ترجمه رایگان مقالات برق مخابرات و مخابرات نوری

[maryam440]

Traffic channels provide two-way connections between space vehicles and subscriber equipment
that support Iridium services. These channels transport the system voice and data services along
with the signaling data necessary to maintain the connection and control the services.
The uplink and downlink Traffic Channels use identical burst structures. Each burst is 8.28 ms
long and contains 414 channel bits. The bursts are divided into four major data fields: Preamble,
Unique Word, Link Control Word and Payload Field. The preamble and unique word are used in
the receiving demodulator for burst acquisition. The preamble and unique word patterns are
different for the uplink and downlink. The Link Control Word provides a very low data rate
signaling channel that is used to support link maintenance, the associated control channel and
Page 19
handoff. The payload field furnishes the primary Traffic Channel that carries the mission data
and signaling messages.
The Link Control Word field provides a low rate signaling channel used for control of the
subscriber link. The uplink and downlink Traffic Channels use the same Link Control Word
format. The Link Control Word is used to support link maintenance, handoff and the ACK/NAK
of the associated control channel transmission protocol. The Link Control Word field is
protected by forward error control (FEC) code.
The Traffic Channel payload field provides the primary Traffic Channel. This field carries the
mission data and mission control data. This field supports a channel bit rate of 3466.67 bps.
Typically error correction coding and other overhead functions provide a nominal information
throughput on this channel of 2400 bps.
Mission data may be either vocoded voice data or data services. For voice service, the
proprietary Iridium vocoder uses FEC to ensure good (based on mean opinion score for a basic
telephony voice call, where 1 is bad and 5 is excellent, good is roughly a 4), or adequate, quality
vocoded voice performance tailored for the Iridium communication channels. For data service,
the L-band transport employs a frame check sequence to provide essentially error free data
transport service.
The basic interface to the SDU and the circuit switched channel setup/teardown are provided at a
modem application level using the Iridium AT command set2. Some Iridium data services also
provide additional service specific interfaces to facilitate user access. In summary, the Iridium
communication channel appears to the end users as an efficient and reliable data transport.
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[saanaz jun]

سلام دوستان. من یه مقاله انگلیسی درباره فرآیند پواسون می خوام. می تونین کمکم کنین؟

 

[nazliii]

سلام دوستان. من یه مقاله انگلیسی درباره فرآیند پواسون می خوام. می تونین کمکم کنین؟

اسم مقاله رو پیدا کنین و تو تالار مقالات بزارین دوستان براتون میگیرن

 

[saanaz jun]

A NOTE ON NON-PARAMETRIC BAYESIAN ESTIMATION
FOR POISSON POINT PROCESSES
SHOTA GUGUSHVILI AND PETER SPREIJ
Abstract. We derive the posterior contraction rate for non-parametric Bayesian estimation of the intensity function of a Poisson point process.
1. Introduction
Poisson point processes (see e.g. Kingman (1993)) are among the basic modelling tools in areas as different as astronomy, biology, image analysis, reliability theory, medicine, physics, and others. A Poisson point process X on the space X = [0,1]d (this is good enough for our purposes) with the Borel σ-field B(X) of its subsets is a random integer-valued measure on X (we assume the underlying probability space (Ω,F,Q) in the background), such that (i) for any disjoint subsets B1,B2,...,Bm ∈ B(X), the random variables X(B1),X(B2),...,X(Bm) are independent, and (ii) for any B ∈B(X), the random variable X(B) is Poisson distributed with parameter Λ(B), where Λ is a finite measure on (X,B(X)), called the intensity measure of the process X. Intuitively, the process X can be thought of as random scattering of points in X, where scattering occurs in a special way determined by properties (i)–(ii) above. In practical applications knowledge of the intensity Λ is of importance. The latter typically cannot be assumed known beforehand and has to be estimated based on the observational data on the process X. A popular assumption in the literature (see e.g. the references on p. 263 in Kutoyants (1998)) is that one has independent observations X1,...,Xn on the process X over X at his disposal, on basis of which an estimator of Λ has to be constructed. We will denote for brevity X = (X1,X2,...,Xn). In case Λ is absolutely continuous with respect to some dominating measure and has a density λ, one might also be interested in estimation of λ. We will assume that Λ is absolutely continuous with respect to the Lebesgue measure on X and will call λ the intensity function. From now on we concentrate on estimation of the intensity function. Two broad approaches to estimation of λ, parametric and non-parametric, can be discerned in the literature. In the parametric approach, one assumes that the unknown intensity function λ can be parametrised by a finite-dimensional parameter θ (where, for instance, θ ranges in some subset Θ of Rp), so that λ = λθ, and the corresponding
Date: April 30, 2013. 2000 Mathematics Subject Classification. Primary: 62G20, Secondary: 62M30. Key words and phrases. Intensity function; Non-parametric Bayesian estimation; Poisson point process; Posterior contractaion rate. The research of the first author was supported by The Netherlands Organisation for Scientific Research (NWO).

