Web authentication IC to make NFC authentication scalable

Swiss semiconductor company, EM Microelectronic, announces full volume production of em|linq, the NFC tag authentication IC. em|linq offers to brands the possibility to engage with their customers via NFC and to offer advanced product authentication.

NFC tags are a popular support for consumer engagement, turning any smartphone into a portal for accessing brand content. When the NFC tag content is dynamic, the tags also provide enhanced protection against cloning.

Scalability requires robust, cost-effective products but the authentication component adds cost and complexity typical to smartcards, making the return on investment proposition more difficult. In response, em|linq combines powerful cryptographic mechanisms typically reserved to smartcard products with the convenience and affordability of RFID products, says EM Microelectronic.

It is based on proven, open standards, allowing for full degree of freedom in the implementation of the authentication service. The key management and provisioning. critical for the security architecture, can be handled and fully controlled by the company who implements the solution, regardless of its position in the value chain, whether inlay or label manufacturer, integrator, brand or retailer.

Programming the cryptographic keys into the chips is segregated from programming the URL for the authentication service, providing additional flexibility and security for the system implementation.

The IC also opens up integration possibilities for electronic labels. Its small form factor provides superior mechanical robustness, says EM Electronic. Its power efficiency enables small antenna form factor to enhance communication performance. Electrical characteristics are compatible with most of the antenna designs on the market, reducing the engineering effort.

The authentication engine is built on top of a traditional RFID architecture rather than by simplifying a cumbersome smartcard one. Adding the authentication functionality remains an extension of a traditional RFID use case, with no unnecessary overhead. The tags are produced using the same process flow and with the same quality and cost-effectiveness as the standard RFID products. EM Electronic says its RF performance allows for very small inlay constructions, for ease of integration and to reduce the cost.

em|linq is NFC Type-2 compliant. Optimised cryptographic hardware implementation provides best-in-class web authentication brand protection to consumers’ smartphones, claims EM Electronic, using a dynamically generated HMAC-SHA1 code appended to the URL stored in the NDEF container.

http://www.emmicroelectronic.com

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Solid state lidar has range of 150m+, says RoboSense

At CES 2021, RoboSense announced the standard operating procedure (SOP) version of its automotive-grade, MEMS solid-state lidar, RS-LiDAR-M1.

Features include a ranging capability of more than 150m for 10 per cent reflectivity targets and a range of up to 200m for detecting vehicle objects. The average resolution of M1 is 0.2 x 0.2 degrees (horizontally and vertically) with a wide field of view of 120 x 25 degrees  (horizontally and vertically) to create a high resolution of 0.75M pixel points per second.

The RS-LiDAR-M1 has reduced the minimum detection distance to less than 0.5m by upgrading the optical system design and signal processing system. RoboSense has innovated the signal processing algorithms and optical system design, enabling the M1 to distinguish laser emission signals from ambient lights, without interference from strong sunlight.

M1 filters laser pulses from other lidars to prevent crosstalk. The SOP version is claimed to be the world’s thinnest automotive lidar, measuring 108mm in depth, 110mm wide and 45mm high for ease of integration into the vehicle body.

Power consumption is less than 15W. Supporting functions include over-the-air firmware updates, blockage detection, smart cleaning, smart heating, performance detection, power management and network management.

RoboSense has adopted VDA6.3 as the basis of project management and control, implemented IATF16949 quality management system and the ISO26262 standard, and has integrated ISO16750 and other automotive-grade reliability specifications, to carry out a series of verification tests on M1. Tests include random mechanical vibration, mechanical shock, splash water, stone impact, high and low temperature storage and operation, solar radiation, EMC, chemical corrosion and salt spray. The cumulative test time of all M1 test samples has exceeded 300,000 hours, reports the company. The longest-running prototype has been in continuous operation for more than 700 days, and the total road test mileage has exceeded 200,000km.

Founded in 2014, RoboSense provides smart lidar sensor systems, incorporating lidar sensors, artificial intelligence (AI) algorithms and IC chipsets, that transform conventional 3D lidar sensors to full data analysis and comprehension systems.

http://robosense.ai/

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Sensor harvests energy in ADAS wheels

Power generation and sensing are combined in the InWheelSense energy harvesting and sensing module for automotive wheels in advanced driver assistance system (ADAS) applications.

