scholarly journals Noise sensitivity of the top eigenvector of a Wigner matrix

2020 ◽  
Vol 177 (3-4) ◽  
pp. 1103-1135
Author(s):  
Charles Bordenave ◽  
Gábor Lugosi ◽  
Nikita Zhivotovskiy
Keyword(s):  
Noise Sensitivity ◽  
Wigner Matrix

scholarly journals Intuitionistic Fuzzy C-Means Algorithm Based on Membership Information Transfer-Ring and Similarity Measurement

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 696
Author(s):  
Haipeng Chen ◽  
Zeyu Xie ◽  
Yongping Huang ◽  
Di Gai
Keyword(s):  
Brain Tumor ◽  
Information Transfer ◽  
Magnetic Resonance Images ◽  
Low Noise ◽  
Similarity Measurement ◽  
Noise Sensitivity ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Membership Information ◽  
Fuzzy C Means Algorithm

The fuzzy C-means clustering (FCM) algorithm is used widely in medical image segmentation and suitable for segmenting brain tumors. Therefore, an intuitionistic fuzzy C-means algorithm based on membership information transferring and similarity measurements (IFCM-MS) is proposed to segment brain tumor magnetic resonance images (MRI) in this paper. The original FCM lacks spatial information, which leads to a high noise sensitivity. To address this issue, the membership information transfer model is adopted to the IFCM-MS. Specifically, neighborhood information and the similarity of adjacent iterations are incorporated into the clustering process. Besides, FCM uses simple distance measurements to calculate the membership degree, which causes an unsatisfactory result. So, a similarity measurement method is designed in the IFCM-MS to improve the membership calculation, in which gray information and distance information are fused adaptively. In addition, the complex structure of the brain results in MRIs with uncertainty boundary tissues. To overcome this problem, an intuitive fuzzy attribute is embedded into the IFCM-MS. Experiments performed on real brain tumor images demonstrate that our IFCM-MS has low noise sensitivity and high segmentation accuracy.

The Epidemiology of Noise Sensitivity in New Zealand

2020 ◽  
Vol 54 (6) ◽  
pp. 482-489
Author(s):  
Daniel Shepherd ◽  
Marja Heinonen-Guzejev ◽  
Kauko Heikkilä ◽  
David Welch ◽  
Kim N. Dirks ◽  
...  
Keyword(s):  
New Zealand ◽  
Social Determinants ◽  
Social Deprivation ◽  
Personal Characteristics ◽  
Social Position ◽  
Noise Sensitivity ◽  
Noise Annoyance ◽  
High Deprivation ◽  
Cultural Determinants ◽  
And Task

<b><i>Background:</i></b> Sensitivity to noise, or nuisance sounds that interrupt relaxation and task-related activities, has been shown to vary significantly across individuals. The current study sought to uncover predictors of noise sensitivity, focussing on possible social and cultural determinants, including social position, education, ethnicity, gender, and the presence of an illness. <b><i>Method:</i></b> Data were collected from 746 New Zealand adults residing in 6 areas differentiated by social position. Participants responded to questions probing personal characteristics, noise sensitivity, illness, neighbourhood problems, and noise annoyance. It was hypothesized that those in high-deprivation areas and/or experiencing illness report higher levels of noise sensitivity. <b><i>Results:</i></b> Approximately 50 and 10% of the participants reported being moderately or very noise sensitive, respectively. Significant predictors of noise sensitivity included age, length of residence, level of social deprivation, and self-reported illness. <b><i>Conclusion:</i></b> There is evidence of social determinants of noise sensitivity, including social position and residential factors.

