Note
Click here to download the full example code
1.2.3. Using the PropConst convenience class¶
This example demonstrates how the PropConst class, implemented in module fmas.propagation_constant, can be used to wrap and analyse a user supplied propagation constant.
The use of this class is optional. py-fmas can be used without this class. However, this class makes it convenient to display and analyze a given propagation constant. In order to use PropConst, the propagation constant needs to be available as a callable function.
We first start by importing the functionality of numpy and fmas into the current namespace. In particular, we also import the convenience class PropConst, defined in module propagation_constant.
import numpy as np
import fmas
from fmas.propagation_constant import PropConst
from fmas.tools import plot_details_prop_const
Below we will demonstrate how to use PropConst to wrap a user supplied propagation constant and analyze it. The methods defined by this convenience class refer to common project-sepecific tasks that reoccur regularly in the desing-stage of propagation scenarios.
In particular we will wrap and analyze the propagation constant of a NL-PM-750 nonlinear photonic crystal fiber (PCF), defined by the subsequent function.
def define_beta_fun_NLPM750():
r"""Custom propagation constant for NL-PM-750 photonic crystal fiber.
Implements rational Pade-approximant of order [4/4] for the refractive
index of a NL-PM-750 nonlinear photonic crystal fiber (PCF), see Ref. [1].
References:
[1] NL-PM-750 Nonlinear Photonic Crystal Fiber, www.nktphotonics.com.
Returns:
:obj:`callable`: Propagation constant for NL-PM-750 PCF.
"""
p = np.poly1d((1.49902, -2.48088, 2.41969, 0.530198, -0.0346925)[::-1])
q = np.poly1d((1.00000, -1.56995, 1.59604, 0.381012, -0.0270357)[::-1])
n_idx = lambda w: p(w)/q(w) # (-)
c0 = 0.29979 # (micron/fs)
return lambda w: n_idx(w)*w/c0 # (1/micron)
Let us note that above, we defined the refractive index profile as a rational function in terms of a Pade-approximant of order \([m=4/n=4]\). Such an approximation has several benefits. For example, it gives a better approximation of the refractive index than truncating a Taylor expansion in the variable \(\omega-\omega_0\) for some reference frequency \(\omega_0\), avoids rapid divergence for large frequencies, and helps to avoid unnecessary numerical stiffness.
Next, we initialize the propagation constant as beta_fun and generate a cooresponding instance of the PropConst convenience class, wrapping the function beta_fun.
beta_fun = define_beta_fun_NLPM750()
pc = PropConst(beta_fun)
We then visually assess the group-velocity (GV) and group-velocity dispersion (GVD) of the propagation constant in the angular frequency range \(\omega \in [1.2,3.2]~\mathrm{rad/fs}\). GV and GVD are implemented by the class methods vg, and beta2, respectively. To generate a quick plot of both, the GV and GVD, we use the function plot_details_prop_const, which is defined in module tools.
w = np.linspace(1.3, 3.2, 200)
plot_details_prop_const(w, pc.vg(w), pc.beta2(w))
<IP_ADDRESS>. Finding zero-dispersion points¶
A quick visual assessment of the GVD in the bottom subfigure allows to roughly locate the first zero-dispersion point within the angular frequency interval \([1.4,1.7]~\mathrm{rad/fs}\). The second zero-dispersion point surely falls into the interval \([2.2,2.5]~\mathrm{rad/fs}\). From these rough estimates we can determine the exact roots of \(\beta_2\) as shown below:
w_Z1 = pc.find_root_beta2(1.4, 1.7)
w_Z2 = pc.find_root_beta2(2.2, 2.5)
print('w_Z1 = ', w_Z1)
print('w_Z2 = ', w_Z2)
Out:
w_Z1 = 1.4907331965874615
w_Z2 = 2.385682823499155
<IP_ADDRESS>. Finding group-velocity matched frequencies¶
For the desing of propagation scenarios that demonstrate, e.g., the interaction of a soliton and a dispersive wave accross a zero-dispersion point, it is useful to be able to compute a group-velocity matched partner frequency for a give frequency. Using the PropConst convenience class this can be done as shown below. Consider, e.g., a soliton with center frequency \(\omega_{\rm{S}}=2.1~\mathrm{rad/fs}\). Then, a group-velocity matched frequency in the domain of normal dispersion (for \(\omega>2.386\)), which surely is contained in the interval \(\omega\in[2.4,3.0]\), can be computed as follows:
w_S = 2.1
w_GVM = pc.find_match_beta1(w_S, 2.4, 3.0)
print('w_GVM = ', w_GVM)
Out:
w_GVM = 2.6757196927682507
We might then reassure us that both frequencies exhibit the same group-velocity like so:
print(np.abs(pc.vg(w_S)-pc.vg(w_GVM)) < 1e-6 )
Out:
True
<IP_ADDRESS>. Computing local expansion coefficients of \(\beta(\omega)\)¶
Taylor expansion coefficients of the proapgation constant at a specific frequency can be computed as shown below. Consider, e.g., the frequency \(\omega_{\rm{S}}=2.1~\mathrm{rad/fs}\), located in the domain of anomalous dispersion. The local expansion coefficients of \(\beta\) up to order \(n_{\rm{max}}=6\) at that specific point are obtained by
beta_coeffs = pc.compute_expansion_coefficients(w_S, n_max=5)
for idx, coeff in enumerate(beta_coeffs):
print("beta_{:d} = {:10.7f} fs^{:d}/micron".format(idx, coeff, idx))
Out:
beta_0 = 10.1264331 fs^0/micron
beta_1 = 4.9586473 fs^1/micron
beta_2 = -0.0073170 fs^2/micron
beta_3 = 0.0081117 fs^3/micron
beta_4 = 0.0017631 fs^4/micron
beta_5 = -0.0030165 fs^5/micron
Total running time of the script: ( 0 minutes 0.297 seconds)


