CertiConVis: the future of computer vision
CertiConVis (abbreviated to CCV) is software that has been designed to analyse images from cameras or videos. Over several years of development, our experts gradually created a cutting-edge product that now effectively streamlines processes in many segments.
Define the objects and the behaviour that you want to monitor with camera systems and let the system work entirely on its own. As soon as a predefined situation occurs in the scene under observation, such as a vehicle turning the wrong way down a one-way street or a person entering a prohibited zone without authorisation, CertiConVis automatically alerts the user to the situation.
Objects and their behaviour, entry zones, directions of movement, and more can all be defined in advance. Analysis can be done in real time or from recorded footage. In the latter case, CertiConVis automatically, retrospectively displays all moments defined by the user during the selected time period. This is what is known as forensic analysis.
The advantage of automatically recording situations is the almost zero error rate and the fact that it is not demanding on time: there is no need to monitor hours of footage and pick out the required situations “by hand”.
CertiConVis is taking the use of camera systems to a whole new level all around the world. Define objects and how they behave in the monitored scene and let the system do the work for you.
Functions of the CertiConVis system
We will teach your cameras to automatically detect
and many other situations.


ELITE COMPUTER SYSTEM LAUNCHES A MINI COMPUTER IN INDIA
Taiwan's Elite Computer System (ECS) has launched a pocket computer in India. The company claims that this is the world's smallest pocket-size Windows-based computer. This personal computer named Liva Q (Liva Q) has been given Intel Apolo Lake processor.
Pocket Computer Liva Q Features:
- 4 GB RAM
- 32 GB eMMC storage
- MicroSD card can be increased to 128 GB of memory
- Liva queen size is 70x70x31.4mm
- Weight 260 grams
- This computer has the advantage that it can play 4K content on the monitor.
However, one thing to note is that Liva Q is the only pocket-sized CPU. With it, you have to buy monitors, mouse, and keyboard separately.
Prices of Liva Q:
- Liva is the two variants of Q.
In one, you will get the operating system, while the other variant will have to install the operating system.
Variants of LIVA Q 4GB RAM and 32GB internal storage are priced at 15,500 and you will find Windows 10 operating system. It can take the taxon.
- When the variant without operating system costs Rs 13500.
- The pocket-sized computer has a dual network option with HDMI 2.0 port.
- For connectivity, ,it has been given the RJ45 LAN connector.
- This mini-PC has both LAN and WiFi options to connect to the Internet.
Click Here View in Detail


The demand for AI-based learning is rising as artificial intelligence is taking leapfrog jumps in every sector, including eLearning. An AI-based learning management system can execute various activities that need human intellect. It is capable of providing answers to human-related issues such as voice recognition, interpretation, decision-making, and much more. Institutions are understanding the role of AI in eLearning and thus opting for an AI-based learning management system. AI has the potential to completely transform learning and take it to the next level. In fact, it’s already started showing its ability. Here are the ways through which AI is changing the eLearning sector.
Rather than giving a one-size-fits-all approach, eLearning platforms like language lab software use AI to enable information depending on the prior knowledge and requirements of every learner.
Personalized learning is a data-driven method of learning that continually monitors each student’s progress and adapts information to match each student’s knowledge and preferences using machine learning algorithms. As a result, if a student is learning a new skill, the platform will change the content and difficulty levels based on their progress until they have learned the subject completely. Creating a customized learning route for each learner with specific topics not only saves time but also boosts students’ motivation.
When it comes to analyzing the performance of learners, instructors experience a typical limitation of time management and the information isn’t very thorough.
However, by using an Artificial Intelligence-based eLearning platform, instructors are able to swiftly analyze massive volumes of data and uncover patterns and trends to continually optimize and improve learning experiences. Also, corporate executives may use strong data analytics to not only build and manage their eLearning material but also to collect critical information regarding student progress and understanding.
Instructors can use artificial intelligence to get accurate information about each student’s performance, strengths, weaknesses, and even attendance concerns. It facilitates decision-making as well as the capacity to optimize and respond before the employee loses interest and abandons the course.
Many things that humans are handling currently will likely be handled by artificial intelligence in the future. So the people will be better trained and capable of handling more complicated jobs such as English lab services. Companies can provide personalized learning by using the power of artificial intelligence, saving the time of both the instructors and learners and enabling them to focus on more vital duties. This will result in better outcomes and higher productivity.


Find a solution for your needs.
The AAMS-SALVARANI pump tester is designed to define the capacity of pumps at different counter pressures.
Therefore the AAMS- SALVARANI pump tester is equipped with an inductive flow sensor and an electronical pressure sensor.
Both sensors are connected to a monitor that continuously displays pressure and flow rate.
The pump tester is available in different versions from 16 to 80 bar.
The flow meter measures in ranges starting from 8 l/min up to 1300 l/min. (Other ranges are available on demand)
The monitor has a memory for 100 combined pressure and flow rate values. The stored values can be printed and/ or transferred to a PC (on request).
The sensors are of 0,5% precision class (with calibration certificate).
With the built-in pressure regulator, a counter pressure can be set.
Thanks to the transparent part air sucked by the pump can be watched
Quick fit connectors are provided for a fast and reliable connection with the pump lines.
The pump tester works on a 12V battery and is mounted in a durable box
We’ll respond to your request as soon as possible.


Enjoy complete hands-free wireless charging and keep your devices charged up even on the road. Charge at 15 W speed, 3X faster than regular wireless chargers. The built-in magnet provides strong adhesion and automatically locks onto the MagSafe device. The no-arm design lets you view the device in any angle. High speed Qi wireless charging and reliable hands-free access with VentMag-15W.
Specially designed for iPhone 12 series: iPhone 12, iPhone 12 Pro, iPhone 12 Mini, iPhone 12 Pro Max
Can also be used as a desk charger for all Qi-enabled devices, like, Samsung Galaxy S21/S20/S10, Galaxy A52 5G, Samsung Galaxy Note 20 series, Google Pixel 5, Google Pixel 4A, Oppo Find X3 Pro, OnePlus 9 series, Xiaomi Mi 11, Sony Xperia 1 III


Years before the Apple TV, the company launched the Macintosh TV. At that time, the tool with a price tag of $ 2099 did not hold. Three months later, Apple pulled the Macintosh TV from the market.
Apple’s first personal assistant venture was the Newton. The feature of the device that came with the handwriting recognition feature was actually not working. The technology of that time was not yet advanced.
While the iMac was revolutionary in personal computers, the accompanying mouse was just as revolutionary.
The round mouse that Apple shipped with it was so dysfunctional that it was impossible to use the mouse without looking.
iPod Shuffle (Gen 3)
It is known that Apple released iPod shuffles with the insistence of offering a screen-free music experience.
However, the third generation of this instrument did not even have a button. The error made in this device, which can be controlled with headphones, has been noticed and the keypad is back on the 4th generation iPod shuffs.
The Magic Mouse 2 and charging problem
Apple’s Magic Mouse didn’t look bad in terms of design, but that was as long as you didn’t charge the device.
Apple designers placed the mouse’s charging port on the bottom. Therefore, charging the mouse meant that your work on the computer stopped for a while.