Railway noise annoyance modeling: Accounting for noise sensitivity and different acoustical features

2018 ◽  
Vol 144 (6) ◽  
pp. 3381-3390 ◽  
Author(s):  
P.-A. Vallin ◽  
C. Marquis-Favre ◽  
J. Bleuse ◽  
L.-A. Gille
Keyword(s):  
Noise Sensitivity ◽  
Railway Noise ◽  
Noise Annoyance

Proportional–Integral Controller Design for Combustion-Timing Feedback, From n-Heptane to Iso-Octane in Compression–Ignition Engines

2017 ◽  
Vol 140 (5) ◽  
Author(s):  
Gabriel Ingesson ◽  
Lianhao Yin ◽  
Rolf Johansson ◽  
Per Tunestål
Keyword(s):  
Ignition Delay ◽  
Controller Design ◽  
Negative Temperature ◽  
Operating Conditions ◽  
Measurement Noise ◽  
Noise Sensitivity ◽  
Proportional Integral ◽  
Primary Reference ◽  
The Difference ◽  
Primary Reference Fuels

The problem of designing robust and noise-insensitive proportional–integral (PI) controllers for pressure-sensor-based combustion-timing control was studied through simulation. Different primary reference fuels (PRF) and operating conditions were studied. The simulations were done using a physics-based, control-oriented model with an empirical ignition-delay correlation. It was found that the controllable region in between the zero-gain region for early injection timings and the misfire region for late injection timings is strongly PRF dependent. As a result, it was necessary to adjust intake temperature to compensate for the difference in fuel reactivity prior to the controller design. With adjusted intake temperature, PRF-dependent negative-temperature coefficient (NTC) behavior gave different system characteristics for the different fuels. The PI controller design was accomplished by solving the optimization problem of maximizing disturbance rejection and tracking performance subject to constraints on robustness and measurement-noise sensitivity. Optimal controller gains were found to be limited by the high system gain at late combustion timings and high-load conditions; furthermore, the measurement-noise sensitivity was found to be higher at the low-load operating points where the ignition delay is more sensitive to variations in load and intake conditions. The controller-gain restrictions were found to vary for the different PRFs; the optimal gains for higher PRFs were lower due to a higher system gain, whereas the measurement-noise sensitivity was found to be higher for lower PRFs.

Genetic Component of Noise Sensitivity

2005 ◽  
Vol 8 (3) ◽  
pp. 245-249 ◽  
Author(s):  
Marja Heinonen-Guzejev ◽  
Heikki S. Vuorinen ◽  
Helena Mussalo-Rauhamaa ◽  
Kauko Heikkilä ◽  
Markku Koskenvuo ◽  
...  
Keyword(s):  
Study Design ◽  
Twin Study ◽  
Genetic Model ◽  
Hearing Impaired ◽  
Genetic Component ◽  
Noise Sensitivity ◽  
Quantitative Genetic ◽  
Genetic Modeling ◽  
Health Related ◽  
Age Range

AbstractWe investigated the genetic component of noise sensitivity using a twin-study design. The study sample consisted of 573 same-sexed twin pairs from the Finnish Twin Cohort. The 131 monozygotic (MZ) and 442 dizygotic (DZ) twin pairs with an age range of 31 to 88 years replied to a questionnaire on noise and health-related items in 1988. The noise sensitivity of respondents was defined as high, quite high, quite low or low. MZ pairs were more similar with regards noise sensitivity than DZ pairs, and quantitative genetic modeling indicated significant familiality. The best z-fitting genetic model provided an estimate of heritability of 36% (95% CI = .20–.50) and when hearing impaired subjects were excluded this rose to 40% (95% CI = .24–.54). In conclusion, noise sensitivity does aggregate in families and probably has a genetic component.

scholarly journals The Noise-Sensitivity Phase Transition in Compressed Sensing

2011 ◽  
Vol 57 (10) ◽  
pp. 6920-6941 ◽  
Author(s):  
David L. Donoho ◽  
Arian Maleki ◽  
Andrea Montanari
Keyword(s):  
Phase Transition ◽  
Compressed Sensing ◽  
Noise Sensitivity
Sign in / Sign up

Export Citation Format

Share Document