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Rusty Runs Away
Ruskin Bond’s, ‘Rusty Runs Away’ is the second book
followed by the prosperous first book, ‘Rusty, the Boy from
the Hills.’ The author has engendered the book in a way
that could make abecedarian verbalizers of English to
have no quandary reading this book. The story takes place
around Rusty, the protagonist of this novel. In every
chapter, Rusty meets some characters, who show him
love and care and calm him from the loss of his parents.
Though Mr. Harrison, the guardian of Rusty comports
impudent and firm, Rusty reveres him. He pays special
care to the characters like Ko Ki, Miss. Mackenzie, Puran,
a woman on Platform No.8, Daljit, Omar, Somi, Ranbir and
Kishen, who in turn returns the same with love. Rusty
longs for the love of someone, who could be amicable,
doting and caring for him because he is isolated from his
family and experiences solitude under his guardian.
Though his guardians, Mr. and Mrs. Harrison, are none
other than his own aunt and uncle, he finds arduous to join
or go about with them. Except his elusion from the school
at Arundel along with Daljit, he remains an agreeable and
astute and keenly intellective boy. Rusty accommodates a
model to the children and does not take any opportunity of
spoiling them.
I conjecture that this book could be popular for the
teenager children who are placid and placid but
sometimes having arduousness that others cannot avail.
For the adults this book could be insipid but it is
worthwhile reading. This book’s categorical reason that
makes me to cerebrate this book is good is that it does not
spoil children by giving them an erroneous model.
As an English leaner, I would relish to give five stars for it
does have some worth like edifying something. It neither
makes me bore nor gives nonessential messages and so
it deserves five stars. If I were to be an adult, I might be
giving three and a moiety stars.
-Sourav Chakraborty


