AAEON, a leading developer in embedded solutions, announces the ASDM-L-CFS smart display module, the first board built to the Intel® SDM Large (SDM-L) form factor to support socket type 8th and 9th Generation Intel® Core™ processors. The ASDM-L-CFS provides uses with a powerful, flexible solution to powering a range of applications including Edge Computing.
The ASDM-L-CFS supports the 8th/9th Generation Intel® Core™ i3/i5/i7, Celeron® and Pentium® processors (formerly Coffee Lake and Coffee Lake Refresh). It is the first board to feature desktop performance processors on the SDM-L form factor, and pairs the processor with up to 32 GB DDR4 SODIMM memory to help maximize computing performance. It is also the first SDM-L board to support Wireless vPro technology from Intel, allowing for remote management of the system without needing to connect directly to it.
Using the 8th/9th Generation Intel Core processors provide a significant boost over previous generations, along with a more powerful Intel® UHD graphics controller to deliver high resolution images for brilliant displays. Display output is handled thanks to HDMI 2.0 and DP1.2 support through the edge connector.
The ASDM-L-CFS provides users with a host of features to unlock greater flexibility and expansion options. The board offers users both an M.2 2242 B Key and M.2 2230 E Key, allowing users to quickly add storage and Wi-Fi capabilities. The ASDM-L-CFS also features an onboard Gigabit Ethernet LAN port, and two USB 3.2 ports (up to 10Gb/s support with Q370 socket).
The ASDM-L-CFS is backed by the industry leading support only available through AAEON. From fast technical service to manufacturer services and OEM/ODM support, AAEON provides developers and systems integrators the reliable service needed to shorten deployment times and reduce time to market. AAEON offers end-to-end support for the ASDM-L-CFS, with a pre-built chassis or the ready-built carrier board, ASDM-CRB-A11; AAEON can also assist clients with developing their own carrier boards and chassis.
Septentrio, a leader in high-precision GNSS* positioning solutions, announces today two important open source resources for its GPS/GNSS module receivers. The first, ROSaic, is a ROS (Robot Operating System) driver for the mosaic-X5 module as well as other Septentrio GNSS receivers. The second project, mosaicHAT, is an open source hardware reference design combining mosaic-X5 with a Raspberry Pi single-board computer. Both projects facilitate integration of centimeter-level reliable positioning into robotic and other machine automation applications.
ROSaic driver operates on ROS, a widely used programming environment within the industry as well as academics, commonly used for integrating robot technology and developing advanced robotics and autonomous systems. ROS allows data from numerous sensors to be combined allowing high levels of autonomy.
The mosaicHAT project facilitates accurate and reliable GNSS positioning for robotics and automation on a hardware level. Numerous engineers today use Raspberry Pi for prototyping and initial integrations. The mosaicHAT board is an easy way for integrators to get started with Septentrio’s mosaic-X5 GNSS module. By plugging mosaicHAT into a compatible Raspberry Pi, users have access to high-accuracy positioning with a high update rate, ideal for machine navigation and control. The small 56×65 mm board exposes basic interfaces such as USB, serial, and general-purpose communication pins. The reference design, footprint and documentation are available for easy board printing or further customization.
“We are excited about both the ROSaic driver and the mosaicHAT being part of the GitHub community and we highly appreciate the initial authors work as well as the future contributors. Both projects are available as open source, thus empowering the community to easily fit autonomous or robotic systems with highly accurate and reliable GNSS positioning technology,”
notes Gustavo Lopez, Market Access Manager at Septentrio.
STMicroelectronics’ top-class GSMA embedded SIM (eSIM or eUICC) product is designed for all industrial devices
The ST4SIM-200M is an STMicroelectronics top-class GSMA embedded SIM (eSIM or eUICC) product designed for all industrial devices. It is compliant with the GSM Association (GSMA) remote provisioning specification SGP.02 v3.2. The ST4SIM-200M can remotely manage profiles of different MNOs while ensuring the appropriate security level to all eUICC stakeholders (user, MNO, OEM, hardware integrator, service provider, and so on). Include in this ST4SIM-200M, Truphone, trusted partners, provide and operate device-onboarding and service-provisioning platforms.
