What is Amateur Radio ?
It is many different things to the individual, millions of people throughout the world who enjoy this very diverse communications hobby.
Below is a summary of how it began and has kept with the times to remain an enjoyable leisure time activity. In Canada an amateur radio licence (Certificate) is granted for life, there is no renewal requirement also there is no age requirement, radio operators can be of any age, and knowledge of Morse code is no longer required. There are three classes or levels of Certificate’s (No longer Licence) “Certificate of Proficiency of Amateur Radio” Basic, Basic with Honours, Advanced and also Morse Code 5 w.p.m endorsement, it is not required to have the Morse Code endorsement to operate using Morse Code.
Basic : The holder of an Amateur Radio Operator Certificate with Basic Qualification is limited to basically bands above 30mhz (Code endorsement with Basic allows you to operate below 30mhz) and a transmit limit of 250 Watts (DC)
Basic with Honours: To achieve “Basic with Honours” is receiving a mark of 80% or more on a Basic Exam, or you may chose to do a min 5 words per minute Morse code exam. (Note: Morse code is not a requirement for any class of Amateur Radio Licence in Canada.)
Advanced: Allows a more technical approach to Amateur Radio, where you are permitted to install and operate repeaters, run higher power design and build transmitters, along with more technical privileges.
Who are Amateur Radio Operators?
They are ordinary citizens, including some of your neighbours or work colleagues, and people all over the world. Are Amateur Radio Operators – also known as Ham Radio Operators.
One of the marvelous things about the hobby is that radio signals don’t stop at country borders – being a Amateur Radio Operator is like having an international passport.
You can visit the world on the airwaves, make casual acquaintances or life-long friendships, without even leaving home. Many long-time Amateur Radio Operators will tell you that some of their best friends are people they have never met in person.
Around the world Amateur Radio Operators have set up their own transmitting and receiving stations at home, in their cars, and even use hand-held radios to keep in touch while on foot. The friends they make could be someone across town, in a far-flung exotic country, or even astronauts on the International Space Station or Space Shuttle missions who are Amateur Radio Operators also!
How do Amateur Radio Operators contact each other?
When the hobby began around the turn of the 19th century the only form of communication radio amateurs (they were then known as amateur wireless experimenters) was Morse code, the same method used by the telegraph.
This form of communication has survived to still be in use today – and has become an international language enabling people who can’t speak the same language, to communicate.
Up until the 1920’s wireless telegraphy was the only way to transmit and receive information on the airwaves. But Amateur Radio Operators pioneered voice communications in the mid-1920s at the time when broadcast stations began.
Although the transmission and reception techniques have changed over the years with technical developments, voice communication remains the major method of communicating on the amateur bands.
In times of natural disasters, Amateur Radio Operators throughout the world provide support communications, and sometimes the only communications immediately after a disaster.
The radio systems of emergency services are also extremely busy, and additional or supplementary communication can be readily provided by radio amateurs using their own equipment, and skills.
The linking of computers to Amateur Radio has become popular. Often it is done using the sound card on a PC and software. There are several digital operating modes, each having its particular use. Mostly they provide keyboard communications.
If you would like to know more, do some research on FT8, PSK31, WSJT. The use of digitized voice via Amateur Radio is also expected to become common in the near future. The IRLP (internet radio linking project) and EchoLink are linking Amateur Radios and Repeaters through the internet.
The sending of pictures via radio was being done by Amateur Radio Operators long before television began.
This interesting aspect of Amateur Radio has several variations, from single-frame pictures through to full-colour real-time video that can be received on a domestic television receiver with UHF capabilities. There is also software available that permits fax to be sent over the radio.
Soon after the launch by the former Soviet Union of Sputnik 1, the world’s first man-made orbiting satellite, Amateur Radio Operators entered the space age with the OSCAR (Orbiting Spacecraft Carrying Amateur Radio) series of satellites.
The tradition of designing and building amateur satellites continues today. They are being launched as a piggyback load when major communications satellites are put into orbit. International contacts are possible by sending a signal to a satellite and having it relayed back to earth providing communications over many thousands of kilometers.
The method used to determine, at a distance, the source of a transmitted signal, are broadly as direction finding (DF), and have application in navigation systems.
But Amateur Radio Operators also effectively use DF when they take part in a popular activity called Foxhunting. This involves locating within a time limit a small hidden transmitter.
In some countries DFing is called Radio Sport, and involves a lot of footwork over reasonably lengthy courses, and is likened to a mix of DFing and another sport – orienteering.
Foxhunts can also be held over relatively short courses requiring Hounds to do all of their DFing while on foot.
Foxhunting basically uses a directional beam antenna, both vehicle mounted or out-the-window, and receiver to DF the general hiding spot of the Fox.
QRP (Low Power)
QRP is low power transmitting usually 5 watts or less, some Amateur Radio Operators challenge themselves or their equipment to make contact overseas with as little transmit power as possible, sometimes with less than 1 watt.
This is the world of “QRP” or Low Power Operation, where the goal is to reach as far as you can with as little transmitter power as possible.
Why? Yes, it seems strange when 100 watts is the norm, to want to transmit with five watts or much less – milliwatts. But it is the challenge of making your antenna as efficient a radiator as possible.
You may also like the challenge of making your own radios. QRP is a challenge to succeed with limited resources, and for many of its devotees it satisfies their desire to experiment, learn, and have fun.
And there’s much more!
Contests, awards, QSLing, eyeball-QSOs, Hamventions, special event stations, skills that can be used at work or school, and the list goes on, currently the production crew of the TV Show “Last Man Standing” (KA6LMS) operate and make contact with others with the actual Amateur Radio that is used on the set of the TV Show.


Really hear it.
Welcome to Wireworld Pro Audio – a division of Wireworld Cable Technology, the technology and innovation leader in high-end audio/video cable founded in 1992. While Wireworld cables have been used in recording and mastering studios for well over a decade, there is now a division specifically for the pro audio and music industries.
With a range of products from instrument cables to microphone/ balanced interconnects, digital audio cables, speaker cables and shielded power cords, our patented designs provide superior translation and the intense tone that musicians crave.
DNA HELIX – The goal of Wireworld’s patented DNA* Helix cable technology is to enable us to experience music without the losses and colorations normally caused by cables. Controlled listening tests show that the primary sonic effects of cables are caused by electromagnetic effects. The effect called eddy current resistance, which increases as strands are twisted, is especially problematic because it masks quiet musical details. To overcome those issues, the strands in the DNA Helix designs are completely parallel, providing the most direct signal path for the lowest eddy current resistance. These parallel strands run within flat insulated conductors layered in patterns that channel electromagnetic energy and reject interference.
Here's how it works.
*Delineated Neutralizing Array
UNI-PATH – The exclusive Uni-Path design used in Wireworld USB and HDMI cables provides similar advantages to the more complex DNA Helix design in smaller scale applications. Uni-Path improves electromagnetic efficiency and shielding, enabling more of the original signal information to be reproduced, improving both sound quality and video imaging.
FLUXFIELD –The Fluxfield design in our power conditioning cords features 24 insulated wires coiled around flat inner cores to maximize inductive and capacitive filtering. Twin high-density shields prevent noise from entering or radiating outward. These unique elements minimize noise and line resonances, especially with longer lengths. The flat, flexible design coils effortlessly for easy handling.
Wireworld was founded with the unique mission of perfecting audio cables through objective listening tests. With the invention of the CES Innovations Award winning Cable Comparator™ (US Patent 5,740,255), we created a better way to test cables for musical preservation. Our objective listening tests are far more revealing than normal cable comparisons because the cables are compared to a virtually perfect test control, a direct connection between components. We call these listening tests ‘Cable Polygraphs’ and we are now sharing 24/96 wave files of recorded cable tests in our Cable Polygraph Library.
Audio professionals around the world have verified the effectiveness of cable polygraph testing. Robert Harley, editor of The Absolute Sound, described cable polygraph testing as “illuminating insight into exactly how each cable affects the sound”. A leading author of audio engineering books, Bobby Owsinski, stated “I was a major skeptic that high end cables could offer any sonic improvement, but Wireworld changed my mind in a big way”. This testing advantage has led to several patents including the DNA Helix cable technology, and it has enabled us to optimize the material blends in our ultra-quiet Composilex 3 insulation. These powerful innovations have enabled Wireworld to create the highest fidelity audio cables in the world.
Highest conductivity premium quality materials.
A common misconception is that the gauge of a speaker wire is all that matters. Heavier is better, right? Wrong. Gauge can make a difference, but the cable design and material quality can have an even greater impact on performance. That is why we focus on developing the most efficient designs and producing them with the best quality materials available in each price range. For example, the conductors in our least expensive cables are oxygen-free copper and our ultimate cables use Ohno Continuous-Cast® 7N (99.99999% pure) solid silver.
Metal conductivity is equally important for plugs. Our silver-clad OFC plug contacts are three times more conductive than the common bright gold over nickel plating used on costume jewelry and most other brands.
Beyond the advantages of the best conductor materials, our second and third generation composite insulation, Composilex 2 and Composilex 3, preserve the purity of the signal by minimizing triboelectric noise better than any conventional low-loss insulation materials, including DuPont Teflon®. This proprietary material provides rewarding improvements in vividness, focus and dynamic contrast. With these innovations, Wireworld cables have advanced the art of reproducing the power and delicacy of music.
To learn more, click
A love of music. A passion for innovation. A flair for industrial design. A quest for detail. The innate ability to prove the naysayers wrong time and time again. This is who we are. Like many audiophiles, David Salz has spent decades refining his music listening experience. He is truly passionate about using objective listening tests to create cables that preserve the finest details and expression of music. The closer we get to bringing the intensity and beauty of live music to your listening room, the closer we have come to achieving our goals.
It began in 1980, when David realized the only way to discover what was being lost by a cable was to remove it altogether. Instead of simply comparing cables, he began testing them against virtually perfect direct connections made by docking components together with custom adapters. The knowledge gleaned from decades of these tests led to several patents as David continued to develop cable designs that sound closer and closer to the ultimate purity of a direct connection. In short, David developed more effective testing that provided real answers and real solutions, not just different results.
In the '90s David partnered with V.P. / Operations, Sara Flaaten, and together they moved the company in a direction of steady growth and progress with dedication to loyal, well-trained staff and excellent customer service; people that take pride in creating a product that truly does live up to its reputation. Try Wireworld cables for yourself if the feeling of a live performance is what you want for your listening experience.
© 2018 Wireworld, Inc. All rights reserved.
"I was a major skeptic, but Wireworld
changed my mind in a big way."
Bobby Owsinski - Producer, Educator, Author
"I couldn't believe my ears."
Fabrizio Sotti - Jazz Guitarist
"Added immeasurably to the quality I can acheive
in mastering. Fantastic results."
Greg Calbi - Senior Mastering Engineer, Sterling Sound Mastering - NYC
"As if there were no cable, just pureness of tone."
Rob Bonfiglio - Wilson Phillips
"Wireworld cables have made
my entire sonic life better."
Stu Hamm - Bass Guitarist


