Systems Design¶
The key to building and maintaining highly secure and reliable systems is simplicity - a good system will have nothing to take away, rather than nothing to add.
Each component you use you will have to secure, update and debug forever. Like open source projects which reject code not because it’s bad, but because they don’t have sufficient capacaties to maintain it, you need to be very careful about each piece of technology you introduce into your stack and carefully weight its benefits against the extra complexity it introduces.
Debugging and securing a system is an order of magnitude more complicated than building it. There’s a common saying that if you build the most clever system you can think of, you’re by definition unqualified to maintain it - and there’s a lot of truth to it.
Why not Kubernetes?¶
We’ve heard about many validators who plan to use Kubernetes for their production setups.
We strongly recommend against the use of Kubernetes and similar technologies for your core validator operations - they solve problems validator operators don’t have. Validator core infrastructure are pets, not cattle. You can’t just deploy a cloud instance, you need to rent dedicated servers and plug HSMs into them. Even if you’re running an active-active setup like ours which tolerates full node outages, you’re unlikely to gain enough from Kubernetes to justify its costs. We recommend traditional configuration management tools like Ansible.
Even plain Docker usage should be carefully evaluated - while Docker itself is quite stable, even in 2018, the overlay filesystems and namespacing technology it uses haven’t stabilized yet (not for lack of trying, but any complicated piece of code in the kernel needs decades to mature; people are still finding bugs in ext4 to this day). Any large-scale production user of either Docker and Kubernetes will have tales of Docker daemon crashes in production, weird kernel issues that required node reboots and scheduler bugs that required them to read the Kubernetes source code at 3am in the morning. This is perfectly fine for the kind of stateless infrastructure Docker and Kubernetes are designed for - they are built to tolerate single node losses, or build systems. In fact, many production deployments can auto-update and reboot cluster nodes (like CoreOS/Container Linux does).
However, none of this applies to core validator infrastructure. cosmos-sdk compiles to a single binary, you don’t need Docker to deploy it (it’s quite useful for building it, though). You’re not going to need to scale your validator setup up and down across a fleet of thousands of machines. Being able to list all processes running on your nodes and knowing exactly what they’re doing is beautiful, and while it’s certainly possible with Kubernetes, it’s a lot easier with with a stripped-down, hardened node running nothing but your validator software.
Advanced security setups like eBPF seccomp policies, auditing, SELinux policies and just plain debugging get a lot easier when there are no kernel namespaces, Docker daemons, runC wrappers and overlay filesystems to reason about.
Both Docker and Kubernetes also add a lot of unnecessary attack surface (properly securing Kubernetes alone is pretty complicated - it’s a REST API which hands out omnipotent tokens which have root access to your nodes - that’s the opposite of defense in depth!).
Certus One has extensive experience using Docker, Kubernetes and Red Hat’s OpenShift k8s distribution in production and we’re confident that we could pull it off, just like we have no doubt that other teams will. However, we believe that a better result can be achieved without, with time better spent on security hardening and tooling.
That being said, we do use Kubernetes for our auxiliary infrastructure like our monitoring stack, continous integration, testing setup and various other internal infrastructure needs. It’s perfect for that - our monitoring stack in particular consists of many small services talking to each other, and Kubernetes is perfect for that. We just don’t want it anywhere where its failure would affect the core of our business - running secure validators.
Network topology¶
We recommend a flat network topology where each node has its own unique IP address, with full end-to-end connectivity (only hindered by your firewall rules).
You should prefer a fully routed L3 topology over switched L2 topologies, especially those of the “magic SDN” variety which use a separate control plane (like OpenVSwitch, ZeroTier and most datacenter SDN technologies in general).
L2 topologies have no advantage over L3 routing for applications that have no special networking requirements. Their main users are service providers which need to span L2 network across their data centers to provide their customers with virtual networks. For everyone else, L3 networks are the better choice. L2 networks are brittle, hard to segment (they’re basically one large failure domain), and hard to debug.
L3 networks are built to scale, have well-defined interfaces and control plane protocols, and great troubleshooting tools (routing tables, traceroutes and BGP are more intuitive than an opaque SDN control plane which pretends to be a gigantic flat L2 network, but isn’t). The potential blast radius in a L3 network is much smaller than in a L2 network, where a stray spanning-tree packet can potentially take down an entire network.
For example, a GCE VPC is completely routed. Each virtual machine has its own transfer network with the VPC router, and the only broadcast packet you’ll ever see are the ones sent by either the router, or the instance. The VPC router maintains a /32 route for each VMs IP addresses. This is why you can’t set next hop routes in your virtual machines, but need to add them in the VPCs routing table - while it pretends to be a large /24 network, it’s in fact a routed network where each instance has its own little network.
Via VPC peerings, you can connect multiple networks to their respective IP ranges, which is “just” another entry in the VPC routing table pointing to the other VPC’s router.
Network address translation is best avoided. It adds an extra layer of complexity, makes it harder to attribute traffic once it left a NAT boundary and is another state machine on top of TCP and Tendermint’s consensus protocols.
It creates all sorts of operational headaches - you need to maintain a separate list of public/private IPs and port forwardings, you can’t easily whitelist traffic from single nodes once it crossed a NAT boundary (you would need to log NAT source ports), and so on.
NAT is not a security tool, firewalls are. You can achieve the same level of network isolation with and without NAT. It’s hard to avoid - most cloud provider’s VPC networks use RFC 1918 addresses internally with NAT for external connectivity, and you need some sort of NAT at the network edges - but you should keep its use to a minimum and ensure that nodes can communicate without NAT inside of your network.
Kubernetes is another good example - each pod has its own IP address in a large flat network, which can be either routed or switched, depending on the network plugin you use - Calico is what we use. Each pod can talk to each other across this flat network as long as the network policy allows it. However, pods need to talk to the outside world sometimes, so each node does outgoing NAT for the traffic originating from pods running on it. The outside world also needs to talk to some of the services running in the cluster, so there’s a load balancer which accepts traffic on its public IP address and load balances it to the internal IPs.
Secret management¶
Avoid working with secrets, especially bearer tokens, especially if they can be used on public APIs without any additional layer of security (looking at you, public cloud platforms and k8s).
Use service-level certificate-based authentication to authenticate your services against a centralized secrets storage like Hashicorp Vault.
If possible, use Vault’s auto-provisioning feature which creates individual short-lived credentials for each service.
If you use stateless tokens like JWT, make sure to hardcode the signature algorithm and either use very short-lived tokens that expire after a few minutes, with stateful refresh tokens that you can invalidate, or another invalidation mechanism like revocation lists or a mechanism that invalidates all tokens issued before a certain timestamp (for emergencies).
Red Hat IDP/Keycloak is a good OAuth/JWT server which implements both short-lived tokens as well as not-before timestamps.
If you have a lot of services that need to authenticate at each other, you might reach a point where a service mesh like Istio is worth looking into.
Todo
This article is a work in progress


| Package | com.whirled.contrib.platformer.client |
| Class | public class ClientPlatformerContext |
| Inheritance | ClientPlatformerContext PlatformerContext |
| Property | Defined by | ||
|---|---|---|---|
| board : Board
[static]
| PlatformerContext | ||
| boardSprite : BoardSprite
[static]
| ClientPlatformerContext | ||
| collisionEnum : Class [static]
| PlatformerContext | ||
| controller : GameController
[static]
| PlatformerContext | ||
| dfac : DynamicFactory
[static]
| PlatformerContext | ||
| effectEnum : Class [static]
| PlatformerContext | ||
| gctrl : GameControl [static]
| PlatformerContext | ||
| inControl : Boolean [static]
| PlatformerContext | ||
| keyboard : KeyboardController
[static]
| ClientPlatformerContext | ||
| local : Boolean [static]
| PlatformerContext | ||
| myId : int [static]
| PlatformerContext | ||
| net : MessageManager
[static]
| PlatformerContext | ||
| persist : PersistenceManager
[static]
| PlatformerContext | ||
| pfac : PieceFactory
[static]
| PlatformerContext | ||
| platformer : PlatformerController
[static]
| PlatformerContext | ||
| prefs : Preferences
[static]
| ClientPlatformerContext | ||
| sound : SoundController
[static]
| ClientPlatformerContext | ||
| soundEffectEnum : Class [static]
| PlatformerContext |
| boardSprite | property |
public static var boardSprite:BoardSprite
| keyboard | property |
public static var keyboard:KeyboardController
| prefs | property |
public static var prefs:Preferences
| sound | property |
public static var sound:SoundController


In this section, you’ll have a chance to practice the concepts you’ve learned in the videos. First, review the core concepts covered that you’ll need to keep in mind. Then go through the exercises below.
Remember, these are for your own benefit. Feel free to skip them if you don’t find a particular exercise valuable or you get stuck for too long.
REPL stands for Read-Eval-Print-Loop and is the interactive environment
you get when you type python
in the terminal / command
prompt. Remember, on macOS and Linux, you start Python 3’s REPL by typing
python3
.
Variables are names that we use to refer to data that could change or is complex to write directly. Values are the data that is currently assigned to that variable. We used pythontutor.com to explore this.
Examples:
x = 7
y = 11
z = x + 2*y
name = 'Sarah'
Python comes with many included libraries (hundreds!). To use one of these libraries, such as sys
, you must tell
Python you want to load it. This is done with the
import
keyword.
Example:
import sys
print(f"The current version of Python is {sys.version_info}")
Getting input from users is done with the input
function.
Example:
name = input("What is your name? ")
print(f"Hello {name}")
Certain operations (like math and string concatenation) require the correct data types.
Data is converted to numerical types using the type name (int, float, etc). Here are a few examples:
text = '7.2'
whole_number = int(text) # value = 7
number = float(text) # value = 7.2
Now it’s your turn. Here are some ideas to practice.
datetime
and the function
datetime.datetime.now()
to determine the current year and
print that to REPL using an f-string.


{%- extends 'base.html' -%} {%- block content -%}
Here is a random new id that hasn't been used yet:
{{ new_id }}


jsqubits sample
Applying the Hadamard operator followed by the T operator to |0> gives
2
2048
mod 100 =


This is a Bootstrap starter template with jGrowl integrated. As you can see it's extremely easy to make jGrowl look and feel like part of Bootstrap.


IM Solution¶
Server¶
Jitsi could be better than openfire projects.
Clients¶
None are suitable?
- No “one coding, twice use”
For React Native Sunsetting, no react native for mobile. So it is possible using Jitsi Mobile SDK or Smack(Movim/Android?).
- Not Movim
Movim uses PHP for web, but should keep an eye on Movim for Android.
The Activity uses WebView & WebChromeClient. Here is an more about WebView.
4. JSXC is for prosody, also looks promising over javascript.
6. Smack is a java client.
7. react-jitsi is a good example of Jitsi Meet JS API.
React-jitsi’s Demo failed.
8. [no open source]`react chat <https://www.npmjs.com/package/react-chat-engine>`_ is promising but just a library.