Back in the day, we have to pay Rs 50,000 to get the best smartphones but now that’s not necessary, the same features and performance are available for under Rs 20,000.
There are phones with a 108MP camera setup, an in-display fingerprint sensor, premium design, 7000 mAh battery, and massive 65W fast charging support and all the features are available under the 20k price range.
I will update this list every month, but if you want live deals or want to chat with me, you can follow topbestinINDIA on Telegram for live deals. Here is the discussion Telegram Group. See you there…
Redmi Note 10 Pro
|Display Size||6.67-inches FHD+ AMOLED 120Hz Display|
|Processor||Qualcomm Snapdragon 732G Chipset|
|OS||MIUI 12-based on Android 11|
|Cameras (Rear)||64MP Primary |
|Cameras (Front)||16MP Selfie|
|Ram & Storage||6GB Ram + 64GB Storage | 6GB Ram + 128GB Storage | 8GB Ram + 128GB Storage|
|Battery||5,020 mAh battery (33W Fast Charging)|
Xiaomi had launched three phones under Redmi Note 10 series and the Redmi Note 10 Pro is one of them. There is no huge difference between the Pro version & the Max version, the only thing that makes both of these smartphones different is the camera setup.
Max version has 108MP primary sensor while the Pro version has 64MP primary sensor and that’s it, everything else is the same, not a slight difference.
Now, if you ask me, you can save two thousand by buying Redmi Note 10 Pro but if you want that 108MP camera sensor then you can pay an extra 2k.
I know, Xiaomi has also launched the Redmi Note 11 Series but this series is by far worst series Redmi has launched in decades, so skip the Redmi Note 11 Series.
For those who are wondering, there is no huge difference in the camera department. Both the Pro & Max version will give the same image quality. It’s just that, Redmi is charging an extra 2k for 108MP and that’s just a marketing gimmick, so don’t fall for that.
Now, if you’re okay with a 4G smartphone then still better to buy Redmi Note 10 Pro but if you want 5G then go with POCO X4 Pro 5G because that offers real value at the real price tag.
POCO X4 Pro 5G
|Specifications||6.67-inches FHD+ AMOLED Display,|
120Hz Refresh Rate,
Qualcomm Snapdragon 695 5G (6 nm),
MIUI 13 – Android 11,
64MP+8MP+2MP Rear Cameras,
16MP Front Cameras,
5000mAh battery (67W),
|Reasons to Buy||120Hz AMOLED Display,|
7 5G Bands,
Snapdragon 695 Processor,
Solid Battery Life,
Superfast 67W Charging Support,
Wide Vine L1 Support,
|Reasons to Avoid||Decent Cameras,|
Night Photography needs improvement,
Videos come out Shaky,
This is what Redmi Note 11 Pro suppose to be but they launched under the POCO brand. The POCO X4 Pro 5G has almost everything from a great display, solid battery life, good build, superfast charging, and even a powerful processor but it doesn’t have the cameras good cameras.
Related: POCO X4 Pro 5G Review with Pros and Cons
Now it’s not like the cameras are bad but compared to some other phones like RealMe 9 Pro 5G, POCO needs to work on cameras.
Still, the phone has better specs compared to RealMe 9 Pro 5G expect cameras.
I mean, the only phone that comes with a 120hz AMOLED display with 5G support.
If your budget is Rs 20,000 and if you want the overall best smartphone then don’t look for anything else, you can consider the POCO X4 Pro 5G over Redmi Note 10 Pro & RealMe 9 Pro 5G.
Unless and until you don’t like the MIUI.
Motorola G71 5G
|Display Size||6.40-inches FHD+ AMOLED display|
60Hz Refresh Rate
|Processor||Snapdragon 695 SoC|
|OS||Stock Android 11|
|Cameras (Rear)||50MP Primary Sensor|
|Cameras (Front)||16MP Selfie|
|5G||Yes, 13 5G Bands.|
|Battery||5,000 mAh battery (33W Fast Charging)|
Motorola is back with a bang, newly launched Motorola G71 5G has almost everything you can think of from the 13 5G band to a powerful processor.
The only thing is, it has a plastic back, no compass sensor and it only has a single speaker other than that, Motorola G71 5G has everything even good cameras.
Related: Motorola G71 5G Review with Pros and Cons
The Snapdragon 695 SoC is powerful enough to handle almost all mobile games while there is a 5,000 mAH battery with 33W fast charging support.
Now, the display has only a 60Hz refresh rate but it’s an AMOLED panel.
Overall, for the price of Rs 18,999, it’s a good smartphone, if you want pure stock Android experience. Also, the phone has stock UI which means, with no ads, you will get a smooth Android experience.
RealMe 9 Pro 5G
|Specifications||6.60-inches FHD+ LCD Display|
120Hz Refresh Rate
Qualcomm Snapdragon 695 5G (6 nm)
RealMe UI 2.0– Android 11
64MP+8MP+2MP Rear Cameras
16MP Front Cameras
5000mAh battery (33W)
|Reasons to Buy||120Hz Refresh Rate|
13 5G Bands
Snapdragon 695 Processor
Solid Battery Life
Wide Vine L1 Support
|Reasons to Avoid||Videos come out Shaky|
Low Display Brightness
No Dual Speakers
Only 33W Charging Speed
The phone does have good cameras but they have added an LCD display, plastic build, and single speakers which is not a good thing to have in 2022’s budget smartphone.
While the Snapdragon 695 SoC is powerful enough to handle almost every single task without any problems.
Overall, if you want better cameras and you can compromise the speakers, display, and charging speed then RealMe 9 Pro 5G is a good option otherwise, POCO X4 Pro 5G is the way to go.
RealMe 9 5G Speed Edition
|Display||6.60-inches FHD+ LCD|
600 pick brightness
Touch Sampling Rate: 360Hz
|Processor||Qualcomm Snapdragon 778G SoC|
|OS||RealMe UI 2.0 – Based on Android 11|
|Cameras (Rear)||48MP Primary Sensor|
|Cameras (Front)||16MP Selfie Shooter|
|Battery Size||5,000 mAh|
30W Wired Fast Charging
If you’re looking for the best 5G Gaming smartphone under Rs 20,000 then there is no better option than RealMe 9 5G Speed Edition.
The Snapdragon 778G is a powerful processor and it can handle almost all mobile games without any problem, so no issue with performance.
Related: RealMe 9 5G Speed Edition Review with Pros and Cons
Coming to RealMe 9 5G SE, like every single gaming smartphone, this phone also doesn’t have great cameras, also there is no stereo speaker and the back and frame are made out of plastic.
Overall, for the price of Rs 19,999, this is a good option to consider for gaming only.
Conclusion: Which one should you buy?
If you want overall the best smartphone and you don’t care about 5G then close your eyes and go with Redmi Note 10 Pro but if you want the overall best 5G smartphone then there is no better option than the POCO X4 Pro 5G as of now.
Do note, that POCO X4 Pro 5G doesn’t have the best cameras, so if you want better cameras then do consider the RealMe 9 Pro 5G but this has an LCD display, only 30W charging, and a plastic back & frame.
In short, every smartphone will have its Pros and Cons, in the end, you have to decide which feature you want the most.
Winner: POCO X4 Pro 5G (review)
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To get up to date with tech, do follow us at MobileDrop. If you want to buy a new mobile, tv, or DSLR, then do follow TopBestinIndia on Telegram for live deals.
Find Smartphone on Your Budget


Ministry of Water & Electricity Emergency Warning System
In 2011, the Ministry of Water and Electricity commissioned Dar Al Riyadh to install an emergency warning system to notify their employees, visitors and residents in case of the flood, natural disaster and emergency occurred in the 5 biggest Dams in the Kingdom. The chosen technology is a radio frequency based system, which is linked to various Civil Defense control centers and 24/7 monitored by security operators.
Scope of work: Design, supply and installation of a warning system which includes 28 high power omni directional speakers with local electronic cabinet attached in a 15 meters mast which can also be triggered locally and integrated with level monitoring system. The system was installed in strategic locations to maximize the coverage of the area.
- Safety & Security Solutions
- Ministry of Water and Electricity