Truphone offers worldwide cellular IoT connectivity with state-of-the-art SIM technology and simple, easy-to-activate data plans for devices. The most complex challenges of building IoT cellular devices are addressed by building a dedicated IoT SIM, a global IoT network, and an IoT platform to provide a truly global IoT cellular connectivity service.
Texas Instruments’ converter features 800 nA ultra-low quiescent current
Texas Instruments’ TPS61099x is a synchronous boost converter with 1 µA ultra-low quiescent current. The device is designed for products powered by alkaline batteries, NiMH rechargeable batteries, Li-Mn batteries, or rechargeable Li-Ion batteries where a high efficiency under light-load condition is critical to achieving long battery life operation.
TPS61099x boost converter uses a hysteretic control topology to obtain maximal efficiency at minimal quiescent current. The device consumes 1 µA quiescent current under the light-load condition and can achieve up to 75% efficiency at 10 µA load with fixed output voltage version. This converter supports up to 300 mA output current from 3.3 V to 5 V conversion and achieves up to 93% at 200 mA load. The TPS61099x offers down mode and pass-through operations for different applications.
In down mode, the output voltage can still be regulated at the target value even when the input voltage is higher than the output voltage. In pass-through mode, the output voltage follows the input voltage. The TPS61099x exits down mode and enters pass-through mode when VIN > VOUT + 0.5 V. The TPS61099x supports a true shutdown function when it is disabled, which disconnects the load from the input supply to reduce the current consumption. The TPS61099x offers an adjustable output voltage version and fixed output voltage versions. The device is available in a 6-ball 1.23 mm x 0.88 mm WCSP package and a 6-pin 2 mm x 2 mm WSON package. Features
Ultra-low IQ into VOUT pin: 600 nA
Ultra-low IQ into VIN pin: 400 nA
Operating input voltage: 0.7 V to 5.5 V
Adjustable output voltage: 1.8 V to 5.5 V
Fixed output voltage versions available
Switch peak current limit: 0.8 A (minimum)
Regulated output voltage in down mode
True disconnection during shutdown
Efficiency: up to 75% at 10 µA load with fixed output voltage versions
Efficiency: 93% from 10 mA to 300 mA load
Packages: 6-ball 1.23 mm x 0.88 mm WCSP and 2 mm x 2 mm WSON
Murata’s CT310 is a 2D angular sensor in a dual full-bridge configuration from Crocus Technology developed on its patented XtremeSense 2D TMR technology. The operating magnetic field for this 2D sensor is 25 mT to 90 mT and has an angular error less than 0.25° after compensation over the full operating temperature range. It has differential outputs for both sine (SIN) and cosine (COS) axes and operates with a supply voltage range from 1.0 V to 5.5 V. It is packaged in an 8-lead TSSOP package and for applications where space is critical, a low profile, small form factor 8-lead DFN package that is 2.00 mm x 2.00 mm x 0.45 mm in size is available. Features
Angular error less than 0.25° (after compensation) over full temperature range
Dual full-bridge resistor network
Operating magnetic field: 25 mT to 90 mT
Differential outputs for SIN and COS axes
Supply voltage: 1.0 V to 5.5 V
Package options: 8-lead TSSOP, 8-lead DFN, 2.00 mm x 2.00 mm x 0.45 mm
A NAS unit is a computer connected to a network that provides only file-based data storage services to other devices on the network. Although it may technically be possible to run other software on a NAS unit, it is usually not designed to be a general-purpose server. For example, NAS units usually do not have a keyboard or display, and are controlled and configured over the network, often using a browser. NAS is specialized for serving files either by its hardware, software, or configuration. It is often manufactured as a computer appliance – a purpose-built specialized computer.
A full-featured operating system is not needed on a NAS device, so often a stripped-down operating system is used. For example, FreeNAS or NAS4Free, both open source NAS solutions designed for commodity PC hardware, are implemented as a stripped-down version of FreeBSD. NAS systems contain one or more hard disk drives, often arranged into logical, redundant storage containers or RAID. NAS uses file-based protocols such as NFS (popular on UNIX systems), SMB (Server Message Block) (used with MS Windows systems), AFP (used with Apple Macintosh computers), or NCP (used with OES and Novell NetWare). NAS units rarely limit clients to a single protocol.
