used for filtering noise. In the experimental study spectrums were significantly cleared without any instrumental and digital filters, it was done only by analysis of combination of the spectrums. This study can provide more reliable diagnostics of industrial machines.

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15

Joshi, Gyanendra Prasad, Srijana Acharya, Changsu Kim, and Sung Won Kim. "Reselling Spectrum Information to the Cognitive Users by Maintaining Spectrum Database." Advanced Science Letters 21, no. 6 (June 1, 2015): 1681–83. http://dx.doi.org/10.1166/asl.2015.6154.

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16

Shen, Bo, Jinguang Lv, Jingqiu Liang, Baixuan Zhao, Yupeng Chen, Kaifeng Zheng, Yingze Zhao, Yuxin Qin, Weibiao Wang, and Guohao Liu. "Research on the Processing of Image and Spectral Information in an Infrared Polarization Snapshot Spectral Imaging System." Applied Sciences 14, no. 7 (March 24, 2024): 2714. http://dx.doi.org/10.3390/app14072714.

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In order to solve the problems of a low target recognition rate and poor real-time performance brought about by conventional infrared imaging spectral detection technology under complex background conditions or in the detection of targets of weak radiation or long distance, a kind of infrared polarization snapshot spectral imaging system (PSIFTIS) and a spectrum information processing method based on micro-optical devices are proposed in this paper, where the synchronous acquisition of polarization spectrum information is realized through the spatial modulation of phase with a rooftop-shaped multi-stage micro-mirror and the modulation of the polarization state of light with a micro-nanowire array. For the polarization interference image information obtained, the infrared polarization spectrum decoupling is realized by image segmentation, optical path difference matching, and image registration methods, the infrared polarization spectrum reconstruction is realized by Fourier transform spectral demodulation, and the infrared polarization image fusion is realized by decomposing and reconstructing the high- and low-frequency components of the polarization image based on the Haar wavelet transform. The maximum spectral peak wavenumber error of the four polarization channels of the polarization spectrum reconstruction is less than 2 cm−1, and the polarization angle error is within 1°. Ultimately, compared with the unprocessed polarization image unit, the peak signal-to-noise ratio is improved by 45.67%, the average gradient is improved by 8.03%, and the information entropy is improved by 56.98%.

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Ryu, Hyun, Sunjae Yoon, Hee Suk Yoon, Eunseop Yoon, and Chang D. Yoo. "SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (March 24, 2024): 14857–65. http://dx.doi.org/10.1609/aaai.v38i13.29405.

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Data augmentation is a crucial component in training neural networks to overcome the limitation imposed by data size, and several techniques have been studied for time series. Although these techniques are effective in certain tasks, they have yet to be generalized to time series benchmarks. We find that current data augmentation techniques ruin the core information contained within the frequency domain. To address this issue, we propose a simple strategy to preserve spectral information (SimPSI) in time series data augmentation. SimPSI preserves the spectral information by mixing the original and augmented input spectrum weighted by a preservation map, which indicates the importance score of each frequency. Specifically, our experimental contributions are to build three distinct preservation maps: magnitude spectrum, saliency map, and spectrum-preservative map. We apply SimPSI to various time series data augmentations and evaluate its effectiveness across a wide range of time series benchmarks. Our experimental results support that SimPSI considerably enhances the performance of time series data augmentations by preserving core spectral information. The source code used in the paper is available at https://github.com/Hyun-Ryu/simpsi.

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18

Yang, Xin Dong, Ai Guo Shi, Zuo Chao Wang, Bao Zhang Yang, and Ben Hui Zhang. "Study on the Extraction Method of Wave Spectrum Based on WaMoSII Wave Measuring Information." Applied Mechanics and Materials 444-445 (October 2013): 1745–50. http://dx.doi.org/10.4028/www.scientific.net/amm.444-445.1745.