GHGRP 2011: Reported Data
For reporting year (RY) 2011, approximately 8,000 facilities in nine industry sectors reported 3.3 billion tons carbon dioxide equivalent (CO2e) of direct emissions. The facilities reported direct emissions of carbon dioxide, methane, nitrous oxide, and fluorinated gases. Additional facilities reported GHG quantities associated with products supplied. Over 6,200 facilities across 29 source categories reported GHG data for RY2010. In 2011, 12 additional source categories began monitoring GHG data and reported GHG data for the first time in 2012. The addition of these 12 new source categories brings the total coverage of the GHGRP to 41 source categories. This table lists the nine industrial sectors and includes which of the 41 source categories are included in each sector.
EPA prepares the annual Inventory of U.S. Greenhouse Gas Emissions and Sinks (Inventory) report to track total national emissions of greenhouse gases. The Inventory presents national estimates of GHG emissions starting in 1990 and for each subsequent year. The GHGRP represents a subset of the national estimates and now includes approximately 85-90 percent of total U.S. GHG emissions with the addition of the 12 new source categories in 2011. More information on the U.S. Greenhouse Gas Inventory. Learn more about differences between the Inventory and the GHGRP.
2011 Data Highlights Reported to GHGRP
All 2011 data presented here reflects data reported to the GHGRP as of 1/16/13.
- 2011 Reporting: Location of direct-emitting facilities
This map shows the locations of direct-emitting facilities. The size of a circle corresponds to the quantity of emissions reported by that facility.
- About 4% of the facilities emitted 57% of 2011 reported emissions.
In 2011, the 295 largest-emitting facilities — those emitting more than 2.5 million metric tons CO2e — accounted for almost 1.9 billion metric tons CO2e. These emissions represent about 57% of the total 3.3 billion metric tons CO2e reported.
These high-emitting facilities are mainly power plants, petroleum refineries, and facilities in the metals and chemicals industry sectors.
- Power plants accounted for about 67% of direct emissions reported by stationary sources in 2011.
Power plants accounted for 2.2 billion metric tons CO2e, which represents about 67% of the 3.3 billion metric tons CO2e reported for 2011.
Emissions reported by power plants represent 33% of total U.S. GHG emissions. See the U.S. GHG Inventoryreport for more information about total U.S. emissions.
Select the industry sectors in the table below for a profile of sector emissions.
Download this table (XLSX).
Table 1: GHGRP Sector Classifications
| Industry Sector | Number of Reporters | Emissions (million metric tons CO2e) |
|---|---|---|
| Power Plants | 1,594 | 2,221 |
| Petroleum and Natural Gas Systems | 1,880 | 225 |
| • Onshore Petroleum & Nat. Gas Prod. | 448 | 94 |
| • Offshore Petroleum & Nat. Gas Prod. | 99 | 6.3 |
| • Natural Gas Processing | 372 | 62 |
| • Natural Gas Trans./Compression | 424 | 24 |
| • Underground Natural Gas Storage | 44 | 1.4 |
| • Natural Gas Local Distribution Co. | 168 | 14 |
| • Liquefied Natural Gas Imp./Exp. Eq. | 7 | 0.7 |
| • Liquefied Natural Gas Storage | 5 | ** |
| • Other Petroleum & Nat. Gas Systems | 331 | 23 |
| Refineries | 145 | 182 |
| Chemicals | 458 | 180 |
| • Adipic Acid Production | 3 | 12 |
| • Ammonia Manufacturing | 22 | 25 |
| • Fluorinated GHG Production | 16 | 6.6 |
| • HCFC-22 Prod./HFC-23 Dest. | 5 | 6.9 |
| • Hydrogen Production | 103 | 34 |
| • Nitric Acid Production | 36 | 12 |
| • Petrochemical Production | 64 | 53 |
| • Phosphoric Acid Production | 13 | 2.0 |
| • Silicon Carbide Production | 1 | 0.1 |
| • Soda Ash | 4 | 5.1 |
| • Titanium Dioxide Production | 7 | 2.4 |
| • Other Chemicals | 213 | 21 |
| Waste | 1,593 | 103 |
| • Industrial Waste Landfills | 173 | 8.5 |
| • Municipal Landfills | 1,208 | 81 |
| • Solid Waste Combustion | 68 | 10 |
| • Wastewater Treatment | 151 | 3.7 |
| Metals | 297 | 115 |
| • Aluminum Production | 10 | 6.7 |
| • Ferroalloy Production | 10 | 2.3 |
| • Iron and Steel Production | 128 | 91 |
| • Lead Production | 13 | 1.0 |
| • Magnesium Production | 9 | 2.5 |
| • Zinc Production | 6 | 0.9 |
| • Other Metals Production | 121 | 10 |
| Minerals | 362 | 98 |
| • Cement Production | 96 | 56 |
| • Glass Production | 110 | 8.4 |
| • Lime Manufacturing | 73 | 31 |
| • Other Minerals | 83 | 3.6 |
| Pulp and Paper | 230 | 44 |
| • Pulp and Paper Manufacturing | 110 | 31 |
| • Other Paper Producers | 120 | 13 |
| Other | 1,377 | 126 |
| • Food Processing | 299 | 30 |
| • Ethanol Production | 162 | 18 |
| • Manufacturing | 280 | 17 |
| • Universities | 109 | 9.4 |
| • Military | 43 | 2.7 |
| • Other | 156 | 11 |
| • Underground Coal Mines | 175 | 28 |
| • Electronics Manufacturing | 49 | 5.4 |
| • Electrical Equipment Manufacturers | 5 | 0.3 |
| • Electrical Equipment Use | 102 | 3.9 |
* Biogenic emissions are NOT included in the total emissions.
** Total reported emissions are less than 0.05 million metric tons CO2e.
Suppliers: 2011 Reporting
Suppliers are those entities that supply products into the economy which if combusted, released or oxidized emit greenhouse gases into the atmosphere. Emissions associated with these fuels and industrial gases do not occur at the supplier’s facility but instead throughout the country, where used. An example of this is gasoline, which is supplied into the U.S. economy by a relatively small number of entities and consumed by many individual vehicles throughout the country.
The majority of GHG emissions associated with the transportation, residential and commercial sectors are accounted for by these suppliers.
The GHG quantity reported by suppliers may not always result in GHG emissions, and the emissions may not take place during the reporting year. However, the data from suppliers provide important information on the structure and flow of products through the economy, because these products may ultimately result in greenhouse gas emissions. In addition, reporting by fossil fuel and industrial gas suppliers can account for greenhouse gas emissions by the many low-emitting sources that are not required to report emissions under the GHGRP. Emissions reported by suppliers can be accessed through the suppliers section of FLIGHT.
How You Can Obtain The GHGRP Data
Use FLIGHT to view maps of facility locations, obtain summary data for individual facilities, create customized searches, and display search results graphically.
All non-CBI GHG data submitted to the GHGRP is now available on Envirofacts in a format that allows users to easily search, sort, and download data. Users have access to all reporting year 2010 data and the majority of reporting year 2011 data. The remaining reporting year 2011 data will be made available shortly.


#include <CollisionBoundsManiVisitor.h>
|
| static cute::c2Manifold | collisionBetween (std::variant< cute::c2AABB, cute::c2Circle > const &left, std::variant< cute::c2AABB, cute::c2Circle > const &right) |
| |
|
| std::variant< cute::c2AABB, cute::c2Circle > const & | m_bounds |
| |
| cute::c2Manifold & | m_manifold |
| |
Definition at line 8 of file CollisionBoundsManiVisitor.h.
◆ CollisionBoundsManiVisitor() [1/3]
| app::vis::CollisionBoundsManiVisitor::CollisionBoundsManiVisitor |
( |
std::variant< cute::c2AABB, cute::c2Circle > const & |
bounds, |
|
|
cute::c2Manifold & |
manifold |
|
) |
| |
◆ ~CollisionBoundsManiVisitor()
| app::vis::CollisionBoundsManiVisitor::~CollisionBoundsManiVisitor |
( |
| ) |
|
|
default |
◆ CollisionBoundsManiVisitor() [2/3]
◆ CollisionBoundsManiVisitor() [3/3]
◆ collisionBetween()
| cute::c2Manifold app::vis::CollisionBoundsManiVisitor::collisionBetween |
( |
std::variant< cute::c2AABB, cute::c2Circle > const & |
left, |
|
|
std::variant< cute::c2AABB, cute::c2Circle > const & |
right |
|
) |
| |
|
static |
◆ operator()() [1/2]
| void app::vis::CollisionBoundsManiVisitor::operator() |
( |
cute::c2AABB const & |
aabb | ) |
|
◆ operator()() [2/2]
| void app::vis::CollisionBoundsManiVisitor::operator() |
( |
cute::c2Circle const & |
circle | ) |
|
◆ operator=() [1/2]
◆ operator=() [2/2]
◆ m_bounds
| std::variant<cute::c2AABB, cute::c2Circle> const& app::vis::CollisionBoundsManiVisitor::m_bounds |
|
private |
◆ m_manifold
| cute::c2Manifold& app::vis::CollisionBoundsManiVisitor::m_manifold |
|
private |
The documentation for this class was generated from the following files:


Portfolio Projects
click on card images for details
Online Portal where teachers can ask for volunteer help from parents by posting a request
-
Technologies Used:
- Front End Built with React
- User authentication with Passport.js
- Custom CSS Grid Framework
- Axios for API calls/routing
- Express/Node Server back end
- MongoDB/Mongoose Database
Personal website built for my brother, Kyle Etges, a musician in TN. Check him out!
Still working on tablet level responsiveness
-
Technologies Used:
- HTML5/CSS Grid
- Front End built on React
- Express.js
- Custom CSS Framework
Social network for developers graduating bootcamps
-
Technologies Used:
- Front End Built with React
- User authentication with Passport.js
- Bulma (scss framework)
- Sass for Styling
- Axios for API calls/routing
- Express/Node Server back end
- MongoDB/Mongoose Database
Web Tool built for my company, Vision Care Specialists, to correctly calculate AARP Discount
for opticians.
v1.0 just released!
-
Technologies Used:
- HTML5/CSS Grid
- Materialize.css
- Javascript & jQuery
Html Templates using xhtml and table-based templates for multiple mail clients
Sign up for TrailsOffRoad.com to see it in action! :)
-
Technologies Used:
- HTML for email clients
- CSS
- Table-based layout
No Github Repo
Searches database for ingredient and displays recipe ideas via Sequelize database
-
Technologies Used:
- HTML/CSS
- Javascript/jQuery
- Express.js
- Materialize
- mySQL & Sequelize
The Simpsons React Memory Game
-
Technologies Used:
- HTML/CSS
- React/ JSX
- Javascript ES6
- Bootstrap 4.0
- Animate.css
Night/Event planner based on user's location.
-
Technologies Used:
- jQuery
- Firebase
- Materialize.css (css framework)
- AJAX for API calls
- email.js
Video Game Article Scraper
-
Technologies Used:
- HTML/CSS
- Javascript/jQuery
- Express.js
- Bootstrap 4.0
- Handlebars
- node.js
- mongoDB & Mongoose
-
Technologies Used:
- Materialize
- Express.js Server
- Javascript
- node.js
- npm packages: express, body-parser, path
Webpage built for my photography hobby.
-
Technologies Used:
- Bootstrap
- Javascript/jQuery
- Built using a Bootstrap template provided by freehtml6.co
Node CLI Application that can display tweets, search Spotify for songs, or omdb for Movie
information
-
Technologies Used:
- Javascript
- node.js
- npm packages: inquirer, dotenv, twitter, spotify-node-api, request
Interactive Hangman Game
-
Technologies Used:
- Javascript/jQuery
- Bootstrap
- Mobile Responsive
- HTML5/CSS3
Express Server with JawsDB mySQL database
-
Technologies Used:
- Javascript
- Express.js
- Handlebars
- mySQL
- Bootstrap
- HTML/CSS
Giphy Animal Gif Search
-
Technologies Used:
- jQuery
- AJAX for API call
- Javascript
- Bootstrap


MyInstants SinusBot Player
GitHub
Select Bot:
Logout
Search
All sounds
Favourites
...
Address
Port
Login
Password
Login
(c) 2016,
HarpyWar
.