The Benefits of Hiring a Computer System Repair Professional
A computer repair service specialist is someone who repair services as well as preserves computer systems and also computer networks. Some of the duties of a computer system fixing technician consist of building equipment, installing software application, and keeping computer networks. Some specialists also aid to build as well as fix server computers. This job path calls for a college level as well as is excellent for somebody that appreciates addressing problems with modern technology. In many cases, computer system repair work professionals can resolve a trouble in simply a few mins. However, some issues may take a little bit more time as well as call for the proficiency of a technician. This implies that you should spend much less cash for computer repair work over time. It is constantly better to obtain your computer system checked by a computer fixing expert regularly. An additional benefit of going to a computer system repair work shop is that they are versatile with their hours. Some are open 24/7. You never know when a computer emergency situation may happen. Maybe in the center of the night, before you go to bed, and even while you read an e-mail. If you remain in a rush, a 24-hour computer repair service store can help you out. Computer system repair shops can additionally aid you establish networks if you’re new to using them. The workers can help you establish networks and reveal you exactly how to utilize them. Computers with information loss are likewise a major issue these days, so you need to select a shop that has technicians that concentrate on data healing. Professionals that offer these solutions can ensure you a high quality of service. You can also obtain a consultation, or look into on the internet evaluations to establish which computer system repair shops are reputable. If you do not have time to fix your computer system yourself, you can employ a computer service center in Omaha, Nebraska. These stores generally equip the components that you require for your computer system, as well as they can repair it much faster than you could. Besides, the computer service center will be able to detect problems that you may not be able to settle on your own. A computer service center that uses free diagnostic services is a terrific area to start. With their solutions, you can be certain that the tech who repairs your computer has the proficiency and also the understanding to deal with any type of computer system trouble that it has. Frequently, you can also obtain a complimentary quote for your repairs prior to you take it to a computer shop. To come to be a computer system repair service technician, you’ll require to have excellent technical abilities. You’ll require to know just how to repair various computer system areas, consisting of peripheral tools, running systems, and software. You’ll additionally require to know how to diagnose issues and think of an option. Some professionals start their jobs by doing computer hardware repair work themselves. Others aid friends and family with their computer problems. Along with being knowledgeable about computers and hardware, it assists to take courses or participate in seminars that concentrate on computer repair. Computer repair service technicians can troubleshoot and mount numerous types of computer tools. They can also help you develop and also maintain personal computers, and also set up computer networks.


Voltage installation inspections, high temperature measurements and circuit board analysis on the one hand. Solar panel system inspections, complete building envelope thermography and construction thermography for energy saving advice on the other. The testo 885 thermal imaging camera is excellently equipped for both professional industrial and building thermography.
*testo 885 professional thermal imaging camera: resolution 320 x 240 pixels, optional SuperResolution enhanced to 640 x 480 pixels, thermal sensitivity less than 30 mK
*Optional high temperature measurements up to 1 200 °C
*Panorama image function, SiteRecognition technology, humidity calculation, JPEG storage, optional: sequence storage in the unit and much, much more
*Built-in digital camera, optional interchangeable lens, many added features, wide range of accessories included in delivery (carry case, SD card, pro software etc.)
Enviro Engineering General Trading LLC (EEGT) is the authorized and exclusive distributor of TESTO measuring instruments (Germany) in the United Arab Emirates (UAE) and other GCC countries for more than 20 years.
For more information, please contact us through the following details:
Phone No.: +971 4 4201188
Email ID: firstname.lastname@example.org


Visualization capabilities for real-time design collaboration, analysis, and simulation.
Usability, manufacturability, and design reviews are generally conducted across geographic boundaries with physical prototypes- which pose major obstacles. PTC DIVISION MockUp overcomes these challenges with capabilities for real-time design collaboration, analysis and simulation – regardless of location. Compatible with many CAD systems and graphical standards, PTC DIVISION MockUp reduces development costs and time.
PTC DIVISION Reality builds upon PTC DIVISION MockUp to allow engineers to immerse themselves within a virtual environment and experience the product’s capabilities and validate product operations. In an increasingly global and competitive market, utilizing a realistic and interactive virtual reality prototype can reduce product development costs and accelerate time to market.
Features & Benefits
- Improve collaboration by conducting real-time, interactive, multi-site, collaborative reviews of entire product design
- Accelerates time-to-market through rapid product visualization and dynamic product analysis, allowing potential issues to quickly be resolved
- Reduces product development costs by significantly reducing the need for physical models with immersive and functionally rich virtual prototypes that can be quickly created on-demand
- Improved product quality by allowing engineers to investigate realistic part behaviors and real time mechanisms earlier in the product lifecycle, uncovering any potential problems in the development process


2-way signal amplifier Signal transmitter
The 512DMX signal distributor is dedicated to the function of shaping and amplifying the DMX512 digital light control signal and the photoelectric isolation between the DMX signal distribution output and the distribution output interface (input interface).
It is suitable for the shaping and amplification processing of DMX512 signal attenuation after long-distance transmission, and the use occasions that require complete electrical isolation between the distribution output interface, the input interface and the distribution output interface, to avoid the subsequent stage to control the system DMX signal Interference and destruction, thereby improving the reliability of the entire DMX digital lighting control system.
1 All-aluminum housing, unique appearance, novel and beautiful;
2 Standard DMX512 / 1990 digital signal;
3 DMX512 signal shaping and amplification, optimize DMX signal, and extend DMX signal transmission distance;
4 DMX512 signal distribution function, sharing a set of control system with multiple DMX interfaces;
5 Using photoelectric high-voltage isolation technology, independent inductive power supply, independent DMX signal ground, independent distribution, isolation, shaping and amplification of professional DMX;


A restaurant floor should run like clockwork. However, monitoring its operational performance is complex and challenging. As a result most managerial decisions are based on gut-feel and guesswork.
Servo is an AI-powered platform which transforms the way Hospitality businesses manage their Front-of-House, to help them
operate at peak performance and drive incremental profit.
At the core of Servo’s engine are
state-of-the-art Computer Vision algorithms which seamlessly integrate with existing CCTV infrastructure and analyze the entire operation systematically.
Integrates with the business existing CCTV cameras
Analyzes the Front-of-House activity using powerful Computer Vision algorithms.
Identifies pain points and underperformance patterns.
Compares results between different sites and shows against industry benchmarks.


AT-3500 Underground Cable / Pipe Locator System
Locates underground wires, cables and pipes with or without voltage. Locating depth even 4.9 meters, 2 year warranty.
Three testing modes available for best accuracy:
- Passive power
- Passive radio
- Active with signal generator
Included accessories: R-3500 receiver, T-3500 transmitter, 2 measurement cables, 2 alligator clips, grounding rod, 10 AA batteries, 6 D batteries and nylon bag.
Recommended accessory: AMP SC-3500 signal input clamp.


HP EliteBook 2560p fails to reboot on new kernels
My EliteBook 2560p fails to reboot on new kernels. We need to fix the ACPI of hp/kbc1126.
After bisecting the kernel, I found this kernel commit is to blame:
commit d60cd06331a3566d3305b3c7b566e79edf4e2095 (HEAD, refs/bisect/bad)
Author: Kai-Heng Feng email@example.com
Date: Fri Oct 30 15:06:57 2020 +0800
PM: ACPI: reboot: Use S5 for reboot
I haven't tested with the OEM firmware yet.