Welcome to PiControl Solutions LLC
We provide Software and Consulting Services for: PID
Tuning Simulation/Optimization, PID Control, PID
Auto-Tuning, Advanced Process Control (APC),
Online Control Quality and Performance Monitoring,
Online Process Optimization, OPC Communications,
Online Oscillation Detection & Adaptive Control, Closed-Loop
Multivariable Transfer Function Identification(System
Identification), Training Software Products and
Process Control Training Courses for Operators, Technicians
Specialties: Advanced Control on Polymer Processes (Polyethylene, Polypropylene etc.), Electric Power Plants, Air Separation, Styrene, Refining, Olefins, Pulp and Paper, most Basic and Intermediate Chemical processes.
SOFTWARE PRODUCTS: We offer several process control products in
Click on Products button to see Demos, Details and Brochures. We offer free trial software.
- PID Tuning Optimization
- PID Tuning Training Simulator for Tuning Practice and Certification
- Process Control CBT (Computer-Based Training)
- Closed-Loop Multivariable Dynamics Identification, Transfer Function
- Identification using Closed-Loop or Open-Loop Data
- System Identification
- PID Control and Advanced Control Quality Monitoring
- Online Oscillation Detection and Adaptive Control
- Multivariable Model-Predictive Control
- Rule-Based Sequence Control and Recipe-Based Sequence Control
- Online Real-Time Process Optimization and Control
- OPC (OLE for Process Control) - server to server/Excel communications, fast data monitoring, online analyzer communication and validation
- Laboratory Information Management System (LIMS)
- Online Thermodynamics calculations for Vapor Pressure, Enthalpy, Dew Point, Bubble Point etc.
PROCESS CONTROL TRAINING (Industry and Colleges):
Click on Training to see more training details and schedules.
Our products PITOPS, SIMCET and CBT are excellent for classroom and online web-based training for both industry and colleges. Control Engineers, DCS technicians, PLC technicians, operators and students - all will benefit using our training products and training courses. Our training software not only serves training needs but also serves as full-blown tools that can be used in the plant and control rooms. We offer both classroom training courses and online web-based training courses on a regular scheduled frequency every month all year round.
We can examine your process/plant and recommend cost-effective modern advanced control solutions to save you money on new process control-related projects, improve your existing controls to increase plant automation, improve process stabilization and help you move your process in the direction of increased profits. We can optimize and tune all PID controllers in your plant. We can also examine your DCS/PLC and host computer communications and recommend new, powerful OPC (OLE for Process Control) software products for cost-effective and reliable data communications including remote plant data access and monitoring.
APC (Advanced Process Control) PROJECTS:
We can design, implement, commission and support complete APC projects for any chemical, petrochemical, oil-refining, pharmaceutical, electric power, pulp and paper or related-industrial process. Our services include PID tuning optimization, Advanced Process Control (APC) implementation in DCS/PLC or using OPC on a host server computer, real-time communications using OPC, Laboratory Information Management Systems, Production Maximization and Online Process Optimization. We can install complete rule-based or sequence-based controllers to automate your batch or semi-batch processes, conduct automated product grade transitions for any process in a novel, low-cost and robust solution like no other competitor. We possess excellent technical know-how and skills for Polymer Advanced Process Control (polyethylene, polypropylene etc.), Electric Power Plants, Air Separation, Styrene, Refining, Olefins, Pulp and Paper, most Basic and Intermediate Chemical processes.
PiControl has invented new, unique and powerful system identification algorithm that works amazingly well even with complete closed-loop data for multivariable inputs and outputs. The revolutionary system identification algorithm can work with much shorter data sets compared to the current ARMAX (auto-regressive moving average with exogenous inputs) method. PiControl system identification algorithm is a novel technological breakthrough and its performance is far better and it is far simpler than the ARMAX method and also other methods like vector step-response coefficient methods. The technique can be used easily by engineers and even students and technicians without advanced educational degrees nor extensive experience. The algorithm does not need data normalization, is not affected by noise levels and can work well even in the presence of significant unmeasured external disturbances.
We provide online thermodynamics calculations to calculate Vapor Pressure, Enthalpies, Dew Point, Bubble Point and related calculations including online heat and mass balance. These calculations can be run on a server computer on the process control network, reading live data from a DCS/PLC and then writing the calculation results back into the DCS/PLC. Communication is modern, fully OPC-based.
We have skills and experience in many areas: Polymers (Polyethylene, Polypropylene), Polyolefins, Styrene Monomer, Air Separation (Nitrogen, Oxygen, Argon), Hydrogen, Ammonia, Olefins, Aromatics, LNG, Urea, Alcohol, Refining, Cement, Power Plants, Specialty Chemicals, Pharmaceuticals, Oil and Gas. Our skilled and experienced engineers can work with you and design a new control system for any plant or process worldwide.
We believe in simplicity, robustness, low-cost and ease of use by your operators and engineers. We vehemently avoid "black-box" approach and complex tools that need constant support from outside support engineers or software that is hard to understand and use. This helps you minimize your initial costs and also ongoing costs. Our software products, technology and methodology are simple and need little support. Most maintenance work can be done by your own people at the plant. We do provide round-the clock support for you in case you need any technical assistance.
CONNECT TO ANY DCS/PLC/OTHER SOFTWARE:
Our software products, technology and ideas can work with any DCS (distributed control system) or PLC (programmable logic controller) manufactured in any country, including Honeywell (TDC3000, Experion), Foxboro/Invensys, Yokogawa, Emerson/Fisher DeltaV, Siemens/Modicon, Allen Bradley, Unitronics, Rockwood, ABB, Bailey, etc. Our software can also seamlessly connect to Aspen Tech, Matrikon, Kepware, Vista-Control, OSI, and other common products. Connectivity is provided via both OPC and Excel.
SOFTWARE PRODUCT AND TECHNOLOGY APPLICATION:
Our software products and technology can be applied to
Distillation Control, Reactor Control, Combustion Control, Boiler
Control, Compressor Control, Heat Exchanger Control, Dew
Point/Bubble Point Control, Pipeline Control, Environmental
Emissions Control, Motor Control, Turbo-machinery Control.
For any questions, please send us an email: Info@PiControlSolutions.Com


Matterport brings 3D capture to iPhone
iPhone users can now use a Matterport app to create, edit, and share high fidelity 3D digital twins of any physical space. The Matterport Capture app also allows users to embed notes, links, labels or videos, and share the data.
Visitors can explore the spaces in immersive 3D and digitally measure walls, doors, windows or furniture. iPads are also supported by the Matterport Capture app, available in the Apple App Store.
RJ Pittman, CEO of Matterport, said: “With billions of square feet captured in more than 80 countries, Matterport has created the standard for 3D capture to digitise the built world. Matterport for iPhone introduces the world to our advanced spatial data capture and Cortex AI technologies in an easy-to-use app that enables anyone to capture and share 3D spaces with friends, family or colleagues.”
Once downloaded, the Matterport Capture app can be used immediately in a number of powerful ways:
- Real estate agents can scan a property almost as easily as taking photos with their iPhone, to create and publish an accurate 3D digital twin of a property listing
- Homeowners can share a digital twin of their kitchen to get a quote for a remodel or scan damage to send to their insurance company for a more accurate estimate for repair
- iPhone users can freely share their digital twins with friends and colleagues, along with prospective tenants, owners, and more
- Interior designers can capture a space and take it with them to make sure furnishings fit
- Contractors can efficiently document stages of progression during the construction or renovation process
- Property owners can create an immersive virtual tour of their spaces to improve their booking rate on rental sites
- Businesses can easily capture a 3D digital twin of their office to help with recruiting, hiring, wayfinding, space planning and building company culture
- And anyone can capture and share places that are important to them — whether it’s a special room in their home, a favourite spot they frequent, or an incredible space they experienced on vacation.
Japjit Tulsi, CTO of Matterport, said:“We have been hard at work advancing the capability of the Matterport platform to support 3D capture from a range of new digital capture devices over the past two years. Matterport for iPhone marks an important milestone in our capability to create stunning 3D digital twins of any space using the phone in your pocket.”
Matterport for iPhone is powered by Cortex, the company’s AI platform and patented deep learning neural network. It analyses 3D spatial data captured from Matterport’s flagship Pro2 camera and a wide variety of third party devices including Lidar cameras, 360 cameras, and now smartphones. With millions of spaces captured, Cortex consistently and accurately creates the 3D digital twin and handles complex tasks, from 2D to 3D reconstruction, advanced image processing, automatic colour correction, object and room labelling, and more. Cortex can even generate professional looking photo galleries and shareable videos from within the digital twin, along with measurements and dimensions of entire spaces; and automatic face blurring for privacy.
Matterport clients include Redfin, HH Angus, Sotheby’s, Arup and Marriott, have captured billions of square feet of space in over 80 countries.