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The wave measuring system can supply massive wave data information, but these data cannot be applied to navigation practice directly. This thesis set WaMoSII(The Wave and Surface Current Monitoring System) as an example , introduced the system and principle of WaMoSII, reprocessed the supplied wave measuring information, and established wave information database based on SQL Server. Through study on the MTF (modulation transfer function), it proved the distribution similarity between practical wave spectrum and the supplied frequency and spectral density information of WaMoSII, then we implemented the function that extracting relative spectrum information then transforming into absolute spectrum information automatically based on MATLAB program, it can provide convenience for wave measuring radar information for navigating application.

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19

Sarkar, I. N. "Biodiversity informatics: organizing and linking information across the spectrum of life." Briefings in Bioinformatics 8, no. 5 (June 20, 2007): 347–57. http://dx.doi.org/10.1093/bib/bbm037.

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20

Lim, Chang-Yong, and Seung-Jung Shin. "Demand survey of spectrum information and a study on plan for disclosing spectrum information to the public." Journal of the Institute of Internet Broadcasting and Communication 17, no. 1 (February 28, 2017): 253–59. http://dx.doi.org/10.7236/jiibc.2017.17.1.253.

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21

Xie, B. S., S. Y. Zhou, and L. X. Wu. "AN INTEGRATED MINERAL SPECTRAL LIBRARY USING SHARED DATA FOR HYPERSPECTRAL REMOTE SENSING AND GEOLOGICAL MAPPING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B5-2020 (August 24, 2020): 69–75. http://dx.doi.org/10.5194/isprs-archives-xliii-b5-2020-69-2020.

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Abstract. Mineral spectral library (MSL) is the foundation of hyperspectral remote sensing, and a significant tool of storing and managing massive mineral spectral data to facilitate the matching or identifying of unknown rocks and minerals conveniently and fast. However, mineral spectral data are scattered and stored in different spectral libraries worldwide, which behave different spectral resolutions, mineral categories and measurement parameters, and hinder its application in field investigation, mineral identification, landcover identification and geological mapping. An integrated MSL using shared data is developed currently in Central South University, China, to improve the properties of MSL. We collected the shared spectral data and related information (e.g., mineral attribute data, spectrometer information, etc.) worldwide, performed data cleaning measures to retain the qualified spectral data and consolidated all the data in a common framework so as to establish a reliable and comprehensive dataset, and developed an integrated MSL for data management and diversified applications. The user can analysis the target spectrum with the spectrum absorption characteristic parameters, and match the measured spectral curve with the reference spectrum in the integrated MSL to find the most similar spectrum curve. It’s crucial to note that a new spectrum classifier was designed to limit the scope of matching for improving the efficiency of identification when the experimental sample lacks the specific information. The integrated MSL is developed in B/S and C/S website environments. A demonstration of functions of the integrated MSL and its preliminary applications are introduced in the article.

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Benoit, Landry, Étienne Belin, David Rousseau, and François Chapeau-Blondeau. "Information-Theoretic Modeling of Trichromacy Coding of Light Spectrum." Fluctuation and Noise Letters 13, no. 04 (October 20, 2014): 1450025. http://dx.doi.org/10.1142/s0219477514500254.

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Trichromacy is the representation of a light spectrum by three scalar coordinates. Such representation is universally implemented by the human visual system and by RGB (Red Green Blue) cameras. We propose here an informational model for trichromacy. Based on a statistical analysis of the dynamics of individual photons, the model demonstrates a possibility for describing trichromacy as an information channel, for which the input–output mutual information can be computed to serve as a measure of performance. The capabilities and significance of the informational model are illustrated and motivated in various situations. The model especially enables an assessment of the influence of the spectral sensitivities of the three types of photodetectors realizing the trichromatic representation. It provides a criterion to optimize possibly adjustable parameters of the spectral sensitivities such as their center wavelength, spectral width or magnitude. The model shows, for instance, the usefulness of some overlap with smooth graded spectral sensitivities, as observed for instance in the human retina. The approach also, starting from hyperspectral images with high spectral resolution measured in the laboratory, can be used to devise low-cost trichromatic imaging systems optimized for observation of specific spectral signatures. This is illustrated with an example from plant science, and demonstrates a potential of application especially to life sciences. The approach particularizes connections between physics, biophysics and information theory.

