scholarly journals Location Security under Reference Signals’ Spoofing Attacks: Threat Model and Bounds

2021 ◽  
Author(s):  
Stefania Bartoletti ◽  
Giuseppe Bianchi ◽  
Danilo Orlando ◽  
Ivan Palamà ◽  
Nicola Blefari-Melazzi
Keyword(s):  
Threat Model ◽  
Reference Signals

Implementation of a single-channel active noise control system with multiple reference sensors

2020 ◽  
Vol 68 (5) ◽  
pp. 358-366
Author(s):  
H.E. Oh ◽  
W.B. Jeong ◽  
C. Hong
Keyword(s):  
Single Channel ◽  
Control Method ◽  
Reference Signal ◽  
Active Noise Control ◽  
Multiple Source ◽  
Weighted Sum ◽  
Reference Signals ◽  
Feedforward Controller ◽  
Channel Control ◽  
Source Signals

When multiple sources contribute competitively to the noise level, multi-channel control architecture is needed, leading to more cost and time for control computation. We, hence, are concerned with a single-channel control method with a single-reference signal obtained from a linear combination of the multiple source signals. First, we selected 3 source signal sensors for the reference signals and the error sensor, selected a proper actuator and designed the controllers: 3 cases of single-channel feedforward controllers with a single-reference signal respectively from the source signals, a multi-channel feedforward controller with the reference signals from the source signals, and the proposed controller with the reference signal from weighted sum of the source signals. The weighting factors and the filter coefficients of the controller were determined by the FxLMS algorithm. An experiment was then performed to confirm the effectiveness of the proposed method comparing the control performance with other methods for a tower air conditioner. The overall sound pressure level (SPL) detected by the error sensor is compared to evaluate their performance. The reduction in the overall SPL was obtained by 4.74 dB, 1.96 dB and 6.62 dB, respectively, when using each of the 3 reference signals. Also, the overall SPL was reduced by 7.12 dB when using the multi-reference controller and by 7.66 dB when using the proposed controller. Conclusively, under the multiple source contribution, a single-channel feed forward controller with the reference signal from a weighted sum of the source signals works well with lower cost than multi-channel feedforward controller.

scholarly journals High Ripple-Density Resolution for Discriminating Between Rippled and Nonrippled Signals: Effect of Temporal Processing or Combination Products?

2021 ◽  
Vol 25 ◽  
pp. 233121652110101
Author(s):  
Dmitry I. Nechaev ◽  
Olga N. Milekhina ◽  
Marina S. Tomozova ◽  
Alexander Y. Supin
Keyword(s):  
Low Frequency ◽  
Noise Signal ◽  
Density Variation ◽  
Choice Procedure ◽  
Masker Level ◽  
Reference Signals ◽  
Forced Choice Procedure ◽  
The Mean ◽  
Density Resolution

The goal of the study was to investigate the role of combination products in the higher ripple-density resolution estimates obtained by discrimination between a spectrally rippled and a nonrippled noise signal than that obtained by discrimination between two rippled signals. To attain this goal, a noise band was used to mask the frequency band of expected low-frequency combination products. A three-alternative forced-choice procedure with adaptive ripple-density variation was used. The mean background (unmasked) ripple-density resolution was 9.8 ripples/oct for rippled reference signals and 21.8 ripples/oct for nonrippled reference signals. Low-frequency maskers reduced the ripple-density resolution. For masker levels from −10 to 10 dB re. signal, the ripple-density resolution for nonrippled reference signals was approximately twice as high as that for rippled reference signals. At a masker level as high as 20 dB re. signal, the ripple-density resolution decreased in both discrimination tasks. This result leads to the conclusion that low-frequency combination products are not responsible for the task-dependent difference in ripple-density resolution estimates.

A threat model method for ICS malware

2021 ◽  
Author(s):  
Yassine Mekdad ◽  
Giuseppe Bernieri ◽  
Mauro Conti ◽  
Abdeslam El Fergougui
Keyword(s):  
Threat Model ◽  
Model Method

scholarly journals Does It Help Smokers If We Stigmatize Them? A Test of the Stigma-Induced Identity Threat Model Among U.S. and Danish Smokers

2019 ◽  
Vol 37 (3) ◽  
pp. 294-313 ◽  
Author(s):  
Marie Helweg-Larsen ◽  
Lia J. Sorgen ◽  
Charlotta Pisinger
Keyword(s):  
Identity Threat ◽  
Threat Model

A source contribution quantitative calculation method for mechanical systems based on the simplified independent component analysis with reference

2016 ◽  
Vol 230 (18) ◽  
pp. 3222-3240
Author(s):  
Jie Zhang ◽  
Zhousuo Zhang ◽  
Wei Cheng ◽  
Guanwen Zhu ◽  
Zhengjia He
Keyword(s):  
Independent Component Analysis ◽  
Prior Knowledge ◽  
Complex Structure ◽  
Component Analysis ◽  
Independent Component ◽  
Vibration Measurement ◽  
Contrast Function ◽  
Source Contribution ◽  
Quantitative Calculation ◽  
Reference Signals

The quantitative calculation of the source contribution is very important and critical for the identification of the main vibration sources and the reduction of vibration and noise in submarine. It is difficult to calculate the source contribution because of the submarine’s complex structure and the large amount of vibration sources. As a typical blind source separation method, independent component analysis (ICA) has recently been proved to be an effective method to solve the source identification problem in which the source signals and mixing models are unknown. However, the outcomes of the ICA algorithm are affected by random sampling and random initialization of variables. In our study, the prior knowledge of the vibration sources can be obtained through the vibration measurement of submarine. Obviously, information in addition to mixed signals from sensors can lead to a more accurate separation. Therefore the contrast function of ICA can be enhanced by the reference signals obtained by the prior knowledge. In this paper, a closeness measurement between the independent components and the reference signals obtained by the prior knowledge is introduced, and the closeness measurement is constructed to have the same optimization direction with the traditional contrast function: negentropy. The closeness measurement is used to enhance the contrast function and then the enhanced contrast function is optimized by means of the Newton iteration and the deflation approach. Thus the simplified independent component analysis with reference (ICA-R) algorithm is obtained. After that a method to quantitatively calculate the source contribution is proposed based on the outcomes of the simplified ICA-R. Finally, the effectiveness of the proposed method is verified by the numerical simulation studies. The performance offered by the proposed method is also investigated by the experiment: it appear as a very appealing tool for the quantitative calculation of the source contribution.

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