Thea for Rhino integrated plugin takes full advantage of Thea Render functionality and allows you to create high-quality photorealistic renders within Robert McNeel & Associates Rhinoceros® 6 / 7. With advanced features such as interactive render, true physically-based materials, innovative material layering, IES & HDRI light support along with a versatile rendering system comprised of unbiased and GPU engines, rendering within Rhino has become really powerful.
Windows 8.1/10 64-bit, Intel SSE3 CPU (or compatible)
for Presto GPU
Nvidia CUDA Graphics Card (Compute Capability 3.x / 5.x / 6.x / 7.x / 8.0 / 8.6 ) with latest graphics driver or
AMD Graphics Card (Beta support for selected GPUs) with latest OpenCL and graphics drivers.
Note: for OpenCL rendering, render-on-the-fly displacement is supported only for UV bitmap textures.
You may download the OpenCL binaries from the following link along with instructions on how to install them
https://www.thearender.com/resources/opencl-binaries/ (Windows only)
Nvidia OptiX Denoiser
Nvidia 5.0 minimum compute capability required for Nvidia OptiX denoising in interactive rendering.
Nvidia 3.0 minimum compute capability card required for Nvidia OptiX denoising in production rendering.
Intel Open Image Denoiser
Intel SSE4.1 CPU (or compatible) required for Intel Open Image Denoiser.
Compatible with Rhino 6.33 and 7.17 and later.


As a market leader in automated ultrasound reprocessing systems, Nanosonics’ trophon® technology helps protect patients by delivering consistent high level disinfection (HLD) of ultrasound probes with every automated cycle.
The trophon®2 device is manufactured in Australia and with an expanding global network of offices in major markets including USA, Canada, UK, France, Germany, Ireland, Japan and Australia along with an expanding distributor base across the world, Nanosonics has an established global supply chain to continue to provide unparalleled service and supply.
The global trophon device installed base has now increased to over 25,000 units worldwide with more and more facilities continuing to trust the trophon technology to deliver reliable, automated HLD.
The fully enclosed trophon technology generates a 'sonically activated' hydrogen peroxide (H2O2) mist which accesses all surfaces of the suspended probe, ensuring all crevices and imperfections of the probe surface are high level disinfected. The only by-products of the HLD cycle are oxygen and water. The trophon devices therefore effectively delivers HLD without damaging the sensitive probe surface, whilst reducing patients, staff and the environment to harmful chemicals.*
As a fully automated reprocessing system designed with the user in mind, trophon2 technology offers advanced workflow efficiencies. With traceability across the operator, probe and cycle parameters, trophon devices help demonstrate user compliance for survey and audit reporting.
*Compared to manual soaking methods.
With over 25,000 trophons now installed in major markets across the world, more than 80,000 patients every day are protected from the risk of ultrasound probe cross contamination.
The trophon® family includes trophon® EPR and trophon®2 which share the same core technology of 'sonically activated' hydrogen peroxide.


These pressure transmitters feature a flat, non-clogging diaphragm. This is designed for use with viscous fluids or media containing particulates that could clog the pressure port of the standard NPT version. The transmitters are engineered to meet Class I Division 2 non-incendive protection requirements in hazardous environments. Each undergoes extensive quality control testing and calibration to achieve a linearity of < 0.25% full scale. In addition, each pressure transmitter is temperature compensated to assure accuracy and long term stability when exposed to severe ambient temperature variations.


The Most user-friendly LCD controllers in the world
Our company develops LCD controllers with a heavy focus on being "easy to understand and easy to use". Externally, it seems like a dedicated IC, but it actually contains a programmable area, which has many advantages. The image frame buffer appears to be local memory from the Host-CPU, therefore software design is also very easy.
LCD controllers without supply and obsolescence risks
This is the advantage of an LCD controller not found with a standard LSI. Even if by chance the programmable production is discontinued, we will be able to provide the same functions as before by using a replacement. Therefore, at worst, the only change is in the PCB design, a change of software is unnecessary. Similarly, even if the LCD is discontinued, we will be able to provide the same functions as before by substituting for another one. Your valuable hardware and software assets are protected.
Kenic system can cater to custom-designs
Kenic system can cater to custom designs due to the use of programmable architecture.Our company has handled many custom-designs built to work well with the customer's system. Our company offers trustworthy and proven products.
Competitively-priced LCD controllers
Our LCD controllers with programmable architecture have an even greater advantage due to the rapid lowering of the cost of high speed SRAM and SDRAM. This product is price competitive even for customers with large production volumes.
As the appearance and handling is the same as dedicated LSI, practically forget that complicated system. Customers can rest assured that this LCD controller isn't as risk of obsolescence. In addition, we also supply finished PCB products, as well as LCD bundles. Please refer to our company's webpage for details.
"Provide customers with the world's easiest product for designing LCD systems". This is our policy, and it will not change. We develop our products while keeping track of current global trends. By digesting the information into easy-to-use starter kits and manuals, we provide customers with not only quality products but important know-how as well.
About Our Products
LCD Controller IC
Our LCD controllers have been adopted mainly by customers in the medical equipment, shipping, and special measuring instrument fields in Japan, who need to produce small-quantity lots and continue production for a long period of time.we carry products from high performance LVDS built-in controllers to low cost controllers that are limited to 64 display colors.
LED Backlight Power Supply Board
DCDC converter substrate for LED backlights for LCD panel. Compact and lightweight, and Kenic system original.


With further advancements and refinements, AI and ML are set to shape the future of the casino industry, providing enhanced experiences for both casinos and their patrons.” “Cloud-based casino solutions are becoming increasingly popular among online casino operators. This is due to the many advantages that cloud-based solutions offer, such as scalability, cost savings, and improved security. By harnessing the power of the cloud, online casino operators can take advantage of the latest technologies and services to provide a better gaming experience for their customers. Cloud-based casino solutions allow operators to scale their operations quickly and easily. This is because the cloud provides a virtual environment that can be used to quickly deploy new services and applications.
This means that operators can quickly add new games and features to their online casino without having to invest in additional hardware or software. This scalability also allows operators to quickly respond to customer demand and adjust their services accordingly. Cloud-based casino solutions also offer cost savings. By using 에볼루션 the cloud, operators can reduce their IT costs by eliminating the need for expensive hardware and software. This can result in significant savings for operators, as they no longer need to purchase and maintain expensive hardware and software. Additionally, cloud-based solutions can be used to reduce the cost of hosting and maintenance, as the cloud can provide a secure and reliable environment for hosting and maintaining the online casino. Finally, cloud-based casino solutions offer improved security.
By using the cloud, operators can ensure that their customers’ data is secure and protected. This is because the cloud provides a secure environment that is not vulnerable to malicious attacks. Additionally, the cloud can provide a secure environment for storing customer data, which can help to protect customers’ personal information. Overall, cloud-based casino solutions offer many advantages for online casino operators. By harnessing the power of the cloud, operators can take advantage of the latest technologies and services to provide a better gaming experience for their customers. Additionally, cloud-based solutions can help to reduce costs and improve security, making them an attractive option for online casino operators.”


The role of the turbine is to transform the energy of water, steam or wind into mechanical energy that will make the generator spin. The generator transforms the mechanical energy into electricity. In hydropower plants, this combination of generator and turbine is called a generating unit.
In this generating unit, water rushes through the penstock and into the scrollcase. It turns the turbine blades and is then drawn to the turbine axis to exit through the underneath draft tube. The mechanical energy produced by the tremendous force that rushing water exerts on the turbine is transmitted to the generator, which then converts it into electrical energy.
The generator is connected to the turbine drive shaft. It has a moving part–the rotor–and a fixed part–the stator. The rotor's outer surface is covered with electromagnets. The stator's inner surface, or cylinder wall, is made up of copper windings. When the rotor turns inside the stator, the electrons in the copper windings "vibrate." Their movement generates an electric current, similar to the one created by Michael Faraday in his 1831 experiment on electromagnetic induction, but on a much larger scale.
Installation of a Kaplan turbine
All the generating units in a power system must be synchronized. In other words, it's essential that they maintain an exact rotation speed. Why? To ensure adequate power quality. Equipment that runs on electricity is designed to use alternating current of a specific frequency. This frequency depends on the generating unit's rotation speed, i.e., the number of times per second that rotor magnets travel past the stator windings. This frequency is expressed in cycles per second, or hertz (Hz), named after the German physicist Heinrich Hertz, who proved the existence of radio waves.
In North America, the standard alternating-current cycle is 60 times per second, but in Europe it is 50 times per second. This means that a clock designed to work at 60 Hz will be slower when plugged into a European socket.
Rotors at La Grande-3 generating station
At La Grande-3, the rotors have 32 pairs of electromagnets. To supply a 60-Hz alternating current, they must therefore rotate at a speed of 112.5 revolutions per minute (RPM).
Here is the formula that was used by the engineers:
32 pairs of electromagnets x 112.5 RPM
3,600 RPM or 60 revolutions per second (60 Hz).
Michael Faraday, a British physicist and chemist, discovers the induction phenomenon.
The scientist is the first to create an electric current by moving a magnet back and forth inside a metal winding. The innovative principles behind Faraday's discovery are quickly implemented and used to help meet the production needs of the industrial era. The first electric generator, precursor to today's generating units, was created based on these principles. Faraday's experiments sparked the invention, by other researchers, of the first electric motor and the first transformer (essential for the transmission of electricity).
© Hydro-Québec, 1996-2023. All rights reserved.