Signal digitization is carried out in two steps: discretization and element-wise quantization.
At the first step of the digitization, called signal discretization, continuous signals are converted into a set of real numbers that form signal discrete representation. At the second stage, called scalar (element wise) quantization, this set of numbers is, number by number, converted into a set of quantized numbers, which finally results in a digital signal that corresponds to the initial continuous one.
The conversion of continuous signals into discrete ones can be modeled as computing coefficients that represent signals in their expansion over a set of basis functions.
Let be a set of basis functions used for signal discretization. Then coefficients of signal discrete representations are computed as
Equation 2‑1
This equation is a mathematical formulation of operations performed by signal sensors. Functions describe spatial sensitivity of sensors, i.e. sensor point spread function, or sensor aperture.
Signal discretization with discretization basis assumes that a reciprocal set of reconstructing basis functions is defined, with which signal can be reconstructed from its discrete representation coefficients as following:
where approximation symbol in Equation 2‑2. indicates that signal reconstructed from its discrete representation is, generally, not a precise copy of the initial signal, is a number of representation coefficients needed for signal reconstruction with a desired accuracy and functions are point spread functions of signal reconstruction devices (such as, for instance, computer display), or their reconstruction aperture functions.
The sampling and reconstruction functions (, )are usually produced from a single “mother” function by means of its modification with one or another method. Typical basis functions can be:
Shift (convolutional) basis functions – each function is a shifted version of the “mother function”:
; . An example for such a function is the impulse rect-functions:
Equation 2‑3
Scale (multiplicative) basis functions- method for generating basis functions from one mother function is scaling the argument of functions:
Equation 2‑4
Examples for such functions are the Fourier series, DCT, and Walsh.
Wavelets - wavelet basis functions built using a combination of shift and scaling of a mother function. An example of such function is the Haar functions.
Fourier series expansion over a finite interval is an example of signal expansion over scaling basis functions ) [1]:
Equation 2‑5
where is the interval within which signal is approximated by the Fourier series and coefficients are complex numbers computed as:
Equation 2‑6
They can be regarded as ()-normalized samples of the signal Fourier transform spectrum taken at frequencies. The fundamental property of Fourier series signal expansion is that, while it converges to the signal within interval of expansion, outside this interval Fourier series, owing to periodicity of basis functions, converges to a periodical function with a period.
The discrete Fourier transform and its inverse are given by:
Equation 2‑7
Equation 2‑8
Correspondingly, canonical direct and inverse 2-D DFTs are defined as:
Equation 2‑9
Equation 2‑10
The Discrete Cosine Transform defined for signal of samples as [2]:
Equation 2‑11
The inverse DCT is defined as
Equation 2‑12
Equation 2‑13
Discrete Cosine Transform exhibits the best energy compaction capability. In fact, DCT has the edge over other known transforms in the most of image processing applications.
Walsh functions are binary functions that assume only two values; either 1, or -1. Walsh functions are generated by multiplication
Equation 2‑14
of clipped sinusoidal functions called the Rademacher functions:
Equation 2‑15
where is interval on which functions are defined and is the -th digit of the so-called Gray code of index . Gray code digits are generated from digits of ordinary binary representation of the number
Equation 2‑16
according to the following rule:
Equation 2‑17
where stands for modulo 2 addition, according to which .
Hadamard Transform
The Hadamard Transform is defined as:
Equation 2‑18
Where:
The number of signal samples be an integer power of 2: .
and represent signal and transform domain indices on the base 2 through their binary digits and,:
Equation 2‑19
The 1D Hadamard transform is a -dimensional DFT over two samples in each dimension. (see appendix) . From this it follows that inverse Hadamard Transform is identical to the direct one.
The basis functions of Hadamard transform
Equation 2‑20
are ordered in the same way as exponential basis functions of DFT are: according to the natural ascending order of their index.
We call better corresponds to the principle of ordering according to transform coefficients’ energy. The transform with such ordering of the basis functions, called Walsh Transform or Walsh-Hadamard Transform ,
Equation 2‑21
can also be computed using Fast Hadamard Transform algorithm, though it must be complemented with permutation of transform coefficients according to their Gray code ordering. Similarly to Hadamard Transform, Inverse Walsh Transform is identical to the direct one.
Haar functions defined on interval are generated from shifted rectangular impulse functions and Rademacher functions in the following way:
Equation 2‑22
where is index of the most significant non-zero digit (bit) in binary representation of and is a residual from division of by .
Haar functions are orthogonal on the interval . Graphs of first eight Haar functions are shown in Figure 2‑1.
|
Figure 2‑1 First eight Haar functions. Boxes unify Haar functions of the same scale. |
The figure clearly reveals the way in which Haar functions are built. One can see from the figure, that Haar functions form groups according to the scale indexed by symbol (outlined by boxes in Figure 2‑1) and that functions within each group are generated by shift in coordinates by multiple of. One can also see that signals reconstructed from their discrete representation over Haar basis functions belong to the same family of piece-wise constant functions as signals reconstructed in the base of rectangular impulse basis functions and Walsh functions.
Numerical values of Haar functions can be found at each point by expressing Haar functions, as in the case of Walsh functions, via a binary code of the argument:
Equation 2‑23
A formal definition of discrete Haar basis functions:
Equation 2‑24
with as index of the most significant non-zero digit (bit) in binary representation of , being modulo value of , a residual from division of by and being an integer part of . Table 4.2 provides examples of , and for these functions.
Table 2‑1 Examples of, and in binary representation of numbers
|
0 |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
|
|
Binary code |
000 |
001 |
010 |
011 |
100 |
101 |
110 |
111 |
|
0 |
0 |
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The “Discrete Representation” experiment compares the energy compaction capability of four digital transforms – DFT, DCT, Haar and Walsh, as described at 2.1.1.
Energy compaction capability is the capability of transforms to concentrate signal energy in few signal decomposition coefficients.
This experiment also gives a good comparison between the visual effects cause by zeroing smallest absolute value coefficients of each transform.
The experiment procedure is:
The test image is transformed using four different transforms.
The absolute values of the transforms are sorted in descending order.
The coefficients of the smallest absolute value are zeroed until the total energy of the transform reaches to a threshold defined by the user
The image is restored from the result transform coefficients.
Figure 2‑2: “Discrete Representation”. Top left image is the test image. Bottom left plot is the fraction of spectrum energy as a function of ordered spectral coefficient. Center top image is the DFT restored image after zeroing smallest coefficients to achieve 98% energy. Center bottom image is the Haar restored image after zeroing smallest coefficients to achieve 98% energy. Top right image is the DCT restored image after zeroing smallest coefficients to achieve 98% energy. Bottom right image is the Walsh restored image after zeroing smallest coefficients to achieve 98% energy.
DCT and Haar displays better energy compaction capability with respect to minimum absolute value of the coefficient form all the other transform. Haar, however, create un-natural artifacts to the restored image. This reason, along with other, makes the DCT a natural choice for image discretization.
Figure 2‑3: “Discrete Representation”– spectrum of previous images. Top left image is the test image. Bottom left plot is the fraction of spectrum energy as a function of ordered spectral coefficient. Center top image is the DFT spectrum after zeroing smallest coefficients to achieve 98% energy. Center bottom image is the Haar spectrum after zeroing smallest coefficients to achieve 98% energy. Top right image is the DCT spectrum after zeroing smallest coefficients to achieve 98% energy. Bottom right image is the Walsh spectrum after zeroing smallest coefficients to achieve 98% energy.
Then discrete representation coefficients of a signal are:
and its reconstructed version is
where is the point spread function (PSF) of signal/image reconstruction/display device, is the PSF of signal sampling device and is signal sampling interval.
Integration over x in Equation 2‑25 and summation over k in Equation 2‑26 are performed within signal boundaries.
Without violating the generality one can, in order to simplify further mathematical analysis, rewrite integration in Equation 2‑25 and summation in Equation 2‑26 in infinite limits:
assuming that signal and its samples are equal to zero outside the real intervals of support. Modify now Equation 2‑27 and Equation 2‑28 in the following way:
Equation 2‑29
Equation 2‑30
which introduces “virtual” signals
Equation 2‑31
and
Equation 2‑32
Further analysis of signal sampling is much simplified in Fourier Transform domain. Compute Fourier spectra of virtual signals and. Spectrum of signal, whose instantaneous values form sampled representation of signal, is, by the convolution theorem, a product
Equation 2‑33
of spectrum of the signal and the frequency response of the sampling device, Fourier Transform of its PSF :
Fourier spectrum of the virtual signal involved in reconstruction of signal from its sampled representation can be found as (see Appendix)
Equation 2‑35
The sum in the latter expression can be regarded as a Fourier series expansion on interval of a function with expansion coefficients, equal to 1, i.e. Dirac delta. Therefore this series converges to a periodical replication of delta-function with a period. In this way we arrive at the identity
Equation 2‑36
called the Poisson summation formula.
Inserting this formula into Equation 2‑34, obtain that spectrum of the virtual signal is composed of periodical replicas of the spectrum
Equation 2‑37
This analysis reveals that
Coefficients of discrete representation of signal over shift basis functions are instantaneous vales of a signal obtained from the signal by its “pre-filtering” in the sampling device (and not those of the signal).
Signal sampling can be interpreted as converting pre-filtered input signal into a virtual signal whose Fourier spectrum is composed of periodically repeated replicas of initial signal spectrum modified by the frequency response of signal sampling device ;the replication period is defined by the sampling interval and forms what is called signal “baseband”.
- Signal reconstruction from its sampled representation can be treated as a “post-filtering” of the virtual discrete signal in the reconstruction device:
Equation 2‑38
This “post-filtering” can be described in Fourier Transform domain as multiplication
Equation 2‑39
of spectrum of the virtual signal by frequency response of the signal reconstruction device:
Equation 2‑40
The described interpretation of signal sampling and signal reconstruction from its samples is illustrated by flow diagrams in Figure 2‑4 and Figure 2‑5 and by plots in Figure 2‑6.
Figure 2‑4: Flow diagram of the sampling device
Figure 2‑5: Mathematical model of the device for restoring continuous signals from their samples
|
Figure 2‑6: Fourier domain representation of signal sampling and reconstruction: upper plot – initial signal spectrum, its spectrum after “pre-filtering” in the sampling device and frequency responses of the sampling device and of the ideal low-pass filter that defines the signal base band; lower plot – periodical replicas of sampled signal spectrum and frequency responses of the signal reconstruction device and of the ideal low-pass reconstruction filter |
Now one can easily see that distortions of the reconstructed signal compared to the initial non-sampled signal are due to the following reasons:
Modifications of the signal spectrum by its pre-filtering in the sampling device;
Penetrating tails of the pre-filtered signal spectrum periodical replicas inside the signal baseband, which causes spectra aliasing;
Remains of the signal spectrum periodical replicas outside the signal baseband not perfectly filtered out by the “post-filtering” in the reconstruction device.
For signals, whose spectral energy does not grow with frequency, signal distortions due to sampling can be minimized in terms of signal reconstruction mean square error, if signal pre-filtering and post-filtering are performed by the ideal low-pass filters with frequency response
Equation 2‑41
Thus, with the ideal low-pass pre- and post- filters, signal samples are obtained as
Equation 2‑42
and signal reconstruction from its samples is performed as
Reconstruction of a continuous signal from its samples according to Equation 2‑43 is called sinc-interpolation.
Ideal low-pass filters do not distort signal spectrum within the baseband and remove all spectral components outside the baseband thus preventing signal spectrum from aliasing. Signal pre-filtering with the ideal low-pass filter makes the signal band-limited.
In a special case of band-limited signals, the result of sampling are samples of signals themselves and signals are perfectly (distortion less) reconstructed from the result of sampling:
Equation 2‑44
Sampling theorem allows also evaluating the volume of signal discrete representation, i.e. the number of signal samples N needed to represent signals of length X and bandwidth F:
Signal space-bandwidth product is a fundamental parameter that defines signal degrees of freedom.
The “Image Sampling” experiment compares image sampling using pre and post-filters and with no pre-post filtering.
Figure 2‑7: “Image Sampling”. Top left image is the test image, bottom left image is the DFT transform of the test image. Top middle image is unfiltered subsampled version of the test image with subsample factor of 3. Bottom middle image is the unfiltered subsampled
As described in 2.2.1, aliasing is caused by penetrating tails of the pre-filtered signal spectrum periodical replicas inside the signal baseband, if not ideal pre and post filters are used.
The aliasing problem can be explained using 1D signal in the time domain, using Figure 2‑8.
Figure 2‑8: Aliasing 1D time domain example. There are two different sinusoids that fit the same set of samples.
Figure 2‑8 represent two signals – first (red) is a high frequency signal, and the other (blue) with small frequency. Any sampling of the high frequency signal with smaller than Nyquist sampling rate, will cause any restoration of the signal to be ambiguous, and an interpreter of the sampled data can restore the blue signal instead of the red.
The “Aliasing 1D” experiment illustrates aliasing affect in 1D sampling. Using a general structure of a 1D signal, where K is an increasing constant, and t is the sampling interval; the aliasing can be illustrated when the signal frequency is higher than the sampling rate.
This general structure of the signal, which is composed of a cosine part and a DC part, is useful to examine, since this signal structure in the Fourier transform is comprised out of 3 delta functions – one in the zero frequency, and two others in a frequency determined by the argument of the cosine.
The experiment uses the mentioned signal structure with an increasing parameter K, a displays the signal, its Fourier transform relative to the unstamped Fourier transform with sampling rate indicator, and Fourier image of current and previous signals Fourier transforms.
Once the K value is big enough so the frequency of the signal is higher than the sampling rate, aliasing occurs.
Figure 2‑9: “Aliasing 1D”. Top plot is the signal. Second from the top plot is the signal DFT after sampling (green), signal DFT before sampling (blue), and sampling rate mark (red). Bottom image is an “upper” view signal spectra for.
Figure 2‑10: “Aliasing 1D”. Top plot is the signal. Second from the top plot is the signal DFT after sampling (green), signal DFT before sampling (blue), and sampling rate mark (red). Bottom image is an “upper” view signal spectra for.
It can be seen from Figure 2‑9 and Figure 2‑10 that while both of the signals ( and have different frequency, the aliasing artifact cause their reconstruction to appear the same. The explanation for that is that the high frequencies of the signal are interpreted as low frequencies, which makes the high frequency signal to appear with low frequency.
As mentioned at 2.1 Discrete Representation the capability of transforms to concentrate signal energy in few signal decomposition coefficients is called energy compaction capability. This property was examined at 2.1 Discrete Representation with regards to the absolute value of the coefficients. Another aspect of energy compaction capability is with regards to coefficient energy, i.e., the more energy a transform have in lower frequencies, the better the transform is.
Theoretically, Karhunen-Loeve transforms and related Hottelling and SVD transform have the best energy compaction capability among linear transforms (Karhunen-Loeve transforms, Hottelling and SVD transform are covered at appendix…). However in practice of image processing they remain to be a theoretical bench mark because of implementation problems and other transforms are used, such as Discrete Fourier, Discrete Cosine, Discrete Walsh and Discrete Haar transforms.
Among those transforms, the DCT , introduced by N. Ahmed, T. Natarajan and K.R. Rao, proved to have a very good energy compaction capability and is very frequently considered as an approximation to Karhunen-Loeve transform.
The “Image LPF” experiment illustrates four transforms (DFT, DCT, Haar, Walsh) energy compaction capability by applying an ideal low pass filter in the transform. Two low pass filter representation are used – rectangular and circular.
Where it the rectangular low pass filter in the Fourier domain.
The rectangular low pass filter in the special domain is:
Figure 2‑11: Rectangular low pass filter. Right image is the rectangular low pass filter in the frequency domain. Left image is the rectangular low pass filter in the special domain.
Circular low pass filter is defined as:
Where it the circular low pass filter in the Fourier domain.
The circular low pass filter in the special domain is:
where is the first order Bessel function of the first kind.
This function is also known as Mexican hat.
Figure 2‑12: Circular low pass filter. Right image is the circular low pass filter in the frequency domain. Left image is the circular low pass filter in the special domain.
Figure 2‑13: "Image LPF". Top left is the test image. Bottom left is a fraction of signal energy as function of bandwidth plot for all four transforms (DCT-black, DFT-blue, Walsh-red, Haar-dark blue). Second from the left top is the DFT restored image after LPF. Third from the left top is the DCT restored image after LPF. Second from the left bottom is the Haar restored image after LPF. Third from the left bottom is the Walsh restored image after LPF. Top right is the DFT LPF mask. Bottom right is the DCT, Haar and Walsh LPF mask. The cutoff frequency was set to 0.5.
Figure 2‑14: "Image LPF". Top left is the test image. Bottom left is a fraction of signal energy as function of bandwidth plot for all four transforms (DCT-black, DFT-blue, Walsh-red, Haar-dark blue). Second from the left top is the DFT restored image after LPF. Third from the left top is the DCT restored image after LPF. Second from the left bottom is the Haar restored image after LPF. Third from the left bottom is the Walsh restored image after LPF. Top right is the DFT LPF mask. Bottom right is the DCT, Haar and Walsh LPF mask. The cutoff frequency was set to 0.7.
It can be seen from the Figure 2‑13 and Figure 2‑14 that DCT and DFT displays the best comparison, energy compaction capability with regards to coefficient frequency, with a small advantage for the DCT. This small advantage is consistent for many natural images. In fact, DCT has the edge over other known transforms in most image processing applications.
|
[1] |
L. P. Yaroslavsky, "Discrete Fourier Transforms," in Theoretical Foundations of Digital Imaging Using MATLAB, CRC Press, 2012, pp. 142, 147. |
|
[2] |
L. P. Yaroslavsky, "Discrete Cosine and Sine Transforms," in Theoretical Foundations of Digital Imaging Using MATLAB, CRC press, 2012, p. 161. |