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8BITDO SN30 Pro GamePad When it comes to 3rd party controllers several years ago it used to be getting a controller at a cheaper price. Fast forward to today 3rd part peripherals are a lot more top quality than ever before. My latest gadget for the Nintendo Switch, which is also compatible with the following: Windows, Android, macOS, & Steam, feels great in the hands it feels like having a SNES controller but without losing any buttons or joysticks. The controller is a wireless bluetooth (4.0) and direct usb-c with a built-in lithium battery. It took me a good 30 minute to pair the controller on my Nintendo Switch but that was self-inflicted by miss reading the first step. And I was able to play several hours on Super Smash with my in-laws and having a blast. It has a battery life of 18 hours with 1 to 2 hours charge as per the packaging, I can't confirm this but my battery hasn't let go yet. The weight of the controller might be the most surprising thing about this gadget, its super light when compared to the Pro Controller from Nintendo, feels twice as light. What's in the box: Includes: SN30 Pro, USB-C cable, Instruction manual Pros: Super light Quality built Motion controls Rumble vibrations Ultra-compatible (Windows, Android, macOS, Steam & Nintendo Switch) The price tag ($54.99) compared to ($89.99 for the Pro Controller) Better D-Pad than the Pro Controller Cons: Can't Power on the Nintendo Switch with Ergonomic (likely caused by its retro design) no HD Rumble Pro Controller vs SN30 Pro If you guys what to compare Nintendo vs 8bitdo controllers here are the biggest difference. The Pro controller obviously delivers a much more comfortable controller due to its modern design compared to the 8bitdo retro feeling. The Pro Controller gives you a longer battery which is twice as much as 40 hours compared to 18 hours. It also delivers the HD rumbles & the amiibo scanner that the SN 30 Pro doesn’t bring it to the table. With that being said the SN 30 Pro gives you a lot, it’s super light, compatible with 5 platforms, has vibrations and motion controller like most controllers it has all the buttons and joystick as the others as well. The D-Pad is even better than what the Pro Controller offers. During my gaming experience, I didn’t find 8bitdo controller uncomfortable but maybe I can see it getting a little less pleasant experience from the Nintendo’s, Sony and Xbox modern controllers. This is a great controller if you wanna save money, have a retro feel experience and have the convenience of changing platform without the need of buying an extra controller.
creeping judas Administrators
The Outcast Administrators


E-Books & Audio Books
Octavian Adrian Postolache and Subhas Chandra Mukhopadhyay, “Sensors for Everyday Life: Healthcare Settings”
English | ISBN: 3319473182 | 2016 | 304 pages | PDF | 10 MB
Sensors for Everyday Life Healthcare Settings
14 Days Free Access to USENET!
Free 300 GB with Full DSL-Broadband Speed!
Sensors were developed to detect and quantify structures and functions of human body as well as to gather information from the environment in order to optimize the efficiency, cost-effectiveness and quality of healthcare services as well as to improve health and quality of life. This book offers an up-to-date overview of the concepts, modeling, technical and technological details and practical applications of different types of sensors. It also discusses the trends for the next generation of sensors and systems for healthcare settings. It is aimed at researchers and graduate students in the field of healthcare technologies, as well as academics and industry professionals involved in developing sensing systems for human body structures and functions, and for monitoring activities and health.
(Buy premium account for maximum speed and resuming ability)
Download This Ebook Here :


Privately held and steadily profitable, LogicVein has been focused on the network management domain for over a decade as a respected provider of innovative solutions. We are pleased to introduce Net LineDancer. Configuring and managing network devices is still an often error prone, manual process.
A sophisticated but simplistic and easy to use Network Configuration and Change Management (NCCM) software designed to help network engineers manage the configurations on the network devices (routers, switches, etc.) in their enterprises.
Net LineDancer is an integrated and cloud ready solution, including the tools, reporting, automation and integration capabilities you need in a low cost and easy to use package.
Besides enabling support for overlapping IP spaces, our SmartBridges allows network operations to occur geographically closer to the devices under management.
SmartChange templates for running unique commands against devices all at once, created with a point and click interface, supporting per-device value parameterization, and Excel spreadsheet import.
Terminal Proxy Auto-Login
Provides an integrated Terminal Proxy that allows users already authenticated into Net LineDancer to SSH/Telnet into network devices.
Several LVI customers are offering NetLD as a service (SaaS) to their customers via public and private clouds.
Buy enough licenses for all the devices. And enough Smart Bridges for each customer.
A great solution for managing your network from a single location even if your network has multiple subnets with overlapping IP spaces.
Full hardware inventory for audit including daughter card serial numbers,as well as power-supplies, and even fan tray assemblies.
Cisco PnP Support
Cisco PnP devices, in combination with a Cisco PnP-compliant server, can provide “hands free” configuration of new equipment in your network.
42 vendors (104 Operating Systems).
Push out tens or thousands of changes safely to your devices safely and quickly.
Up to 100,000+ nodes easily.
What can NetLD do for you
Tools and features include:
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The manufacturing and service industries of Thailand play in an important role in driving the country’s economy as can be seen by the fact that manufacturing and service industries accounted for 86% of the country’s annual GDP on average from 2005-2009. However, manufacturing industries still face various problems on all sides such as low production capacity, competition for market leadership, movement of manufacturing bases from Thailand to countries with lower wages, lack of essential up-stream industries, lack of ability to accommodate and upgrade to advanced production technology and new products.
NSTDA is aware of the importance of R&D work to create technologies in the manufacturing and service industries in order to increase capability and create added value in these industries. It has set up R&D strategies for the manufacturing and service industries for the years 2011-2016, focusing on spearheaded manufacturing industries, namely the hard disk drive industry, the air-conditioning and refrigerator industry and the automotive and automotive parts industries.
The Hard Disk Drive Industry Research Program
The program aims to encourage Thailand to be a competitive R&D hub for hard disk drives in the region and to maintain existing manufacturing bases. Key operation plans include:
1. Supporting and promoting R&D work on technology that will increase production efficiency and technology for testing hard disk drives and their parts.
2. Supporting Thai SMEs in their designing and manufacturing efforts so that they can compete and meet the automation-related needs of local and international manufacturers.
3. Supporting R&D work on the production processes of hard disk drives of 5-10 Tbpsi, which is the new technology that will impact the hard disk drive industry worldwide.
The Air-conditioning and Refrigerator Industry Program
The program aims to encourage the air-conditioning industry to participate in R&D work for air-conditioning products, which will lead to add value for products and exports. Key operation plans include:
1. Doing R&D work on new product technologies, developing parts and manufacturing processes, focusing on developing inverter prototypes and fans, and DC motor parts.
2. Encouraging the development of standards related to the air-conditioning industry to accommodate industrial expansion brought on by new technology development.
3. Creating R&D personnel to support new technology.
The Automotive and Automotive Parts Industry Program
The program focuses on supporting the Thai automotive industry in its efforts to design key automotive parts, drives, electric car safety equipment and energy-saving and environment-friendly automotive parts. Key operation plans include:
1. Developing prototypes and designing technologies and manufacturing electric cars.
2. Developing industrial-level SUV prototypes, increasing manufacturing and designing capabilities to higher standards for local use and overseas export in the future.
3. Developing industrial-level prototypes or technologies related to mechanical and electronic automotive parts.


