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Home - Edge AI specialist FogHorn works with auto firms ADVICS, Porsche; releases Android app
Data AnalyticsEnterpriseInternet of Things (IoT)News & Event CoverageSmart FactoryWired Networks, Fiber

Edge AI specialist FogHorn works with auto firms ADVICS, Porsche; releases Android app

by James Blackman August 13, 2019
written by James Blackman August 13, 2019 Share
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Foghorn has struck a deal with auto-parts maker ADVICS to deploy its edge compute software to enable real-time analytics and artificial intelligence (AI) as part of its “smart factory transition”.

The California-based industrial IoT software developer has also released an Android version of its edge AI software, and completed a pilot with car-maker Porsche around vehicle access using edge-based face-recognition technology.

ADVICS has partnered with Japanese technology distributor Macnica Networks to digitise its manufacturing sites, integrate equipment data, and bring intelligence to its manufacturing operations. Macnica Networks has deployed Foghorn’s Lightning edge compute software with ADVICS to be able to identify and respond to live production issues.

Yuta Endo, vice president, general manager of business development and head of APAC operations at FogHorn, commented: “ADVICS digital transformation to a smart factory reflects their mission to contribute to the reliability of society by pursuing a better safety, environment and comfort through products that delight customers.”

FogHorn has also released an Android version of its Lightning product, Lightning Mobile, for industrial devices, designed to give operational technology (OT) staff on the ‘shop floor’ access to edge-based AI and IoT programmes on handheld mobile devices.

“This approach enables a new class of edge intelligence applications not possible with devices restricted to fixed locations, while making it easy to build and market off-the-shelf common solutions using FogHorn’s software… Support for edge-based deep learning model inferencing solves critical issues such as image recognition of bar codes and health and battery monitoring of high-volume deployments of mobile devices,” said FogHorn.

Sastry Malladi, chief technology officer at FogHorn, said: “This will unleash a wave of new industrial use cases including next-generation bar code scanners, portable factory environmental monitors, manufacturing using a high-density of devices such as smart screwdrivers, advanced fleet applications and more. We see a tremendous global opportunity for this technology.”

Separately, FogHorn has partnered with German car maker Porsche to develop a real-time video recognition solution, using multi-factor authentication, for unlocking and securing vehicles. The 100-day challenge was part of the Startup Autobahn challenge, in Stuttgart, Germany, a European innovation platform connecting automotive organisations with startups.

The FogHorn / Porsche proposal saw drivers enter their vehicles with real-time face detection and infrared video processing at the edge coupled with MFA via a device, such as a personal phone, without the need for a key fob. By leveraging edge computing, connectivity to the cloud or a network is not necessary for the driver to access the vehicle, said FogHorn.

“A pre-registered driver need only walk toward the car for the doors to unlock and resolves safeguarding issues of key fobs being scanned by car thieves,” it said.  Porsche called it a solution to a “persistent challenge” of easier access and tougher security.

In March, NTT-subsidiary NTT Data said it has selected Foghorn’s Lightning software for its on-premise analytics and artificial intelligence (AI) solutions, which are being offered to the manufacturing, telecoms and energy industries.

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James Blackman
James Blackman

James Blackman has been writing about the technology and telecoms sectors for over a decade. He has edited and contributed to a number of European news outlets and trade titles. He has also worked at telecoms company Huawei, leading media activity for its devices business in Western Europe. He is based in London.

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