Thundercomm Announces Qualcomm based Modules for Smart Speakers, LTE IoT, Smart Retail, and 5G Applications

TurboX C865 Snapdragon 865 SoM

Based in California in the US, Thundercomm Technology Co., Ltd. (aka Thundercomm) is a provider of IoT products & technologies for OEM/ODMs, enterprises and developers. The company introduced several Qualcomm based “TurboX Systems-on-Module” for smart speakers, LPWAN IoT devices with NB-IoT and LTE Cat M1 connectivity, smart retail applications, and 5G powered devices. TurboX C404 and C405 SOMs for Smart Speakers and Soundbars Key features and specifications: SoC – Qualcomm Snapdragon C404 / C405 with CPU – Quad-core Arm Cortex-A53 @, 1.4 GHz GPU (C405 Only) – Qualcomm Adreno 306 GPU @ 600 MHz DSP -2x Hexagon QDSP6 v66 – Low Power Audio Subsystem & Audio Compute DSP System Memory & Storage – 1GB LPDDR3 + 8GB eMMC flash in eMCP package; SD card signals Connectivity 2×2 MIMO WiFI 5 802.11 a/b/g/ac + Bluetooth 5.0 + FM via WCN3999 Gigabit Ethernet (RGMII) Display Interfaces (C405 Only) 4-lane MIPI DSI port up to 720p HDMI up to 1080p30 Audio – SLIMBus, …

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ODYSSEY-X86J4105800 SBC Combines Intel Gemini Lake SoC and Arduino Compatible MCU

ODYSSEY-X86J4105864

Would it be good to have an all-in-one Windows platform used to both develop Arduino code and run that code to control I/O of your project? Or alternatively, have a single board computer capable of video processing and real-time I/Os? That platform already exists. UDOO X86 II SBC features an Intel Braswell processor combined with an Arduino Leonardo compatible Microchip ATmega32U4 MCU. But there’s now another, more powerful option courtesy of Seeed Studio with the oddly named ODYSSEY-X86J4105800 SBC equipped with an Intel Celeron J4105 quad-core Gemini Lake processor to run Windows 10 or Linux distributions, and a Microchip SAMD21 ARM Cortex-M0+ microcontroller compatible with Arduino Zero. ODYSSEY-X86J4105800 SBC specifications: SoC – Intel Celeron J4105 quad-core processor @ 1.5/2.5 GHz (Turbo) with 12EU Intel UHD Graphics 600 @ 250-750 MHz; 10W TDP System Memory – 8GB LPDDR4 RAM Storage Optional 64GB eMMC flash (fitted to ODYSSEY-X86J4105864 model), 1x SATA III data connector & 3x SATA power headers mSATA socket Micro SD …

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MarsCat is a Bionic Cat Powered by Raspberry Pi 3 (Crowdfunding)

MarsCat

Elephant Robotics MarsCat is a robotic cat that tries to mimic the behavior of actual cats through artificial intelligence. It can have a life of its own roaming the house, and you can also interact with it as it will play with toys/teasers through its camera, and you can send voice command to ask it to walk, run, look at you, be quiet. etc… but the trick is that it may not always obey like a real cat, and its personality will evolve depending on how you interact with it. Beside being a bionic pet, MarsCat can also be used to teach/learn robotics as the company will release an open-source SDK allowing you to change the Python code running on the Raspberry Pi 3 board powering the cat, and access API’s to control the camera, microphone, touch sensors, as well as the 16 servos connected to a Microchip ATMega2560 MCU via two servo boards. Visual programming for the robot will …

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Snekboard Controls LEGO Power Functions with CircuitPython or Snek Programming Languages (Crowdfunding)

Snekboard

LEGO has provided robotics kit for years, and LEGO blocks and accessories in general are great to build all sort of projects, so it’s not surprising third-parties have come up with LEGO-compatible boards and accessories for makers which include DFRobot BOSON kits, STEMTera smart breadboard, Brixo LEGO blocks with built-in electronics, and more. Snekboard is another option with the Microchip SAMD21 board designed to work with LEGO Power Functions motors and switches are programmable with MicroPyhon based CircuitPython or Snek programming languages. Snekboard hardware specifications: Microcontroller – Microchip SAMD21G18A Arm Cortex-M0 MCU with 256 KB flash and 32 KB RAM. Storage – 2MB SPI flash USB – 1x Micro USB port for power and programming Motor Control – 4x TI DRV8800 DC motor drivers tp provide up to 2.8A for LEGO motors and servos I/O- 8x GPIOs (3.3V) Misc – 2x RGB LEDs, 1x blue LED Power Supply 5V via USB port Single-cell LiPo battery support with USB charging and …