From the mid-1990s, NAS devices began gaining popularity as a convenient method of sharing files among multiple computers. Potential benefits of dedicated network-attached storage, compared to general-purpose servers also serving files, include faster data access, easier administration, and simple configuration. Open-source NAS-oriented distributions of Linux and FreeBSD are available. These are designed to be easy to set up on commodity PC hardware, and are typically configured using a web browser. They can run from a virtual machine, Live CD, bootable USB flash drive (Live USB), or from one of the mounted hard drives. They run Samba (an SMB daemon), NFS daemon, and FTP daemons which are freely available for those operating systems.
NAS units are generally available. However, NAS can get quite expensive, so one solution is the DIY solution, which is cost effective compared to a readily available solution. Recently, a Redditor named CodemanHB posted his project, which demonstrates his personal small-scale solution utilizing a Raspberry Pi and a custom 3D-printed case. His NAS unit comprises of a Raspberry Pi 4 4GB version, an Uctronics PoE HAT, a 240×320 pixel 2″ IPS LCD display module, a 500GB SK Hynix SATA III SSD, and a USB to SATA adapter cable. He also designed the SSD to serve as the main storage device, and also store the OS for running the NAS.
The enclosure He used for the NAS is a 3D-printed case, which was designed in Fusion 360 and made with a Creality3D CR-10 3D printer. He also used a carbon fiber filament which gave the NAS a beautiful finish on the printed parts. You can find more pictures and information on CodemanHB’s Thingiverse page, and on his Reddit post.
So far, there have been quite a number of development boards following Adafruit Feather format with products, like the QuickFeather Cortex-M4 + FPGA board and nRF9160 Feather providing LTEIoT & GPS connectivity. Added to the list now is the new FeatherS2 that is based on Espressif’s ESP32-S2 microcontroller SoC.
As a quick reminder, the ESP32-S2 unlike other ESP processors by Espressif Systems, is the first of its kind to include an in-built USB (OTG) interface and a low-power coprocessor based on the free and open source RISC-V architecture.
The FeatherS2 development board by Unexpected Maker offers an easy platform for makers to develop, play, and build IoT devices. The board comes with an interesting layout that comprises of Espressif ESP32-S2 WiFi SoC, 16 MB of Flash memory (for firmware and file storage), 8 MB of QSPI-based external PSRAM, several I/O and a typical USB Type – C port for power and programming.
Measuring 5.1cm by 2.3 cm, the FeatherS2 comes at a fair size for the features it has. Some of such features and specifications include:
Espressif Systems’ ESP32-S2 single-core Tensilica LX7 processor running @ 240 MHz with RISC-V ultra-low power co-processor
320kB SRAM
128 kB ROM
8MB PSRAM
16MB SPI Flash
2.4 GHz 802.11b/g/n Wi-Fi with integrated 3D antenna
1x USB Type – C port for power and programming
Up to 21x GPIOs
RGB LEDs, Charging / Power LEDs and User LEDs
QWIIC / STEMMA connector
ALS-PT19 Ambient Light Sensor
5V via USB-C port
2x 700 mA 3.3 V LDO regulator
2-pin battery connector
LiPo battery management, and,
Optimised power path for low-power battery usage
Dimensions – Approximately 5.1 cm x 2.3 cm
The developer, giving reasons why he included two LDO voltage regulators in the board has explained that:
“The first one is for the general operation of the board and the ESP32-S2, RAM and Flash. The second LDO is for you to use to connect external 3V3 modules, sensors and peripherals, and it has programmable EN control tied to GPIO21 + it’s connected to the deep sleep capabilities of the S2, so if the S2 goes into deep sleep, the 2nd LDO is automatically shut down for you!”
The Feather-S2 comes pre-installed with the current beta version of CircuitPython6.0 which supports the ESP32-S2, as well as early ESP-IDF and Arduino support for those who might just prefer coding in C++.