The InWheelSense module attaches to the wheel of a vehicle and converts the force of tyre rotation into piezoelectric power and generates battery-less sensing and data collection and transmission from the wheel. According to TDK, delivering an electrical source in this hostile environment is conventionally difficult. The module enables sensing of road surface conditions, wheel alignment, tyre pressure and other conditions in real-time. Smart mobility applications can be implemented when it connects to the roadside infrastructure to help empower smart mobility. The sensors can connect with smart bridges, traffic controls and signage to communicate real-time data and support vehicle-to-pedestrian, vehicle-to-infrastructure and vehicle-to-vehicle networks.

Until now, environmental sensing for ADAS features have largely been driven by perception sensors like lidar and radar, image and infra red cameras. These sensors provide valuable data for ADAS operations but for improved sensing performance during adverse weather or all terrain conditions, non-perception sensors (such as piezo, inertial measurement unit (IMU), ultrasound, and strain gauges) embedded in the tyre or wheel can more accurately digitalise and classify driving and road conditions.

The InWheelSense energy harvesting module uses piezoelectric elements to generate electric power from mechanical motion or force. By placing the device at the boundary between the tyre and the wheel, the module generates electricity using the force received from the road surface as the tyre rotates. It enables scalable power generation according to the load of the driving system by allowing multiple device connections along the wheel’s circumference. It achieves an average continuous power output of 1mW when driving at 65mph / 105km/h. This perpetual source of power is particularly suitable for digitalising driving, road and tyre conditions using a range of non-perception-based sensing, TDK says.

A vehicle’s speed, turning and other changes in operating conditions can cause variations in the electromotive force characteristics of the device. The InWheelSense module can sense various driving conditions using those power changes through analysis of the waveforms from the piezoelectric effect. Waveforms are output when the tyres contact the road surface, so they are continuously generated as the car drives. As the speed increases, the frequency of the waveforms also increases, and when the direction of travel changes, the load on the tyres will change, creating different waveforms that reflect the driving characteristics at that time. A waveform is delivered for each wheel revolution, therefore the InWheelSense module is able to detect not only the speed during driving, but also road surface conditions based on the shape of the output waveform. The different waveforms reflect the driving characteristics at that time.

InWheelSense also allows for real-time collection of data from additional wheel sensors (including accelerometers, barometric pressure and temperature) to the onboard computation unit. This control module platform includes power management, digital compute capacity and low power data transmission using Bluetooth Low Energy. Data can be stored and / or processed through an inference engine in the control module, powered by an edge processor that enables algorithms to make meaningful inferences on the fly. This allows lower-latency control responses without dependency on the cloud during adverse weather conditions. All the power needed to support the data collection, processing, and action (transmission) is supplied by the energy harvesting power generator, confirms TDK.

InWheelSense provides an evaluation kit dedicated for conducting simple evaluations of the energy-harvesting module as a sample that can be attached to existing wheels. This kit enables wireless collection of data outputted from the device and power generation performance without the need for additional equipment.

https://www.tdk.com

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Face recognition package increases accuracy

Image sensing libraries used in the OKAO Vision face recognition package by Omron Electronic Components Europe are claimed to provide “highly accurate” deep learning face recognition. Developers can deploy OKAO Vision on their choice of embedded hardware platform.

The deep learning libraries of OKAO Vision Face Recognition V9.0 address applications that require accuracy under various conditions including poor lighting and when the face is at various angles relative to the detector. These include security and access control, time and attendance monitoring, login/wake up systems, and camera auto focus/auto-exposure control.

The platform can be used to monitor attendance at face to face and online meetings, which will facilitate contact tracing and verification of attendance. Another application will be in automotive design, for example in driver recognition to manage features such as seat adjustment.

The face recognition libraries achieve “excellent” evaluation results with various skin tones and face sizes, says Omron. It delivers a low error rate down to image size as small as 40 pixels. Benchmark testing with Intel and Arm processors has demonstrated that OKAO Vision Face Recognition V9.0 maintains exceptionally fast recognition times despite the enhanced accuracy, reports Omron. This ensures that users in access control applications for example will be barely conscious of the need to wait for validation of their identity.

The complete OKAO Vision Face Recognition V9.0 package contains modular libraries that provide sensing capabilities including expression estimation, age and gender estimation, and photographic image beautification including red eye reduction, facial shaping, eye enlargement, and blemish removal. Users can combine various modules’ functionalities to add value to applications.

OKAO Vision is available as a set of software libraries and can integrate with Linux, Windows and iOS operating systems. Users can leverage Omron’s machine vision package in embedded systems running on custom hardware. Off-the-shelf libraries are already available for various platforms, adds Omron.

http://components.omron.eu

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