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Jing, Li, Che Ying, Jin Meishan, Zhai Yannan, and Ding Changhong. "Application of laser remote sensing technology and super continuous spectrum laser." E3S Web of Conferences 165 (2020): 03002. http://dx.doi.org/10.1051/e3sconf/202016503002.

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Fiber optic super continuous spectrum laser technology is a new technology developed in recent years. It takes into account the advantages of good alignment of laser source, high energy density and wide wavelength range of ordinary white light source, which can not only accurately remote sense distant target, but also obtain hyperspectral information of distant target. Super continuous spectrum of laser radar is a kind of remote sensing monitoring instrument, it can obtain three-dimensional spectral information of the target, and can be accurately detected in the night of distant high spectral information, the all-weather, three-dimensional spectrum detection method with the traditional passive remote sensing compared with single/multiband laser radar technology has incomparable advantages. Based on the development trend of lidar abroad, the development status of super-continuous spectrum lidar in China and the problems in remote sensing detection of oil spill, this paper introduces the feasibility of using super-continuous spectrum laser to monitor oil spill in the sea, which lays a foundation for further research.

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24

Prishchenko, Olga, Nadezhda Cheremskaya, and Svetlana Bukhkalo. "EXAMPLES OF INFORMATION TECHNOLOGIES FOR RECONSTRUCTION FROM THE DATA OF THE SPECTRUM OF SOME CLASSES OF RANDOM FUNCTIONS." Bulletin of the National Technical University "KhPI". Series: Innovation researches in students’ scientific work, no. 1 (October 2, 2022): 38–43. http://dx.doi.org/10.20998/2220-4784.2022.01.06.

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It is known that a stationary random process is represented as a superposition of harmonic oscillations with real frequencies and uncorrelated amplitudes. In the study of nonstationary processes, it is natural to have increasing or declining oscillationсs. This raises the problem of constructing algorithms that would allow constructing broad classes of nonstationary processes from elementary nonstationary random processes. A natural generalization of the concept of the spectrum of a nonstationary random process is the transition from the real spectrum in the case of stationary to a complex or infinite multiple spectrum in the nonstationary case. There is also the problem of describing within the correlation theory of random processes in which the spectrum has no analogues in the case of stationary random processes, namely, the spectrum point is real, but it has infinite multiplicity for the operator image of the corresponding operator, and when the spectrum itself is complex. Reconstruction of the complex spectrum of a nonstationary random function is a very important problem in both theoretical and applied aspects. In the paper the procedure of reconstruction of random process, sequence, field from a spectrum for Gaussian random functions is developed. Compared to the stationary case, there are wider possibilities, for example, the construction of a nonstationary random process with a real spectrum, which has infinite multiplicity and which can be distributed over the entire finite segment of the real axis. The presence of such a spectrum leads, in contrast to the case of a stationary random process, to the appearance of new components in the spectral decomposition of random functions that correspond to the internal states of "strings", i.e. generated by solutions of systems of equations in partial derivatives of hyperbolic type. The paper deals with various cases of the spectrum of a non-self-adjoint operator A, namely, the case of a discrete spectrum and the case of a continuous spectrum, which is located on a finite segment of the real axis, which is the range of values of the real non-decreasing function a (x). The cases a (x) = 0, a (x) = const, a (x) = x and a (x) is a piecewise constant function are studied. The authors consider the recovery of nonstationary sequences for different cases of the spectrum of a non-self-adjoint operator A promising since spectral decompositions are a superposition of discrete or continuous internal states of oscillators with complex frequencies and uncorrelated amplitudes and therefore have deep physical meaning.

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25

Mailer, Colin, Bruce H. Robinson, Benjamin B. Williams, and Howard J. Halpern. "Spectral fitting: The extraction of crucial information from a spectrum and a spectral image." Magnetic Resonance in Medicine 49, no. 6 (May 16, 2003): 1175–80. http://dx.doi.org/10.1002/mrm.10474.