Further details on the FeatherS2 development board may be found on the Unexpected Maker’s website where he is currently selling the board for $20 including shipping.
The Teseo-LIV3F is a standalone positioning receiver IC that works simultaneously in multiple constellations (GPS, Galileo, Glonass, BeiDou and QZSS), being an easy to use GNSS (Global Navigation Satellite System) module. It brings the proven accuracy and robustness of the Teseo chips into the hands of everyone.
Besides being able to integrate this module into your projects, the EVB-LIV3F standalone solution allows you to evaluate its performance using only a computer, pretty easy!
Looking at the specifications of the Teseo-LIV3F, there is:
Simultaneous multiconstellation
-163 dBm tracking sensivity
1.5 m CEP position accuracy
Operating voltage range from 2.1 V to 4.3 V
UART and I2C port available
Tiny LCC 18 pin package, measuring 9.7 mm by 10.1 mm
Extended operating temperature range, from -40 ºC to 85 ºC
Free firmware configuration
17 µW standby current and 75 mW tracking power consumption
Aside from the list of specs, there are some other interesting functionalities to highlight: firstly, the on-board TCXO (Temperature Compensated Crystal Oscillator) allows for superior accuracy and a reduced TTFF (Time to First Fix) that relies on its dedicated 32 kHz RTC (Real Time Clock) oscillator. Other interesting feature is the embedded 16 Mb flash memory, which enables the chip to offer many interesting extra functionalities, such as data logging, 7 days autonomous assisted GNSS, besides firmware reconfigurability and upgrades. The Teseo-LIV3F also provides the Autonomous Assisted GNSS, which enables it to predict satellite data based on previous observation of the satellite. Lastly, the module is a certified solution, that optimizes the time to market of the final applications, with a wide operating temperature range.
Regarding the EVB-LIV3F, all you need to do is plug it to a computer with USB and you are good to go. It comes pre-programmed and pre-configured by ST, so that you can experiment and evaluate its capabilities and performance, resorting to the ST Teseo-Suite PC GUI. It also comes with a GNSS antenna to guarantee the best and easiest user experience. Besides the evaluation module, this kit also contains a STM32F401RE based STM32 Nucleo board, so that you have everything to prototype your next position awareness application.
The Teseo-LIV3F GNSS prototyping solution
As you can see, we are looking at a very interesting part, but where can you apply it? That is easy. Due to its small size, you can fit these modules everywhere, and its also small cost makes it a no brainer to be your number one choice in several applications, such as insurance, tracking of assets and goods, drones, tolling, anti-theft systems, location for people and pets, emergency calls, fleet management, vehicle sharing and public transportation. The possibilities are endless, specially within the bounds of mass market and IoT solutions.
Regarding pricing, the Teseo-LIV3F module itself can be found for around $14 and the full evaluation kit for $48, so overall they are an interesting choice.
Diodes Incorporated introduced DMT47M2LDVQautomotive compliant 40V dual MOSFET in a 3.3mm x 3.3mm package for automotive systems. It smartly integrates two n-channel enhancement-mode MOSFETs with the lowest RDS(ON) (10.9mΩ at VGS of 10V and ID of 30.2A).
The low on-resistance conduction helps in keeping the losses to a minimum in applications like wireless charging or motor control. Besides, switching losses are minimized with the help of a typical gate charge of 14.0nC, at a VGS of 10V and ID of 20A.
The thermally efficient PowerDI 3333-8 package of the device returns a junction-to-case thermal resistance (Rthjc) of 8.43°C/W, thereby enabling the development of end applications with a higher power density than with MOSFETs packaged individually. Furthermore, the PCB area needed for implementing automotive features including ADAS is reduced too.
Key Features of DMT47M2LDVQ Dual MOSFET:
Fast switching speed
100% unclamped inductive switching
High conversion efficiency
Low RDS(ON) that minimizes on-state losses
RDS(ON): 10.9mΩ at VGS of 10V and ID of 30.2A
Low input capacitance
Two n-channel enhancement mode
Thermally efficient PowerDI 3333-8 package
From electric seat control to advanced driver-assistance systems (ADAS), the DMT47M2LDVQ dual MOSFET can reduce the board space footprint in many automotive applications. It is available at the price of $0.45 in 3000-piece quantities.
