Examination of Soft Viterbi Decoder Enhanced with Non-Transmittable Codewords with its Antecedents Algorithms Over Burst Noise Model

Phase reconstruction is in general a non-trivial problem when it comes to devices where the reference is not accessible. A non-convex iterative optimization algorithm is proposed in this paper in order to reconstruct the phase in reference-less spherical multiprobe measurement systems based on a rotating arch of probes. The algorithm is based on the reconstruction of the phases of self-transmitting devices in multiprobe systems by taking advantage of the on-axis top probe of the arch. One of the limitations of the top probe solution is that when rotating the measurement system arch, the relative phase between probes is lost. This paper proposes a solution to this problem by developing an optimization iterative algorithm that uses partial knowledge of relative phase between probes. The iterative algorithm is based on linear combinations of signals when the relative phase is known. Phase substitution and modal filtering are implemented in order to avoid local minima and make the algorithm converge. Several noise-free examples are presented and the results of the iterative algorithm analyzed. The number of linear combinations used is far below the square of the degrees of freedom of the non-linear problem, which is compensated by a proper initial guess. With respect to noisy measurements, the top probe method will introduce uncertainties for different azimuth and elevation positions of the arch. This is modelled by considering the real noise model of a low-cost receiver and the results demonstrate the good accuracy of the method. Numerical results on antenna measurements are also presented. Due to the numerical complexity of the algorithm, it is limited to electrically small- or medium-size problems.

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Model Refinement Learning and an Example on Channel Estimation With Universal Noise Model

IEEE Journal on Selected Areas in Communications ◽

10.1109/jsac.2020.3036964 ◽

2021 ◽

Vol 39 (1) ◽

pp. 31-46

Author(s):

Hui-Ming Wang ◽

Jia-Cheng Jiang ◽

Yu-Ning Wang

Keyword(s):

Channel Estimation ◽

Noise Model ◽

Model Refinement

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Acoustic Modeling of Meteorological Effects on Roadway Noise

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198121994584 ◽

2021 ◽

pp. 036119812199458

Author(s):

Roger L. Wayson ◽

Kenneth Kaliski

Keyword(s):

Road Traffic ◽

Traffic Noise ◽

Model Development ◽

The United States ◽

Noise Model ◽

Road Traffic Noise ◽

Atmospheric Conditions ◽

Noise Propagation ◽

Transportation Research ◽

Meteorological Effects

Modeling road traffic noise levels without including the effects of meteorology may lead to substantial errors. In the United States, the required model is the Traffic Noise Model which does not include meteorology effects caused by refraction. In response, the Transportation Research Board sponsored NCHRP 25-52, Meteorological Effects on Roadway Noise, to collect highway noise data under different meteorological conditions, document the meteorological effects on roadway noise propagation under different atmospheric conditions, develop best practices, and provide guidance on how to: (a) quantify meteorological effects on roadway noise propagation; and (b) explain those effects to the public. The completed project at 16 barrier and no-barrier measurement positions adjacent to Interstate 17 (I-17) in Phoenix, Arizona provided the database which has enabled substantial developments in modeling. This report provides more recent information on the model development that can be directly applied by the noise analyst to include meteorological effects from simple look-up tables to more precise use of statistical equations.

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Forgery Detection in Digital Images by Multi-Scale Noise Estimation

Journal of Imaging ◽

10.3390/jimaging7070119 ◽

2021 ◽

Vol 7 (7) ◽

pp. 119

Author(s):

Marina Gardella ◽

Pablo Musé ◽

Jean-Michel Morel ◽

Miguel Colom

Keyword(s):

Noise Model ◽

Correlated Noise ◽

Forgery Detection ◽

Color Channel ◽

Image Block ◽

Multi Scale ◽

Compressed Images ◽

Inconsistency Analysis ◽

Highly Correlated ◽

The Moment

A complex processing chain is applied from the moment a raw image is acquired until the final image is obtained. This process transforms the originally Poisson-distributed noise into a complex noise model. Noise inconsistency analysis is a rich source for forgery detection, as forged regions have likely undergone a different processing pipeline or out-camera processing. We propose a multi-scale approach, which is shown to be suitable for analyzing the highly correlated noise present in JPEG-compressed images. We estimate a noise curve for each image block, in each color channel and at each scale. We then compare each noise curve to its corresponding noise curve obtained from the whole image by counting the percentage of bins of the local noise curve that are below the global one. This procedure yields crucial detection cues since many forgeries create a local noise deficit. Our method is shown to be competitive with the state of the art. It outperforms all other methods when evaluated using the MCC score, or on forged regions large enough and for colorization attacks, regardless of the evaluation metric.

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