CARL ZEISS VisuMax® is significantly shaping the world of refractive surgery.
This next generation laser system employs high-performance femtosecond laser technology and is characterized by its precision, high speed and gentle treatment technique. It completes the unparalleled range of innovative surgical possibilities and creates the ideal preconditions for tapping into new patient groups.
VisuMax is the logical enhancement of the product range for refractive surgeons – and marks another step into the future of corneal surgery, the progress of which ZEISS has been shaping for more than 20 years.
With Visumax Femtosecond laser we are able to fulfil the highest demands:
- For the smooth running of treatment.
- For the full patient satisfaction.
- To effectively manage the flow of clinical tasks.


OVER 70 YEARS OF INDUSTRY EXPERIENCE COMBINED
The resulting products combine SOC integrated circuitry with various current sensing technologies to produce best-in-class energy monitors. Separate products were developed for single phase and 3 phase mains networks.
The monitors employ high accuracy current transformers or Rogowski coils depending on the application. They provide very high levels of accuracy and have a multitude of features including the measurement of Watt-Hours, Watts, VA, and reactive power, all of which are accessible over Episensor’s high performance ZigBee based network.
Episensor are a leading provider of sensor networks in the field of energy and building management.


As web designers we have learned to embrace the fact that that the web is a constantly evolving place, and that we need to evolve right alng with it. Nowadays, the majority of websites out there is still based on fixed page layouts. This approach has rapidly become obsolete. Due to the explosion of smartphones and tablets over the past few years, the content of web pages is now consumed by a wider array of screen sizes, screen resolutions, and browser types than ever before.
The old school method of fixed page layout design, often leads to a lot of annoying scrolling and zooming, especially for small screen devices. This approach has therefor made room for a new methodology: responsive design.
Responsive design is more than a buzz phrase of the moment. It's the answer to particularly vexing problems in the world of web design, as web designers are, struggling with the ever-growing number of Internet capable screens of varied widths and heights, many able to swap dimensions with the flip of a wrist.
The great advantage of responsive designs is that it allows designers to control how users experience content, based on the device or screen size. This makes it possible to create unique experiences for every device type or screen size used. Content can be reorganized or even eliminated, based on its context. The same website can now serve both small screen and large screen users. Mobile users no longer need to be redirected to mobile-specific pages because they can experience a site that's tailored for the mobile experience, while desktop users can interact with a site designed around a wide screen context. For any particular device, the responsive design shifts the page layout around, so that it makes sense for the people who are looking at it, thus offering a different experience to people using different device types.
On smartphones, the design can change layout based on smaller size, hide some elements, and even take advantage of smartphone features like geolocation and messaging.
On a tablet, the design can change layout and functionality based on the device type or the orientation of the device.
On desktop computers, the design can be enhanced for the larger screen, with increased graphics size and layouts that target wider aspect ratios.


Deep Intelligence uses highly advanced technologies to offer the features that best fit your business needs. Our tool is upgraded continuously to provide our clients with the latest functionalities as soon as our development team creates them. Innovative AI & Machine Learning algorithms are always delivered to your Deep Intelligence.
Your business will always be on top!
Machine Learning emerged in response to the growing technicality of AI. Data became bigger and more complex, and new solutions were needed. It was not enough for machines to imitate human behaviour. The new objective was to imitate how the human mind LEARNS. Machine Learning is a subset of AI, a set of algorithms designed to provide machines with learning capabilities.
How does our platform implement the latest Machine Learning techniques? Deep Intelligence:
Combines complex mathematical algorithms whose objective is to imitate the processes of the human brain.
Optimizes the performance of algorithms and techniques by adapting them for specific tasks.
Feeds algorithms continuously with large amounts of data.
Provides enriched and updated feedback every time: indicates correct/incorrect results.
LEARNS from previous cases about optimal algorithm and data combinations for each case.
PREDICTS the results from the input data because it has the ability to learn continuously.
Deep Intelligence uses different advanced algorithms, such as Classification and Regression (Trees, Ensembles, Linear Regressions, Logistic Regressions, Deep Neural Networks), Time Series Forecasting, Cluster Analysis, Anomaly Detection, Topic Modelling, Association Discovery, Principal Component Analysis (PCA) and Neural Networks.
In short, Deep Intelligence optimizes the predictive capacity of Machine Learning techniques and algorithms, to help the user take the best decisions for their business. Deep Intelligence not only predicts the results from the data, it is also able to choose the data that produce the results you want for your business.
Choosing the correct visualization format is essential, it allows you to visualize your data in the simplest, most intuitive and complete way possible. This is the goal of Deep Intelligence.
Choosing the visualization format preferred by the users makes the use of the platform more user-friendly and comfortable.
The combination of both efficiency and ease of use makes Deep Intelligence a unique and essential tool. Our advanced visualization techniques have been designed to facilitate decision making by representing useful and comprehensive business information in a relatively simple and intuitive format.
Forget about confusing graphs that only waste your valuable time.
Deep Intelligence incorporates multiple visualization formats and continuously updates and includes new types of graphics , adapting them to your business sector if necessary.
Not so long ago, the concept of Artificial Intelligence (AI) has been limited to science fiction movies, but today it is a part of daily life. To put it simply, AI is intelligence implemented by machines. An ideal "intelligent" machine would be capable of perceiving its environment and responding to its needs by completing adequate tasks.
Popularly, the term "Artificial intelligence" is applied when a machine imitates the "cognitive" functions that we associate with the human mind: "perceiving", "reasoning", "learning"… In short, "solving problems". From a practical point of view, Artificial Intelligence (AI) is implemented through numerous techniques, however, what they do is more important than how they do it.
Deep Intelligence adopts the most pragmatic vision of AI: the ability to correctly interpret external data, learn from it, and use extracted knowledge to achieve a specific goal: identifying important information and optimizing decision making for your business.
Normally, companies accumulate a lot of data over the years. Sometimes they just don’t know they have them. Quite often, they are not aware of the fact that they can extract very valuable business information from them. In most cases, data is stored carelessly: it is not digitized and left forgotten, in formats that are incompatible with each other... Deep Intelligence helps you identify the sources from which you can obtain your data and extract the most important information efficiently.
Data pre-processing, cleansing and optimization are the key stages of the Artificial Intelligence processes used by Deep Intelligence:
Data is collected from multiple data sources: from complex databases to plain text or CSV files.
Valuable data is identified and irrelevant data is deleted (cleansing).
Data is merged.
Loading times are reduced through process optimization.
Formats are adapted to simplify the execution of Machine Learning algorithms.
Advanced visualization techniques are applied to provide simple and intuitive graphic representations.
The main goal of the activates performed by Deep Intelligence is to transform your raw data into highly valuable business information, to help you make the best decisions and increase the performance and profit of your business.
Deep Learning goes a level beyond AI and Machine Learning. It imitates the human mind to improve the results of algorithms. One of the techniques initially used by Machine Learning was the so-called Neural Networks. The basic idea behind them was to imitate the functioning of the human brain, specifically that of billions of neurons, connected to each other by synapses. The advances in technology soon proved that it was necessary to expand and improve the design of those first neural networks...
Deep Learning implements much more complex neural networks, it is more similar to the human brain, both in the number of neurons and in the complexity of the relationships and connections between them.
While Machine Learning needs to know in advance the processes of identification and classification, similar to those used by the human brain, Deep Learning algorithms are able to "discover" these mechanisms automatically.
Deep Intelligence provides a complete range of the most advanced algorithms and techniques, which can be adapted flexibly to different tasks according to the desired result.