Various Types of RAID
RAID stands for Redundant Array of Independent Disks and is widely used in commerical sectors. Using a RAID storage subsystem has the following advantages:
- Provides disk spanning by weaving all connected drives into one single volume;
- Increases disk access speed by breaking data into several blocks when reading / writing to several drives in parallel operations. With RAID, storage speed increases as more drives are added;
- Provides fault-tolerance by mirroring or parity configuration.
Common terms that you will need to understand more include:
NRAID stands for Non-RAID. The controller treats each drive as a stand-alone disk, therefore each drive is an independent logical drive. NRAID does not provide data redundancy.
JBOD stands for Just a Bunch of Drives. The capacity of all the drives is combined to become one logical drive (no block striping). In other words, the capacity of the logical drive is the total capacity of the physical drives. JBOD does not provide data redundancy.
RAID 0 provides the highest performance but no redundancy. Data in the logical drive is striped and distributed across several physical drives on the same volume.
RAID 1 mirrors the data stored in one hard drive to another. RAID 1 can only be performed with two hard drives. If there are more than two hard drives, RAID (0+1) will be performed automatically.
RAID (0+1) Disk Striping with Mirroring. This configuration combines RAID 0 and RAID 1 - Mirroring and Striping. RAID (0+1) allows multiple drive failure because of the full redundancy of the hard drives. If there are more than two hard drives assigned to perform RAID 1, RAID (0+1) will be performed automatically.
RAID 3 Disk Striping with Dedicated Parity Disk. One drive member is dedicated to storing the parity data. When a drive member fails, the controller can recover / regenerate the lost data of the failed drive from the dedicated parity drive.
RAID 5 Striping with Interspersed Parity. RAID 5 is similar to RAID 3 but the parity data is not stored in one dedicated hard drive. Parity information is interspersed across the drive array. In the event of a failure, the controller can recover/regenerate the lost data of the failed drive from the other surviving drive.
Fully understanding the physical storage configurations is a must for better manage and control your data storage strategic and emergency planning.
ITPro iDataFIND Rescue Recovery supports all type of RAID Array multi drives configuration.
Your DATA -- ITPro can FIND


We're continuing our staff interviews to celebrate our 60th anniversary, and this week we turn our focus to our engineering team. In this interview with Inventus Power's Ilyas Ayub (Vice President, Technical Center Americas) and Sundy Liu (Vice President, Technical Center Asia), we'll discuss the benefits of having global engineering capabilities and how this has helped us develop safe, reliable, and quality-engineered battery systems across a broad range of portable, motive and stationary applications.
Can you tell us a little bit about your roles and the capabilities of each Technical Center?
Sundy: As Vice President, Technical Center Asia, I'm responsible for global project development and technology. I lead our Asia Pacific Engineering Team in Guangzhou, China which consists of nearly 200 engineers with industry expertise in mechanical, software, and electrical engineering. We can provide complete technical solutions and cover a wide range of product design platforms for battery packs and battery managements systems (BMS), chargers, and power systems. We also have a high-level agency in-house testing lab which streamlines the regulatory process for our customers. Our Technical Center Asia is the only facility certified by TUV for these standards in the South of China and we also have a UL-approved CTDP lab (Client Test Data Program).
Ilyas: As Vice President, Technical Center Americas, I oversee product development in the U.S. for battery packs and chargers. The Americas Engineering Team consists of a variety of talented mechanical, electrical, test and qualification engineers as well as field application engineers (FAEs). Our FAEs play a vital role at Inventus Power as they assist our sales team in winning new opportunities and ensuring we design products to the customer’s requirements. Historically the Americas Technical Center has engineered products for military and medical applications. Today, we are expanding into new applications for lawn and garden, material handling, and light electric vehicles. These new markets require medium to large format batteries to power their applications, and we are excited to be working on such complex projects.
Inventus Power has 300+ engineers worldwide. What are the benefits of having a global engineering team?
Ilyas: We benefit from the ability to co-develop products and utilize each other's strengths accordingly. For example, a product may be designed by the team in Guangzhou and our team in the US will provide customer support. We may also perform the electrical engineering for a project at one tech center and software engineering at the other. We also leverage each other's labs for qualification and agency certification.
Sundy: I agree with Ilyas. Our customers benefit from our co-development capabilities in product design and technology. Ultimately, our shared global resources help us deliver exceptional customer service and superior product design.
Market diversity is a strong suit of Inventus Power, servicing markets from consumer and industrial to medical and military. What engineering lessons have been learned from working with a variety of markets?
Sundy: The engineering team has accumulated extensive design experience in product application technology across different fields over the years. Inventus Power's ability to improve product technology, design system solutions across multiple platforms, and shorten the product design cycle allows us to meet the diverse market demand.
Ilyas: Our broad market diversity has enabled us to take the knowledge and lessons learned in one market/application and incorporate into the design products for other markets. For example, our military applications taught us how to ruggedize products for extreme conditions. Consumer applications taught us how to be cost-sensitive. If we purchase a large volume of cells to support a consumer battery pack, we can leverage the supplier relationship and utilize the cell pricing for other market applications.
Over the years, Inventus Power has moved up the power curve. What considerations need to be made when moving from smaller, simpler battery packs to larger, more complex systems?
Ilyas: Moving up the power curve means your battery packs are going to get more complex. From an electrical perspective, components must handle higher voltage and power. From a mechanical perspective, housing material selection is critical to be able support larger and heavier battery packs. As the battery gets bigger and more complex, there is a lot more software that needs to be developed to ensure proper cell management, communications with the host device, and more accurate state-of-charge (SOC) calculations.
Sundy: To further expand on Ilyas’s response, medium and large format battery packs require more complex system designs. This includes advanced engineering in the Battery Management System (BMS) as these larger battery packs need the correct thermal management controls such as cell balancing & uniform cooling methods, system safety design protections and power management. They also require enhanced communication systems for ensuring high-precision system detection & data collection, SOC calculations, high-speed CAN communication / compatibility with other communication protocols, and reliable State-of-Health (SOH) management. Finally, in addition to size, there are mechanical design considerations such as application of new materials for indoor/outdoor use (waterproof, radiation protection, shock/vibration protection, high impact, and reliable cooling).
Are there any plans for the Inventus Power Technical Centers you can share with us?
Sundy: The Guangzhou Technical Center is focusing on design innovations that will support Inventus Power's expansion into new markets. We are supporting custom designs as well as developing a new line of standard products. Along with the Technical Center Americas, our goal is to lead the industry in advanced battery systems and provide customers with quality engineered products that are safe, reliable, and optimized for their intended application.
Ilyas: Overall, we are working on many complex and engineered systems. Working with our colleagues in Guangzhou, our entire global team is focusing on quality and making our products even safer than what currently exists in the battery industry.


Arc Fault Circuit Interrupter (AFCI)
Arc Fault Circuit Interrupter (AFCI) is a device intended to mitigate high current arcing faults in the complete circuit. High current arcing faults occur from line-to-neutral or line-to-ground. These arcing faults are in parallel with the load.
Enlarge Key Features and Benefits Detects arcing faults that standard circuit breakers are unable to detect. Intended to mitigate the effects of parallel arcs (line-to-ground or line-to-neutral) by de-energizing the circuit when an arc-fault is detected. Industry exclusive LED light to indicate type of trip.
Key Features and Benefits of AFCI 2nd Generation New and improved electronics 1/4” more wiring bend space than our previous design Both lugs at the same angle - for easier wiring Available with interrupting rating of 65kA
Published on 2017-03-23