Select Locale and Amount
How to Use
First select the desired locale and currency, then enter an amount into the
amount field. The formatted value appears below, localized for the selected
locale. Number parsing is also localized, so entering 1,23
is
an error in the en-US
locale, but a valid decimal number in
the de-DE
locale.
Query arguments may be passed to the page to pre-set the input fields. The page accepts the following arguments:
locale
- BCP-47 locale
currency
- ISO 4127 Currency Code or Number
amount
- Amount of Currency (localized number)
The page by default only loads data for English, French, and German
speaking locales. To add additional languages, open the page source and
add a <script>
tag pointing to
"../src/locales/web/{{lang}}.js"
(where
{{lang}}
is the two-letter ISO 3166 language code) immediately after the ones for
en
, fr
, and de
.
Formatted Currency
| Parsed to POSIX | |
|---|---|
| Formatted as Number | |
| Formatted as Currency | |
| Formatted as Accounting |
Locale Data
| Currency Code | |
|---|---|
| Currency Symbol | |
| Decimal Symbol | |
| Grouping Symbol | |
| Number Format | |
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PLANET SICK-BOY: http://www.sick-boy.com "We Put the SIN in Cinema"
Julien Temple's second swing at documenting the rise and fall of British punk purveyors The Sex Pistols is a home run deep into the center field bleachers. Hell, The Filth & The Fury is one of those moon shots that's still going up when you finally lose sight of it – a legendary rip that actually knocks the cover off the ball, leaving the rawhide wrapping to float gently back down into the hands of a lucky kid that, God willing, will one day be inspired to radically overhaul the crappy state of music today. We're about due for another sweeping change – there was a twenty-two year gap between Bill Haley and the Comets `(We're Gonna) Rock Around the Clock' and the Pistols `God Save the Queen (She Ain't No Human Being).' And it's been another twenty-two since the Pistols' song hit number one (or, as shown in Fury, since it didn't hit number one). Don't tell me that it's `Boy Bands,' either, because I'll legally acquire an automatic rifle and pepper MTV's Times Square studio with enough metal to turn Carson Daly into the Tin Man, I swear to God.
See – just talking about The Sex Pistols gets people riled up. In a music world that was populated by the likes of Kansas, The Eagles and Roxy Music, the Sex Pistols burst onto London's music scene at a time when the country was mired in garbage strikes, lethally high unemployment and race riots. The economically drained blue-collar workers had little else to do than hate the upper class and the Royal family, especially when wealth was flaunted right in front of their faces (like seeing the Queen riding around in a gold carriage during her Silver Jubilee celebration).
Filth is the third documentary about the Pistols, but the first to really tell their story. Temple's first attempt was 1980's The Great Rock ‘n' Roll Swindle (the second was a non-Temple film called D.O.A.). When he began filming Swindle, Temple could never have predicted the ending – the break-up of the band and the suicide of its enigmatic bass player, Sid Vicious.  The film premiered not long after Vicious' death, and the `swindle' in question, at least according to McLaren, was the bilking of money by record companies, the mindless youth of England and the actual members of the Pistols – all from a craze that he pretty much implies that he started on his own.
In Filth (the film's title is taken from one of the many London tabloid headlines about the Pistols), the story of the band's twenty-six-month existence is told from the band's perspective. We learn about the childhood of the band members - some things aren't surprising (broken homes) and some are (Lydon was in a coma for a year). But it isn't long before Johnny (Lydon) Rotten, Paul Cook, Steve Jones and Glen Matlock form the Pistols, and the band took off like a depraved, spitting rocket, triggering a London politician to say that the band would be "much improved by sudden death.' Imagine Asa Hutchinson saying something like that about Limp Bizkit.
 He lets you discover the irony of a band rallying against conformity, but unwittingly launching a fashion trend where everyone dressed in similar torn shirts and black leather jackets. He tosses in animated clips of the band and British television comedies, as well as snippets of Laurence Olivier playing Richard III. Temple also bathes the surviving members of the Pistols in shadows during their interview sessions. It looks like they're in the witness protection program, but after a while, you don't even notice it. At least not until Rotten/Lydon starts to get weepy when he talks about Vicious' death. McLaren is interviewed, too, but he's wearing a bondage mask. And the only thing that they can all seem to agree on is that Sid's girlfriend, Nancy Spungen, was the most despicable person on the face of the Earth (see if you can watch her and not think about Courtney Love).
Fury also includes all of the famous Pistol-related incidents that many of you may have only heard about or seen tiny portions of. There's the Bill Grundy television interview, the Thames performance in honor of Her Majesty's Silver Jubilee celebration, and the band's final concert, which consisted of just one song. The film also includes a very touching post-Pistol breakup interview with Vicious, as well as appearances by future stars Billy Idol, Siouxsie Sioux and Shane McGowan.
1:48 - R for pervasive strong language, drugs and sexual content
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Leverage agile frameworks to provide a robust synopsis for high level overviews. Iterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition. Organically grow the holistic world view of disruptive innovation via workplace diversity and empowerment.
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Iterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition.
Iterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition.
Iterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition.
Iterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition.
Leverage agile frameworks to provide a robust synopsis for high level overviews. Iterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition. Organically grow the holistic world view of disruptive innovation via workplace diversity and empowerment.


