scholarly journals Derivation of Probability Density Function of Signal-to-Interference-Plus-Noise Ratio for the MS-to-MS Interference Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Ho-Kyung Son ◽  
Che-Young Kim
Keyword(s):  
Monte Carlo ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Interference Effect ◽  
Numerical Verification ◽  
Analytical Derivation ◽  
Mobile Stations ◽  
Noise Ratio ◽  
Analytical Result

This paper provides an analytical derivation of the probability density function of signal-to-interference-plus-noise ratio in the scenario where mobile stations interfere with each other. This analysis considers cochannel interference and adjacent channel interference. This could also remove the need for Monte Carlo simulations when evaluating the interference effect between mobile stations. Numerical verification shows that the analytical result agrees well with a Monte Carlo simulation. Also, we applied analytical methods for evaluating the interference effect between mobile stations using adjacent frequency bands. The analytical derivation of the probability density function can be used to provide the technical criteria for sharing a frequency band.

Monte Carlo Probability Density Function Method for Gas Turbine Combustor Flowfield Predictions

1997 ◽  
Vol 13 (2) ◽  
pp. 218-225 ◽  
Author(s):  
Anil K. Tolpadi ◽  
Sanjay M. Correa ◽  
David L. Burrus ◽  
Hukam C. Mongia
Keyword(s):  
Monte Carlo ◽  
Probability Density Function ◽  
Probability Density ◽  
Gas Turbine ◽  
Density Function ◽  
Function Method ◽  
Gas Turbine Combustor

scholarly journals Mobile/Wireless Robot Navigation

Robotics ◽  
2013 ◽  
pp. 366-374
Author(s):  
Amina Waqar
Keyword(s):  
Monte Carlo ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Efficient Method ◽  
Feedback Loop ◽  
Loop Filter ◽  
Mobile Wireless ◽  
External Loop ◽  
Monte Carlo Localization

Sensor-based localization has been found to be one of the most preliminary issues in the world of Mobile/Wireless Robotics. One can easily track a mobile robot using a Kalman Filter, which uses a Phase Locked Loop for tracing via averaging the values. Tracking has now become very easy, but one wants to proceed to navigation. The reason behind this is that tracking does not help one determine where one is going. One would like to use a more precise “Navigation” like Monte Carlo Localization. It is a more efficient and precise way than a feedback loop because the feedback loops are more sensitive to noise, making one modify the external loop filter according to the variation in the processing. In this case, the robot updates its belief in the form of a probability density function (pdf). The supposition is considered to be one meter square. This probability density function expands over the entire supposition. A door in a wall can be identified as peak/rise in the probability function or the belief of the robot. The mobile updates a window of 1 meter square (area depends on the sensors) as its belief. One starts with a uniform probability density function, and then the sensors update it. The authors use Monte Carlo Localization for updating the belief, which is an efficient method and requires less space. It is an efficient method because it can be applied to continuous data input, unlike the feedback loop. It requires less space. The robot does not need to store the map and, hence, can delete the previous belief without any hesitation.

scholarly journals Συνεισφορά της οπτικής τoμογραφίας OCT (optical computed tomography) στην μονοφωτονική τομοσπινθηρογραφία

2019 ◽  
Author(s):  
Αριστοτέλης-Νικόλαος Ραψομανίκης
Keyword(s):  
Computed Tomography ◽  
Monte Carlo ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Ray Tracing ◽  
Optical Tomography ◽  
Time Resolved ◽  
Optical Computed Tomography ◽  
Pet Ct

Η ανατομική πληροφορία στις καθιερωμένες συνδυαστικές μοριακές απεικονίσεις γίνεται συνήθως με ιοντίζουσες ακτινοβολίες (ακτίνες-Χ, SPECT/CT, PET/CT), οι οποίες επιβαρύνουν κατά κανόνα τον ασθενή με επιπρόσθετη ακτινοβόληση. Η σύγχρονη έρευνα εστιάζεται στη χρήση οπτικών μεθόδων για την υλοποίηση της ζητούμενης μορφολογικής εικόνας, όπου σκεδαζόμενο φως από την υπό εξέταση περιοχή ανακατασκευάζεται κατάλληλα για να αποδώσει τη ζητούμενη ανατομική πληροφορία. Σκοπός της Διδακτορικής αυτής Διατριβής είναι η μελέτη μιας καινοτόμου μεθόδου οπτικής απεικόνισης, όπου, παρά την υψηλή σκεδαστικότητα του μέσου, η χρονικά αξιοποιημένη λαμβανόμενη πληροφορία να δύναται να χρησιμοποιηθεί επιτυχώς για την μορφολογική ανασύσταση του σκεδαζόμενου μέσου σε τομογραφικό επίπεδο. Απώτερος στόχος της μελέτης αυτής είναι η ανάπτυξη ενός υβριδικού τομογραφικού συστήματος αποτελούμενου από μία γ-Camera (SPECT) και ενός συστήματος τηλεκεντρικού φωτισμού με υπέρυθρη παλμική ακτινοβολία και σύστημα οπτικής ανάλυσης με χρονική πληροφορία (Time Resolved Optical Tomography - TROT). Βασικός αρωγός στην κατανόηση των φυσικών διεργασιών στο στάδιο αυτό, πέραν του πειραματικού ελέγχου της εφαρμοσιμότητας συμβατικών μαθηματικών αλγορίθμων για την ανακατασκευή της τομογραφικής εικόνας με υπέρυθρη και οπτική ακτινοβολία, αποτέλεσε η ανάπτυξη ενός εκτεταμένου εργαλείου προσομοίωσης με τεχνικές Monte-Carlo, το οποίο, πέραν από τη λεπτομερή παρακολούθηση της τροχιάς ενός φωτονίου (ray-tracing) σε ισχυρά σκεδαστικό μέσο, καινοτομεί σε δύο βασικά σημεία: Εισάγει με στοχαστικό τρόπο, υπό τη μορφή συνάρτησης πυκνότητας πιθανότητας (probability density function), την μακροσκοπική προσομοίωση της κυριαρχούσης στο μέσο σκέδασης Mie, αποφεύγοντας τοιουτοτρόπως μεγάλους υπολογιστικούς χρόνους. Παράλληλα, υπολογίζει το χρόνο πτήσης (Time-of-Flight) για κάθε προσομοιούμενο φωτόνιο, παρακολουθώντας όλες τις γνωστές φυσικές αλληλεπιδράσεις στις οποίες αυτό υπεισέρχεται. Το λογισμικό αυτό (PhoSim) με την μορφή ενός ολοκληρωμένου πακέτου εφαρμογής αποτελεί τον πυρήνα της μελέτης του εξεταζόμενου θέματος στην Διδακτορική αυτή Διατριβή.