A Forward-Looking Infrared (FLIR) camera, coupled with a 4K sensor offers the ability to capture both thermal images and simultaneously record high-resolution video.
Thermal imagery highlights heat or vapor loss, and quantifies it with
precise non-contact temperature measurement. Documentation is
automatic. Pilots can monitor and measure critical or hazardous
objects at a safe distance.
Infrared inspections are highly efficient, non-destructive, and provide a visual qualification of damaged areas of your asset or projects so you can assess and repair.
• Roof, building, and bridge inspections and detect structural issues
• Utility and solar energy inspections to detect electrical/transmission issues
• Monitor infrastructure
• Evaluate efficiency of HVAC systems
• Search and rescue, pet recovery
• Wildlife and environmental management
• Evaluate plant and soil conditions


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.


Diploma in Electronics and Communication Engineering
Electronics & Communication Engineering is the basic of all fields. Only through electronics, Control over anything can be maintained and without communication, there is nobody and nothing in this world.
The Department of Electronics and Communication Engineering has been established in the year 2001. It has grown leaps and bounds with a student strength of 144 and a faculty strength of 7.
The elective subjects are chosen only for the benefits of students.
* Computer Installation and Servicing
* Power Electronics
* Digital Communication
* Computer Networks
* Digital Signal Processing
* Television Engineering
Our department is equipped with the following Laboratories.
(i) Electronics Lab – I
(ii) Electronics Lab – II
(iii) Electronics Lab - III
Our lab is equipped with the instruments such as Cathode Ray Oscilloscope, Function Generator, Micro Controller, Microprocessor & Digital Trainer kits, Dual power supplies and hundreds of meters, Advanced Communication engineering kits and Advanced industrial Electronics Kits.
Our Association BLAZE formed by our students and faculties organizes Seminars/Guest Lectures to widen their knowledge and perspective understanding of Engineering Principles, so that they are trained to produce innovative solutions to Engineering problems.
Our final year students achieve cent percent result in all the subject. Our student secured state ranks between 2002 – 2005.


This amazing new light from San Francisco based LumiGrow Inc. is quickly becoming the industry standard for high tech LED horticulture lighting solutions. I’ve read on many blogs and threads that this lighting system is very effective. The company has partnerships with University California Davis, Duke University and several commercial growers. Although more expensive than standard horticulture lighting, LumiGrow has a return on investment of 12 to 18 months. This system can change the color of the LED’s for differing growth cycles…growing, budding, etc. Pretty amazing, IMHO.
More info can be found here.


You need to learn about a smart thermostat. A smart thermostat is what you need for your HVAC system. A lot of people do not realize how important the thermostat is for the heater and air conditioner. The thermostat runs how much you heat and cool. It also regulates the temperature control in your home. The thermostat can help you in day to day life, or not. A digital thermostat does its job and nothing more. You see the temperature and you can manually adjust it. There are no other perks with the thermostat. Older homeowners prefer this system thermostat with easy to read numbers. A wireless thermostat uses a remote change the climate. It is a little more advanced and nice to use. A smart thermostat is the best option for technology savvy people. The thermostat can learn your heating and cooling behaviors. Just manually adjust your heating and cooling for the week. The thermostat learns then at what times you like what temperatures. Then it will start doing it for you. Maybe you want the temperature to be changed. All you need to do is grab your phone, laptop or tablet. You can adjust your thermostat from all of those products. Another added benefit is the thermostat can be used for HVAC zone control and it will tell you about humidity levels. Also the smart thermostat informs all homeowners when the HVAC device needs air filter changes. You won’t forget this small, but necessary task anymore. Consider buying a smart thermostat for your heater and A/C device in your home.


Data science plays a significant role in today’s fast-paced digital world. Data Science Services leverages the power of interdisciplinary fields, including statistics, mathematics, and computer science, to transform large piles of complex data into valuable insights, patterns, and predictions. The process of data science involves a series of steps, just like a scientific experiment, to make data-driven decisions. It starts with the collection of data from various sources, followed by cleaning, analyzing, and interpreting using advanced algorithms and machine learning tools. It brings enormous benefits to businesses in optimizing their processes, identifying trends in the market, and spotting hidden opportunities. Our seasoned team of data scientists leverages advanced analytics, machine learning, and artificial intelligence to extract valuable insights from your data. From predictive modeling to data visualization, we transform raw data into actionable strategies, empowering you to make informed decisions.
What drives customers to seek this essential service?
As our world becomes more complex and fast-paced, businesses need to adapt quickly to stay ahead of the competition. This is where data science comes in – the practice of using data to gain insights and make informed decisions. With the vast amounts of data available today, businesses can no longer rely on traditional methods of analysis. Data science allows companies to extract meaningful insights from both structured and unstructured data, providing a deeper understanding of consumer behavior and market trends.
Data science offers a range of benefits to organizations, including the ability to optimize marketing campaigns, improve operational efficiency, and develop predictive models for future trends. By leveraging data science services, businesses can use data as a strategic asset, driving innovation and informed decision-making. With the insights gained through data science, companies can make better decisions, gain a competitive edge, and ultimately achieve greater success. It is clear that data science is becoming an increasingly important tool for organizations in today’s data-driven business landscape.
The Sanfony Technologies Solution: How does it address the service needs?
At Sanfony Technologies, we pride ourselves on our unwavering dedication to delivering advanced data science solutions that are tailored to meet the unique needs of businesses. Our highly skilled team of data scientists and analysts possess in-depth expertise when it comes to data collection, cleaning, and analysis, utilizing numerous statistical and machine learning techniques to accomplish the task at hand. Additionally, we place a strong emphasis on collaborating closely with our clients to ensure we have a solid understanding of their objectives, allowing us to develop customized data science models and algorithms that will yield optimal results for their businesses. Our innovative approach to leveraging advanced tools and technologies has consistently enabled our clients to uncover valuable insights while streamlining processes, so they can make informed, data-driven decisions with unwavering confidence. At Sanfony Technologies, we are dedicated to helping businesses stay ahead of the game by offering cutting-edge data science solutions.


Dr Karsten Schulz
Director of Digital Technologies Institute, Brisbane
According to CS unplugged, “The binary number system plays a central role in how information of all kinds is stored on computers. Understanding binary can lift a lot of the mystery from computers, because at a fundamental level they’re really just machines for flipping binary digits on and off.”
In this session, we will get to know binary data with a hands-on approach.
We will make our own binary data, translate it into decimal numbers, perform binary addition, subtraction, and data storage.
We’ll then use binary data to program a simple computer and learn how binary data becomes code.
The approach is fun, interactive, tactile, with many experiments.
Participants will grow their understanding of the binary number system and the internal working of computers through experimentation.
ACTDIK014, ACTDIP020, ACTDIP029, ACTDIK024, ACTDIK034, ACTDIP038
In choosing your personal programme, please note that this presentation will be made on Friday morning, and then repeated on Saturday afternoon.
About the Presenter:
Associate Professor Dr Karsten Schulz (PhD) is an engineer, computer scientist, and educator.
He is presently heading up the Digital Technologies Institute and has previously created Young ICT Explorers and the Bebras Australia Computational Thinking Challenge.
Karsten has been working in the Australian ICT/Digital Technologies space since 2008. He is passionate about digital systems and how computers work deep, deep inside.
Join Dr Karsten Schulz at the ECAWA 2017 State Conference at ECU Mount Lawley.
ECAWA 2017 State Conference
Dates: Friday the 23rd and Saturday the 24th of June, 2017
Venue: ECU Mount Lawley Campus – a map of the venue is available. Download the Map
Parking: Free parking is available only in Parking Area 7 – the RED Zone. A map showing the parking area and the venue is available. Download the Map
Buy Tickets: Choose your ticket and pay online at http://bit.ly/ECAWA2017ConferenceTickets
Presenters and Presentations: http://bit.ly/E
Record keeping: Certificates recording your participation in this event will be made available at the event
Enquiries: contact conference@
LOGIN Light: http://bit.ly/LOGINLigh