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Set up ad-hoc Dask cluster¶
This guide documents how to set up an ad-hoc cluster of machines that can run Dask processes.
Requirements¶
For this guide, we will assume you have access to the following:
At least two machines connected to a network that allows them to talk to each other over ssh
Ability to run on each machine
gds_env
containers (for this example, we will usegds_dev
but the same should work withgds_py
andgds
)
Intuition¶
Dask has several ways of setting up a cluster, including over ssh or with orchestration tools such as Kubernetes. For a more thorough coverage, please visit the official docs. In this guide, we will connect a few machines that can talk to each other over ssh. This is handy, for example, if you have a few computers running on the same local network.
The structure a Dask cluster relies on a single “entry point”, the “scheduler”, that recruits resources from a series of additional machines, the “workers” (including itself potentially).
The sequence of actions will be as follows:
Start the scheduler
Start workers on different machines, attaching them to the scheduler
Use the cluster on a Python session with a Dask
Client
Scheduler¶
We will run the scheduler inside a Jupyter Lab session that will then use the cluster, although this need not be the case. For illustration, we will refer to the IP on which this machine can be reached at with <scheduler-ip>
.
Fire up a terminal and type:
docker run --rm -ti -p 8888:8888 -p 8787:8787 -p 8786:8786 darribas/gds_dev:latest start.sh
This will spin up a gds_dev
container with a JupyterLab instance, opening ports 8888 (JupyterLab), 8787 (Dask dashboard) and 8786 (scheduler).
Inside JupyterLab, open a terminal window and launch the scheduler with:
dask-scheduler
Workers¶
Now go to each of the machines in the network you want to recruit as workers. Open a terminal and run:
docker run --network="host" --rm -ti -p 8786:8786 darribas/gds_dev:latest start.sh dask-worker <scheduler-ip>:8786
This will launch a container that starts a worker process and attaches to the scheduler (through the <scheduler-ip>
link).
It’s important to include the --network="host"
parameter so that the worker container can be reached from outside the machine through ssh.
If you want to start a worker on the same machine as a scheduler, use dask-worker <scheduler-ip>:8786
(not localhost).
Use the cluster¶
Once the cluster is available, we can use it on a Python session.
Open a notebook or Python console in the same JupyterLab instance as the scheduler
Import Dask’s
Client
from dask.distributed import Client
Set up a client for the session
client = Client("tcp://<IP_ADDRESS>:8786")
Replace tcp://<IP_ADDRESS>:8786
by the URL where the scheduler is running at (you can see this on the scheduler log feed).
You can check the status of the cluster at
<scheduler-ip>:8787
Now the cluster is set up and linked to the session, when you run a Dask operation, its computation will be distributed across the cluster.


With the advent of big data era, multi-view data become ubiquitous in real world. How to mine the valuable information inside these multi-view data is of significant importance. As a key component of various intelligent data analyticor mining algorithms, multi-view representation learning attracted considerable attentions, especially along with the resurgence of deep learning.
Although multi-view representation learning has gained great progress, there are still some open problems that requires further investigation. For instance, most existing multi-view representation learning methods can not deal with missing values and unaligned views directly. Big data era calls for efficient multi-view representation learning methods. Deep multi-view representation learning methods have huge parameters to tune and lack interpretability. Compared with other machine learning tasks like classification, multi-view representation learning does not have a clear criterion or good evaluation. All these problems call for novel multi-view representation learning theory and algorithms.
Multi-view representation learning has been successfully applied to many applications including computer vision, natural language processing, speech recognition, signal processing. Extending its applications to more areas needs more novel advanced multi-view representation learning theory, algorithms and high-quality datasets.
This workshop aims to provide a forum for researchers and practitioners from different domains to share their new findings and communicate to advance the further development of multi-view representation learning.
Topics of interest include, but not limited to, the following:
- Multi-view metric learning, multi-view sparse coding, multi-view manifold learning,multi-view subspace learning, multi-view dimension reduction
- Multi-view representation learning for explanation
- Multi-view representation learning on data with noise or missing values
- Multi-view representation learning for prediction, clustering and recommendation
- Visualization of multi-view representation learning
- Efficient multi-view representation learning
- Multi-view representation evaluation or criteria
- New high-quality multi-view representation learning datasets
- Optimization for multi-view representation learning
- Multi-view representation learning and its applications in medical informatics, computervision, natural language processing, etc.
Submission information:
We invite both full papers (max 8 pages) describing mature work and short papers (max 5-6 pages) describing work-in-progress or case studies. Only original and high-quality papers formatted using the IEEE2-column format (
Latex Template), including the bibliography and any possible appendices will be considered for reviewing. Paper submission site is at
https://wi-lab.com/cyberchair/2022/icdm22/scripts/ws_submit.php?subarea=S.
Important dates:
- September 17, 2022: Workshop papers submission
- October 8, 2022: Notification of workshop papers acceptance to authors
- October 10, 2022: Camera-ready deadline and copyright form
- October 10, 2022: Early bird registration finishes
- November 28-December 1, 2022: Workshops date
All dates are 11:59pm Pacific Daylight Time.
Workshop MRL Program:
Workshop Chair:
- Guoqing Chao, Schoolof Computer Science and Technology, Harbin Institute of Technology
- Xingquan Zhu, Departmentof Computer & Electrical Engineering and Computer Science, Florida Atlantic University
- Weiping Ding, Schoolof Information Science and Technology, Nantong University
- Jinbo Bi, School of Engineering, University of Connecticut
- Shiliang Sun, School of Computer Science and Technology, East China Normal University
Program Committee:
- Qingjiu Zhang, Shanghai Gene Automation Technology Co. Ltd
- Songtao Wang, National University of Singapore
- Zhijin Wang, University of Jimei
- Xijiong Xie, Ningbo University
- Jianjun Su, Harbin Institute of Technology
- Yu-Jie Xiong,Shanghai University of Engineering Science
- Xiayu Liang, Harbin Institute of Technology
- Kezhi Zhang, Nanning Normal University
- Xuejiao Yu, Harbin Institute of Technology


--- layout: page title: "About" description: "This is what I do." header-img: "/img/about-bg.jpg" ---
Hi, I'm Gitte, I'm a Cloud Solution Architect at Microsoft, helping our customers with all tech stuff related to Azure. I do design sessions, build proof of concepts and help them in architectural decisions. It's a fun job, where I get to be on top of things.
In a previous role, I used to be a dev grrrrl, team lead and architect. Customer focus has always been one of my priorities, but also keeping a realistic view on technology.
This blog was started a couple of years ago. A previous version of it can be found at [proq.blogspot.net][proq]. blogwise it's been very quiet around here for a couple of years, but I'm hoping to pick up where I left off.
And on a personal note, I'm the mom of a tremendously cute daughter.  We live, together with the best dad in the world, close to Brussels.


#include <loginscreen.h>
◆ LoginScreen()
| LoginScreen::LoginScreen |
( |
QWidget * |
parent = nullptr , |
|
|
Farm * |
f = nullptr |
|
) |
| |
|
explicit |
This is the default constructor for the Screen Login.
◆ ~LoginScreen()
| LoginScreen::~LoginScreen |
( |
| ) |
|
This is the default destructor for the Screen Login.
◆ getFarm()
| Farm * LoginScreen::getFarm |
( |
| ) |
|
◆ on_exitButton_clicked
| void LoginScreen::on_exitButton_clicked |
( |
| ) |
|
|
privateslot |
Function that closes the program.
◆ on_loginButton_clicked
| void LoginScreen::on_loginButton_clicked |
( |
| ) |
|
|
privateslot |
◆ TestLoginScreenGUI
◆ farm_
This attribute contains the the Farm.
◆ ui_
The documentation for this class was generated from the following files:


Nonogram Maker
What is a Nonogram?
Width
Height
Clear
Invert
Generate from image
Export as JSON
Export as PNG
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Empty (Right click)
Unsolvable
Calculating...


