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Tiny NXP Semiconductors QorIQ LS1012A Network Processor

29.02.2016

NXP Semiconductors has announced the QorIQ LS1012A network processor. The novelty, according to the company, is intended to create completely new classes of products that combine battery power with high-speed data transfer interfaces.

QorIQ LS1012A is suitable for IoT gateways, portable multimedia platforms, high-performance portable storage and so on. Also, QorIQ LS1012A is the first network processor, the dimensions of which allow it to be integrated directly on the printed circuit board of conventional hard drives, which allows you to create network drives (Ethernet Drive) without the need for separate hardware solutions.

The processor configuration includes a Cortex-A53 core and a data encryption accelerator with a bandwidth of 2 Gb / s. The configuration also includes various interfaces (2.5 Gigabit Ethernet, PCIe, SATA 6 Gb / s and USB 3.0). At the same time, the power consumption of the processor does not exceed 1 W, and the dimensions of the microcircuit are 9,6 x 9,6 mm. NXP claims that its new product is the only processor available on the market with this feature set.

QorIQ LS1012A customers will be available starting April.

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Identification of a person by gait 05.10.2020

A new technology for identifying people by gait was developed by a team of Japanese scientists led by Osaka University professor Yasushi Yagi.

The technology is able to identify people based on images from surveillance cameras, and, allegedly, using only a small piece of video. It is expected that the method will be used in criminal investigations.

According to Yagi, who specializes in visual information processing, gait recognition has been used in criminal investigations in Japan since 2009 and has been introduced as evidence in trials. To obtain accurate results, it was necessary to have images that capture at least four steps - two steps each with the right and left feet.

The team used video footage of about 5000 people walking and analyzed it using machine learning algorithms. The scientists were reportedly able to extrapolate data on four steps from a single frame of a person's gait. The team said that based on this extrapolation, they can identify people with about 80% accuracy.

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