Signal-to-Clutter Ratio in Pseudo Random Doppler Flowmeter

1986 ◽  
Vol 8 (4) ◽  
pp. 272-284
Author(s):  
Dominique Jean Cathignol
Keyword(s):  
Velocity Measurement ◽  
Signal To Noise Ratio ◽  
Binary Sequence ◽  
Vessel Diameter ◽  
Sine Wave ◽  
Ultrasonic Field ◽  
Pseudorandom Binary Sequence ◽  
Range Cell ◽  
Simultaneous Decrease ◽  
Better Than

In order to increase the signal-to-noise ratio of Doppler velocimeters, several authors have proposed to use the emission of a sine wave phase modulated by a pseudorandom binary sequence with an on/off ratio equal to unity. The velocity measurement of the scatterers located in the range cell volume may be perturbed by all the other reflecting clutter targets which are moving in the ultrasonic field. The ratio between the Doppler signal issuing from the range cell and that from the mobile clutter targets will be defined as the signal-to-mobile clutter ratio (SCR)m. We have evaluated the (SCR)m by computing the ambiguity function of the pseudorandom binary sequence as a function of the vessel diameter, the number of phase states and the normalized particle velocity. The results show: (i) for high velocities, the (SCR)m decreases rapidly. (ii) an increase of the number of states of the code does not entail a proportional improvement in the (SCR)m because of the simultaneous decrease of the range cell;(iii) it is confirmed that pseudorandom flowmeters perform better than pulsed Doppler systems for small, even deep-lying, vessels. However, they do not allow for a precise velocity measurement in the case of vessel diameters larger than three Centimeters.

Efficient Steganography Algorithm Based On DCT And Entropy Thresholding Technique

2018 ◽  
Vol 8 (1) ◽  
pp. 45
Author(s):  
Satvir Singh
Keyword(s):  
Relative Entropy ◽  
Experimental Work ◽  
Random Function ◽  
Signal To Noise Ratio ◽  
Binary Sequence ◽  
Threshold Value ◽  
Absolute Difference ◽  
Secret Message ◽  
Signal To Noise ◽  
Confidential Information

Steganography is the special art of hidding important and confidential information in appropriate multimedia carrier. It also restrict the detection of  hidden messages. In this paper we proposes steganographic method based on dct and entropy thresholding technique. The steganographic algorithm uses random function in order to select block of the image where the elements of the binary sequence of a secret message will be inserted. Insertion takes place at the lower frequency  AC coefficients of the  block. Before we insert the secret  message. Image under goes dc transformations after insertion of the secret message we apply inverse dc transformations. Secret message will only be inserted into a particular block if  entropy value of that particular block is greater then threshold value of the entropy and if block is selected by the random function. In  Experimental work we calculated the peak signal to noise ratio(PSNR), Absolute difference , Relative entropy. Proposed algorithm give high value of PSNR  and low value of Absolute difference which clearly indicate level of distortion in image due to insertion of secret message is reduced. Also value of  relative entropy is close to zero which clearly indicate proposed algorithm is sufficiently secure. 

scholarly journals Velody 2—Resilient High-Capacity MIDI Steganography for Organ and Harpsichord Music

2020 ◽  
Vol 11 (1) ◽  
pp. 39
Author(s):  
Eric Järpe ◽  
Mattias Weckstén
Keyword(s):  
Mean Absolute Error ◽  
Signal To Noise Ratio ◽  
High Capacity ◽  
Absolute Error ◽  
Music Technology ◽  
Alternative Methods ◽  
File Size ◽  
User Friendly ◽  
Different Levels ◽  
Better Than

A new method for musical steganography for the MIDI format is presented. The MIDI standard is a user-friendly music technology protocol that is frequently deployed by composers of different levels of ambition. There is to the author’s knowledge no fully implemented and rigorously specified, publicly available method for MIDI steganography. The goal of this study, however, is to investigate how a novel MIDI steganography algorithm can be implemented by manipulation of the velocity attribute subject to restrictions of capacity and security. Many of today’s MIDI steganography methods—less rigorously described in the literature—fail to be resilient to steganalysis. Traces (such as artefacts in the MIDI code which would not occur by the mere generation of MIDI music: MIDI file size inflation, radical changes in mean absolute error or peak signal-to-noise ratio of certain kinds of MIDI events or even audible effects in the stego MIDI file) that could catch the eye of a scrutinizing steganalyst are side-effects of many current methods described in the literature. This steganalysis resilience is an imperative property of the steganography method. However, by restricting the carrier MIDI files to classical organ and harpsichord pieces, the problem of velocities following the mood of the music can be avoided. The proposed method, called Velody 2, is found to be on par with or better than the cutting edge alternative methods regarding capacity and inflation while still possessing a better resilience against steganalysis. An audibility test was conducted to check that there are no signs of audible traces in the stego MIDI files.