 

[star700]

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مشاهده پیوست 1.pdf​

 

[pinkng123]

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[FONT=&quot]T[/FONT][FONT=&quot]h[/FONT][FONT=&quot]i[/FONT][FONT=&quot]s paper proposes a novel two-step distributed detection scheme for cooperative spectrum-sensing networks. In the first step, indi- vidual contributions from nodes within a neighborhood are fused through an adaptive combiner, which updates the weights and makes local decisions iteratively. In the second step, local decisions are shared within a neighborhood to yield a consensus decision. Results are presented in terms of complementary receiver operating charac- teristic curves and show the good behavior of the proposed scheme when compared to the optimal linear fusion rule, even if correlated node contributions are considered.[/FONT] [FONT=&quot]Index Terms[/FONT][FONT=&quot]— [/FONT][FONT=&quot]Cognitive radio, spectrum sensing, distributed detection, adaptive signal processing, least mean square algorithms[/FONT]

[FONT=&quot] [/FONT]​

[FONT=&quot] [/FONT]​

[FONT=&quot]1[/FONT][FONT=&quot]. INTRODUCTION[/FONT][FONT=&quot][/FONT]​

[FONT=&quot] [/FONT]​

[FONT=&quot]Cognitive radio (CR) technology arises as a promising alternative for the spectrum allocation problem, since it allows a secondary user (SU) to share and opportunistically use the same band assigned to a primary user (PU) [1]. The efficiency of this dynamic spectrum management depends on how reliably the SU identifies the presence (or absence) of legacy users. Methods for spectrum sensing may in- volve energy detection, matched filter detection or feature detection. Matched filters are the best option if the SU knows the PU’s signal shape a priori, but energy detection offers easy implementation and requires less a priori information [2].[/FONT] [FONT=&quot]Detection performance can be improved by spatial cooperation among SUs, for a combination of their contributions can produce a more reliable decision. This scheme will be referred to herein as cooperative spectrum sensing. Some strategies consider the use of a fusion center, whose task is to collect all the individual sensing information, fuse them and make the decision. Several optimal and suboptimal strategies for centralized data fusion can be found in the literature. In [3] the authors suggest an algorithm for maximizing the probability of detection for linear fusion based on semidefinite programming. An online adaptive linear fusion based on orthogonal projections onto convex sets (POCS) is proposed in [4].[/FONT] [FONT=&quot]T[/FONT][FONT=&quot]h[/FONT][FONT=&quot]e centralized scheme may not be a good solution if the sys- tem is composed of a large number of nodes. Information centered at only one point would require the fusion center to be able to process a very large amount of data, in addition to being more sensitive to link failures. Furthermore, increasing distance between nodes require the radios to use more power and consequently increases network en- ergy consumption [5]. Therefore, a distributed approach arises as a[/FONT]

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[FONT=&quot]file:///C:\DOCUME~1\Ali\LOCALS~1\Temp\msohtmlclip1\01\clip_image002.gif[/FONT][FONT=&quot][/FONT]​