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Raspberry Pi 4 Powered Open Source Hardware Robot Paves the Way for Robot Maids

Raspberry Pi 4 Open Source Hardware Robot

Eventually, we all expect robots to do chores and other manual tasks performed by humans such as preparing and serving food at restaurants, carry objects over rough terrain as opposed to just inside the warehouse with a flat floor, or even moves pieces on a chessboard when other humans are no here to play with us. I’m fully expecting to eventually own a robot maid of sorts to wash dishes, mop the floors, and perform other tasks on my behalf. We are not there yet, but Raspberry Pi 4 powered Pollen Robotics’ Reachy open source-hardware robot is getting us closer to the goal as it can handle small objects and via two robotic arms and a dual-camera head, and can also interact with humans using a microphone and a speaker. Key features and specifications of Reachy robot: Main body SBC – Raspberry Pi 4 SBC with 2GB according to a teardown on Tom’s hardware AI accelerator – Google Coral AI …

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Zidoo M9 Mini PC Review – Part 1: Unboxing & Teardown

Zidoo M9 Review

Over the years, Zidoo has made TV boxes for the consumer market, and digital signage players for businesses. The recently announced Zidoo M9 aims at both markets, plus other applications such as IoT, robotics, and artificial intelligence. Zidoo M9 is either sold as a board or a complete TV box reference design with a case, as it exposes all the usual ports of a TV with HDMI, USB, and Ethernet. But you can do more with internal connectors for cameras, PCIe interfaces, SIM card slot, MPI DSI and eDP connectors, and so on. The company has sent me a review sample, so let’s first check out the accessories provided with the box, and have a look into the hardware design today, before testing the Android firmware in the second part of the review. Zidoo M9 Unboxing The package makes it clear Zidoo M9 “mini PC” looks like a TV box, but it’s also designed for other purposes like automotive infotainment, …

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Some Interesting Talks from FOSDEM 2020 Schedule

FOSDEM 2020 Schedule

We wrote about IoT devroom call for proposals for FOSDEM 2020 a little while ago, and as the free open-source developer meetup is getting closer, FOSDEM 2020 organizers released the schedule. So I’ll look at some of the talks in the relevant devrooms such as the Internet of Things, hardware enablement, Embedded, Mobile and Automotive, as well as RISC-V and others to compose my own little virtual schedule for the 2-day event. Saturday, February 1 10:30 – 10:50 – How lowRISC made its Ibex RISC-V CPU core faster – Using open source tools to improve an open-source core – by Greg Chadwick Ibex implements RISC-V 32-bit I/E MC M-Mode, U-Mode, and PMP. It uses an in-order 2 stage pipe and is best suited for area and power-sensitive rather than high-performance applications. However, there is scope for meaningful performance gains without major impact to power or area. This talk describes work done at lowRISC to analyze and improve the performance of …

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NVIDIA Jetson Xavier NX SoM Delivers up to 21 TOPS for AI Workloads at the Edge

NVIDIA Jetson Xavier NX

NVIDIA has just announced Jetson Xavier NX system-on-module, with the company claiming it is the “world’s smallest, most powerful AI supercomputer for robotic and embedded computing devices at the edge” with a 70x45mm “Jetson Nano” form factor, and delivering either up to 14 TOPS at 10 Watts or 21 TOPS at 15 Watts. The company expects the module to be used in small commercial robots, drones, intelligent high-resolution sensors for factory logistics and production lines, optical inspection, network video recorders, portable medical devices, and other industrial IoT systems. Jetson Xavier NX specifications: SoC – NVIDIA Xavier with 6-core NVIDIA Carmel ARM v8.2 64-bit CPU, 6MB L2 + 4MB L3 caches, and a 384-core NVIDIA Volta GPU with 48 Tensor Cores, 2x NVDLA deep learning accelerators delivering up to 21 TOPS at 15 Watts System Memory – 8 GB 128-bit LPDDR4x @ 51.2GB/s Storage – 16 GB eMMC 5.1 flash Video Encode 2x464MP/sec 2x 4K @ 30 (HEVC) 6x 1080p @ …

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