Signal Reconstruction Algorithm of Finite Rate of Innovation with Matrix Pencil and Principal Component Analysis

2017 ◽  
Vol E100.A (3) ◽  
pp. 761-768 ◽  
Author(s):  
Yujie SHI ◽  
Li ZENG
Keyword(s):  
Principal Component Analysis ◽  
Signal Reconstruction ◽  
Principal Component ◽  
Matrix Pencil ◽  
Reconstruction Algorithm ◽  
Component Analysis ◽  
Finite Rate ◽  
Finite Rate Of Innovation

scholarly journals A Multiobjective Evolutionary Approach for Solving Large-Scale Network Reconstruction Problems via Logistic Principal Component Analysis

2021 ◽  
Author(s):  
Chaolong Ying ◽  
Jing Liu ◽  
Kai Wu ◽  
Chao Wang
Keyword(s):  
Principal Component Analysis ◽  
Network Structure ◽  
Large Scale ◽  
Evolutionary Process ◽  
Principal Component ◽  
Reconstruction Algorithm ◽  
Component Analysis ◽  
Network Reconstruction ◽  
Reconstruction Error ◽  
Large Scale Networks

Currently, the problem of uncovering complex network structure and dynamics from time series is prominent in many fields. Despite the recent progress in this area, reconstructing large-scale networks from limited data remains a tough problem. Existing works treat connections of nodes as continuous values, leaving a challenge of setting a proper cut-off value to distinguish whether the connections exist or not. Besides, their performances on large-scale networks are far from satisfactory. Considering the reconstruction error and sparsity as two objectives, this paper proposes a subspace learning based evolutionary multiobjective network reconstruction algorithm, termed as SLEMO-NR, to solve the aforementioned problems. In the evolutionary process, we assume that binary-coded individuals obey the Bernoulli distribution and can use the probability and natural parameter as the alternative representations. Moreover, our approach utilizes the logistic principal component analysis (LPCA) to learn a subspace containing the features of network structure. The offspring solutions are generated in the learned subspace and then can be mapped back to the original space via LPCA. Benefitting from the alternative representations, a preference-based local search operator is proposed to concentrate on finding solutions approximate to the true sparsity. The experimental results on synthetic networks and six real-world networks demonstrate that, due to the well-learned network structure subspace and the preference-based strategy, our approach is effective in reconstructing large-scale networks compared to six existing methods.

Industrial Ultrasonic Imaging Based on Compressed Sensing

2012 ◽  
Vol 256-259 ◽  
pp. 2328-2332
Author(s):  
Guang Zhi Dai ◽  
Wei Yi Lin ◽  
Guo Qiang Han
Keyword(s):  
Compressed Sensing ◽  
Imaging System ◽  
Signal Reconstruction ◽  
Ultrasonic Imaging ◽  
Reconstruction Algorithm ◽  
Physical Structure ◽  
Finite Rate ◽  
Array Technology ◽  
Ultrasonic Phased Array ◽  
Finite Rate Of Innovation

Industrial ultrasonic imaging system based on compressed sensing(IUICS),is still lack of available implementation, due to its difficulty in hardware realization.However,thanks to the recent finite rate of innovation and ultrasonic phased array technology,it is possible to apply Compressive Sensing framework to industrial ultrasonic imaging system.In this paper,we propose an available scheme of industrial ultrasonic imaging,which includes the sampling of signal,reconstruction algorithm and its physical structure, based on Compressed Sensing.

scholarly journals A Multiobjective Evolutionary Approach for Solving Large-Scale Network Reconstruction Problems via Logistic Principal Component Analysis

2021 ◽  
Author(s):  
Chaolong Ying ◽  
Jing Liu ◽  
Kai Wu ◽  
Chao Wang
Keyword(s):  
Principal Component Analysis ◽  
Network Structure ◽  
Large Scale ◽  
Evolutionary Process ◽  
Principal Component ◽  
Reconstruction Algorithm ◽  
Component Analysis ◽  
Network Reconstruction ◽  
Reconstruction Error ◽  
Large Scale Networks

Currently, the problem of uncovering complex network structure and dynamics from time series is prominent in many fields. Despite the recent progress in this area, reconstructing large-scale networks from limited data remains a tough problem. Existing works treat connections of nodes as continuous values, leaving a challenge of setting a proper cut-off value to distinguish whether the connections exist or not. Besides, their performances on large-scale networks are far from satisfactory. Considering the reconstruction error and sparsity as two objectives, this paper proposes a subspace learning based evolutionary multiobjective network reconstruction algorithm, termed as SLEMO-NR, to solve the aforementioned problems. In the evolutionary process, we assume that binary-coded individuals obey the Bernoulli distribution and can use the probability and natural parameter as the alternative representations. Moreover, our approach utilizes the logistic principal component analysis (LPCA) to learn a subspace containing the features of network structure. The offspring solutions are generated in the learned subspace and then can be mapped back to the original space via LPCA. Benefitting from the alternative representations, a preference-based local search operator is proposed to concentrate on finding solutions approximate to the true sparsity. The experimental results on synthetic networks and six real-world networks demonstrate that, due to the well-learned network structure subspace and the preference-based strategy, our approach is effective in reconstructing large-scale networks compared to six existing methods.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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