Cubic Telecom to develop advanced in-car services for next generation vehicles

Cubic Telecom to develop advanced in-car services for next generation vehicles

Cubic Telecom to develop advanced in-car services for next generation vehicles

Using the cutting-edge application processing and wireless capabilities of the Qualcomm® Snapdragon™ automotive modems, Cubic aims to offer a new advanced connectivity management solution to aid vehicle manufacturers, and content and system providers, to future-proof their products and technologies over the lifetime of the vehicle.

The solution is designed to support automakers with over-the-air feature updates and applications, network and vehicle analytics, and the ability to help drive connected vehicle services worldwide – using a single architecture that takes advantage of multiple wireless operator deployments across regions. The connectivity management solution has been optimized and showcased using a Qualcomm® Snapdragon™ X16 Gigabit LTE modem.

Barry Napier, CEO of Cubic Telecom, said:
“Qualcomm Technologies has been a steadfast supporter of Cubic Telecom and we are delighted to move our working relationship forward to offer an innovative solution to the automotive industry.”

“Joining forces with the leading semiconductor company in telematics means that our new Cubic Telecom’s solution can become widely available to automotive customers in the near future.”

“Cars are now platforms for innovation, new business models and services, and connectivity is the foundation,” said Nakul Duggal, vice president, product management, Qualcomm Technologies, Inc. “Cubic Telecom’s unique connectivity management solution complements our automotive platform to empower automakers with the ability to provide connected car services on a global scale, and the flexibility to work with multiple network operators per region.”

A demonstration of Cubic’s new connectivity management solution is planned to be exhibited during CES® 2018 at the Qualcomm Technologies’ automotive booth #5616.

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IoT Business News

Advanced microcontroller kit for 8051 developers

The latest from ARTech Electronics Solution Provider is the A-123.  Kit A-123 is an advanced version of A-100, where all peripherals have been brought on one single board.

For better replacement of microcontroller IC ZIP, the ‘Advance Intel 8051 Kit-A123′ uses a socket. Separate 5V & 12V power supply is use to drive different peripherals.

The experimental boards include almost all major embedded attributes like serial communication, interrupt base multiple seven segment display. Different types of display are used to demonstrate displaying options like 16×2 LCD display. The other display options are single seven segment, 4 multiple seven segment and Dot matrix display.


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Internet Of Things | IoT India

AEye unveils iDAR advanced perception and planning for driverless cars

Aeye autonomous cars, Idar

AEye, a San Francisco computer vision start-up backed by Airbus Ventures and Intel Capital, has launched iDAR. The next generation vision technology offers advanced perception and motion planning for autonomous vehicles.

The majority of autonomous car manufacturers consider LiDAR (light detection and ranging) technology a vital part of the self-driving fleets of the future. These laser scanners act as the ‘eyes’ for onboard computers, by building a 360-degree image of the world around the vehicle.

However, LiDAR technology is yet to be perfected. As well as being bulky, expensive and short in supply in a demand-heavy market, questions remain over its long-term future as the eyes of autonomous vehicles.

Despite working in tandem with computers, cameras and radar systems, it can struggle in adverse weather conditions; fog, rain and even dust interfere with LiDAR’s ability to build a comprehensive image of the world.

Read more: MWC 2017: The car in front is autonomous; or soon will be

Enter AEye

In a similar move to industry giant Velodyne, AEye has launched a second generation ‘solid state’ LiDAR system, which aims to solve these issues.

Its MOEMS (micro-opto-electromechanical system) LiDAR has been integrated with a low-light camera and artificial intelligence. The result, according to AEye, is vision hardware that can dynamically adapt in real-time to “deliver higher accuracy, longer range, and more intelligent information to optimize path planning software.”

AEye’s iDAR system does this by overlaying 2D images onto the 3D point cloud data captured by the LiDAR. The embedded AI then ploughs through thousands of computer vision algorithms to form efficient path-planning software.

“AEye’s unique architecture has allowed us to address many of the fundamental limitations of first-generation spinning or raster scanning LiDAR technologies,” said Luis Dussan, AEye founder and CEO.

“These first-generation systems’ silo sensors use rigid asymmetrical data collection that either oversample or undersample information. This dynamic exposes an inherent trade-off between density and latency in legacy sensors, which restricts or eliminates the ability to do intelligent sensing.”