Is there a cure for slow coupe rear windows. So far I have stripped down removed the motors cleaned commutators brushes etc, lubricated all rollers and springs..........not a nice job as a bit fiddly all back together but still slow!!
Can electric motors get "tired" or magnets get weak.
is there a better motor that could be adapted?
Any help appreciated
Hope this may be of help to you.
Daimler Sovereign S1 4.2 1971
I would imagine that the general wiring would be the same.
The switches take all the current for the motors through them and over time they wear and the contacts get dirty. Speaking to an old mechanic friend he said they were always slow, even when new.
What I did was to run a larger power cable to each of the doors and used the switch wires in the doors to operate a couple of relays. One for up and one for down. This ensured that the motor receives full battery voltage when operated.
The windows now work better than I expected. As fast as any modern car. In fact, they work so well that I'm considering fitting a limit switch to the ends of the window travel as they now close with with a noticeable thump.
I can dig out the schematic for this if anyone wants to give this a try.
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Have you thought about solar camping? That is, using solar products if you are in a tent, cabin, or RV. No matter how or where you camp, using solar lights, solar radios, solar flashlights, even solar showers is a great way to go. There are several advantages to go solar camping and this article and others will explore those advantages.
Solar pond lights are LED lights in a waterproof casing which float on top of the water. They draw in the sun’s energy, store that energy in a small battery, and then the battery powers the lights all night long. A light sensor built into the casing tells the lights when to turn on.
Another danger that kerosene lamps give you is fire hazard. Make yourself be known to the safety that solar lights give, that kerosene, gasoline, and diesel fuel that are stored for lamp use cannot give.
Putting your solar panel into the sun, use the battery clamps (those similar to those you use to jump start your car, a good panel will come with these) to charge. Once charged, disconnect from the solar panel and connect to the inverter. Again, the inverter should have the clamps like the solar panel. From this set up, you can use the inverter to power up the items you need. Depending on where you buy your items, this generator should only cost about $200.
Need of sunlight. Best Solar Christmas Lights are powered by re-chargeable batteries. These batteries are charge via a small solar panel affixed to the light. If the area does not get adequate sun, the batteries may not charge adequately.
Having a well lit yard can make a really great impression all on its own. Just remember back to the Christmas lights that you saw a few months ago. Obviously a Christmas design won’t work all year round but you can attract people’s attention to your house with a good landscaping light plan.
Having a solar generator is a source of security for you and your family. Integrating solar into your emergency kit will put you ahead of the curve from those who don’t have this process readily available. Don’t keep the information to yourself though. Pass this on to all of your friends and neighbors. A prepared neighborhood is a safe neighborhood.


The SCP is essentially a data base server which responds to the SSP by sending the instructions for completing the call.
The SCP is based on a commercial reliable computer platform which meets the downtime specifications typical of telecommunications networks, including the capability to add processing power or change the software without interrupting service.
The smallest configuration of the SCP handles around 25,000 BHCA. As traffic grows in a network, each SCP can be grown to a capacity of around 1,000,000 BHCA with an average response time of less than 300 ms. A typical configuration is to put two or three SCPs in the network in a load-sharing configuration.
As traffic grows beyond this limit, the SCPs can be reconfigured so that each one handles a subset of the services. This is possible if the Global Title Translation function is implemented so that calls can be directed to the correct SCP. This type of reconfiguration can be effected without disturbing service.
The SCP complies with ITU-T and ETSI standards for the INAP and Italtel guarantees complete compatibility with new releases as the standards mature. It uses SCCP and TCAP according to the ITU-T White Book and TCAP according to the ETSI INAP standards.
The interface with the SMP is based on the standard OSI X.25 protocol so that it can be connected to equipment from other vendors.
The operating system of the SCP is Unix, which means that a wide range of support and development tools is available. Since the SMP is also Unix-based, there is portability of software between the two machines.
The use of Unix eases maintenance and installation problems, such as connections with existing LANs.
Italtel Societa' Italiana Telecomunicazioni Spa
Claudio Chiarenza, BUCT BPM
Comments to author:Claudio.Chiarenza@settimo.italtel.it
Contact for Information: Claudio Chiarenza - BUCT BPM
All contents copyright (C) 1995.Italtel S.p.a. All rights reserved. Revised: August 30, 1995


Electrone offers a range of flexible and highly configurable keypads and data input devices with a variety of features and connectivity options to meet the varying requirements of their customers.
The 720 series of terminals offer a wide range of options including the ability to connect directly onto an INTRANET or the INTERNET via a configurable RJ45 connection with or without PoE capabilities.
Graphic and Numeric 2-line displays, USB, PS2 and serial connectivity as well as the greater flexibility of a network RJ45 connectivity option are all available in the Electrone keypad product range. There is even the ability to add a magnetic stripe reader, biometrics reader or chip card reader on some models if required.
However, there is further flexibility and customization available through the range of different pin pad options and layouts. By default, all 720 series key pads come with a standard set of commands which can be used to integrate with existing applications or used to build the key pad into new application developments. However, Electrone is able to offer a customisation service where the firmware can be expanded to meet individual user requirements. In addition, the units can be ordered with the firmware option that sends SMTP messages to a central “mail server” as an alternative means of integration. The 720 with SMTP firmware is available with IP and POE connection options. It is also available in a range of housings including the new outdoor metal housing and key pad which is designed for more challenging environments.
The 720 series also comes in a range of housings to suit a variety of market requirements:
It can be ordered as a desktop model with either a standard key pad or a wipe-clean membrane key pad option.
In addition, it can also be ordered in a choice of two wall-mount housings for use in conjunction with such solutions as a remote monitoring or access control application. There is now an outdoor housing which has a metal casing and key pad, plus a reinforced display, for use in more challenging outdoor environments.
Electrone has now integrated the 720S serial key pad range with the range of printers available from Zebra Technologies. This integration gives a range of solutions where a printer and key pad can be used as a standalone solution for label, ticketing or wristband printing.
Click here to discover more about the Electrone & Zebra printer integration.
Electrone has already integrated the 720 Series with the IMMIX remote monitoring and event management solution from SureView Systems.
Download specification sheets and user guides
Download a pin pad options & layout sheet
720 PoE Model Available
Whilst the complete range of the 720 offers users a multitude of options it is the 720IP and PoE products that Electrone believes are truly unique in the marketplace.
720 TCP/IP & PoE Network Keypad
The 720 TCP/IP or PoE becomes a specialised work tool with the capability to tie into online software; no pc required, it just needs a network connection. This provides a flexible solution for enhanced access to network applications and web-based services allowing bi-directional communication from the host and across the Internet.
The keypad features a 2 x 16 character display, a 17 key layout with customisable firmware and key legends. A magnetic stripe reader and other security options can be incorporated to read an ID card, in-house account card or even a fingerprint. Custom versions of key signals and commands can be created as needed. It has an on-board memory which allows settings to be adjusted either prior to shipping or remotely over the Internet afterwards, to reduce setup time and costs. Wipe clean models are available for demanding environments such as in schools, hospitals and industrial areas. The 720 series is specially designed to meet the strict RoHS environmental standard thus adhering to responsible production methodology.
Add PoE to your Network
In addition, Electrone is able to offer a range of PoE injectors to allow you to turn your regular IP network into a PoE solution to drive the 720 PoE terminals via a standard network connection. This offers customers a low-cost route to upgrading only the required network points to PoE. It allows a simple, convenient and unobtrusive cabling solution to be installed for locations with a 720 PoE keypad.
Some typical Applications where the 720IP has been used
Electrone Europe were able to replace the British Gymnastics Association’s traditional scoring system with the 720 PoE-keypads, which feature LCD character display and a 17-key arrangement. (Read more here)
The 720 network interface keypad provides an automated online printing solution:
Users can print documents from hotels across the globe by accessing a website and then simply entering a PIN code into the keypad located in the hotel’s lobby.
The growing move to cashless transactions created a request for a network payment system on a University Campus:
With the incorporation of a magnetic stripe reader, the 720 TCP/IP terminals could be connected to the campus network, placed anywhere on campus and allowed for a pre-paid account system for students.
Pre-paid telephone for online kiosk service:
Customers accessed online service and entered their ID and PIN code into the keypad.
Connect the device to a wireless router, it becomes wireless within your network:
A company needed a keypad at a guard gate at a nearby remote site but it was costly to install new cabling. The 720 terminal provided the access to plug into the wireless router and tie into the network.
In addition to the range of 720 non-transactional keypads, Electrone also has a number of other key pad solutions (EALTM 032). Electrone also sources a range of payment terminals for use in transactional environments where financial payments need to be made or received. These come in a range of configurations with Magnetic Stripe reader and Chip and Pin/Smart Card options available.
Prices are indicative and subject to change