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26

Xiao, Huijun, Tangsen Huang, and Ensong Jiang. "Rural Acoustic Landscape Analysis Based on Segmentation and Extraction of Spectral Image Feature Information." Applied Bionics and Biomechanics 2022 (October 8, 2022): 1–15. http://dx.doi.org/10.1155/2022/1742711.

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Spectrogram is an image that can record voice information, which can be analyzed by analyzing the received image. Spectrograms are used in mechanical fault diagnosis systems to answer questions such as the location, type, and extent of the fault. It is the main tool for analyzing vibration parameters. In actual use, there are three types of spectrograms, namely linear amplitude spectrum, logarithmic amplitude spectrum, and self-power spectrum. The ordinate of the linear amplitude spectrum has a clear physical dimension and is the most commonly used. In this paper, the feature extraction information of rural acoustic landscape is mainly carried out through spectral images, which can effectively improve the segmentation efficiency, ensure the integrity of information, and determine the feasibility of establishing acoustic landscape in rural areas. This article aims to study the analysis of rural acoustic landscape in Guilin, Guangxi, based on the segmentation and extraction of spectral image feature information, through the segmentation and extraction of spectral image feature information, and then analyze the advantages and disadvantages of rural acoustic landscape. In this article, the Gabor wavelet filtering method is proposed to filter and analyze the spectral image. Through the detailed analysis of the insect and bird calls of the forest community near the village of Guilin, Guangxi, finally, the satisfaction and attention of the rural villagers to the acoustic landscape are investigated. The experimental results show that the sound of insects and birds reaches the maximum in spring and the minimum in autumn and winter. Moreover, the attention of rural villagers to acoustic landscape is also very high, with satisfaction of 87.12% and attention of 92.68%.

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Patil, Kishor Puna, Snehal Barge, Knud Erik Skouby, and Ramjee Prasad. "Spectrum Occupancy Information in Support of Adaptive Spectrum Sensing for Cognitive Radio." Network Protocols and Algorithms 6, no. 1 (April 30, 2014): 76. http://dx.doi.org/10.5296/npa.v6i1.4863.

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Semenov, Z. V., O. A. Neklyudov, and V. G. Garanin. "New Modules “Spectrum Export” and “Information from Spectrum” Developed for “Atom” Software." Industrial laboratory. Diagnostics of materials 83, no. 1 p.II (January 1, 2017): 138–40. http://dx.doi.org/10.26896/1028-6861-2018-83-1-ii-138-140.

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Yang, Jichen, and Rohan Kumar Das. "Improving anti-spoofing with octave spectrum and short-term spectral statistics information." Applied Acoustics 157 (January 2020): 107017. http://dx.doi.org/10.1016/j.apacoust.2019.107017.

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30

Zeng, Yong, Yixin Li, Zhongyuan Jiang, and Jianfeng Ma. "GNS: Forge High Anonymity Graph by Nonlinear Scaling Spectrum." Security and Communication Networks 2021 (September 30, 2021): 1–11. http://dx.doi.org/10.1155/2021/8609278.

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It is crucial to generate random graphs with specific structural properties from real graphs, which could anonymize graphs or generate targeted graph data sets. The state-of-the-art method called spectral graph forge (SGF) was proposed at INFOCOM 2018. This method uses a low-rank approximation of the matrix by throwing away some spectrums, which provides privacy protection after distributing graphs while ensuring data availability to a certain extent. As shown in SGF, it needs to discard at least 20% spectrum to defend against deanonymous attacks. However, the data availability will be significantly decreased after more spectrum discarding. Thus, is there a way to generate a graph that guarantees maximum spectrum and anonymity at the same time? To solve this problem, this paper proposes graph nonlinear scaling (GNS). We firmly prove that GNS can preserve all eigenvectors meanwhile providing high anonymity for the forged graph. Precisely, the GNS scales the eigenvalues of the original spectrum and constructs the forged graph with scaled eigenvalues and original eigenvectors. This approach maximizes the preservation of spectrum information to guarantee data availability. Meanwhile, it provides high robustness towards deanonymous attacks. The experimental results show that when SGF discards only 10% of the spectrum, the forged graph has high data availability. At this time, if the distance vector deanonymity algorithm is used to attack the forged graph, almost 100% of the nodes can be identified, while when achieving the same availability, only about 20% of the nodes in the forged graph obtained from GNS can be identified. Moreover, our method is better than SGF in capturing the real graph’s structure in terms of modularity, the number of partitions, and average clustering.