Vice President of Graphics Research
Mixed-primary Factorization for Dual-frame Computational Displays.
Fu-Chung Huang, Dawid Pajak, Jonghyun Kim, Jan Kautz, and David Luebke.
ACM Transactions on Graphics (SIGGRAPH 2017), Los Angeles, CA (2017).
Wide field of view varifocal near-eye display using see-through deformable membrane mirrors.
David Dunn, Cary Tippets, Kent Torell, Petr Kellnhofer, Kaan Akşit, Piotr Didyk, Karol Myszkowski, David Luebke, and Henry Fuchs.
IEEE Transactions on Visualization and Computer Graphics (Selected Proceedings, IEEE Virtual Reality 2017), Los Angeles, CA (2017). IEEE VR 2017 Best Paper Award!
Real-time global illumination using precomputed light field probes.
Morgan McGuire, Michael Mara, Derek Nowrouzezahrai, and David Luebke.
ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D 2017), San Francisco, CA (to appear).
Towards foveated rendering for gaze-tracked virtual reality.
Anjul Patney, Marco Salvi, Joohwan Kim, Anton Kaplanyan, Chris Wyman, Nir Benty, David Luebke, and Aaron Lefohn.
ACM Transactions on Graphics (SIGGRAPH Asia 2016), Macao, China (December 2016).
See also our Emerging Technologies Exhibit at SIGGRAPH 2016.
|Deep G-Buffers for Stable Global Illumination Approximation. Michael Mara, Morgan McGuire, Derek Nowrouzezahrai, and David Luebke. High Performance Graphics 2016, Dublin, Ireland (June 2016).|
|Infinite Resolution Textures. Alexander Reshetov and David Luebke. High Performance Graphics 2016, Dublin, Ireland (June 2016).|
|Hybrid Modulation for Near Zero Display Latency. Trey Greer, Josef Spjut, David Luebke, Turner Whitted. Society for Information Display (SID 2016) 47:1 pp 76-78, San Francisco, CA (May 2016).|
|CloudLight: A System for Amortizing Indirect Lighting in Real-Time Rendering . Cyril Crassin, David Luebke, Michael Mara, Morgan McGuire, Brent Oster, Peter Shirley, Peter-Pike Sloan, and Chris Wyman. Journal of Computer Graphics Techniques (JCGT) 4:4 (September-December 2015).|
|An Adaptive Acceleration Structure for Screen-space Ray Tracing . Jan Kautz, Sven Widmer, Dawid Pajak, Andre Schulz, Kari Pulli, Michael Goesele, and David Luebke. High Performance Graphics 2015, Los Angeles, CA (August 2015).|
||Slim Near-Eye Display Using Pinhole Aperture Arrays . Kaan Akşit, Jan Kautz, and David Luebke. Applied Optics, Vol. 54 No. 11 (April 10, 2015).|
|Pinlight Displays: Wide Field of View Augmented-Reality Eyeglasses using Defocused Point Light Sources . Andrew Maimone, Douglas Lanman, Kishore Rathinavel, Kurtis Keller, David Luebke, and Henry Fuchs. ACM Transactions on Graphics (SIGGRAPH 2014 Proceedings), Vancouver, Canada (August 2014).|
|Cascaded Displays: Spatiotemporal Superresolution using Offset Pixel Layers. Felix Heide, Douglas Lanman, Dikpal Reddy, Jan Kautz, Kari Pulli, and David Luebke. ACM Transactions on Graphics (SIGGRAPH 2014 Proceedings), Vancouver, Canada (August 2014).|
Near-Eye Light Field Displays.
Douglas Lanman, David Luebke.
ACM Transactions on Graphics (SIGGRAPH Asia 2013 Proceedings), Hong Kong (November 2013).
See press and videos on our SIGGRAPH 2013 Emerging Technologies Exhibit!
|PixelPie: Maximal Poisson-disk Sampling with Rasterization. Cheuk Yiu Ip, M. Adil Yalçin, David Luebke, Amitabh Varshney. High Performance Graphics 2013, Anaheim, CA (November 2013).|
GPU Ray Tracing.
Steven Parker, Heiko Freidrich, David Luebke, Keith Morley, James Bigler, Jared Hoberock, David McAllister, Austin Robison, Andreas Dietrich, Greg Humphreys, Morgan McGuire, Martin Stich.
Communications of the ACM, Vol. 56 No. 5 (May 2013).
CACM Research Highlights featured our SIGGRAPH 2010 OptiX paper as one of “the most important research results published in CS in recent years,” with a Technical Perspective by Matt Pharr.
|Toward Practical Real-Time Photon Mapping: Efficient GPU Density Estimation. Michael Mara, David Luebke, and Morgan McGuire. ACM Symposium on Interactive 3D Graphics and Games (I3D 2013 proceedings), Orlando, FL (March 2013)|
|Scalable Ambient Obscurance. Morgan McGuire, Michael Mara, and David Luebke. High Performance Graphics 2012, Paris, France (June 2012)|
|Subpixel Reconstruction Antialiasing. Matthäus G. Chajdas, Morgan McGuire, and David Luebke. ACM Symposium on Interactive 3D Graphics and Games (I3D 2011 proceedings), San Francisco, CA (February 2011)|
|A Local Image Reconstruction Algorithm for Stochastic Rendering. Peter Shirley, Timo Aila, Jonathan Cohen, Eric Enderton, Samuli Laine, David Luebke, and Morgan McGuire. ACM Symposium on Interactive 3D Graphics and Games (I3D 2011 proceedings), San Francisco, CA (February 2011)|
OptiX: A General Purpose Ray Tracing Engine. Steven G. Parker,
James Bigler, Andreas Dietrich, Heiko
Friedrich, Jared Hoberock, David Luebke,
David McAllister, Morgan McGuire, Keith Morley, Austin Robison, and
Transactions on Graphics (SIGGRAPH 2010 Proceedings), Los Angeles,
CA (August 2010).
Downloads, code examples, and forums at the OptiX home page.
|Optical Image Processing Using Light Modulation Displays. Gordon Wetzstein, Wolfgang Heidrich, and David Luebke. Computer Graphics Forum, Vol. 29 No. 6 (2010).|
|Real-Time Stochastic Rasterization on Conventional GPU Architectures. Morgan McGuire, Eric Enderton, Peter Shirley, and David Luebke. High Performance Graphics 2010, Saarbruecken Germany (June 2010).|
|HLBVH: Hierarchical LBVH Construction for Real-Time Ray Tracing. Jacopo Pantaleon and David Luebke. High Performance Graphics 2010, Saarbruecken Germany (June 2010).|
Stochastic Transparency. Eric Enderton, Erik Sintorn, Peter Shirley,
and David Luebke. The 2010 ACM SIGGRAPH Symposium on Interactive 3D
Graphics and Games (I3D 2010), Washington, DC (February 2010).
Best Paper Award, I3D 2010
|Hardware-accelerated global illumination by image space photon mapping. Morgan McGuire and David Luebke. High Performance Graphics 2009, New Orleans, LA (August 2009).|
Fast BVH construction on
GPUs. Christian Lauterbach, Michael Garland, Shubho Sengupta, David
Luebke, and Dinesh Manocha. Eurographics 2009, Munich, Germany (March
Editing and Relighting of Homogeneous Translucent
Materials. Rui Wang, Ewen Cheslack-Postava, Rui Wang, David Luebke,
Qianyong Chen, Wei Hua, Qunsheng Peng, Hujun Bao. Computer Graphics
International 2008, published as The Visual Computer 24 (7-9),
pp. 565-575 (June 2008).
|GPU Computing. John D. Owens, Mike Houston, David Luebke, Simon Green, John E. Stone, and James C. Phillips. Proceedings of the IEEE, 96(5):879–899, May 2008.|
Advanced Techniques for Realistic Real-Time
Skin Rendering. Eugene d'Eon
and David Luebke. GPU Gems 3, Addison-Wesley.
Special thanks to actor Doug Jones for allowing us to use his likeness.
Rendering of Human Skin. Eugene d'Eon, David Luebke, and Eric
Enderton. Eurographics Symposium on Rendering 2007, Grenoble, France
Also available: The video excerpt of our SIGGRAPH 2007 Electronic Theater piece demonstrating the technique [WMV format, 20 MB].
|A Hardware Redundancy and Recovery Mechanism for Reliable Scientific Computation on Graphics Processors. Jeremy Sheaffer, David Luebke, and Kevin Skadron. Graphics Hardware 2007, San Diego CA (August 2007).|
A Survey of General-Purpose Computation on Graphics Hardware. John
D. Owens, David Luebke, Naga Govindaraju, Mark Harris, Jens Krüger,
Aaron E. Lefohn, and Tim Purcell. Computer Graphics Forum,
26(1):80-113 (March 2007).
The CGF article updates and extends our previous STAR:
A Survey of General-Purpose Computation on Graphics Hardware. John D. Owens, David Luebke, Naga Govindaraju, Mark Harris, Jens Krüger, Aaron E. Lefohn, and Tim Purcell, Eurographics 2005 State of the Art Report (STAR), Dublin, Ireland (August 2005).
|How GPUs Work. David Luebke and Greg Humphreys, IEEE Computer, Vol. 40 No. 2, pp 96-100, February 2007.|
||The Visual Vulnerability Spectrum: Characterizing Architectural Vulnerability for Graphics Hardware. Jeremy Sheaffer, David Luebke, and Kevin Skadron. Proceedings of Graphics Hardware 2006, Vienna, Austria (September 2006).|
|Effiicent Wavelet Rotation for Environment Map Rendering. Rui Wang, Ren Ng, David Luebke, and Greg Humphreys. Proceedings of the 2006 Eurographics Symposium on Rendering, Nicosia, Cyprus (June 2006; published as Rendering Techniques 2006, Ed. Wolfgang Heidrich and Tomas Akenine-Moller, Springer-Verlag, Vienna).|
Applications of Small-Scale Reconfigurability to
Kevin Dale, Jeremy Sheaffer, Vinu Vijay Kumar, David Luebke, Greg
Humphreys, and Kevin Skadron. International Workshop on Applied
Reconfigurable Computing (ARC2006) (March 2006).
Selected as one of 10 best workshop papers to be extended for a special edition of the International Journal of Electronics.
Small-Scale Reconfigurability for Improved Performance and Double Precision in Graphics Hardware. Kevin Dale, Jeremy Sheaffer, Vinu Vijay Kumar, David Luebke, Greg Humphreys, and Kevin Skadron. International Journal of Electronics (to appear).
|A High-Accuracy, Low-Cost Localization System for Wireless Sensor Networks. Radu Stoleru, Tian He, John A. Stankovic, and David Luebke. ACM SenSys 2005 (November 2005).|
|All-Frequency Relighting of Glossy Objects. Rui Wang, John Tran, and David Luebke. ACM Transactions on Graphics 25(2) (April 2006).|
|The Ultimate Display: Where Will All The Pixels Come From? Ben Watson and David Luebke, IEEE Computer 38(8) (August 2005).|
|All-Frequency Interactive Relighting of Translucent Objects with Single and Multiple Scattering. Rui Wang, John Tran, and David Luebke, ACM Transactions on Graphics 24(3) (SIGGRAPH 2005), Los Angeles, CA (August 2005).|
|Adaptive Frameless Rendering. Abhinav Dayal, Cliff Woolley, Ben Watson, and David Luebke, Proceedings of the 2005 Eurographics Symposium on Rendering, Konstanz, Germany (June 2005; published as Rendering Techniques 2005, Ed. Kavita Bala and Philip Dutre, Springer-Verlag, Vienna).|
|A GPU-Accelerated Render Cache. Tenghui Zhu, Rui Wang and David Luebke. Pacific Graphics 2005 (short paper), Macao, China (October 2005).|
Thermal Management for Graphics-Processor
Architectures. Jeremy Sheaffer, Kevin Skadron, and David Luebke,
Proceedings of the 2005 IEEE International Symposium on Performance
Analysis of Systems and Software (ISPASS 2005), Austin, TX (March
See also Qsilver, the public-domain graphics architecture simulator used in the paper.
|A Flexible Simulation Framework for Graphics Architectures. Jeremy W. Sheaffer, David Luebke, and Kevin Skadron, Proceedings of Graphics Hardware 2004, Grenoble, France (August 2004).|
|All-Frequency Relighting of Non-Diffuse Objects Using Separable BRDF Approximation. Rui Wang, John Tran, and David Luebke, Proceedings of the 2004 Eurographics Symposium on Rendering (June 2004, Sweden; published as Rendering Techniques 2004, Ed. Henrik Wann Jensen and Alexander Keller, Springer-Verlag, Vienna).|