Artificial neural network-based seismic detector

1995 ◽  
Vol 85 (1) ◽  
pp. 308-319 ◽  
Author(s):  
Jin Wang ◽  
Ta-Liang Teng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Input Data ◽  
Seismic Event ◽  
Signal To Noise Ratio ◽  
Type B ◽  
Earthquake Detection ◽  
Seismic Event Detection ◽  
Artificial Neural ◽  
Better Than

Abstract An artificial neural network-based pattern classification system is applied to seismic event detection. We have designed two types of Artificial Neural Detector (AND) for real-time earthquake detection. Type A artificial neural detector (AND-A) uses the recursive STA/LTA time series as input data, and type B (AND-B) uses moving window spectrograms as input data to detect earthquake signals. The two AND's are trained under supervised learning by using a set of seismic recordings, and then the trained AND's are applied to another set of recordings for testing. Results show that the accuracy of the artificial neural network-based seismic detectors is better than that of the conventional algorithms solely based on the STA/LTA threshold. This is especially true for signals with either low signal-to-noise ratio or spikelike noises.

scholarly journals A new and homogeneous metallicity scale for Galactic classical Cepheids

2018 ◽  
Vol 616 ◽  
pp. A82 ◽  
Author(s):  
B. Proxauf ◽  
R. da Silva ◽  
V. V. Kovtyukh ◽  
G. Bono ◽  
L. Inno ◽  
...  
Keyword(s):  
Effective Temperature ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Physical Parameters ◽  
Classical Cepheids ◽  
Microturbulent Velocity ◽  
Metal Abundances ◽  
Pulsation Cycle ◽  
Intrinsic Error ◽  
Better Than

We gathered more than 1130 high-resolution optical spectra for more than 250 Galactic classical Cepheids. The spectra were collected with the optical spectrographs UVES at VLT, HARPS at 3.6 m, FEROS at 2.2 m MPG/ESO, and STELLA. To improve the effective temperature estimates, we present more than 150 new line depth ratio (LDR) calibrations that together with similar calibrations already available in the literature allowed us to cover a broad range in wavelength (5348 ≤ λ ≤ 8427 Å) and in effective temperature (3500 ≤ Teff ≤ 7700 K). This gives us the unique opportunity to cover both the hottest and coolest phases along the Cepheid pulsation cycle and to limit the intrinsic error on individual measurements at the level of ~100 K. As a consequence of the high signal-to-noise ratio of individual spectra, we identified and measured hundreds of neutral and ionized lines of heavy elements, and in turn, have the opportunity to trace the variation of both surface gravity and microturbulent velocity along the pulsation cycle. The accuracy of the physical parameters and the number of Fe I (more than one hundred) and Fe II (more than ten) lines measured allowed us to estimate mean iron abundances with a precision better than 0.1 dex. We focus on 14 calibrating Cepheids for which the current spectra cover either the entire or a significant portion of the pulsation cycle. The current estimates of the variation of the physical parameters along the pulsation cycle and of the iron abundances agree very well with similar estimates available in the literature. Independent homogeneous estimates of both physical parameters and metal abundances based on different approaches that can constrain possible systematics are highly encouraged.

Moth-Flame Optimization Algorithm Based Multilevel Thresholding for Image Segmentation

2017 ◽  
Vol 8 (4) ◽  
pp. 58-83 ◽  
Author(s):  
Abdul Kayom Md Khairuzzaman ◽  
Saurabh Chaudhury
Keyword(s):  
Image Segmentation ◽  
Computational Complexity ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Multilevel Thresholding ◽  
Bacterial Foraging Optimization ◽  
Kapur’S Entropy ◽  
Moth Flame Optimization Algorithm ◽  
Popular Image ◽  
Better Than

Multilevel thresholding is a popular image segmentation technique. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are applied to reduce computational complexity of multilevel thresholding. A new method of multilevel thresholding based on Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The goodness of the thresholds is evaluated using Kapur's entropy or Otsu's between class variance function. The proposed method is tested on a set of benchmark test images and the performance is compared with PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based methods. The results are analyzed objectively using the fitness function and the Peak Signal to Noise Ratio (PSNR) values. It is found that MFO based multilevel thresholding method performs better than the PSO and BFO based methods.