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31

Vizine-Goetz, Diane. "Spectrum." Journal of Library Administration 34, no. 1-2 (December 2001): 49–56. http://dx.doi.org/10.1300/j111v34n01_08.

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32

Zhang, Jing, Renjie Zheng, Xu Chen, Zhaolong Hong, Yunsong Li, and Ruitao Lu. "Spectral Correlation and Spatial High–Low Frequency Information of Hyperspectral Image Super-Resolution Network." Remote Sensing 15, no. 9 (May 8, 2023): 2472. http://dx.doi.org/10.3390/rs15092472.

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Hyperspectral images (HSIs) generally contain tens or even hundreds of spectral segments within a specific frequency range. Due to the limitations and cost of imaging sensors, HSIs often trade spatial resolution for finer band resolution. To compensate for the loss of spatial resolution and maintain a balance between space and spectrum, existing algorithms were used to obtain excellent results. However, these algorithms could not fully mine the coupling relationship between the spectral domain and spatial domain of HSIs. In this study, we presented a spectral correlation and spatial high–low frequency information of a hyperspectral image super-resolution network (SCSFINet) based on the spectrum-guided attention for analyzing the information already obtained from HSIs. The core of our algorithms was the spectral and spatial feature extraction module (SSFM), consisting of two key elements: (a) spectrum-guided attention fusion (SGAF) using SGSA/SGCA and CFJSF to extract spectral–spatial and spectral–channel joint feature attention, and (b) high- and low-frequency separated multi-level feature fusion (FSMFF) for fusing the multi-level information. In the final stage of upsampling, we proposed the channel grouping and fusion (CGF) module, which can group feature channels and extract and merge features within and between groups to further refine the features and provide finer feature details for sub-pixel convolution. The test on the three general hyperspectral datasets, compared to the existing hyperspectral super-resolution algorithms, suggested the advantage of our method.

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Yu, Xilin, Zhenning Mei, Chen Chen, and Wei Chen. "Ranking Power Spectra: A Proof of Concept." Entropy 21, no. 11 (October 29, 2019): 1057. http://dx.doi.org/10.3390/e21111057.

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To characterize the irregularity of the spectrum of a signal, spectral entropy and its variants are widely adopted measures. However, spectral entropy is invariant under the permutation of the power spectrum estimations on a predefined grid. This erases the inherent order structure in the spectrum. To disentangle the order structure and extract meaningful information from raw digital signal, a novel analysis method is necessary. In this paper, we tried to unfold this order structure by defining descriptors mapping real- and vector-valued power spectrum estimation of a signal into a scalar value. The proposed descriptors showed its potential in diverse problems. Significant differences were observed from brain signals and surface electromyography of different pathological/physiological states. Drastic change accompanied by the alteration of the underlying process of signals enables it as a candidate feature for seizure detection and endpoint detection in speech signal. Since the order structure in the spectrum of physiological signal carries previously ignored information, which cannot be properly extracted by existing techniques, this paper takes one step forward along this direction by proposing computationally efficient descriptors with guaranteed information gain. To the best of our knowledge, this is the first work revealing the effectiveness of the order structure in the spectrum in physiological signal processing.

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Han, T. S. "Folklore in Source Coding: Information-Spectrum Approach." IEEE Transactions on Information Theory 51, no. 2 (February 2005): 747–53. http://dx.doi.org/10.1109/tit.2004.840860.

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Datta, Nilanjana, and Renato Renner. "Smooth Entropies and the Quantum Information Spectrum." IEEE Transactions on Information Theory 55, no. 6 (June 2009): 2807–15. http://dx.doi.org/10.1109/tit.2009.2018340.