|Monticello Through the Window. Nathaniel Williams, Chad Hantak, Kok-Lim Low, John Thomas, Kurtis Keller, Lars Nyland, David Luebke, and Anselmo Lastra, Proceedings of the 4th International Symposium on Virtual Reality, Archaeology and Intelligent Cultural Heritage (VAST 2003), Brighton, UK (November 2003).|
|Efficient Reconstruction of Indoor Scenes with Color . Rui Wang and David Luebke, Proceedings of the 4th International Conference on 3-D Digital Imaging and Modeling (3DIM 2003), Banff, Canada (October 2003).|
|A Multigrid Solver for Boundary Value Problems Using Programmable Graphics Hardware. Nolan Goodnight, Cliff Woolley, Greg Lewin, David Luebke, and Greg Humphreys, Proceedings of Graphics Hardware 2003, San Diego, CA (July 2003)|
|Perceptually Guided Simplification of Lit, Textured Meshes. Nathaniel Williams, David Luebke, Jonathan Cohen, Mike Kelley, and Brenden Schubert, Proceedings of the 2003 ACM SIGGRAPH Symposium on Interactive 3D Graphics, Monterey, CA (April 2003).|
|Interruptible Rendering. Cliff Woolley, David Luebke, Benjamin Watson, and Abhinav Dayal, Proceedings of the 2003 ACM SIGGRAPH Symposium on Interactive 3D Graphics, Monterey, CA (April 2003).|
Level of Detail for 3D Graphics.
David Luebke, Martin Reddy, Jonathan Cohen, Amitabh Varshney,
Benjamin Watson, and Robert Huebner.
Morgan-Kaufmann Publishers, San Francisco (July 2002).
New! Kindle version now available.
Visit the accompanying web site for tools, links, errata, and more.
|Perceptually Driven Simplification for Interactive Rendering. David Luebke and Ben Hallen, Proceedings of the 2001 Eurographics Workshop on Rendering (June 2001, London; published as Rendering Techniques 2001, Ed. Steven Gortler and Karol Myszkowski, Springer-Verlag, Vienna).|
Particles for Interactive Non-Photorealistic Rendering. Derek Cornish,
Andrea Rowan, and David Luebke, Proceedings
of Graphics Interface 2001
Also available: a video screen capture, in DIVX format, of the NPR system in action (29 MB).
|A Developer’s Survey of Polygonal Simplification Algorithms. David Luebke, IEEE Computer Graphics &Applications (May 2001).|
|View-Dependent Simplification of Arbitrary Polygonal Environments. David Luebke and Carl Erikson, Proceedings of SIGGRAPH 97, ACM Press, NY (August 1997).|
Portals and Mirrors:
Simple, Fast Evaluation of Potentially Visible Sets, David P. Luebke
and Chris Georges, Proceedings of the 1995 Symposium on Interactive 3D
Graphics, ACM Press, NY (April, 1995). [postscript,
Note: see the pfPortals library.
Winner, I3D Test of Time Award - awarded to the paper from the first five years of the Symposium judged to have had the most lasting impact.
Interactive Indirect Lighting Computed in the Cloud
. Cyril Crassin, David Luebke, Michael Mara, Morgan McGuire, Brent Oster, Peter Shirley, Peter-Pike Sloan, and Chris Wyman.
SIGGRAPH 2013 Technical Talk, Los Angeles CA (July 2013)
|Temperature-Aware GPU Design. Jeremy Sheaffer, David Luebke, Kevin Skadron, ACM SIGGRAPH 2004 Posters, Los Angeles, CA (2004).Finalist, ACM Student Research Competition 2003|
A Geometric Level of Detail System at the OpenGL API
Level. Jonathan Cohen, Nathaniel Duca, David Luebke, Brenden
IEEE Visualization 2003, Seattle, WA (2003).
Best poster award, IEEE VIS 2003
Also available: a song lauding the merits of GLOD.
See GLOD, a full-featured public-domain software toolkit for LOD control with a minimalist OpenGL-style API.
Driver-Level Interface for Geometric Level of Detail. Jonathan
Cohen, David Luebke, Nathaniel Duca, Brenden Schubert, SIGGRAPH 2003
Technical Sketch, San Diego, CA (2003).
Also available: an earlier tech report with more detail.
Interruptible Rendering. J. Cliff Woolley, David Luebke, and Ben
Watson, SIGGRAPH 2002 Technical Sketch, San Antonio,
Also available: a large (20 Mb) MPEG video for both 2002 sketches.
Improving Frameless Rendering by Focusing on Change.
Abinav Dayal, Ben Watson, and David Luebke, SIGGRAPH 2002 Technical
Sketch, San Antonio, TX
Also available: a large (20 Mb) MPEG video for both 2002 sketches.
SIGGRAPH 2016 Emerging Technologies Exhibit. We
demonstrated a set of perceptually-based methods for improving
foveated rendering, which uses an eye tracker and renders a
high-detail image near the user's center of gaze (the fovea
and a low-detail image elsewhere (the periphery), in virtual
reality. We specifically address problems seen in prior work of temporal
instability (caused by low resolution rendering) and contrast loss
(caused by filtering). |
This exhibit foreshadowed the final paper in SIGGRAPH Asia 2016.
|SIGGRAPH 2013 Emerging Technologies Exhibit. We demonstrated dramatically thinner and lighter head-mounted displays capable of depicting accurate accommodation, convergence, and binocular-disparity depth cues. Our approach replaces bulky conventional optics with a microlens array and computationally synthesized light field display.|
|The Scanning Monticello project uses image-based methods and a laser scanning device to create an extremely detailed 3D computer model of Monticello, Thomas Jefferson's Virginia home. Applications of this technology range from historic preservation for art and archeology, to telecollaboration, to forensic reconstruction of crime scenes, to virtual tourism. As an example of this last application, we worked with researchers from UNC-Chapel Hill to create a Virtual Monticello exhibit for Jefferson's America & Napoleon's France at the New Orleans Museum of Art (NOMA). This exhibition commemorated the 200th anniversary of the Louisiana Purchase and was visited by over 110,000 people from April 12-August 31 in 2003.|
Fast Global Illumination Approximations on Deep G-Buffers. Michael Mara, Morgan McGuire, Derek Nowrouzezahrai, and David Luebke.
NVIDIA Research Technical Report NVR-2014-001.
See also our earlier tech report on this approach.
CloudLight: A system for amortizing indirect lighting in real-time rendering
. Cyril Crassin, David Luebke, Michael Mara, Morgan McGuire, Brent Oster, Peter Shirley, Peter-Pike Sloan, and Chris Wyman.
NVIDIA Research Technical Report NVR-2013-001.
||GLOD: A Minimal Interface for Geometric Level of Detail. Jon Cohen, David Luebke, Nat Duca, Brenden Schubert, and Chris Niski. Also available: an accompanying video.|
Level of Detail for the Masses. Jon Cohen, David Luebke, Nat Duca,
and Brenden Schubert. Johns Hopkins Computer Graphics Lab Technical
Report JHU-CS-GL03-4 (May 2003).
This tech report has been largely superceded. See the above GLOD links for a more up-to-date introduction to GLOD.
|A Multigrid Solver for Boundary Value Problems Using Graphics Hardware. Nolan Goodnight, Gregory Lewin, David Luebke, and Kevin Skadron, University of Virginia Technical Report CS-2003-03 (January 2003).|
Driven Simplification of Lit, Textured Meshes.
Jonathan D. Cohen, Nathaniel Williams, Mike Kelley, and Brenden
Schubert, University of Virginia Technical Report CS-2002-03
Submitted to IEEE Visualization 2002.
Also available: a very large (98 Mb) MPEG video.
|Perceptually Driven Interactive Rendering. Ben Hallen and David Luebke, University of Virginia Technical Report CS-2001-01 (2001).|
|Perceptually Driven Simplification Using Gaze-Directed Rendering. David Luebke, Ben Hallen, Dale Newfield, and Benjamin Watson, University of Virginia Technical Report CS-2000-04 (2000).|
||Robust View-Dependent Simplification for Very Large-Scale CAD Visualization. David Luebke, University of Virginia Technical Report CS-99-33 (1999).|
View-Dependent Simplification of Arbitrary Polygonal Environments. David Luebke. University of North Carolina Department of Computer Science Technical Report #TR98-029 (1998).
Other technical reports:
Approximately 34 US patent applications filed since July 2006, including:
Systems and methods for voting among parallel threads.
United States Patent 8,200,947 (June 12, 2012).
Capture system and method equipped with at least one steerable
United States Patent 8,118,440 (February 21, 2012).
Display system and method equipped with at least one steerable
United States Patent 8,100,543 (January 24, 2012).
System, method, and computer program product for generating a
ray tracing data structure utilizing a parallel processor
United States Patent 8,072,460 (December 6, 2011).
Apparatus and method for approximating a convolution function
utilizing a sum of Gaussian functions.
United States Patent 8,064,726 (November 22, 2011).
Accelerated Occlusion Culling Using Directional Discretized
Occluders and System Therefor.
United States Patent 6,574,360 (June 3, 2003).
System and Method for Reducing Execution Divergence in Parallel
United States Patent Application 20100064291.
System, Method, and Computer Program Product for Performing a
Scan Operation on a Sequence of Single-Bit Values Using a Parallel
United States Patent Application 20090132878.
System, Method, And Computer Program Product For Generating A
Ray Tracing Data Structure Utilizing A Parallel Processor
United States Patent Applicaton 20090106530.
Image processing of an incoming light field using a spatial
United States Patent Application 20090097092.
Fellow of the IEEE (2016), "for contributions to GPU computing and computer graphics."
NVIDIA Distinguished Inventor (2008).
SIGGRAPH 2010 OptiX paper selected for CACM Research Highlights (2012).
Best Paper Award, ACM SIGGRAPH Symposium on Interactive 3D Graphics (2010)
Test of Time Award, ACM SIGGRAPH Symposium on Interactive 3D Graphics (2005)
National Science Foundation CAREER Award (2001-2006)
Department of Energy Early Career PI Award (2002-05)
UVA Teaching + Technology Initiative Fellowship (2001)
UVA University Teaching Fellowship (2000-01)
UVA ACM Undergraduate Teaching Award (1998-99)
Please see my CV for a complete list and explanations of my awards and honors.
Please see my CV for a detailed list of my department, school, University, and community activities.
The Colorado College
Computer Science at
The University of North Carolina
The Walkthrough Project
Real-Time Rendering & Game Technology [S04]
Fall 2005: Introduction to Computer Science
Spring 2005: Introduction to Computer Graphics [S03][S00][F99]
Fall 2004: Computer Graphics for Film Production
Spring 2003: Computer Science Seminar [S03] [S02]
Spring 2003: Interactive Ray Tracing
Spring 2002: Introduction to Algorithms [S00]
Fall 2001: 3-D Animation and Special Effects
Spring 2001: Advanced Computer Graphics [S99]
Spring 2001: Modern Research in Computer Graphics [F98]
Assistant Professor of Computer Science
I would particularly like to thank the Stanford Computer Graphics Laboratory for the use of many of the 3D models on this page .