Investment helps research scientists
Intertek MSG's Paul Marsh - a Senior Research Scientist within the Microscopy Team
A six-figure investment in sophisticated new equipment is helping scientists to identify new ways of helping their customers.
Intertek MSG, which is based at The Wilton Centre, has recently acquired an electron microscope to enhance the services offered to customers working in Nanotechnology and Materials Science.
The purchase, made jointly with fellow Wilton Centre tenant NanoCentral, will enable Intertek MSG to offer researchers the ability to examine an extended range of materials in even greater detail.
Director Dr Simon Allen said: "For many years, electron microscopes have enabled us to look in minute detail at structures too small for a light microscope but until now we could only study dry or dried samples.
"The exciting thing about our new analytical instrument is that for the first time we can also examine the micro-structures of wet materials. This greatly expands the range of industries that we can support and is excellent news both for our business and our customers."
Formerly part of ICI, Intertek MSG is one of The Wilton Centre's most established residents, having been based at the complex since the 1970s.
Dave Willis, Wilton Centre Manager, said he was delighted that Intertek MSG was based at the centre, adding: "It flies the flag for excellence in scientific research and, with continued investment like this to support them, the team can only go from strength to strength."


Hardware EngineeringEnd to end solutions from initial architecture in developing hardware platforms from small factor to complex PCB designs, Schematics, PCB Layout and Gerber, 3D & 2D visualisation and SI/PI engineering.
Microprocessor & Controller PlatformsSamsung Exynos, Qualcomm Snapdragon, Mediatek, ST Micro, Silicon Labs, Texas Instruments OMAP & CC32xx, Espressif system’s ESP32 series, ARM 7/9/11, 8/16/32 bit controllers from PIC family, ATMEL, ST, TI MSP series, NXP LPC series.
Embedded EngineeringIn depth expertise in embedded platform, firmware development and wireless connectivity across multiple protocols.
Software EngineeringOur software services include end to end custom software development, cloud architecture, web application and mobile application development.
TelecomNBIoT, LTE & 5G based Nodes and Gateways
WirelessZigbee, Z-Wave, Wi-Fi, BLE, BT ,LoRaWAN
NetworkingProtocol Development, Customisation
OthersUSB, UART,SPI,I2C,CAN,D TMF, Audio & Video streaming
IoT & AI Enablement
Design and implementation of digital services spanning IoT, Artificial Intelligence, Mobility, Machine Learning, Deep Learning, Non Intrusive Load Monitoring (NILM) & Cloud computing,
IoTDesign and development for edge devices and sensors, cloud and mobile or web based applications.
Cloud ComputingServices ranging from cloud platform architecture design and connected app development to SaaS services.
Machine LearningExpertise in building, training, optimization and deployment of machine learning models using the latest tools and technologies.
MobilityExpertise in UI/UX development, graphics and front end development of connected mobile applications across platforms like iOS, Android and Windows.
Deep LearningExpertise in creating artificial neural network models that enables advanced independent AI decision making.
Data EngineeringExpertise in complete data life cycle management, from data acquisition, storage and modeling to interpretation, visualization and analytics.
We engage with our customers from the product conceptualization stage and help them in product design, development and execution of interoperable and scalable IoT devices/solutions. Our team has in-depth experience in hardware, firmware, middleware, cloud, Voice assistant, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning and mobile applications.


You can contact us to apply for this job posting.Apply
Role and responsibilities
At ASML we like to make the impossible possible. Our lithography technology – which uses light to print tiny patterns on silicon – is fundamental to mass producing semiconductor chips. With it, the world’s top chipmakers are creating better performing, cheaper chips. In fact, ASML’s technology is probably used for the device you’re using right now!
We design and engineer machines that are on the cutting edge of technology. These machines do things like beaming a tiny droplet of tin with a powerful laser so it turns into plasma and as a consequence emit just the frequency of light that we need. That is requiring complex hardware and of course complex software and software development environments. We are searching for the brightest minds on the planet to help us achieve our mission.
Designing and building complex software requires state of the art software tools. That’s what we do at IT Product &Technology Software Innovation: using the Scaled Agile Framework way of working to provide a world class development environment with efficient pipelines to our embedded software engineers. Making sure software can be stored, edited, validated, automatically tested, built and deployed across all our software stacks and machines. Extending and transforming the existing IDE into a world class environment by continuously innovating and introducing new tools to like GitHub Actions, Bazel, Docker, Kubernetes and much more. Our goal? That our magic empowers our embedded colleagues to do their magic.
Does this sound exciting to you? Do you find our challenges motivating and do you like to become part of the success story of ASML? Then this is an opportunity for you to apply!
You will be responsible for driving software faults to zero from an automated test-based approach, while help improving the test competence within the department. This consists of several aspects on which you will focus:
Develop test automation scripts and implement them as part of our CI/CD pipeline;
Assist in manual testing if required by the team;
Review test-related deliverables, observe automated regression test results, first analysis, follow-up actions, bug advocacy.
Continuously improve the test automation framework/architecture in close cooperation with the test architect and the teams;
Help drive the competence of automated testing within IT P&T Software Innovation and be a sparring partner for architects and teams;
Be part of the ASML software test community and be a representative of the IT Product & Technology Software Innovation in that community.
Education and experience
Working at the cutting edge of tech, you’ll always have new challenges and new problems to solve – and working together is the only way to do that. You won’t work in a silo. Instead, you’ll be part of a creative, dynamic work environment where you’ll collaborate with supportive colleagues. There is always space for creative and unique points of view. You’ll have the flexibility and trust to choose how best to tackle tasks and solve problems.
To thrive in this job, you’ll need the following skills: