This is also the case in the quality control department of a French manufacturer of plastic caps. Reliably detecting cracks and micro-cracks on plastic caps in 40 different colours and shades running at high speed on a production line is a real challenge.

APREX Solutions from Nancy, France has successfully achieved this goal with the help of image processing technology and artificial intelligence. The basic images are provided by a USB 3 industrial camera from IDS Imaging Development Systems.

SOLOCAP is a subsidiary of La Maison Mélan Moutet – “flavour conditioner since 1880” – and manufactures all types of plastic caps for the food sector at its industrial site in Contrexéville.

Among them, a top-class screw cap suitable for any glass or PET bottle. Thanks to a clampable lamella ring arranged around the bottle collar, it enables simple, fast, absolutely tight and secure seal.

However, the slats must be reliably and extremely carefully checked for cracks, tears and twists during production. This is the only way to guarantee absolute tightness.

The previous inspection system could not meet these high requirements. APREX Solutions realised the new solution with artificial intelligence individually on the basis of in-house software algorithms.

The necessary specifications were developed in advance in cooperation with the customer. This also included several inspection stages, one of which, for example, was the reject control to avoid false reports. The introduction took place in two phases:

First, the specific “SOLOCAP application” was trained with the help of the intelligent APREX Track AI solution. The software includes various object detector, classifier and standard methods that operate at different levels.

Networked accordingly, they ultimately deliver the desired result tailored to the customer. Four control levels with several test points guarantee a reliability rate of over 99.99 percent.

In the second step, this application was implemented in the production line right after the first assembly run with APREX Track C&M. The latter was specially developed for the diverse image processing requirements in the industrial sector.

This includes, among other things, the control and safeguarding of a production line up to the measurement, identification and classification of defects in the production environment.

The software suite delivers the desired results quickly and efficiently, without time-consuming development processes. After a short training of the AI methods, the complete system is ready for use at the customer.

In the case of SOLOCAP, it combines an IDS UI-3280CP-C-HQ industrial camera, powerful ring illumination and a programmable logic controller (PLC) to provide comprehensive control over all inspection processes.

At the same time, it records all workflows in real time and ensures complete traceability. Only one camera is needed for this. However, APREX TRACK C&M could handle up to 5 cameras.

Romain Baude, founder APREX Solutions, says: “The difficulty of this project consisted mainly in the very subtle expression of the defects we were looking for and in the multitude of colours. With our software suite, it was possible to quickly set up an image processing application. Despite the complexity.”

The image from the camera provides the basis for the evaluations. It captures every single cap directly in the production line at high speed and makes the smallest details visible to the software.

The UI-3280CP-C-HQ industrial camera integrated into the system with the 5 MP IMX264 CMOS sensor from Sony sets new standards in terms of light sensitivity, dynamic range and colour reproduction.

The USB 3 industrial camera provides excellent image quality with extraordinarily low-noise performance – at frame rates up to 36 fps. CP stands for “Compact Power”.

This is because the tiny powerhouse for industrial applications of all kinds is fast, reliable and enables a high data rate of 420 MByte/s with low CPU load.

Users can choose from a large number of modern CMOS sensors from manufacturers such as Sony, CMOSIS, e2v and ON Semiconductor with a wide range of resolutions.

Its innovative, patented housing design with dimensions of only 29 x 29 x 29 millimetres makes it suitable for tasks in the fields of automation, automotive, medical technology and life sciences, agriculture, logistics as well as traffic and transport, among others.

Screwable cables ensure a reliable electrical connection. Thanks to the IDS-characteristic plug & play principle, the cameras are automatically recognised by the system and are immediately ready for use.

Romain Baude says: “The excellent colour reproduction of the UI-3280CP-C-HQ and its high resolution of 5 MP were decisive factors for us in choosing the camera. At the same time, the model enabled a quick, uncomplicated integration into our system.”

Outlook

Anthony Vastel, head of technology and industry at SOLOCAP, sees a lot of potential in the new inspection system.

He says: “APREX’s AI-based approach has opened new doors for our 100 percent vision-based quality control. Our requirements for product safety, but also for reject control, especially in the case of false reports, were quickly met.

“We are convinced that we can go one step further by continuing to increase the efficiency of the system at SOLOCAP and transferring it to other production lines.”

AI offers quality assurance, but also all other industries in which image processing technology is used, new, undreamed-of fields of application.

It makes it possible to solve tasks in which classic, rule-based image processing reaches its limits. Thus, high-quality results can be achieved with comparatively little effort – quickly, creatively and efficiently.

APREX Solutions and IDS have recognised this and offer solutions with intelligent products that make it easier for customers to enter this new world. Image processing and AI – a real dream team on course for growth.

The post Camera-based cap control with artificial intelligence appeared first on Artificial Intelligence.

Looking for powerful technology for your new project? The right framework makes application development simpler and faster. Your choice of tech stack can also influence the price and future possibilities of improving your business solutions.

You have heard about Python and you think it might be the best language to start your new app with? Here you’ll find some information about the most popular Python-based frameworks.