Securing Enterprise Beans
Enterprise beans are Java EE components that implement EJB technology. Enterprise beans run in the EJB container, a runtime environment within the GlassFish Server. Although transparent to the application developer, the EJB container provides system-level services, such as transactions and security to its enterprise beans, which form the core of transactional Java EE applications.
Enterprise bean methods can be secured in either of the following ways:
Declarative security (preferred): Expresses an application component’s security requirements using either deployment descriptors or annotations. The presence of an annotation in the business method of an enterprise bean class that specifies method permissions is all that is needed for method protection and authentication in some situations. This section discusses this simple and efficient method of securing enterprise beans.
Because of some limitations to the simplified method of securing enterprise beans, you would want to continue to use the deployment descriptor to specify security information in some instances. An authentication mechanism must be configured on the server for the simple solution to work. Basic authentication is the GlassFish Server’s default authentication method.
This tutorial explains how to invoke user name/password authentication of authorized users by decorating the enterprise application’s business methods with annotations that specify method permissions.
To make the deployer’s task easier, the application developer can define security roles. A security role is a grouping of permissions that a given type of application users must have in order to successfully use the application. For example, in a payroll application, some users will want to view their own payroll information (employee), some will need to view others’ payroll information (manager), and some will need to be able to change others’ payroll information (payrollDept). The application developer would determine the potential users of the application and which methods would be accessible to which users. The application developer would then decorate classes or methods of the enterprise bean with annotations that specify the types of users authorized to access those methods. Using annotations to specify authorized users is described in Specifying Authorized Users by Declaring Security Roles.
When one of the annotations is used to define method permissions, the deployment system will automatically require user name/password authentication. In this type of authentication, a user is prompted to enter a user name and password, which will be compared against a database of known users. If the user is found and the password matches, the roles that the user is assigned will be compared against the roles that are authorized to access the method. If the user is authenticated and found to have a role that is authorized to access that method, the data will be returned to the user.
Using declarative security is discussed in Securing an Enterprise Bean Using Declarative Security.
Programmatic security: For an enterprise bean, code embedded in a business method that is used to access a caller’s identity programmatically and that uses this information to make security decisions. Programmatic security is useful when declarative security alone is not sufficient to express the security model of an application.
In general, security management should be enforced by the container in a manner that is transparent to the enterprise beans’ business methods. The programmatic security APIs described in this chapter should be used only in the less frequent situations in which the enterprise bean business methods need to access the security-context information, such as when you want to grant access based on the time of day or other nontrivial condition checks for a particular role.
Programmatic security is discussed in Securing an Enterprise Bean Programmatically.
Some of the material in this chapter assumes that you have already read Chapter 22, Enterprise Beans, Chapter 23, Getting Started with Enterprise Beans, and Chapter 39, Introduction to Security in the Java EE Platform.
As mentioned earlier, enterprise beans run in the EJB container, a runtime environment within the GlassFish Server, as shown in Figure 41-1.
This section discusses securing a Java EE application where one or more modules, such as EJB JAR files, are packaged into an EAR file, the archive file that holds the application. Security annotations will be used in the Java programming class files to specify authorized users and basic, or user name/password, authentication.
Enterprise beans often provide the business logic of a web application. In these cases, packaging the enterprise bean within the web application’s WAR module simplifies deployment and application organization. Enterprise beans may be packaged within a WAR module as Java class files or within a JAR file that is bundled within the WAR module. When a servlet or JavaServer Faces page handles the web front end and the application is packaged into a WAR module as a Java class file, security for the application can be handled in the application’s web.xml file. The EJB in the WAR file can have its own deployment descriptor, ejb-jar.xml, if required. Securing web applications using web.xml is discussed in Chapter 40, Getting Started Securing Web Applications.
The following sections describe declarative and programmatic security mechanisms that can be used to protect enterprise bean resources. The protected resources include enterprise bean methods that are called from application clients, web components, or other enterprise beans.
For more information on this topic, read the Enterprise JavaBeans 3.1 specification. This document can be downloaded from http://jcp.org/en/jsr/detail?id=318. Chapter 17 of this specification, “Security Management,” discusses security management for enterprise beans.
Securing an Enterprise Bean Using Declarative Security
Declarative security enables the application developer to specify which users are authorized to access which methods of the enterprise beans and to authenticate these users with basic, or username-password, authentication. Frequently, the person who is developing an enterprise application is not the same person who is responsible for deploying the application. An application developer who uses declarative security to define method permissions and authentications mechanisms is passing along to the deployer a security view of the enterprise beans contained in the EJB JAR. When a security view is passed on to the deployer, he or she uses this information to define method permissions for security roles. If you don’t define a security view, the deployer will have to determine what each business method does to determine which users are authorized to call each method.
A security view consists of a set of security roles, a semantic grouping of permissions that a given type of users of an application must have to successfully access the application. Security roles are meant to be logical roles, representing a type of user. You can define method permissions for each security role. A method permission is a permission to invoke a specified group of methods of an enterprise bean’s business interface, home interface, component interface, and/or web service endpoints. After method permissions are defined, user name/password authentication will be used to verify the identity of the user.
It is important to keep in mind that security roles are used to define the logical security view of an application. They should not be confused with the user groups, users, principals, and other concepts that exist in the GlassFish Server. An additional step is required to map the roles defined in the application to users, groups, and principals that are the components of the user database in the file realm of the GlassFish Server. These steps are outlined in Mapping Roles to Users and Groups.
The following sections show how an application developer uses declarative security to either secure an application or to create a security view to pass along to the deployer.
Specifying Authorized Users by Declaring Security Roles
This section discusses how to use annotations to specify the method permissions for the methods of a bean class. For more information on these annotations, refer to the Common Annotations for the Java Platform specification at http://jcp.org/en/jsr/detail?id=250.
Method permissions can be specified on the class, the business methods of the class, or both. Method permissions can be specified on a method of the bean class to override the method permissions value specified on the entire bean class. The following annotations are used to specify method permissions:
@DeclareRoles: Specifies all the roles that the application will use, including roles not specifically named in a @RolesAllowed annotation. The set of security roles the application uses is the total of the security roles defined in the @DeclareRoles and @RolesAllowed annotations.
The @DeclareRoles annotation is specified on a bean class, where it serves to declare roles that can be tested (for example, by calling isCallerInRole) from within the methods of the annotated class. When declaring the name of a role used as a parameter to the isCallerInRole(String roleName) method, the declared name must be the same as the parameter value.
The following example code demonstrates the use of the @DeclareRoles annotation:
@DeclareRoles("BusinessAdmin") public class Calculator { ... }
The syntax for declaring more than one role is as shown in the following example:
@DeclareRoles({"Administrator", "Manager", "Employee"})
@RolesAllowed("list-of-roles"): Specifies the security roles permitted to access methods in an application. This annotation can be specified on a class or on one or more methods. When specified at the class level, the annotation applies to all methods in the class. When specified on a method, the annotation applies to that method only and overrides any values specified at the class level.
To specify that no roles are authorized to access methods in an application, use the @DenyAll annotation. To specify that a user in any role is authorized to access the application, use the @PermitAll annotation.
When used in conjunction with the @DeclareRoles annotation, the combined set of security roles is used by the application.
The following example code demonstrates the use of the @RolesAllowed annotation:
@DeclareRoles({"Administrator", "Manager", "Employee"}) public class Calculator { @RolesAllowed("Administrator") public void setNewRate(int rate) { ... } }
@PermitAll: Specifies that all security roles are permitted to execute the specified method or methods. The user is not checked against a database to ensure that he or she is authorized to access this application.
This annotation can be specified on a class or on one or more methods. Specifying this annotation on the class means that it applies to all methods of the class. Specifying it at the method level means that it applies to only that method.
The following example code demonstrates the use of the @PermitAll annotation:
import javax.annotation.security.*; @RolesAllowed("RestrictedUsers") public class Calculator { @RolesAllowed("Administrator") public void setNewRate(int rate) { //... } @PermitAll public long convertCurrency(long amount) { //... } }
@DenyAll: Specifies that no security roles are permitted to execute the specified method or methods. This means that these methods are excluded from execution in the Java EE container.
The following example code demonstrates the use of the @DenyAll annotation:
import javax.annotation.security.*; @RolesAllowed("Users") public class Calculator { @RolesAllowed("Administrator") public void setNewRate(int rate) { //... } @DenyAll public long convertCurrency(long amount) { //... } }
The following code snippet demonstrates the use of the @DeclareRoles annotation with the isCallerInRole method. In this example, the @DeclareRoles annotation declares a role that the enterprise bean PayrollBean uses to make the security check by using isCallerInRole("payroll") to verify that the caller is authorized to change salary data:
@DeclareRoles("payroll") @Stateless public class PayrollBean implements Payroll { @Resource SessionContext ctx; public void updateEmployeeInfo(EmplInfo info) { oldInfo = ... read from database; // The salary field can be changed only by callers // who have the security role "payroll" Principal callerPrincipal = ctx.getCallerPrincipal(); if (info.salary != oldInfo.salary && !ctx.isCallerInRole("payroll")) { throw new SecurityException(...); } ... } ... }
The following example code illustrates the use of the @RolesAllowed annotation:
@RolesAllowed("admin") public class SomeClass { public void aMethod () {...} public void bMethod () {...} ... } @Stateless public class MyBean extends SomeClass implements A { @RolesAllowed("HR") public void aMethod () {...} public void cMethod () {...} ... }
In this example, assuming that aMethod, bMethod, and cMethod are methods of business interface A, the method permissions values of methods aMethod and bMethod are @RolesAllowed("HR") and @RolesAllowed("admin"), respectively. The method permissions for method cMethod have not been specified.
To clarify, the annotations are not inherited by the subclass itself. Instead, the annotations apply to methods of the superclass that are inherited by the subclass.
Specifying an Authentication Mechanism and Secure Connection
When method permissions are specified, basic user name/password authentication will be invoked by the GlassFish Server.
To use a different type of authentication or to require a secure connection using SSL, specify this information in an application deployment descriptor.
Securing an Enterprise Bean Programmatically
Programmatic security, code that is embedded in a business method, is used to access a caller’s identity programmatically and uses this information to make security decisions within the method itself.
Accessing an Enterprise Bean Caller’s Security Context
In general, security management should be enforced by the container in a manner that is transparent to the enterprise bean’s business methods. The security API described in this section should be used only in the less frequent situations in which the enterprise bean business methods need to access the security context information, such as when you want to restrict access to a particular time of day.
The javax.ejb.EJBContext interface provides two methods that allow the bean provider to access security information about the enterprise bean’s caller:
getCallerPrincipal, which allows the enterprise bean methods to obtain the current caller principal’s name. The methods might, for example, use the name as a key to information in a database.
The following code sample illustrates the use of the getCallerPrincipal method:
@Stateless public class EmployeeServiceBean implements EmployeeService { @Resource SessionContext ctx; @PersistenceContext EntityManager em; public void changePhoneNumber(...) { ... // obtain the caller principal. callerPrincipal = ctx.getCallerPrincipal(); // obtain the caller principal's name. callerKey = callerPrincipal.getName(); // use callerKey as primary key to find EmployeeRecord EmployeeRecord myEmployeeRecord = em.find(EmployeeRecord.class, callerKey); // update phone number myEmployeeRecord.setPhoneNumber(...); ... } }
In this example, the enterprise bean obtains the principal name of the current caller and uses it as the primary key to locate an EmployeeRecord entity. This example assumes that application has been deployed such that the current caller principal contains the primary key used for the identification of employees (for example, employee number).
isCallerInRole, which the enterprise bean code can use to allow the bean provider/application developer to code the security checks that cannot be easily defined using method permissions. Such a check might impose a role-based limit on a request, or it might depend on information stored in the database.
The enterprise bean code can use the isCallerInRole method to test whether the current caller has been assigned to a given security role. Security roles are defined by the bean provider or the application assembler and are assigned by the deployer to principals or principal groups that exist in the operational environment.
The following code sample illustrates the use of the isCallerInRole method:
@Stateless public class PayrollBean implements Payroll { @Resource SessionContext ctx; public void updateEmployeeInfo(EmplInfo info) { oldInfo = ... read from database; // The salary field can be changed only by callers // who have the security role "payroll" if (info.salary != oldInfo.salary && !ctx.isCallerInRole("payroll")) { throw new SecurityException(...); } ... } ... }
You would use programmatic security in this way to dynamically control access to a method, for example, when you want to deny access except during a particular time of day. An example application that uses the getCallerPrincipal and isCallerInRole methods is described in Example: Securing an Enterprise Bean with Programmatic Security.
Propagating a Security Identity (Run-As)
You can specify whether a caller’s security identity should be used for the execution of specified methods of an enterprise bean or whether a specific run-as identity should be used. Figure 41-2 illustrates this concept.
In this illustration, an application client is making a call to an enterprise bean method in one EJB container. This enterprise bean method, in turn, makes a call to an enterprise bean method in another container. The security identity during the first call is the identity of the caller. The security identity during the second call can be any of the following options.
By default, the identity of the caller of the intermediate component is propagated to the target enterprise bean. This technique is used when the target container trusts the intermediate container.
A specific identity is propagated to the target enterprise bean. This technique is used when the target container expects access using a specific identity.
To propagate an identity to the target enterprise bean, configure a run-as identity for the bean, as described in Configuring a Component's Propagated Security Identity. Establishing a run-as identity for an enterprise bean does not affect the identities of its callers, which are the identities tested for permission to access the methods of the enterprise bean. The run-as identity establishes the identity that the enterprise bean will use when it makes calls.
The run-as identity applies to the enterprise bean as a whole, including all the methods of the enterprise bean’s business interface, local and remote interfaces, component interface, and web service endpoint interfaces, the message listener methods of a message-driven bean, the timeout method of an enterprise bean, and all internal methods of the bean that might be called in turn.
Configuring a Component’s Propagated Security Identity
You can configure an enterprise bean’s run-as, or propagated, security identity by using the @RunAs annotation, which defines the role of the application during execution in a Java EE container. The annotation can be specified on a class, allowing developers to execute an application under a particular role. The role must map to the user/group information in the container’s security realm. The @RunAs annotation specifies the name of a security role as its parameter.
Here is some example code that demonstrates the use of the @RunAs annotation.
@RunAs("Admin") public class Calculator { //.... }
You will have to map the run-as role name to a given principal defined on the GlassFish Server if the given roles are associated with more than one user principal.
Trust between Containers
When an enterprise bean is designed so that either the original caller identity or a designated identity is used to call a target bean, the target bean will receive the propagated identity only. The target bean will not receive any authentication data.
There is no way for the target container to authenticate the propagated security identity. However, because the security identity is used in authorization checks (for example, method permissions or with the isCallerInRole method), it is vitally important that the security identity be authentic. Because no authentication data is available to authenticate the propagated identity, the target must trust that the calling container has propagated an authenticated security identity.
By default, the GlassFish Server is configured to trust identities that are propagated from different containers. Therefore, you do not need to take any special steps to set up a trust relationship.
Deploying Secure Enterprise Beans
The deployer is responsible for ensuring that an assembled application is secure after it has been deployed in the target operational environment. If a security view has been provided to the deployer through the use of security annotations and/or a deployment descriptor, the security view is mapped to the mechanisms and policies used by the security domain in the target operational environment, which in this case is the GlassFish Server. If no security view is provided, the deployer must set up the appropriate security policy for the enterprise bean application.
Deployment information is specific to a web or application server.


|
M480 BSP V3.05.005
The Board Support Package for M480 Series
|
Macros | |
| #define | TIMER_SET_CMP_VALUE(timer, u32Value) |
| Set Timer Compared Value. More... | |
| #define | TIMER_SET_PRESCALE_VALUE(timer, u32Value) |
| Set Timer Prescale Value. More... | |
| #define | TIMER_IS_ACTIVE(timer) |
| Check specify Timer Status. More... | |
| #define | TIMER_SELECT_TOUT_PIN(timer, u32ToutSel) |
| Select Toggle-output Pin. More... | |
| #define | TIMER_SET_OPMODE(timer, u32OpMode) |
| Select Timer operating mode. More... | |
Functions | |
| __STATIC_INLINE void | TIMER_Start (TIMER_T *timer) |
| Start Timer Counting. More... | |
| __STATIC_INLINE void | TIMER_Stop (TIMER_T *timer) |
| Stop Timer Counting. More... | |
| __STATIC_INLINE void | TIMER_EnableWakeup (TIMER_T *timer) |
| Enable Timer Interrupt Wake-up Function. More... | |
| __STATIC_INLINE void | TIMER_DisableWakeup (TIMER_T *timer) |
| Disable Timer Wake-up Function. More... | |
| __STATIC_INLINE void | TIMER_StartCapture (TIMER_T *timer) |
| Start Timer Capture Function. More... | |
| __STATIC_INLINE void | TIMER_StopCapture (TIMER_T *timer) |
| Stop Timer Capture Function. More... | |
| __STATIC_INLINE void | TIMER_EnableCaptureDebounce (TIMER_T *timer) |
| Enable Capture Pin De-bounce. More... | |
| __STATIC_INLINE void | TIMER_DisableCaptureDebounce (TIMER_T *timer) |
| Disable Capture Pin De-bounce. More... | |
| __STATIC_INLINE void | TIMER_EnableEventCounterDebounce (TIMER_T *timer) |
| Enable Counter Pin De-bounce. More... | |
| __STATIC_INLINE void | TIMER_DisableEventCounterDebounce (TIMER_T *timer) |
| Disable Counter Pin De-bounce. More... | |
| __STATIC_INLINE void | TIMER_EnableInt (TIMER_T *timer) |
| Enable Timer Time-out Interrupt. More... | |
| __STATIC_INLINE void | TIMER_DisableInt (TIMER_T *timer) |
| Disable Timer Time-out Interrupt. More... | |
| __STATIC_INLINE void | TIMER_EnableCaptureInt (TIMER_T *timer) |
| Enable Capture Trigger Interrupt. More... | |
| __STATIC_INLINE void | TIMER_DisableCaptureInt (TIMER_T *timer) |
| Disable Capture Trigger Interrupt. More... | |
| __STATIC_INLINE uint32_t | TIMER_GetIntFlag (TIMER_T *timer) |
| Get Timer Time-out Interrupt Flag. More... | |
| __STATIC_INLINE void | TIMER_ClearIntFlag (TIMER_T *timer) |
| Clear Timer Time-out Interrupt Flag. More... | |
| __STATIC_INLINE uint32_t | TIMER_GetCaptureIntFlag (TIMER_T *timer) |
| Get Timer Capture Interrupt Flag. More... | |
| __STATIC_INLINE void | TIMER_ClearCaptureIntFlag (TIMER_T *timer) |
| Clear Timer Capture Interrupt Flag. More... | |
| __STATIC_INLINE uint32_t | TIMER_GetWakeupFlag (TIMER_T *timer) |
| Get Timer Wake-up Flag. More... | |
| __STATIC_INLINE void | TIMER_ClearWakeupFlag (TIMER_T *timer) |
| Clear Timer Wake-up Flag. More... | |
| __STATIC_INLINE uint32_t | TIMER_GetCaptureData (TIMER_T *timer) |
| Get Capture value. More... | |
| __STATIC_INLINE uint32_t | TIMER_GetCounter (TIMER_T *timer) |
| Get Counter value. More... | |
| uint32_t | TIMER_Open (TIMER_T *timer, uint32_t u32Mode, uint32_t u32Freq) |
| Open Timer with Operate Mode and Frequency. More... | |
| void | TIMER_Close (TIMER_T *timer) |
| Stop Timer Counting. More... | |
| int32_t | TIMER_Delay (TIMER_T *timer, uint32_t u32Usec) |
| Create a specify Delay Time. More... | |
| void | TIMER_EnableCapture (TIMER_T *timer, uint32_t u32CapMode, uint32_t u32Edge) |
| Enable Timer Capture Function. More... | |
| void | TIMER_DisableCapture (TIMER_T *timer) |
| Disable Timer Capture Function. More... | |
| void | TIMER_EnableEventCounter (TIMER_T *timer, uint32_t u32Edge) |
| Enable Timer Counter Function. More... | |
| void | TIMER_DisableEventCounter (TIMER_T *timer) |
| Disable Timer Counter Function. More... | |
| uint32_t | TIMER_GetModuleClock (TIMER_T *timer) |
| Get Timer Clock Frequency. More... | |
| void | TIMER_EnableFreqCounter (TIMER_T *timer, uint32_t u32DropCount, uint32_t u32Timeout, uint32_t u32EnableInt) |
| This function is used to enable the Timer frequency counter function. More... | |
| void | TIMER_DisableFreqCounter (TIMER_T *timer) |
| This function is used to disable the Timer frequency counter function. More... | |
| void | TIMER_SetTriggerSource (TIMER_T *timer, uint32_t u32Src) |
| This function is used to select the interrupt source used to trigger other modules. More... | |
| void | TIMER_SetTriggerTarget (TIMER_T *timer, uint32_t u32Mask) |
| This function is used to set modules trigger by timer interrupt. More... | |
| int32_t | TIMER_ResetCounter (TIMER_T *timer) |
| Reset Counter. More... | |
| #define TIMER_IS_ACTIVE | ( | timer | ) |
Check specify Timer Status.
| [in] | timer | The pointer of the specified Timer module. It could be TIMER0, TIMER1, TIMER2, TIMER3. |
| 0 | Timer 24-bit up counter is inactive |
| 1 | Timer 24-bit up counter is active |
This macro is used to check if specify Timer counter is inactive or active.
| #define TIMER_SELECT_TOUT_PIN | ( | timer, | |
| u32ToutSel | |||
| ) |
Select Toggle-output Pin.
| [in] | timer | The pointer of the specified Timer module. It could be TIMER0, TIMER1, TIMER2, TIMER3. |
| [in] | u32ToutSel | Toggle-output pin selection, valid values are: |
This macro is used to select timer toggle-output pin is output on TMx or TMx_EXT pin.
| #define TIMER_SET_CMP_VALUE | ( | timer, | |
| u32Value | |||
| ) |
Set Timer Compared Value.
| [in] | timer | The pointer of the specified Timer module. It could be TIMER0, TIMER1, TIMER2, TIMER3. |
| [in] | u32Value | Timer compare value. Valid values are between 2 to 0xFFFFFF. |
This macro is used to set timer compared value to adjust timer time-out interval.
| #define TIMER_SET_OPMODE | ( | timer, | |
| u32OpMode | |||
| ) |
Select Timer operating mode.
| [in] | timer | The pointer of the specified Timer module. It could be TIMER0, TIMER1, TIMER2, TIMER3. |
| [in] | u32OpMode | Operation mode. Possible options are |
| #define TIMER_SET_PRESCALE_VALUE | ( | timer, | |
| u32Value | |||
| ) |
Set Timer Prescale Value.
| [in] | timer | The pointer of the specified Timer module. It could be TIMER0, TIMER1, TIMER2, TIMER3. |
| [in] | u32Value | Timer prescale value. Valid values are between 0 to 0xFF. |
This macro is used to set timer prescale value and timer source clock will be divided by (prescale + 1)
before it is fed into timer.
| __STATIC_INLINE void TIMER_ClearCaptureIntFlag | ( | TIMER_T * | timer | ) |
| __STATIC_INLINE void TIMER_ClearIntFlag | ( | TIMER_T * | timer | ) |
| __STATIC_INLINE void TIMER_ClearWakeupFlag | ( | TIMER_T * | timer | ) |
| void TIMER_Close | ( | TIMER_T * | timer | ) |
| int32_t TIMER_Delay | ( | TIMER_T * | timer, |
| uint32_t | u32Usec | ||
| ) |
Create a specify Delay Time.
| [in] | timer | The pointer of the specified Timer module. It could be TIMER0, TIMER1, TIMER2, TIMER3. |
| [in] | u32Usec | Delay period in micro seconds. Valid values are between 100~1000000 (100 micro second ~ 1 second). |
| 0 | Success, target delay time reached |
| TIMER_TIMEOUT_ERR | Delay function execute failed due to timer stop working |
This API is used to create a delay loop for u32usec micro seconds by using timer one-shot mode.
| void TIMER_DisableCapture | ( | TIMER_T * | timer | ) |
| __STATIC_INLINE void TIMER_DisableCaptureDebounce | ( | TIMER_T * | timer | ) |
| __STATIC_INLINE void TIMER_DisableCaptureInt | ( | TIMER_T * | timer | ) |
| void TIMER_DisableEventCounter | ( | TIMER_T * | timer | ) |
| __STATIC_INLINE void TIMER_DisableEventCounterDebounce | ( | TIMER_T * | timer | ) |
| void TIMER_DisableFreqCounter | ( | TIMER_T * | timer | ) |
| __STATIC_INLINE void TIMER_DisableInt | ( | TIMER_T * | timer | ) |
| __STATIC_INLINE void TIMER_DisableWakeup | ( | TIMER_T * | timer | ) |
| void TIMER_EnableCapture | ( | TIMER_T * | timer, |
| uint32_t | u32CapMode, | ||
| uint32_t | u32Edge | ||
| ) |
Enable Timer Capture Function.
| [in] | timer | The pointer of the specified Timer module. It could be TIMER0, TIMER1, TIMER2, TIMER3. |
| [in] | u32CapMode | Timer capture mode. Could be |
| [in] | u32Edge | Timer capture trigger edge. Possible values are |
This API is used to enable timer capture function with specify capture trigger edge
to get current counter value or reset counter value to 0.
| __STATIC_INLINE void TIMER_EnableCaptureDebounce | ( | TIMER_T * | timer | ) |
| __STATIC_INLINE void TIMER_EnableCaptureInt | ( | TIMER_T * | timer | ) |
| void TIMER_EnableEventCounter | ( | TIMER_T * | timer, |
| uint32_t | u32Edge | ||
| ) |
Enable Timer Counter Function.
| [in] | timer | The pointer of the specified Timer module. It could be TIMER0, TIMER1, TIMER2, TIMER3. |
| [in] | u32Edge | Detection edge of counter pin. Could be ether |
This function is used to enable the timer counter function with specify detection edge.
| __STATIC_INLINE void TIMER_EnableEventCounterDebounce | ( | TIMER_T * | timer | ) |
| void TIMER_EnableFreqCounter | ( | TIMER_T * | timer, |
| uint32_t | u32DropCount, | ||
| uint32_t | u32Timeout, | ||
| uint32_t | u32EnableInt | ||
| ) |
This function is used to enable the Timer frequency counter function.
| [in] | timer | The base address of Timer module. Can be TIMER0 or TIMER2 |
| [in] | u32DropCount | This parameter has no effect in M480 series BSP |
| [in] | u32Timeout | This parameter has no effect in M480 series BSP |
| [in] | u32EnableInt | Enable interrupt assertion after capture complete or not. Valid values are TRUE and FALSE |
This function is used to calculate input event frequency. After enable this function, a pair of timers, TIMER0 and TIMER1, or TIMER2 and TIMER3 will be configured for this function. The mode used to calculate input event frequency is mentioned as "Inter Timer Trigger Mode" in Technical Reference Manual
| __STATIC_INLINE void TIMER_EnableInt | ( | TIMER_T * | timer | ) |
| __STATIC_INLINE void TIMER_EnableWakeup | ( | TIMER_T * | timer | ) |
Enable Timer Interrupt Wake-up Function.
| [in] | timer | The pointer of the specified Timer module. It could be TIMER0, TIMER1, TIMER2, TIMER3. |
This function is used to enable the timer interrupt wake-up function and interrupt source could be time-out interrupt,
counter event interrupt or capture trigger interrupt.
| __STATIC_INLINE uint32_t TIMER_GetCaptureData | ( | TIMER_T * | timer | ) |
| __STATIC_INLINE uint32_t TIMER_GetCaptureIntFlag | ( | TIMER_T * | timer | ) |
Get Timer Capture Interrupt Flag.
| [in] | timer | The pointer of the specified Timer module. It could be TIMER0, TIMER1, TIMER2, TIMER3. |
| 0 | Timer capture interrupt did not occur |
| 1 | Timer capture interrupt occurred |
This function indicates timer capture trigger interrupt occurred or not.
| __STATIC_INLINE uint32_t TIMER_GetCounter | ( | TIMER_T * | timer | ) |
| __STATIC_INLINE uint32_t TIMER_GetIntFlag | ( | TIMER_T * | timer | ) |
Get Timer Time-out Interrupt Flag.
| [in] | timer | The pointer of the specified Timer module. It could be TIMER0, TIMER1, TIMER2, TIMER3. |
| 0 | Timer time-out interrupt did not occur |
| 1 | Timer time-out interrupt occurred |
This function indicates timer time-out interrupt occurred or not.
| uint32_t TIMER_GetModuleClock | ( | TIMER_T * | timer | ) |
Get Timer Clock Frequency.
| [in] | timer | The pointer of the specified Timer module. It could be TIMER0, TIMER1, TIMER2, TIMER3. |
This API is used to get the timer clock frequency.
| __STATIC_INLINE uint32_t TIMER_GetWakeupFlag | ( | TIMER_T * | timer | ) |
Get Timer Wake-up Flag.
| [in] | timer | The pointer of the specified Timer module. It could be TIMER0, TIMER1, TIMER2, TIMER3. |
| 0 | Timer does not cause CPU wake-up |
| 1 | Timer interrupt event cause CPU wake-up |
This function indicates timer interrupt event has waked up system or not.
| uint32_t TIMER_Open | ( | TIMER_T * | timer, |
| uint32_t | u32Mode, | ||
| uint32_t | u32Freq | ||
| ) |
Open Timer with Operate Mode and Frequency.
| [in] | timer | The pointer of the specified Timer module. It could be TIMER0, TIMER1, TIMER2, TIMER3. |
| [in] | u32Mode | Operation mode. Possible options are |
| [in] | u32Freq | Target working frequency |
This API is used to configure timer to operate in specified mode and frequency. If timer cannot work in target frequency, a closest frequency will be chose and returned.
| int32_t TIMER_ResetCounter | ( | TIMER_T * | timer | ) |
Reset Counter.
| [in] | timer | The pointer of the specified Timer module. It could be TIMER0, TIMER1, TIMER2, TIMER3. |
| 0 | Timer reset success |
| TIMER_TIMEOUT_ERR | Timer reset failed |
This function is used to reset current counter value and internal prescale counter value.
| void TIMER_SetTriggerSource | ( | TIMER_T * | timer, |
| uint32_t | u32Src | ||
| ) |
This function is used to select the interrupt source used to trigger other modules.
| [in] | timer | The base address of Timer module |
| [in] | u32Src | Selects the interrupt source to trigger other modules. Could be: |
| void TIMER_SetTriggerTarget | ( | TIMER_T * | timer, |
| uint32_t | u32Mask | ||
| ) |
This function is used to set modules trigger by timer interrupt.
| [in] | timer | The base address of Timer module |
| [in] | u32Mask | The mask of modules (EPWM, EADC, DAC and PDMA) trigger by timer. Is the combination of |
| __STATIC_INLINE void TIMER_Start | ( | TIMER_T * | timer | ) |
| __STATIC_INLINE void TIMER_StartCapture | ( | TIMER_T * | timer | ) |
| __STATIC_INLINE void TIMER_Stop | ( | TIMER_T * | timer | ) |