- receive(DataPacketChatRoom<T>) - Method in interface common.IReceiver
-
Receive a data packet.
- receive(DataPacketChatRoom<T>) - Method in class util.Receiver
-
- receiveMsg(String) - Method in interface model.Connection2MinimodelAdapter
-
receive message from connection
- receivePort - Variable in class model.MainModel
-
default port
- receiver - Variable in class controller.MiniController
-
chat receiver
- receiver - Variable in class model.MiniModel
-
chat receiver object
- Receiver - Class in util
-
A concrete IReceiver class
- Receiver(IUser, IChatRoom, Cmd2ViewAdapter) - Constructor for class util.Receiver
-
Constructor of the Receiver object
- receiverStub - Variable in class model.MiniModel
-
remote chat receiver stub
- receiverStub - Variable in class util.Receiver
-
the receiver stub
- receives - Variable in class util.ChatRoom
-
receiver list
- refresh() - Method in interface model.Cmd2ViewAdapter
-
to refresh the mini view
- refresh() - Method in interface model.IMiniMVCAdapter
-
refresh chat room
- refresh() - Method in interface model.IMiniViewAdapter
-
refresh the chat room content
- refresh() - Method in class model.MiniModel
-
refresh chatroom
- refresh() - Method in class view.MiniGUI
-
refresh chatroom and clear content
- registry - Variable in class model.MainModel
-
registry for searching machine
- REGISTRY_PORT - Static variable in interface provided.rmiUtils.IRMI_Defs
-
Port used for Registry.
- removeChatRoom(IChatRoom) - Method in class model.MainModel
-
remove chatroom
- removeChatRoom(IChatRoom) - Method in interface view.IModelAdapter
-
remove the chat room from app
- removeChatroomList() - Method in interface model.IViewAdapter
-
remove chatroom list
- removeChatroomList() - Method in class view.MainGUI
-
remove all the chatroom list
- removeIReceiverStubLocally(IReceiver) - Method in interface common.IChatRoom
-
Remove a receiver in the local chat room without interacting with remote peers
- removeIReceiverStubLocally(IReceiver) - Method in class util.ChatRoom
-
- RemoveReceiver - Class in util
-
A concrete IRemoveReceiverType class
- RemoveReceiver(IReceiver) - Constructor for class util.RemoveReceiver
-
Constructor of the removereceiver class
- removeRoom(IChatRoom) - Method in class util.User
-
remove a chatroom from chat room list
- removeRoom(JPanel) - Method in class view.MainGUI
-
remove a chatroom from the main app
- rmiUtils - Variable in class model.MainModel
-
RMI util object
- RMIUtils - Class in provided.rmiUtils
-
Concrete implementaion of the IRMIUtils, which are utilities to make it
easier to start/stop the RMI system, access local/remote Registries and
get the local IP address.
- RMIUtils(Consumer<String>) - Constructor for class provided.rmiUtils.RMIUtils
-
Constructor for the class.
- RMIUtils(Consumer<String>, boolean) - Constructor for class provided.rmiUtils.RMIUtils
-
Auxiliary constructor used to set the getLocalAddress() method to return non-private addresses,
e.g.
- room - Variable in class model.MainModel.ChatRoomManager
-
- roomCount - Variable in class model.MainModel
-
room count integer
- run() - Method in class provided.rmiUtils.classServer.ClassServer
-
The "listen" thread that accepts a connection to the
server, parses the header to obtain the class file name
and sends back the bytecodes for the class (or error
if the class is not found or the response was malformed).


Create an event QR code
Event title:
Event Start:
Event End:
Event Location:
Event Discription:
Create QR


call p%clone(pout,info)
Create a (deep) copy of the preconditioner object.
Arguments
pout |
type(mld_ xprec_type), intent(out) . |
| The copy of the preconditioner data structure. Note that x must be chosen according to the real/complex, single/double precision version of MLD2P4 under use. | |
info |
integer, intent(out) . |
| Error code. If no error, 0 is returned. See Section 8 for details. |


A JavaScript ALC Tableau Reasoner (pre-alpha)
Please note:
- Expressions are required to have pairs for sub-expressions enclosed by parenthesis. Hence, ((A ⊓ B) ⊓ C) is OK, but (A ⊓ B ⊓ C) will be interpreted as invalid.
- You can type concept names and parenthesis in the input field and either append it to the expression or select a radio button to append an operator.
- The reasoning task supported is concept satisfiability (for now).
- The input field may be used to build expressions. Reasoning tasks are performed on the bold expression displayed below the input field. Click on Reset to provide a new expression.
- Tested and implemented with Firefox <IP_ADDRESS>. Some things probably won't work in IE or other browsers!
Type your expression
Examples:
Example1
Example2
Example3
Example4
Check Satisfiability
Check Subsumption


{{ financial-review.meta.meta_title }}
{% include component/header_xw.html %}
{% include financial-review/main_finance.html %}
{% include financial-review/financial.html %}
{% include financial-review/article_left_light.html %}
{% include financial-review/article_right.html %}
{% include financial-review/graph.html %}
{% include financial-review/article_left.html %}
{% include financial-review/graph_two.html %}
{% include financial-review/article_right_two.html %}
{% include financial-review/graph_three.html %}
{% include financial-review/graph_four.html %}
{% include financial-review/better.html %}
{% include component/back_to_top.html %}
{% include component/footer.html %}
{% lazyScript %}
{% includeScript "../src/js/financial-review.js" %}


Prof. Ajith Abraham
Director
Machine
Intelligence Research Labs
USA
Prof. Oscar Castillo
Professor
Tijuana
Institute of Technology
Mexico
Prof. K.K. Aggarwal
Professor
Guru
Gobind Singh Indraprastha University
India
Prof. Yogesh Singh
Vice
Chancellor
Delhi
Technological University
India
Prof. D.K. Tayal
Prof. M.P.S. Bhatia
Prof. Naveen Rajpal


Hello, Jack_Schumaker
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· Contact Sys Admin
Merced Systems ·


Welcome to my senior project. I've been waiting to take my senior design for years. I even switched my major just because I know this is the only senior design class I am interested in. And here I am, taking EECS 467 Autonomous Robotics as my last class for my college year
Well, enough excitement. The first half semester of this class, we formed a group of four and are required to finish the first two labs as everybody else. The first task is called "setpoint challenge" where we need to command the robot the go along a 1m by 1m square path. The task requires us to implement robot odometry from robot encoder readings, motion planner that can turn command position into robot target action, and PID controller for robot position and velocity.
The actual implementation can be found at our gitlab directory but due to the honor code issue, I can't link the whole repository here. But here is a snippet that would give you some implementation detail
Setpoint Challenge
The odometry given by the wheel encoder is inaccurate and the error accumulate while the robot is driving. Therefore, in order to keep track of the robot actual location for the pid controller, we need some correction methods. Luckily, for this part of the project, we are given the true robot pose calculated by the SLAM algorithm and use it to correct the error of the odometry
We have three PID controller for the system. One is for when the robot is turning 90 degree. One is for the velocity control of the robot when driving straight and the other one is for slightly tuning the robot angle to guarantee that the robot is moving straight.
After tuning the PID controller gains, our robot can finally move in a controlled style. You can see the video below
Escape Challenge
The escape challenge requires the robot to autonomously navigate through an unknown maize and get to the commanded location. Then we are going to remove a wall of the maze and the robot has to detect the removed wall and eventually escape the maize.
Occupancy grid
We generate the occupancy grid as the mapping for the environment. The occupancy grid is generated using the laser scan given by the lidar on the top of the robotic car. We follow the Bresenham algorithm to rasterize the line segment of laser scan between the starting point(robot location) and the lidar beam endpoints. The RPlidar is a low cost 360 degree 2D scanner with a scan rate of 5.5 Hz. The map is generated as follows.
SLAM
Simultaneous Localization And Mapping, short for SLAM is one of the most common techniques used in robotics for robot to navigate in an unknown environment. In this project, we used Occupancy grid method to generate the map and particle filter method to localize the robot. We keep iterating between using new laser scan to update the map and particle filter to update the location of the robot. Particle filter implementation is based on Monte Carlo algorithm. Each particle represents the possible pose of the robot. It can be broken down into three parts: Action model, sensor model and importance sampling.
Action model takes the input of robot odometry and current particles and estimate the new pose of the particles when robot moves. There are four parameters we need to tune, which add the noise to the robot pose estimation. The new particles represents the proposal distribution of the robot current pose
Sensor model takes the current map, current particles and current laser scan as inputs and applies the laser scan to the pose of each particle to evaluate the probability of each particle with current mapping.
Parameters: When the endpoint matches the occupied grid, we increase the particle by a large number (alpha_1) to indicate this is more likely to be the robot true pose. When looking at the neighbor of the end point on the occupied grid, we give the particle a medium value (alpha_2) to indicate that it is reasonably possible to be the robot true pose. When the end point does not hit any occupied grid, then it indicates the particle is less likely to be the robot true pose. Therefore, we give it a very small number (alpha_3).
Change in algorithm: According to Probabilistic Robotics after we calculate the laser end point, we need to find out the closest occupied grid cell and calculate the distance in between. However, this step is computationally expensive and does not work for real time SLAM. Therefore, we improve the algorithm by assigning an absolute value to the particle based on how close the endpoint is to the occupied grid.
Importance sample is a techniques to resample the data based on it's original(different) distribution. After estimate the probability of the particles, we resample the particle so that there are more data at the location with high probability comparing to the number of data at locations with low probability. Then we use the mean of the particles as the estimation of the robot pose
The SLAM yields the following result
A star path planning
We need to plan a path from robot current location to the target location. We need to find the shortest path between the two location as well as avoid the obstacles and ignore the gap that's too small for the robot to drive through. The algorithm we used is A-star.
Our implementation uses boost's fibonacci_heap heap implementation as our priority queue because of one key implementation detail. Boost's implementation has the concept of handles (essentially pointers) to objects in the fibonacci_heap. This is the only parameter to a method update() that forces the fibonacci_heap to update with the new value of the object. This allowed us to update the fscore of cells that are in the heap, and then to update the heap. To keep track of all of our cells, we had a large vector that mapped a grid cellIndex to a handle in fibonacci_heap, or null.
In the actually situation, A star gives the path that is usually too zigzag for the motion controller. Therefore, we throw away every 4 out of 5 poses and that helps to smooth out the robot motion. The following graph shows how the robot moves along the planned path.