How to choose the right framework for your project

Surely, you know by now that there are plenty of technologies that could be used for web and mobile app development, creating advanced solutions for your company management and leveraging the newest methods of running a business. Python is a high-level general-purpose programming language, and it has many frameworks designed to overcome different development problems. It can be used in various projects.

But how do you decide which framework is the best for your company? Before you meet with consultants and developers from a software house, you should answer the following questions:

1. How big and complex will your desired business solution be?
2. Does your in-house team have a lot of experience in IT projects, and you’d like to choose your application structure and libraries, or would you rather the developers select it for you?
3. What quality of performance do you expect?
4. What is your preferred time-to-market?
5. What kind of special features do you expect your application will have?

You don’t have to be an expert on IT solutions for business and choose the only framework by yourself. Idego Groupconsultants have many years of experience in analyzing the business needs of small, medium, and large companies and suggesting the right solutions. All you have to do is to tell them what you require and ask about some tech stack that interests you – keep reading to learn about popular Python-based frameworks.

Flask

This is among the most popular Python-based frameworks in 2020. It provides a core set of functionalities: request handling, routing, WSGI compliance, templating, and flexibility. Using it, developers can make their own choice for matters like storage, database interaction, authentication, authorization, security solutions, and many others – as it allows for easy integration of your chosen solutions. The possibility to do so lets you keep your app free of libraries and pieces of code representing functionalities that your business solution doesn’t use at all. Apart from flexibility, you’ll benefit from scalability as Flask enables developers to scale applications easily.

Which projects should it be considered for?

• Web application development
• Building microservices
• MVP development in a short time
• Medium and large scale projects

Pyramid

This full-stack framework provides routing, renderers, and command-line tools for bootstrapping a project and enables the user to choose the libraries, database layer, and templating system. Developers can use a nice, large set of useful plugins for their projects. This may be a good choice if you expect to integrate your application with systems that would not be integrated easily with frameworks such as Django. By developing in Pyramid, developers can code with a minimalistic approach (it is similar to Flask in that regard). There is no need to deal with components that are not of high business value for your company. This results in better performance.

Which projects should it be considered for?

• Large applications
• For exposing existing Python code as a REST API
• Providing the core for web projects to be developed further

Django

Another full-stack, this Python-based framework is known as a great technology for building complex web applications. Often called “the web framework for perfectionists with deadlines”, Django is great for building advanced business products which need to be created in the shortest possible time. It comes with an automatically generated admin interface that makes quick development easier. Furthermore, it offers its object-relational mapper (ORM) for handling databases and standard solutions for authentication and authorization.

Which projects should it be considered for?

• Rapid development
• Small and large projects
• For web applications that will have to deal with high traffic
• For solutions that will contain a lot of content
• For developing API-only backend applications

Choosing the best technology is not as easy as it might seem. Always remember to analyze all your business needs carefully before making a choice and consult with experts.

The post Which Python-based framework will be the best for your next project? appeared first on Artificial Intelligence.

Scientists, researchers, and engineers are solving the world’s most important scientific, industrial, and big data challenges with AI and high-performance computing (HPC). Businesses, even entire industries, harness the power of AI to extract new insights from massive data sets, both on-premises and in the cloud. NVIDIA Ampere architecture-based products, like the NVIDIA A100 or the NVIDIA RTX A6000, designed for the age of elastic computing, deliver the next giant leap by providing unmatched acceleration at every scale, enabling innovators to push the boundaries of human knowledge and creativity forward.

The NVIDIA Ampere architecture-based products implements ground-breaking innovations. Third-generation Tensor Cores deliver dramatic speedups to AI, reducing training times from weeks to hours and providing massive inference acceleration. Two new precisions – Tensor Float (TF32) and Floating Point 64 (FP64, NVIDIA A100 only) accelerates AI adoption and extends the power of Tensor Cores to HPC.

TF32 works just like FP32 while delivering speedups of up to 10x for AI without requiring any code changes when utilizing sparsity.  Automatic mixed precision and FP16 can be invoked for performance optimization by adding just a couple of lines of code. With support for bfloat16, INT8, and INT4, NVIDIA’s third generation Tensor Cores are an incredibly versatile accelerator for AI training and inference. By bringing the power of Tensor Cores to HPC, the NVIDIA A100 enables matrix operations in up to full, IEEE-certified, FP64 precision.

Every AI, data science, and HPC application can benefit from acceleration, but not every application needs the performance of a full Ampere architecture-based GPU. With Multi-Instance GPU (MIG), supported by the A100, the GPU can be partitioned into up to seven GPU instances, fully isolated and secured at the hardware level with their own high-bandwidth memory, cache, and compute cores. This brings breakthrough acceleration to all applications, big and small, and delivers guaranteed quality of service. IT administrators can offer right-sized GPU acceleration for optimal utilization and expand access to every user and application across bare-metal and virtualized environments.

The A100 SXM4 configuration with 40 GB of GPU memory brings massive amounts of compute performance to data centers. To keep these compute engines fully utilized the DGX A100 provides class leading 1.6 terabytes per second (TB/sec) of memory bandwidth, a 67 percent increase over the previous generation. The A100 also has significantly more on-chip memory, including a 40 megabyte (MB) level 2 cache – 7x larger than the previous generation – to maximize compute performance. The PCIe board version retains the 40 GB of HBM2 GPU memory, with a memory bus width of 5120 bits and a peak memory bandwidth of up to 1555 GB/sec, easily taking the performance crown from the prior generation Tesla V100.

Scaling applications across multiple GPUs requires extremely fast movement of data. Third generation NVIDIA NVLink in the A100 SXM4 doubles the GPU-to-GPU direct bandwidth to 600 gigabytes per second (GB/sec), almost 10x higher than PCIe Gen 4. The PCIe 4.0 A100 implementation also features a total maximum NVLink bandwidth of 600 GB/sec. NVIDIA DGX A100 servers can take advantage of NVLink and NVSwitch technology via NVIDIA HGX A100 baseboards to deliver greater scalability for HPC and AI workloads. For those who prefer to deploy PCIe motherboards the NVIDIA A100 PCIe option fully supports NVLink.

Contemporary AI networks are big and getting bigger, with millions and in some cases billions of parameters. Not all of these are necessary for accurate predictions and inference, and some can be converted to zeros to make models “sparse” without compromising accuracy. Ampere architecture-based Tensor Cores in the NVIDIA A100 or RTX A6000 provide up to 10x higher performance for sparse models. While the sparsity feature more readily benefits AI inference, it can also be used to improve the performance of model training.

NVIDIA Ampere architecture-based second-generation RT Cores in the NVIDIA RTX A6000 and NVIDIA A40 GPUs deliver massive speedups for big data analytics, data science, AI, and HPC use cases where seeing (visualizing) the problem is essential to solving the problem. RT Cores enable real-time ray tracing for photorealistic results and work synergistically with Tensor Cores to deliver AI denoising and other productivity enhancing features.

The post NVIDIA A100, A40 and NVIDIA RTX A6000 Ampere Architecture-Based Professional GPUs Transform Data Science and Big Data Analytics appeared first on Artificial Intelligence.

DriveNets Ltd., a startup developing a software container-based networking platform for carriers, today announced that it has raised $208 million in funding at a valuation exceeding $1 billion. 

D1 Capital Partners led the round with participation from Atreides Management as well as returning DriveNets backers Bessemer and Pitango. The last two firms have made “significant follow-on investments,” according to the startup. Bessemer and Pitango previously jointly led a $110 million funding round for DriveNets in early 2019.

Israel-based DriveNets provides a software platform that carriers and cloud providers can use to manage their networks. AT&T, one of the largest carriers in the U.S., disclosed late last year that it has adopted the platform to help manage its data traffic. DriveNets is hoping to use the new $208 million in funding to expand geographically and win deals with additional service providers around the world.

Large organizations such as carriers traditionally built their networks using pricey proprietary appliances that combine hardware and software in a single integrated package. DriveNets’ networking platform, in contrast, doesn’t come as part of a traditional appliance but can be deployed on low-cost commodity gear that’s more affordable than proprietary hardware. The result, DriveNets says, is a reduction in infrastructure expenses for customers.

The startup implemented its platform as a set of container-based microservices to allow for efficient scaling. When carriers using DriveNets’ platform need more capacity to meet user demand, they simply add more commodity networking gear to their network. The equipment comes from hardware makers that DriveNets itself certifies and the startup also trains the channel partners charged with setting up the hardware at carriers’ sites.

After the deployment is complete, a carrier can deploy its own custom networking applications on top of the platform. The startup says the platform allows customers to run, among other things, core routing applications, which are the applications responsible for managing the most important and highest capacity data routes in a carrier’s network. Those data routes are collectively often referred to as the internet backbone.

Customers can also use DriveNets’ software to build other kinds of applications, such as services to handle so-called edge provider routing. That’s a process wherein a carrier forwards data traffic to another internet provider, which is often necessary to enable connections between devices far apart from each other.

DriveNets positions its platform’s application capabilities as a way to ease 5G rollouts. For the telecommunications sector, a key benefit of 5G is that the technology can enable carriers to bring new revenue-generating offerings to market, such as  edge hosting services and highly customized internet packages tailored to specific customers’ needs. Providing such features requires deploying a fleet of network applications to manage the behind-the-scenes operational details. 

DriveNets has raised more than $320 million from investors to date.

The idea of replacing proprietary systems with lower-cost commodity gear is also gaining traction in other parts of the telecommunications market. Historically, the antennas and base stations that connect subscribers’ devices to carrier networks used proprietary computing hardware to process the incoming data traffic. Using a technology known as vRAN, carriers are now replacing that hardware with commodity servers capable of performing the task more efficiently.

The post DriveNets reels in $208M for its container-based carrier networking platform appeared first on Artificial Intelligence.