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My system has 16GB of RAM, but since I run Linux I rarely use more than about 3GB. So how do I justify such a extreme amounts of memory? Ramdisks.
I run Lubuntu1, but hopefully this works for you too. Basically, I create a ramdisk and mount it in /mnt/ramdisk, then I setup the login/logout scripts to transfer to and from the ramdisk.
Step 1: Create a ramdisk
Add this line to your fstab:
none /mnt/ramdisk tmpfs defaults 0 0
By default a tmpfs will initialize to half of the total system RAM. If this is not desirable, you may use the size option to override the default.
Step 2: Modify .profile
.profile gets executed when a user logs in on a Ubuntu based system. .bash_profile will work for bash only shells. Add this script segment to .profile:
#Setup the RAM disk Cache, if it doesn't exist
if [ ! -d "/mnt/ramdisk/$USERNAME" ] ; then
mkdir /mnt/ramdisk/$USERNAME
cp -a $HOME/.cache /mnt/ramdisk/$USERNAME/
rm -rf $HOME/.cache
ln -s /mnt/ramdisk/$USERNAME/.cache $HOME/.cache
fi
This script snippet will create a folder in the ramdisk to house your .cache folder, then create a symbolic link into it. I saw a significant speed improvement for internet browsing over a disk based cache.
Step 3: Modify .bash_logout
.bash_logout gets executed when a user logs out of a Ubuntu based system. Add this script segment to .bash_logout:
# If the RAM disk cache exists, copy it back to non-volatile
if [ -d "/mnt/ramdisk/$USERNAME/.cache" ] ; then
rm -f .cache
cp -a /mnt/ramdisk/$USERNAME/.cache $HOME
fi
this script snipped will copy your .cache bask to disk when you logout. This does 2 things. Firstly, it preserves your cache between reboots, which helps maintain high performance. Secondly, it reduces the ram usage if multiple people use the system.
Step 4: Enjoy
Enjoy, as application now run faster. But you are not done, you still have piles of empty RAM bits waiting to be used; find other folders on the system begging to be mounted to ram. tmp, /var/run, /var/lock are sexy options2.