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López-Benítez, Miguel, Janne Lehtomäki, Kenta Umebayashi, and Fernando Casadevall. "Smart Spectrum Technologies for Mobile Information Systems." Mobile Information Systems 2016 (2016): 1–2. http://dx.doi.org/10.1155/2016/3402450.

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HAN, T. S. "Large Deviation Theorems Revisited: Information-Spectrum Approach." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E91-A, no. 10 (October 1, 2008): 2704–19. http://dx.doi.org/10.1093/ietfec/e91-a.10.2704.

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Hellerstein, Joseph M., Wei Hong, and Samuel R. Madden. "The sensor spectrum." ACM SIGMOD Record 32, no. 4 (December 2003): 22–27. http://dx.doi.org/10.1145/959060.959065.

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Chen, Yifan. "Modeling Information Flows in Distributed Sensor Networks: Chernoff Information Azimuth Spectrum." IEEE Transactions on Signal Processing 60, no. 9 (September 2012): 4898–912. http://dx.doi.org/10.1109/tsp.2012.2201150.

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Al-Hakeem, Aaya Hamid, Marwa Ahmed Meri, and Hadeel Haider Aldujaili. "Placenta Accreta Spectrum." Medical Science Journal for Advance Research 3, no. 1 (March 6, 2022): 1–11. http://dx.doi.org/10.46966/msjar.v3i1.33.

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Histopathological examination of products of conception from miscarriages is part of routine clinical practice. The extent of additional clinically relevant information provided by this investigation in the setting of recurrent spontaneous abortion remains uncertain. The included studies indicated that such examination identify hydatid form moles, villous dysmorphic features suggesting fetal aneuploidy, chronic histiocytic intervillositis (CHI) and massive per villous fibrin deposition and impaired trophoblast invasion. However, in most cases, morphological assessment cannot reliably determine the cause of the miscarriage or distinguish recurrent from sporadic miscarriage. Studies reporting on the use of additional immunohistochemically methods do not currently provide additional clinically useful diagnostic or prognostic information. Routine histological examination of products of conception in the setting of recurrent spontaneous abortion can provide important clinical information in a minority of cases.

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Marcus, Michael J. "Real time spectrum markets and interruptible spectrum." Journal of Communications and Networks 8, no. 2 (June 2006): 158–62. http://dx.doi.org/10.1109/jcn.2006.6182742.

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Cai, Jian-hua. "Fault diagnosis of rolling bearing based on empirical mode decomposition and higher order statistics." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 229, no. 9 (July 28, 2014): 1630–38. http://dx.doi.org/10.1177/0954406214545820.

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In order to solve the problem of the faulted rolling bearing signal getting easily affected by Gaussian noise, a new fault diagnosis method was proposed based on empirical mode decomposition and high-order statistics. Firstly, the vibration signal was decomposed by empirical mode decomposition and the correlation coefficient of each intrinsic mode function was calculated. These intrinsic mode function components, which have a big correlation coefficient, were selected to estimate its higher order spectrum. Then based on the higher order statistics theory, this method uses higher order spectrum of each intrinsic mode function to reconstruct its power spectrum. And these power spectrums were summed to obtain the primary power spectrum of bearing signal. Finally, fault feature information was extracted from the reconstructed power spectrum. A model, using higher order spectrum to reconstruct power spectrum, was established. Meanwhile, analysis was conducted by using the simulated data and the recorded vibration signals which include inner race, out race, and bearing ball fault signal. Results show that the presented method is superior to traditional power spectrum method in suppressing Gaussian noise and its resolution is higher. New method can extract more useful information compared to the traditional method.

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Kułacz, Łukasz, and Adrian Kliks. "Dynamic Spectrum Allocation Using Multi-Source Context Information in OpenRAN Networks." Sensors 22, no. 9 (May 5, 2022): 3515. http://dx.doi.org/10.3390/s22093515.