A Question of Ethics Election
By Ross Anthony
Here's a movie that raises a question of ethics (or is it morals). Either way, the film steps all over such ideals, perhaps in hopes of redefining them. Appropriately, it ended up poking me with an ethical dilemma: a film with such excellent direction, acting, dialogue and humor, deserves high marks from a reviewer; but my own sense of morality (regarding it's subject matter) is holding me back from giving it the grade.
Allow me to clarify. The director of "Election" pays so close attention to detail that he makes you feel like you're a participant in the film. The timing of the editing is flawless. The script's dialogue is completely real, riddled with the knocks and pings of everyday, high school population stutters and stops. The acting ... appropriately straight on the money. And it's funny. I was laughing, we were all laughing.

But the topic isn't just the election, it's infidelity and statutory rape for starters -- involving students and/or teachers. Perhaps I'm not alone when I say that kind of thing makes me feel uncomfortable. Don't get me wrong -- I'm okay with being uncomfortable during part of a film -- especially if it's an investment toward a larger goal. But, I felt uncomfortable for nearly the duration of this tremendously well put together piece. Nor, did I feel resolved about it at the end. So, I think you see the dilemma.
I'm going to be weak. I'm going to let my intangible, perhaps unjustifiable feelings regarding specifically the touchy topics, prevent me from giving it an 'A'. Forgive me.
Starring Matthew Broderick and Reese Witherspoon. Directed by Alexander Payne. Screenplay by Alexander Payne and Jim Taylor. Produced by Jacobus Rose and Jim Burke at MTV and Paramount. Rated R.
Grade..........................B
-- Copyright © 1999 Ross Anthony, currently based in Los Angeles, has scripted and shot documentaries, music videos, and shorts in 35 countries across North America, Europe, Africa and Asia. For more reviews visit: http://RossAnthony.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
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Please direct comments/criticisms of the review to relevant newsgroups.
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Related links: index of all rec.arts.movies.reviews reviews


Pharmacist Content
Data to be filled carefully and with caution. As accurate as posible
Patient:
Name:
Sex:
ID:
Prescription:
Pharmacist:
Submit patient Details


Just Eat advises shareholders not to accept Prosus offer
by ReutersLONDON (Reuters) - Britain's Just Eat (L:JE) advised shareholders not to accept a 710 pence-a-share cash offer from Prosus (AS:PRX), saying it was inferior to its agreed deal with Takeaway.com (AS:TKWY) to create the largest food delivery player outside China.
"Your Board believes that the Takeaway.com combination provides Just Eat shareholders with greater value creation than the Prosus offer," it said in a letter to investors on Monday, adding that the Prosus offer also significantly undervalued Just Eat on a standalone basis.