Avalanche effectIn cryptography, the avalanche effect refers to a desirable property of cryptographic algorithms, typically block ciphers and cryptographic hash functions. The avalanche effect is evident if, when an input is changed slightly (for example, flipping a single bit) the output changes significantly (eg, half the output bits flip). In the case of quality block ciphers, such a small change in either the key or the plaintext should cause a drastic change in the ciphertext. The actual term was first used by Horst Feistel, although the concept dates back to at least Shannon's diffusion.
If a block cipher or cryptographic hash function does not exhibit the avalanche effect to a significant degree, then it has poor randomization, and thus a cryptanalyst can make predictions about the input, being given only the output. This may be sufficient to partially or completely break the algorithm. It is thus not a desirable condition from the point of view of the designer of the cryptographic algorithm or device.
Constructing a cipher or hash to exhibit a substantial avalanche effect is one of the primary design objectives. This is why most block ciphers are product ciphers. It is also why hash functions have large data blocks. Both these features allow small changes to propagate rapidly through iterations of the algorithm, such that every bit of the output should depend on every bit of the input before the algorithm terminates.
In the image : The SHA1 hash function exhibits good avalanche effect. When a single bit is changed the hash sum becomes totally differen


The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.
Recurrent neural networks, long short-term memory  and gated recurrent  neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation [35, 2, 5]. Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures [38, 24, 15]. Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states ht, as a function of the previous hidden state ht−1 and the input for position t. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples. Recent work has achieved significant improvements in computational efficiency through factorization tricks  and conditional computation , while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains. Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences [2, 19]. In all but a few cases , however, such attention mechanisms are used in conjunction with a recurrent network. In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.
Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states ht, as a function of the previous hidden state ht−1 and the input for position t. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples. Recent work has achieved significant improvements in computational efficiency through factorization tricks  and conditional computation , while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.
Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences [2, 19]. In all but a few cases , however, such attention mechanisms are used in conjunction with a recurrent network.
In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.
The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU , ByteNet  and ConvS2S , all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions . In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section 3.2.
Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations [4, 27, 28, 22].
End-to-end memory networks are based on a recurrent attention mechanism instead of sequencealigned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks .
To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequencealigned RNNs or convolution. In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as [17, 18] and .
3. Model Architecture
Most competitive neural sequence transduction models have an encoder-decoder structure [5, 2, 35]. Here, the encoder maps an input sequence of symbol representations (x1,...,xn) to a sequence of continuous representations z = (z1,...,zn). Given z, the decoder then generates an output sequence (y1,...,ym) of symbols one element at a time. At each step the model is auto-regressive , consuming the previously generated symbols as additional input when generating the next.
The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1, respectively.
3.1 Encoder and Decoder Stacks
Encoder: The encoder is composed of a stack of N = 6 identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, positionwise fully connected feed-forward network. We employ a residual connection  around each of the two sub-layers, followed by layer normalization . That is, the output of each sub-layer is LayerNorm(x + Sublayer(x)), where Sublayer(x) is the function implemented by the sub-layer itself. To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension dmodel = 512.
Decoder: Thedecoder is also composed of a stack of N = 6 identical layers. In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack. Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization. We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position i can depend only on the known outputs at positions less than i.
An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.
3.2.1 Scaled Dot-Product Attention
We call our particular attention 'Scaled Dot-Product Attention' (Figure 2). The input consists of queries and keys of dimension dk, and values of dimension dv. We compute the dot products of the query with all keys, divide each by √ dk, and apply a softmax function to obtain the weights on the values.
3.2.2 Multi-Head Attention
Instead of performing a single attention function with dmodel-dimensional keys, values and queries, we found it beneficial to linearly project the queries, keys and values h times with different, learned linear projections to dk, dk and dv dimensions, respectively. On each of these projected versions of queries, keys and values we then perform the attention function in parallel, yielding dv-dimensional output values. These are concatenated and once again projected, resulting in the final values, as depicted in Figure 2.
Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.
Where the projections are parameter matrices W Qi ∈ R dmodel×dk , W Ki ∈ R dmodel×dk , WV i ∈ R dmodel×dv and WO ∈ R hdv×dmodel.
In this work we employ h = 8 parallel attention layers, or heads. For each of these we use dk = dv = dmodel/h = 64. Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.
3.2.3 Applications of Attention in our Model
The Transformer uses multi-head attention in three different ways: • In 'encoder-decoder attention' layers, the queries come from the previous decoder layer,and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence. This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as [38, 2, 9]. • The encoder contains self-attention layers. In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder. • Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. We need to prevent leftward information flow in the decoder to preserve the auto-regressive property. We implement this inside of scaled dot-product attention by masking out (setting to −∞) all values in the input of the softmax which correspond to illegal connections. See Figure 2.
3.3 Position-wise Feed-Forward Networks
In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically. This consists of two linear transformations with a ReLU activation in between.
While the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1. The dimensionality of input and output is dmodel = 512, and the inner-layer has dimensionality df f = 2048.
3.4 Embeddings and Softmax
Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension dmodel. We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities. In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to . In the embedding layers, we multiply those weights by √ dmodel.
3.5 Positional Encoding
Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence. To this end, we add 'positional encodings' to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension dmodel as the embeddings, so that the two can be summed. There are many choices of positional encodings, learned and fixed .
In this work, we use sine and cosine functions of different frequencies: where pos is the position and i is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from 2π to 10000 · 2π. We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset k, P Epos+k can be represented as a linear function of P Epos.
We also experimented with using learned positional embeddings  instead, and found that the two versions produced nearly identical results (see Table 3 row (E)). We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.
4 Why Self-Attention
In this section we compare various aspects of self-attention layers to the recurrent and convolutional layers commonly used for mapping one variable-length sequence of symbol representations (x1, ..., xn) to another sequence of equal length (z1, ..., zn), with xi, zi ∈ R d , such as a hidden layer in a typical sequence transduction encoder or decoder. Motivating our use of self-attention we consider three desiderata.
One is the total computational complexity per layer. Another is the amount of computation that can be parallelized, as measured by the minimum number of sequential operations required.
The third is the path length between long-range dependencies in the network. Learning long-range dependencies is a key challenge in many sequence transduction tasks. One key factor affecting the ability to learn such dependencies is the length of the paths forward and backward signals have to traverse in the network. The shorter these paths between any combination of positions in the input and output sequences, the easier it is to learn long-range dependencies . Hence we also compare the maximum path length between any two input and output positions in networks composed of the different layer types.
As noted in Table 1, a self-attention layer connects all positions with a constant number of sequentially executed operations, whereas a recurrent layer requires O(n) sequential operations. In terms of computational complexity, self-attention layers are faster than recurrent layers when the sequence length n is smaller than the representation dimensionality d, which is most often the case with sentence representations used by state-of-the-art models in machine translations, such as word-piece  and byte-pair  representations. To improve computational performance for tasks involving very long sequences, self-attention could be restricted to considering only a neighborhood of size r in the input sequence centered around the respective output position. This would increase the maximum path length to O(n/r). We plan to investigate this approach further in future work.
A single convolutional layer with kernel width k < n does not connect all pairs of input and output positions. Doing so requires a stack of O(n/k) convolutional layers in the case of contiguous kernels, or O(logk(n)) in the case of dilated convolutions , increasing the length of the longest paths between any two positions in the network. Convolutional layers are generally more expensive than recurrent layers, by a factor of k. Separable convolutions , however, decrease the complexity considerably, to O(k · n · d + n · d 2 ). Even with k = n, however, the complexity of a separable convolution is equal to the combination of a self-attention layer and a point-wise feed-forward layer, the approach we take in our model.
As side benefit, self-attention could yield more interpretable models. We inspect attention distributions from our models and present and discuss examples in the appendix. Not only do individual attention heads clearly learn to perform different tasks, many appear to exhibit behavior related to the syntactic and semantic structure of the sentences.
This section describes the training regime for our models.
5.1 Training Data and Batching
We trained on the standard WMT 2014 English-German dataset consisting of about 4.5 million sentence pairs. Sentences were encoded using byte-pair encoding , which has a shared sourcetarget vocabulary of about 37000 tokens. For English-French, we used the significantly larger WMT 2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece vocabulary . Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens.
5.2 Hardware and Schedule
We trained our models on one machine with 8 NVIDIA P100 GPUs. For our base models using the hyperparameters described throughout the paper, each training step took about 0.4 seconds. We trained the base models for a total of 100,000 steps or 12 hours. For our big models,(described on the bottom line of table 3), step time was 1.0 seconds. The big models were trained for 300,000 steps (3.5 days).
We used the Adam optimizer  with β1 = 0.9, β2 = 0.98 and = 10−9. We varied the learning rate over the course of training, according to the formula:
This corresponds to increasing the learning rate linearly for the first warmup_steps training steps, and decreasing it thereafter proportionally to the inverse square root of the step number. We used warmup_steps = 4000.
We employ three types of regularization during training:
Residual Dropout We apply dropout  to the output of each sub-layer, before it is added to the sub-layer input and normalized. In addition, we apply dropout to the sums of the embeddings and the positional encodings in both the encoder and decoder stacks. For the base model, we use a rate of Pdrop = 0.1.
Label Smoothing During training, we employed label smoothing of value ls = 0.1 . This hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score.
6.1 Machine Translation
On the WMT 2014 English-to-German translation task, the big transformer model (Transformer (big) in Table 2) outperforms the best previously reported models (including ensembles) by more than 2.0 BLEU, establishing a new state-of-the-art BLEU score of 28.4. The configuration of this model is listed in the bottom line of Table 3. Training took 3.5 days on 8 P100 GPUs. Even our base model surpasses all previously published models and ensembles, at a fraction of the training cost of any of the competitive models.
On the WMT 2014 English-to-French translation task, our big model achieves a BLEU score of 41.0, outperforming all of the previously published single models, at less than 1/4 the training cost of the previous state-of-the-art model. The Transformer (big) model trained for English-to-French used dropout rate Pdrop = 0.1, instead of 0.3.
For the base models, we used a single model obtained by averaging the last 5 checkpoints, which were written at 10-minute intervals. For the big models, we averaged the last 20 checkpoints. We used beam search with a beam size of 4 and length penalty α = 0.6 . These hyperparameters were chosen after experimentation on the development set. We set the maximum output length during inference to input length + 50, but terminate early when possible .
Table 2 summarizes our results and compares our translation quality and training costs to other model architectures from the literature. We estimate the number of floating point operations used to train a model by multiplying the training time, the number of GPUs used, and an estimate of the sustained single-precision floating-point capacity of each GPU 5.
6.2 Model Variations
To evaluate the importance of different components of the Transformer, we varied our base model in different ways, measuring the change in performance on English-to-German translation on the development set, newstest2013. We used beam search as described in the previous section, but no checkpoint averaging. We present these results in Table 3.
In Table 3 rows (A), we vary the number of attention heads and the attention key and value dimensions, keeping the amount of computation constant, as described in Section 3.2.2. While single-head attention is 0.9 BLEU worse than the best setting, quality also drops off with too many heads.
In Table 3 rows (B), we observe that reducing the attention key size dk hurts model quality. This suggests that determining compatibility is not easy and that a more sophisticated compatibility function than dot product may be beneficial. We further observe in rows (C) and (D) that, as expected, bigger models are better, and dropout is very helpful in avoiding over-fitting. In row (E) we replace our sinusoidal positional encoding with learned positional embeddings , and observe nearly identical results to the base model.
6.3 English Constituency Parsing
To evaluate if the Transformer can generalize to other tasks we performed experiments on English constituency parsing. This task presents specific challenges: the output is subject to strong structural constraints and is significantly longer than the input. Furthermore, RNN sequence-to-sequence models have not been able to attain state-of-the-art results in small-data regimes .
We trained a 4-layer transformer with dmodel = 1024 on the Wall Street Journal (WSJ) portion of the Penn Treebank , about 40K training sentences. We also trained it in a semi-supervised setting, using the larger high-confidence and BerkleyParser corpora from with approximately 17M sentences . We used a vocabulary of 16K tokens for the WSJ only setting and a vocabulary of 32K tokens for the semi-supervised setting.
We performed only a small number of experiments to select the dropout, both attention and residual (section 5.4), learning rates and beam size on the Section 22 development set, all other parameters remained unchanged from the English-to-German base translation model. During inference, we increased the maximum output length to input length + 300. We used a beam size of 21 and α = 0.3 for both WSJ only and the semi-supervised setting.
Our results in Table 4 show that despite the lack of task-specific tuning our model performs sur- prisingly well, yielding better results than all previously reported models with the exception of the Recurrent Neural Network Grammar .
In contrast to RNN sequence-to-sequence models , the Transformer outperforms the Berkeley- Parser  even when training only on the WSJ training set of 40K sentences.
In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.
For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles.
We are excited about the future of attention-based models and plan to apply them to other tasks. We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video. Making generation less sequential is another research goals of ours.
The code we used to train and evaluate our models is available at https://github.com/tensorflow/tensor2tensor.
Acknowledgements We are grateful to Nal Kalchbrenner and Stephan Gouws for their fruitful comments, corrections and inspiration.