With AEye’s intelligent sensing, he has said, iDAR can selectively revisit any chosen object twice within 30 microseconds. That equates to a 3,000-fold improvement. “This embedded intelligence optimizes data collection, so we can transfer less data while delivering better quality, more relevant content.”

Read more: Dell Technologies unveils new IoT strategy in New York

Mimicking the visual cortex at the ‘edge’ with iDAR

AEye’s iDAR system mimics a human’s visual cortex. It focuses on and evaluates potential driving hazards and relies on distributed architecture and edge processing to track objects of interest.

“Humans have an instinctive ability to respond to visual cues. By fusing intelligence within the data collection process, iDAR takes us a step closer to this instinctive response,” said AEye director of software, Jon Lareau.

“AEye’s iDAR is also an open and extensible platform, allowing us to integrate best-of-breed sensors to improve performance, increase redundancy and reduce cost. Most importantly, iDAR should help our customers streamline their development process and bring better autonomous vehicles to market, faster.”

AEye has also announced the iDAR Development Partner Program, a move that will no doubt be of interest to the many automotive manufacturers developing autonomous cars. The start-up plans to demo iDAR alongside its automotive product suite at CES 2018 in Las Vegas this January.

Read more: Autonomous driving will create $ 7 trillion “passenger economy”, says Intel

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Internet of Business

Gemalto supplies eSIM technology for new Microsoft Surface Pro with LTE Advanced

Gemalto supplies eSIM technology for new Microsoft Surface Pro with LTE Advanced

Gemalto supplies eSIM technology for new Microsoft Surface Pro with LTE Advanced

Advanced integration of eSIM into Windows 10 delivers an enhanced user experience.

Gemalto, is supplying the eSIM (embedded SIM) solution for Microsoft’s Surface Pro with LTE Advanced, the most connected laptop in its class1 which will begin shipping to business customers in December 2017.

Gemalto’s partnership with Microsoft enabled Surface to become the first fully integrated embedded SIM PC in the Windows ecosystem.

Gemalto’s advanced technology supports seamless activation of mobile subscriptions for users of the innovative Surface Pro with LTE Advanced. This smooth experience leverages Gemalto’s remote subscription management solution in conjunction with Windows 10. Surface customers expect their products to deliver advanced technology and with Gemalto’s eSIM solution, all possible connectivity options are available out-of-box, including the purchase of cellular data from the device itself.

Compliant with the GSMA Remote SIM Provisioning specifications, Gemalto’s eSIM solution is fully integrated with Windows 10. This integration enables the Gemalto solution to have a complete servicing model so that patching and lifecycle management features are available as the technology and standards evolve over time. This capability extends the value promise of Surface as new experiences and capabilities will be available to today’s purchasers of the Surface Pro with LTE Advanced.

“The Surface Pro has redefined the laptop category,” said Paul Bischof, Director, Devices Program Management at Microsoft. “Gemalto’s eSIM solution is helping us to materialize our vision of an uncompromised customer experience.”

Frédéric Vasnier, executive vice president Mobile Service and IoT for Gemalto, said:

“Adoption of eSIM technology is growing rapidly. Mobile operators recognize the potential of seamless connectivity and increased convenience as a way of expanding their customer reach to additional devices. We are at the beginning of a significant technology transformation and the Surface Pro with LTE Advanced represents the start.”

1. Comparison of supported bands and modem speed for Surface Pro with LTE Advanced vs. 12″ and 13″ LTE-enabled laptops and 2-in-1 computers. Service availability and performance subject to service provider’s network. Contact your service provider for details, compatibility, pricing and activation. See all specs and frequencies at
2. Service availability and performance subject to service provider’s network. Contact your service provider for details, compatibility, pricing, and activation. See all specs and frequencies at

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IoT Business News

Autonomous vehicles: Three key use cases of advanced analytics shaping the industry

Driven by analytics, the culture of the automobile, including conventional wisdom about how it should be owned and driven is changing. Case in point, take the evolution of the autonomous vehicle. Already, the very notion of what a car is capable of is being radically rethought based on specific analytics use cases, and the definition of the ‘connected car’ is evolving daily.