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Bearing in mind the stringent problem of limited and inefficiently used radio resources, a multi-source mechanism for the dynamic adjustment of occupied frequency bands is proposed. Instead of relying only on radio-related information, the system that collects data from various sources is discussed. Mainly, using the ubiquitous sources of information about the presence of users (such as city monitoring), it is possible to identify areas that have high or low expected traffic with high probabilities. Consequently, in low-traffic areas, it is not necessary to allocate all available spectrum resources while maintaining the quality of service. This leads to the improved spectral efficiency of the network. As the level of trust in certain information sources may differ among various operators, we propose to implement such functionality in the form of an application. Our contribution is a proposal for an algorithm that limits the use of radio resources through fuzzy and soft connections of multiple sources of contextual information. The simulation results presented in this paper show that it is possible to reduce the spectrum used with a slight and simultaneous reduction in user bitrate, which increases the spectral efficiency of the entire system. Hence, following the concept of an open radio access network, various policies for information merging may be specified.

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Chen, Chaoqun, Qigang Jiang, Zhenchao Zhang, Pengfei Shi, Yan Xu, Bin Liu, Jing Xi, and ShouZhi Chang. "Hyperspectral Inversion of Petroleum Hydrocarbon Contents in Soil Based on Continuum Removal and Wavelet Packet Decomposition." Sustainability 12, no. 10 (May 21, 2020): 4218. http://dx.doi.org/10.3390/su12104218.

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Hyperspectral remote sensing is widely used to detect petroleum hydrocarbon pollution in soil monitoring. Different spectral pretreatment methods seriously affect the prediction and analysis of petroleum hydrocarbon contents (PHCs). This study adopted a combined spectral data preprocessing technique that improves the prediction accuracy of petroleum hydrocarbons in soil. We combined continuum removal and wavelet packet decomposition (CR–Daubechies 3 (db3)) to process the hyperspectral reflectance data of 26 soil samples in the oil production work area in China and judged the correlation between spectral reflectance and petroleum hydrocarbons in soil. Partial least squares regression was used to construct an optimal model for the inversion of PHCs in soil and the leave-one-out cross-validation was used to select the best factor number. The best model of soil petroleum hydrocarbon inversion was determined by comprehensively comparing the initial spectrum, db3 to high-frequency spectrum, db3 to low-frequency spectrum, after-continuum removal spectrum, CR-db3 to high-frequency spectrum, and CR-db3 to low-frequency spectrum comprehensively. The main contributions of this study are as follows: (1) three-layer decomposition with CR-db3 can improve the correlation between spectral reflectance and PHCs and effectively improve the sensitivity of the spectrum to PHCs; (2) the prediction accuracy of the high-frequency spectrum of wavelet packet decomposition for PHCs in soil is higher than that of low-frequency information; (3) the proposed petroleum hydrocarbon prediction model based on CR-db3 processed spectra to obtain high-frequency information is optimal (coefficient of determination = 0.977, root mean square error of calibration = 3.078, root mean square error of cross-validation = 4.727, root mean square error of prediction = 4.498, ratio of performance to deviation = 6.12).

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Zhang, Li Zheng, and Jin Cheng Li. "Ink Spectrum Data Feature Extraction Research." Applied Mechanics and Materials 731 (January 2015): 87–91. http://dx.doi.org/10.4028/www.scientific.net/amm.731.87.

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In the field of printing research, color replication based on spectrum need to solve the issues that how to get cyan, magenta and yellow ink spectrum feature extraction. In this study, based on spectral derivative and spectral image deviation, establish a method to choose special wavelength positions for making up the corresponding relation between ink value and ink spectral reflectivity. The ink scales are printed on papers in the way that the value of the ink dot increase 5% from 0-100%. Ink spectrum reflectivity curves are made up through measuring ink scales. The first-order derivatives of cyan, magenta and yellow ink spectrum reflectivity curves in the range of 380-730nm, in sequence, change smooth at 650nm, 530-560nm, 450nm, and change violently at 530nm, 600nm, 500nm wavelength position. Based on spectral image deviation, the cyan ink spectral wavelength in the range of 630-730nm, the magenta ink spectral wavelength in the range of 500-540nm, include the most information and the least correlation. For the reason that yellow ink spectral images intersect with others, the analysis is combined with first-order derivative of ink spectrum curves. The method proposed in this paper is propitious to get primary ink spectral feature for color spectrum separation.