Recently, more and more Malaysia SMEs started to devise innovative strategies in storing their accounting and financial information.
Businesses are rapidly switching from traditional on-premise accounting method to cloud accounting, with the aim to improve their financial departments’ accountability and transparency.
By taking advantage of cloud-based accounting software, businesses are less dependent on in-house hard drives when managing their accounting and finances. Leveraging the security of cloud-accounting, financial data’s security and accessibility are enhanced.
Traditional and cloud accounting comes with vastly different features. This post will discuss the key differences between these two different methods of recording, processing and storing financial data of a business.
Cloud vs. Traditional Accounting
Traditional accounting involves a dedicated in-house computer hard drive to store data and host the software. The on-premise accounting software is installed onto a desktop computer and is accessible through a desktop application. It does not require an internet connection, which is allow users without internet access to use it offline.
Cloud accounting has the same functionality as on-premise accounting software. Both function as software that records, processes, and stores financial details of a business.
However, with cloud accounting software, data is recorded, processed and stored on a remote server, eliminating the need for in-house computer hard drives. Users will need internet access and devices to operate and access the data.
On-premise software usually requires bigger upfront purchase, known as capital expenditure (CapEx) to a business. It involves up-front investment, responsibility and expertise to operate and maintain in-house computer hardware. When data size grows extensively, the initial invested storage system will need to be replaced or upgraded to accommodate more data. This will have a significant increase in business operations costs.
Conversely, cloud accounting does not require the investment of in-house computer hardware to store financial data. Cloud-based accounting software enables businesses to store data in the cloud and can be easily accessed via any mobile device. Although users have to pay a monthly or annual subscription, it is usually a cheaper option than adopting a traditional on-premise accounting software.
The financial data of the traditional accounting software are vulnerable to theft or physical damage, such as fires. It is a risk when data is stored on a computer’s hardware as it may be stolen or tampered by hackers.
Cloud accounting gives SMEs the accessibility of their financial data regardless of any situations that might affect the business. Virtual platform of cloud data storage is less vulnerable to physical damage.
The additional security levels making it difficult for a hacker to gain access to the data. In the case of any suspicious activity, a cloud accounting provider will be able to quickly detect and prevent a data breach.
Traditional accounting is deployed and maintained in-house at a physical office. Users can only gain access remotely with third-party support to access the solution and a mobile device. Cloud accounting software provides real-time data that can be accessed via the Internet anywhere at any time.
In other words, with a web-based interface device, your Finance personnel can access the software from home, while travelling, and from any location of their choice. The cloud keeps information updated in real-time,in a secure site, making it readily accessible.
As businesses grow, so does financial data size. SMEs using traditional accounting software will need to expand its in-house IT investment to meet the increasing storage size demand regularly. Each time the data size surpasses the limit, you will need to act fast and upgrade the hardware to accommodate the growth.
On the contrary, cloud accounting software is flexible and scalable such that it can accommodate business growth and needs.
5. Environment Friendliness
Traditional accounting requires users to print out financial information on paper, which is not environmentally friendly. Users can go paperless with cloud accounting since the data can be accessed electronically with a mobile device and internet access.
This makes the distribution of accounting information simple, efficient, and more economical compare to the traditional accounting method.
6. Cloud or Traditional Accounting the Future of Finance?
There is a growing trend for the adoption of cloud-based accounting software. Organisations that aims to implement a secure, cost-effective and scalable accounting software should consider adopting a cloud accounting solution.
As technology evolves rapidly to accommodate the ever-growing needs of businesses, even the Finance personnel needs to keep up with the technology trend and reap the benefits of adopting cloud-based software over traditional on-premise solutions.
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