Vehicles can now analyse information from drivers and passengers to provide insights into driving patterns, touch point preferences, digital service usage, and vehicle condition, in virtually real time. This data can be used for a variety of business-driven objectives, including new product development, preventive and predictive maintenance, optimised marketing, up selling, and making data available to third parties. It’s not only powering the vehicle itself, but completely reshaping the industry.

By using a myriad of sensors to inform decisions traditionally made by human operatives, analytics is completely reprogramming the fundamental areas of driving – perception, decision making and operational information. In this article, we discuss a few of the key analytics-driven use cases that we are likely to see in the future as this category, (ahem) accelerates.

The revolution of driverless vehicles

Of course, in the autonomous vehicle, the major aspect missing is the driver, traditionally the eyes and ears of the journey. Replicating the human functions is one of the major ways in which analytics is shaping the industry. Based on a series of sensors, the vehicle gathers data on nearby objects, like their size and rate of speed and categorises them based on how they are likely to behave. Combined with technology that is able to build a 3D map of the road, it helps it then to form a clear picture of its immediate surroundings.

Now the vehicle can see, but it requires analytics to react and progress accordingly taking into account the other means of transportation in the vicinity, for instance. By using data to understand perception, analytics is creating a larger connected network of vehicles that are able to communicate with each other. In making the technology more and more reliable, self-driving vehicles have the potential to eventually become safer than human drivers and replace those in the not so distant future. In fact, a little over one year ago, two self-driving buses were trialed on the public roads of Helsinki, Finland, alongside traffic and commuters. It was the first trials of its kind with the Easymile EZ-10 electric mini-buses, capable of carrying up to 12 people.

Artificial intelligence driving the innovation and decision making

In the autonomous vehicle, one of the major tasks of a machine learning algorithm is continuous rendering of environment and forecasting the changes that are possible to these surroundings. Indeed, the challenge facing autonomous means of transportation is not so much capturing the world around them, but making sense of it. For example, a car can tell when a pedestrian is ready to cross the street by observing behavior over and over again. Algorithms can sort through what is important, so that the vehicle will not need to push the brakes every time a small bird crosses its path.

That is not say we are about to become obsolete. For the foreseeable future, human judgement is still critical and we’re not at the stage of abandoning complex judgement calls to algorithms. While we are in the process of ‘handing over’ anything that can be automated with some intelligence, complex human judgement is still needed. As times goes on, Artificial (AI) ‘judgement’ will be improved but the balance is delicate – not least because of the clear and obvious concerns over safety.

How can we guarantee road safety?

Staying safe on the road is understandably one of the biggest focuses when it comes to automated means of transportation. A 2017 study by Deloitte found that three-quarters of Americans do not trust autonomous vehicles. Perhaps this is unsurprising as trust in new technology takes time – it took many years before people lost fear of being rocketed through the stratosphere at 500 mph in an aeroplane.

There can, and should, be no limit to the analytics being applied to every aspect of autonomous driving – from the manufacturers, to the technology companies, understanding each granular piece of information is critical. But, it is happening. Researchers at the Massachusetts Institute of Technology are asking people worldwide how they think a robot car should handle such life-or-death decisions. Its goal is not just for better algorithms and ethical tenets to guide autonomous vehicles, but to understand what it will take for society to accept the vehicles and use them.

Another big challenge is determining how long fully automated vehicles must be tested before they can be considered safe. They would need to drive hundreds of millions of miles to acquire enough data to demonstrate their safety in terms of deaths or injuries. That’s according to an April 2016 report from think tank RAND Corp. Although, only this month, a mere 18 months since that report was released, professor Amnon Shashua, Mobileye CEO and Intel senior vice president, announced the company has developed a mathematical formula that reportedly ensures that a "self-driving vehicle operates in a responsible manner and does not cause accidents for which it can be blamed."

Transforming transportation and the future

In many industries, such as retail, banking, aviation, and telecoms, companies have long used the data they gather from customers and their connected devices to improve products and services, develop new offerings, and market more effectively. The automotive industry has not had the frequent digital touch points to be able to do the same. The connected vehicle changes all that.

Data is transforming the way we think about transportation and advanced analytics has the potential to make driving more accessible and safe, by creating new insights to open up new opportunities . As advanced analytics and AI become the new paradigm in transportation, the winners will be those who best interpret the information to create responsive, learning, and connected vehicles capable of making autonomous vehicles as simple as getting from A to B. Latest from the homepage