Akita, an IoT device watchdog station raised approximately $700,000 crowdfunding on Kickstarter. With 7000 plus backers, the startup promises to provide instant privacy for connected products.
The device performs three core activities i.e. scans connected gadgets/devices, blocks compromised devices and notifies the users of known issues. Akita comes with full support and help desk monitoring powered by Axius.
This device connects to a LAN port on users’ home router (not inline). The startup describes the device working as follows:
Akita’s Kickstarter received significant backing (both in terms o the number of backers and funds raised from the campaign), though, it only aimed to raise $30,000 initially.
The rise in popularity of privacy and network security devices is understandable. A home network, with several connected devices, need robust protections. That’s where other startups like Dojo and F-Secure also promise to secure network traffic and identify rouge devices.
Readers might visit the Postscapes Connected Device Security guide to understand how other devices in the same niche work and how Akita stacks up against its competitors.
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Voice first interfaces are reinventing the way we engage with devices. Acapela Group, leading player in voice solutions for more than 30 years, is constantly creating new voices to better interact with users, whatever their age or skills, thanks to voices that adapt to the context. Voices that convey meaning, intent and emotions. Voices for […]
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The transformation from products to services has the potential to move enterprises from being providers of products to becoming service providers. At the same time, organizations can switch from one-time sale revenue to recurring monthly revenues for the services they provide.
With analyst firm IDC reporting that manufacturers want to capture upwards of 30% of their revenues from services in the future, there’s a clear direction of travel towards servitization. However, the fire and forget products of today will have to radically alter to fit this model. They must become fully connected and integrated into the wider IoT ecosystem, sharing their data and delivering powerful business value. This evolution is explored in greater depth in the PTC whitepaper: Service Transformation: Evolving Your Service Business in the Era of Internet of Things.
PTC explores how everything from domestic washing machines to cars, heavy plant, healthcare equipment and factory machines will be connected and disseminating data for analysis. However, it is the higher value equipment, such as mining equipment, that has the strongest initial business case.
Specialised areas of the market have a high potential for success. Even though equipment can be dispersed across geographies, it is relatively small in volume. In addition, that equipment is valuable and tends to present a substantial cost in capex for its owner. Finally, the cost also makes it easier to extract savings and efficiencies that make an investment in IoT viable and attractive.
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Infineon Technologies AG expanded its safe automated driving collaboration with NVIDIA, announcing that its AURIX ™ TC3xx series automotive microcontroller (MCU) will be used in the NVIDIA DRIVE™ Pegasus AI car computing platform. The supercomputer for autonomous vehicles meets the requirements of Level 5 autonomous driving as defined by the Society of Automotive Engineers (SAE).
Infineon now supplies the safety microcontroller, safety power supply IC, and selected vehicle communication interface ICs for several NVIDIA DRIVE systems. The devices support increasing levels of autonomous driving capability, ranging from auto cruise functionality to auto chauffeur and full autonomy.
Development time reduced by up to 40%
The collaboration enables users of the platform to access AURIX capabilities through an AUTOSAR-compliant software stack. This potentially allows re-use of higher-level application code and can likely reduce development time by 20% to 40% compared to traditional platforms.
“Infineon has a 30+ year history of delivering the safety and reliability technology that is critical at all levels of autonomous driving. We make cars safer, smarter and greener,” said Ritesh Tyagi, head of the Silicon Valley Automotive Innovation Centre (SVAIC) at Infineon. “Collaboration between Infineon and NVIDIA through multiple generations of DRIVE car computers provides the automotive industry with a consistent platform for development and market deployment across all classes of driver-assist and fully autonomous systems.”
“NVIDIA’s DRIVE AI vehicle supercomputers deliver up to 100-times more computational horsepower than the most advanced cars on the road today,” said Gary Hicok, senior vice president of Hardware Development at NVIDIA. “Their multiple levels of redundancy and safety functionality demand a proven, widely deployed safety architecture, like that of the AURIX TC3xx series.”
Infineon microcontrollers power automated driving
The multicore microcontrollers help the platform meet the highest possible functional safety standard (ISO 26262 ASIL-D) for Advanced Driver Assistance Systems (ADAS) and self-driving systems. The AURIX TC3xx series offers significant upgrades in performance compared to the previous generation to enable scalable and efficient platforms.
Key features of the AURIX microcontrollers are relevant to implementing both ADAS and Automated Driving (AD) functionality. That includes advanced support for ASIL-D applications assisted by more than 3,000 DMIPS of safety computational performance, self-test mechanisms in hardware for logic and memory, integrated monitoring, and redundant peripherals.
In addition, the latest generation of AURIX offers a greater level of integration, enhancements in high-speed connectivity interfaces, and advanced security capabilities.
The microcontroller handles a key layer of the safety supervision framework of the platform and performs monitoring functions for the SoC. In turn, AURIX plays an important role for DRIVE Pegasus to achieve system-level ASIL-D safety.
It also manages the power-up sequence and monitoring of warning signals for the self-driving platform. In addition, the AURIX microcontroller provides the main in-vehicle interfaces for multiple network communication channels to the system, such as CAN FD, Gigabit Ethernet, and FlexRay.
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In an effort to continue to grow their business in existing and new markets, DAZN – a live and on-demand sports streaming service – wanted a fast, low-maintenance way to enable their small data team to run predictive analytics and machine learning projects at scale.
The company wanted to find a way to allow data analysts who were not necessarily technical or experienced in machine learning to be able to contribute in meaningful ways to impactful data projects. Ultimately, they wanted to support an underlying data culture with advanced analytics and machine learning at the heart of the business.
Until recently, the sports entertainment industry was dominated by cable or satellite TV systems and companies; if a customer wanted to watch a particular sporting event, he had little or no choice in how to do so. Now that consumers are breaking free from traditional TV, they are increasingly turning to specialised services streaming exactly the content they’re looking for, whether live or on-demand. And while they are willing to pay for these services, it means that entertainment companies – in the absence of the a fore mentioned virtual monopoly of TV broadcasts – are held to increasingly higher standards when it comes to quality and offerings.
In other words, because customers can turn elsewhere, entertainment companies have had to up their game, so to speak. Today, that means bringing innovation by way of predictive analytics and machine learning to optimise every aspect of the business, from marketing to customer service to product offerings. To do this efficiently, they must also bring this innovation at scale, hiring fewer people to do more such that insights grow exponentially along with the amount of data being collected.
The need for Big Data with a small staff
DAZN knew that in order to accomplish their goals quickly, they would need technologies that were simple and in the cloud. They turned to Amazon Web Services (AWS) and Dataiku in combination for their simplicity in setup, connection, integration, and usability, and they got up and running in under one hour.
With AWS and Dataiku, the small data team built and now manages more than 30 models in parallel, all without needing to do any coding so that the processes are completely accessible to non-technical team members.
They use these models as the basis for a variety of critical processes throughout all areas of the business, specifically:
Content attribution to determine what fixtures are driving sales, enabling contextual information on key fixtures in each market.
Advanced customer segmentation to identify user behaviours, particularly regarding content and devices on which customers use the product.
Propensity modeling to identify customers that are likely to churn, enabling improved customer targeting for retention activities.
Survival analysis to understand customer stickiness, enabling calculation of expected revenues to understand customer return on investment.
Natural language processing on social networks for market research
Results of more effective team members = More data science
AWS and Dataiku have noticeably shifted the data culture at DAZN and have brought innovations in advanced analytics and machine learning into the spotlight throughout […]
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