[FONT=&quot]T[/FONT][FONT=&quot]his work was supported, in part, by the Academy of Finland, Center of Excellence Smart Radios and Wireless Research at the Aalto University, and by CNPq and FAPERJ, Brazil.[/FONT] [FONT=&quot]goo[/FONT][FONT=&quot]d alternative for the final decision made by each of several small neighborhoods [5]. Data is shared only among nodes within a neigh- borhood.[/FONT] [FONT=&quot]Although a rich literature can be found on the distributed spec- trum sensing, few works relate to adaptive node cooperation strate- gies, which can offer better results under dynamic propagation en- vironments [4]. In their recent research, Cattivelli and Sayed [6] proposed a distributed spectrum sensing technique with adaptive co- operation among nodes by reformulating the detection problem as a parameter estimation problem. However, their work considers that information about the user’s signal is available at every node, which may not be a valid assumption in some cases (e.g., several signal models sharing the same spectrum).[/FONT] [FONT=&quot]In this paper, we propose a novel cooperative network featur- ing distributed two-step combining with online adaptive cooperation. Each node employs a simple and conventional energy detector. The main goal of the suggested two-step combining is to distribute the decision tasks taking advantage of spatial diversity information from different neighborhoods. In its turn, the proposed online adaptive linear combining produces decisions after each update instant thus avoiding the need to wait until a large amount of data is processed.[/FONT] [FONT=&quot]T[/FONT][FONT=&quot]h[/FONT][FONT=&quot]e performance of the proposed network is investigated with simulations with uncorrelated and correlated node contributions. The results are presented by means of complementary receiver op- erating characteristic (C-ROC) curves and compared to the optimal linear fusion rule described in [3].[/FONT]

 

[star700]

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These two plane layers must be electrically bonded together with plated-through via holes, and it is recommended that these vias should be no more than λ/30 apart (for example, no more than 10mm for frequencies less than 1GHz), preferably much closer

Plumbing that falls short of this will leak water

so it helps to ensure that the pad patterns for the filters will accommodate a variety of such devices.​

 

[ham!d]

 = or
Where, Te = K1 +K2.M
fd = K3/ (1+pT3). [Efd – K4]
Here TM is prime mover input and Te is electrical
Output torque, H is inertia constant,  and are
peed and rotor angle respectively.
ii) Excitation System:
Fig.2: Block diagram of excitation system
xcitation system is capable of responding rapidly to
disturbance so as to enhance transient stability and
f modulating the generator field so as to enhance
mall scale stability. The duty of an exciter is to
rovide necessary field current in rotor winding of an
lternator. Terminal voltage transducer senses
enerator terminal voltage rectifies and filters it to dc
uantity. Exciter provides dc power to synchronous
machine field winding, constituting the power angle
f excitation system
iii) Power System Stabilizer (PSS):
ower system stabilizers (PSS) were developed to
id in damping these oscillations viamodulations of
xcitation system ofgenerator s. The action of a
SS is to extend the angular stability limits of a
owersystem by providing supplemental damping to
he oscillation of synchronous machinerotors through
he generator excitations.
3. Fuzzy Logic Controller(FLC)

Fig. 3: Fuzzy Logic Controller Diagram
he figure (3) shows block diagram of fuzzy logic
ontroller. It consists of four basic components:
Knowledge Base, Fuzzification Interface,De-
uzzification Interface and Decision Making Logic.
he fuzzy controller is a two input and single output
omponent. It is mostly a MISO system [5].
Knowledge Base: It includes definitions of fuzzy
membership function and the necessary rules which
specified the control goals using linguistic variables.
It also stores the knowledge about all input-output
fuzzy relationships.

Fuzzification Interface: It converts the crisp
quantities into fuzzy quantities. There are several
ways to assign membership values to fuzzy variables
in comparison with the probability density functions
to random variables. The process of membership
value assignment is done by intuition, logical
reasoning, procedural methods or algorithm
approach.

Defuzzification Interface: It has the capability to
reduce a fuzzy set into a crisp single-valued,
quantity. It may also be termed as “rounding it off”.
The selection of method is done on the basis of the
computational complexity involved and applicability
to the situations considered.

Decision Making Logic: This module converts the
inferred decision from linguistic variables. It is the
kernel of an FLC system and it has the capability to
simulate human decisions by performing
approximate reasoning to achieve desired control
strategy.

4. Conventional Power System Stabilizer

Fig. 4: Block Diagram of Conventional PSS

The basic structure of conventional PSS is shown in
figure (4). It contains three components: phase
compensation block, signal washout block and gain
block. The phase lag between exciter input and
generator electrical output provides by phase
compensation block with appropriate phase lead
characteristic.The signal washout block serves as
high pass filter. The stabilizer gain Kst determines
the amount of damping. The transfer functions of
conventional PSS are:



Here TWis washout filter time constant.