scholarly journals Uncertainty evaluation of the conducted emission measurements

2013 ◽  
Vol 28 (2) ◽  
pp. 182-190 ◽  
Author(s):  
Aleksandar Kovacevic ◽  
Dejan Despotovic ◽  
Zoran Rajovic ◽  
Koviljka Stankovic ◽  
Ana Kovacevic ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Density Function ◽  
Measurement Uncertainty ◽  
Probability Density ◽  
Density Function ◽  
Least Squares Method ◽  
Combined Method ◽  
Conducted Emission ◽  
Number Of Classes

For the evaluation of measurement uncertainty in measuring the conduction emission, in this paper we propose a new model which uses mixed distribution. Evaluation of probability density function for the measurand has been done using Monte Carlo method and a modified least-squares method (combined method). In addition, the number of data n and the number of classes of histogram k which were used for simulation, were varied.

Improved MCMC Method for Parameter Estimation Based on Marginal Probability Density Function

2011 ◽  
Author(s):  
Dawn An ◽  
Joo-Ho Choi
Keyword(s):  
Monte Carlo ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Uncertain Parameters ◽  
Marginal Probability ◽  
Mcmc Method ◽  
Proposal Distribution ◽  
Engineering Problems ◽  
Probability Density Function Pdf

In many engineering problems, sampling is often used to estimate and quantify the probability distribution of uncertain parameters during the course of Bayesian framework, which is to draw proper samples that follow the probabilistic feature of the parameters. Among numerous approaches, Markov Chain Monte Carlo (MCMC) has gained the most popularity due to its efficiency and wide applicability. The MCMC, however, does not work well in the case of increased parameters and/or high correlations due to the difficulty of finding proper proposal distribution. In this paper, a method employing marginal probability density function (PDF) as a proposal distribution is proposed to overcome these problems. Several engineering problems which are formulated by Bayesian approach are addressed to demonstrate the effectiveness of proposed method.

Imaging obstructed ventilation with NMR using inert fluorinated gases

2000 ◽  
Vol 88 (6) ◽  
pp. 2279-2286 ◽  
Author(s):  
Dean O. Kuethe ◽  
Arvind Caprihan ◽  
H. Michael Gach ◽  
Irving J. Lowe ◽  
Eiichi Fukushima
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Signal To Noise Ratio ◽  
Brightness Variation ◽  
Signal To Noise ◽  
Pixel Value ◽  
Noise Ratio ◽  
Normal Ventilation ◽  
Q Values

We partially obstructed the left bronchi of rats and imaged an inert insoluble gas, SF6, in the lungs with NMR using a technique that clearly differentiates obstructed and normal ventilation. When the inhaled fraction of O2is high, SF6 concentrates dramatically in regions of the lung with low ventilation-to-perfusion ratios (V˙a/Q˙); therefore, these regions are brighter in an image than whereV˙a/Q˙ values are normal or high. A second image, made when the inhaled fraction of O2 is low, serves as a reference because the SF6 fraction is nearly uniform, regardless ofV˙a/Q˙. The quotient of the first and second images displays the low-V˙a/Q˙ regions and is corrected for other causes of brightness variation. The technique may provide sufficient quantification ofV˙a/Q˙ to be a useful research tool. The noise in the quotient image is described by the probability density function for the quotient of two normal random variables. When the signal-to-noise ratio of the denominator image is >10, the signal-to-noise ratio of the quotient image is similar to that of the parent images and decreases with pixel value.

Sign in / Sign up

Export Citation Format

Share Document