About Daniel Kinyanjui
Bio Data
I am a student at Moringa School. I am aspiring Software Developer.
I am really enjoying what am learning especially HTML, CSS and GIT. I can't wait to see what is in store for me especially bootstrap and Javascript.
My Hobbies include
- Running
- Reading
Click here to learn more about me.
Click here for my portfolio.
© Copyright 2020 Designed by DK


Incredibly detailed overview of the capabilities of the Java language. A bit overwhelming at times, but finds focus with several code examples. I'd recommend Head First Java as your first Java book, but if you're looking for a more contemporary look at the language, this is a great next book.
It covers most aspects of Java, maybe a little shallow on some topics, but already good enough for intermediate level programmers. It might be a little bit difficult for beginners. The author had to skip some stuff to finish those topics in 800 pages.
Good description of Java programming language and various Java technologies. Sometimes long and verbose, but I think it's good for such book format


This passphrase decrypts the sensitive information contained in your wallet file, including your address, hexseed and mnemonic phrase. Only enter a password if you've selected a secure wallet file.
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| expect_cpp_tests_pass {testthat} | R Documentation |
Test compiled code in the package package
. A call to this function will
automatically be generated for you in tests/testthat/test-cpp.R
after
calling use_catch()
; you should not need to manually call this expectation
yourself.
expect_cpp_tests_pass(package) run_cpp_tests(package)
package |
The name of the package to test. |


Grid and Flex Grid: Nesting in collapsed row
These are nested standard and collapsed rows. All rows should be aligned on the right and left.
Single column in a Collapsed Row
Single column in a Row
Two columns in a Row
Two columns in a Row
Two columns in a Collapsed Row
Two columns in a Collapsed Row


{% extends "base.html" %} {% block content_title %}{% endblock %} {% block content %}
Still no idea what to really put here


webdevjeff.us
Web Developer Jeff George
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Sept. 11, 2015


This tool calculates levelized cost of energy (LCOE) for photovoltaic (PV) systems based on cost, performance, and reliability inputs for a baseline and a proposed technology.
Choose your inputs and watch the effect on LCOE to determine whether a proposed technology is cost-effective, to perform trade-off analysis among different technology options, and to do break-even analysis of cost or performance. Each button adjusts a single input to make the baseline and proposed LCOE equal.
For more details, please consult the documentation. You may also be interested in other tools for calculating cost and performance for renewable energy.
Baseline LCOE (USD/kWh)
Proposed LCOE (USD/kWh)
Module price (USD/W)
Total installed system cost (USD/W)
Module price (USD/W)
Total installed system cost (USD/W)
If you use results from this calculator in a publication or proposal, please cite it:
SJ Andrews, B Smith, MG Deceglie, KA Horowitz, and TJ Silverman. “NREL Comparative PV LCOE Calculator.” Version 2.0.1, August 2021
Note: We recommend including the URL for a specific commit if citing results from an unreleased version.


Lauren Jauregui & Sofia Carson Set To Host AMAs Red Carpet Live This Weekend!
Sofia Carson and Lauren Jauregui were just announced as two of the hosts for the AMAs Red Carpet Live!
The two singers will join AJ Gibson, Jaymes Vaughan, Raja Kumari and Nick Viall on the red carpet for the official pre-show, presented by Security Benefit, at the 2019 American Music Awards this weekend,
The two-hour pre-show will stream LIVE from Microsoft Theater in Los Angeles on Sunday, November 24th @ 6PM ET/ 3PM PT on the official American Music Awards Twitter account.
“So I’ll be doing my first hosting gig this weekend👀 SO EXCITED to be co-hosting,” Lauren posted excitedly on her Instagram.
If you missed them, check out all the AMAs nominations on JJJ now and tune in @ 8PM ET/PT on Sunday, November 24th on ABC for the show!