The Influence of Variable-Speed Waveforms on Vibration Suppression in Time-Varying Speed Cutting

2013 ◽  
Vol 690-693 ◽  
pp. 2514-2518
Author(s):  
Juan Cong ◽  
Yun Wang ◽  
Wei Na Yu
Keyword(s):  
Wave Speed ◽  
Vibration Suppression ◽  
Sine Wave ◽  
Time Varying ◽  
Variable Speed ◽  
Suppression Effect ◽  
Speed Variation ◽  
Input And Output ◽  
System Input ◽  
Better Than

Through the research on the change of system input and output energy in time-varying speed cutting, the influence of variable-speed waveforms on vibration suppression effect in time-varying speed cutting is quantitatively analyzed in this paper. A conclusion can be drawn that sine wave speed variation is better than triangle wave speed variation in vibration suppression.

scholarly journals Dietary Nitrate Influence on Oxygen Uptake Response to Pseudorandom Binary Sequence Cycling Protocols

2021 ◽  
Vol 35 (S1) ◽  
Author(s):  
Barry Scheuermann ◽  
Tyler Falor ◽  
Andrew Misko ◽  
Jordan Monnier ◽  
Britton Scheuermann
Keyword(s):  
Oxygen Uptake ◽  
Binary Sequence ◽  
Dietary Nitrate ◽  
Pseudorandom Binary Sequence ◽  
Oxygen Uptake Response

scholarly journals A High-Granularity Timing Detector for the Phase-II upgrade of the ATLAS Calorimeter system: detector concept, description and R&D and beam test results

2021 ◽  
Vol 253 ◽  
pp. 11012
Author(s):  
H. Imam
Keyword(s):  
Signal To Noise Ratio ◽  
Liquid Argon ◽  
High Signal ◽  
Test Results ◽  
Forward Region ◽  
Test Beam ◽  
Pile Up ◽  
Flux Increase ◽  
Silicon Sensor ◽  
Better Than

The particle flux increase (pile-up) at the HL-LHC with luminosities of L = 7.5 × 1034 cm−2 s−1 will have a significant impact on the reconstruction of the ATLAS detector and on the performance of the trigger. The forward region and the end-cap where the internal tracker has poorer longitudinal track impact parameter resolution, and where the liquid argon calorimeter has coarser granularity, will be significantly affected. A High Granularity Time Detector (HGTD) is proposed to be installed in front of the LAr end-cap calorimeter for the mitigation of the pileup effect, as well as measurement of luminosity. It will have coverage of 2.4 to 4.0 from the pseudo-rapidity range. Two dual-sided silicon sensor layers will provide accurate timing information for minimum-ionizing particles with a resolution better than 30 ps per track (before irradiation), for assigning each particle to the correct vertex. The readout cells are about 1.3 mm × 1.3 mm in size, which leads to a high granular detector with 3 million channels. The technology of low-gain avalanche detectors (LGAD) with sufficient gain was chosen to achieve the required high signal-to-noise ratio. A dedicated ASIC is under development with some prototypes already submitted and evaluated. The requirements and general specifications of the HGTD will be maintained and discussed. R&D campaigns on the LGAD are carried out to study the sensors, the related ASICs and the radiation hardness. Both laboratory and test beam results will be presented.

scholarly journals Global spectral clustering in dynamic networks

2018 ◽  
Vol 115 (5) ◽  
pp. 927-932 ◽  
Author(s):  
Fuchen Liu ◽  
David Choi ◽  
Lu Xie ◽  
Kathryn Roeder
Keyword(s):  
Community Detection ◽  
Spectral Clustering ◽  
Signal To Noise Ratio ◽  
Dynamic Networks ◽  
Brain Regions ◽  
Tuning Parameter ◽  
Expression Data ◽  
Dynamic Community Detection ◽  
Dynamic Community ◽  
Better Than

Community detection is challenging when the network structure is estimated with uncertainty. Dynamic networks present additional challenges but also add information across time periods. We propose a global community detection method, persistent communities by eigenvector smoothing (PisCES), that combines information across a series of networks, longitudinally, to strengthen the inference for each period. Our method is derived from evolutionary spectral clustering and degree correction methods. Data-driven solutions to the problem of tuning parameter selection are provided. In simulations we find that PisCES performs better than competing methods designed for a low signal-to-noise ratio. Recently obtained gene expression data from rhesus monkey brains provide samples from finely partitioned brain regions over a broad time span including pre- and postnatal periods. Of interest is how gene communities develop over space and time; however, once the data are divided into homogeneous spatial and temporal periods, sample sizes are very small, making inference quite challenging. Applying PisCES to medial prefrontal cortex in monkey rhesus brains from near conception to adulthood reveals dense communities that persist, merge, and diverge over time and others that are loosely organized and short lived, illustrating how dynamic community detection can yield interesting insights into processes such as brain development.

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