Video Intelligence once considered the exclusive intel of humans has now taken a turn with the advances in Artificial intelligence algorithms and the increasing processing power of AI Gateways.
Intelligence and data-driven decisions based on video and camera are now of prime importance finding its way into applications like Smart Parking, Retail footfall analytics, Traffic Management, and security surveillance. Insights from video and images have the capability of providing you vast amounts of data – both for predictive analytics and historical analytics.
Complementing the infrastructure of multiple cameras present across buildings, airports, retail stores, and other zones, there is now a need for an intelligent gateway to collect the images at a high resolution with connectivity options while also being capable of taking the decision on the edge. With Corazon-AI, we present an efficient Multi-Channel – AI Video Analytics gateway. Through the design of an 8-channel Xilinx Video Codec Unit (VCU) + CNN inference deployed on Corazon-AI, the gateway serves as a low-power heterogeneous compute platform enabling edge computing.
Through the above video integrating our demo applications, we intend to demonstrate the capability and performance of the VCU (Video Codec Unit) available as a hard block IP. The AI Inference Engine and Deep Learning Processing Unit (DPU) are implemented in the PL (programmable logic) side of the device.
Video data from eight RTSP streams from the 8 Cameras are processed alongside high-speed deep Learning analytics performed on each video stream at the edge on Corazon-AI. Given below is an architecture of the video streaming and analytics architecture on Corazon-AI running different models on each of the cameras.
The demo video performing four different algorithms: person detection, object detection, face detection, and vehicle detection (ADAS). All of the AI operations are performed on the eight independent 1080p@30 RTSP video streams simultaneously with four different convolution neural networks running at a different resolution. The overall performance of the 8 channel AI video Analytics application is up to ~80fps on Corazon-AI.
FHD (1080p) IP cameras are used to capture the high-resolution and wider frames of the surveillance streams and the camera uses the Advanced Video Coding (AVC) H.264 standards to encode the video data and transmit over the network using the RTSP. The eight IP cameras are connected via Ethernet cables to a 10-port 1-G Ethernet switch and the encoded RTSP video streams are received onto Corazon-AI via a 1-G PS Ethernet port (RJ45) connected to the Ethernet switch.
The input video streams are decoded using the Xilinx Video Codec Unit (VCU) IP. Video scaling & pre/post-processing on the video is performed by using the software core of the Corazon-AI.
AI-Inference Engine on Corazon-AI – The Deep Learning Processing Unit (DPU) is a configurable computation engine optimized for the convolution neural networks, such as SSD, ResNet, YOLO, VGG, and FPN among others. The high-end 4096 single-core Deep Learning Processing Unit (DPU) implemented in the PL side of the FPGA SoC delivers around 1.2 TOPS of compute performance while running at 300 MHz.
The four different convolution neural networks detailed in Table 1.0 below, SSD, SSD_MOBILENET_V1, Dense Box, and YOLO-V3 are used from the mainstream frameworks such as Caffe, TensorFlow, and Darknet to apply the ML functions on the received video streams using the Xilinx Deep Learning Processing Unit IP and the final output streams are displayed on Display Port monitor.
The Corazon-AI integrated with Xilinx Vitis AI Stack enables faster time to market while reducing complexity. The Xilinx AI Stack includes advanced pre-optimized deep learning models from mainstream frameworks such as Tensor-flow, Caffe, Darknet, and PyTorch.
The Xilinx Vitis AI Stack enables developers to accelerate the development flow of AI applications even without in depth-knowledge of FPGA and deep learning. The Stack support C++/python API’s which provides the programming flexibility to the developers.
Here, we demonstrated the capability of the Corazon AI platform to connect 8 IP cameras and how AI operations are performed on these video streams simultaneously with four different convolution neural networks running at a different resolution.
If you are interested in a high-performance smart AI solution to meet your requirements, please write to us on mktg@iwavesystems.com or contact our Regional Partners.
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