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Chen, Guang Xue, Hai Wen Wang, and Jie Li. "Study on the Efficient Reconstruction Method of Spectral Color Information." Advanced Materials Research 174 (December 2010): 32–35. http://dx.doi.org/10.4028/www.scientific.net/amr.174.32.

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The efficient reconstruction of spectral color information was the key research subject of color production technique based on spectrum. For the reality that the current principal component analysis method and independent component analysis method cannot solve the efficient reconstruction of spectral color information satisfactorily, it put forward the mixed frequency component analysis method in the integrated and unique perspective for the first time. And it solved the efficient reconstruction of spectral color information successfully, besides it provided the further theoretical support of color vision stage theory and a new theoretical explanation of eye color visual constancy

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Schulz, Michael, and Karl Stattegger. "Spectrum: spectral analysis of unevenly spaced paleoclimatic time series." Computers & Geosciences 23, no. 9 (November 1997): 929–45. http://dx.doi.org/10.1016/s0098-3004(97)00087-3.

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Yu, Zhang, Chen Yong, He Panfeng, and Cai Yueming. "Dynamic update scheme of spectrum information based on spectrum opportunity incentive in the database-assisted dynamic spectrum management." China Communications 21, no. 3 (March 2024): 75–90. http://dx.doi.org/10.23919/jcc.ja.2022-0208.

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Lin, Yuxin, Bingo Wing-Kuen Ling, Lingyue Hu, Yiting Zheng, Nuo Xu, Xueling Zhou, and Xinpeng Wang. "Hyperspectral Image Enhancement by Two Dimensional Quaternion Valued Singular Spectrum Analysis for Object Recognition." Remote Sensing 13, no. 3 (January 25, 2021): 405. http://dx.doi.org/10.3390/rs13030405.

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This paper proposes a two dimensional quaternion valued singular spectrum analysis based method for enhancing the hyperspectral image. Here, the enhancement is for performing the object recognition, but neither for improving the visual quality nor suppressing the artifacts. In particular, the two dimensional quaternion valued singular spectrum analysis components are selected in such a way that the ratio of the interclass separation to the intraclass separation of the pixel vectors is maximized. Next, the support vector machine is employed for performing the object recognition. Compared to the conventional two dimensional real valued singular spectrum analysis based method where only the pixels in a color plane is exploited, the two dimensional quaternion valued singular spectrum analysis based method fuses four color planes together for performing the enhancement. Hence, both the spatial information among the pixels in the same color plane and the spectral information among various color planes are exploited. The computer numerical simulation results show that the overall classification accuracy based on our proposed method is higher than the two dimensional real valued singular spectrum analysis based method, the three dimensional singular spectrum analysis based method, the multivariate two dimensional singular spectrum analysis based method, the median filtering based method, the principal component analysis based method, the Tucker decomposition based method and the hybrid spectral convolutional neural network (hybrid SN) based method.

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Feng, Wei Wei, Wei Zhang, Xiao Xue Tian, Hai Long Peng, Hua Xiong, and Ling Xin Chen. "A Portable Spectrum Measurement System Based on Laser-Raman and Fluoresce Spectrum for Cooking Oil Analysis." Advanced Materials Research 588-589 (November 2012): 1152–55. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.1152.

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A system based on spectrum analysis for cooking oil testing which combining laser-induced-fluorescence and laser-Raman technology is established. Several oil samples are testing by this system. The measurement results show that, there are rich information for fluorescence and Raman spectrum for the oil samples and there are obvious difference between the fluoresce spectrums for these samples, which can be used as a reference for oil pollution classification and recognition. This technology can be used for in-situ monitoring equipments.

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