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TLV62595 2.5 V to 5.5 V Input 4 A Step-Down Converter

Posted on 6 March, 2021 by mixos

Texas Instruments’ converter offers 1% output accuracy in 1.5 mm x 1.5 mm QFN package

Texas Instruments’ TLV62595 is a high-frequency synchronous step-down converter that is optimized for a compact solution size and high efficiency. The device integrates switches that can deliver an output current up to 4 A. At medium to heavy loads, the converter operates in pulse width modulation (PWM) mode with a typical 2.2 MHz switching frequency. At light-load, the device automatically enters power-save-mode (PSM) to maintain high efficiency over the entire load current range with a quiescent current as low as 10 µA.

Based on the DCS-control topology, the TLV62595 provides a fast-transient response. The internal reference regulates the output voltage down to 0.6 V with a high feedback voltage accuracy of 1% over the junction temperature range of -40°C to +125°C. The entire solution requires a small 470 nH inductor, a single 4.7 μF input capacitor, and three 10 μF or single 47 μF output capacitors. The device is available in a 6-pin 1.5 mm x 1.5 mm QFN package, offering a solution with high power density.

Features:

Efficiency: up to 97%
Low R_DS(ON) power switches 26 mΩ / 25 mΩ
Input voltage range: 2.5 V to 5.5 V
Adjustable output voltage: 0.6 V to 4 V
DCS-control topology
PSM for light-load efficiency
Duty-cycle for the lowest dropout: 100%
Operating quiescent current: 10 μA
Switching frequency: 2.2 MHz (typ.)
Short-circuit protection (HICCUP)
Active output discharge
Power good output
Thermal shutdown protection
Feedback voltage accuracy: 1% (full temperature range)

more information: https://www.ti.com/product/TLV62595

Latest Micro Rotary DIP Switches are IP67 Rated

Posted on 4 March, 2021 by mixos

CUI Devices’ Switches Group today announced the addition of micro rotary actuator types to its DIP switches product line. Housed in compact 7.2 x 7.2 x 2.9 mm packages, the RDS-7229 series features 4, 10, or 16 positions, a 5.08 mm pitch, and surface mount or through-hole mounting styles. Thanks to their compact footprints and IP67 ratings, these rotary DIP switches are ideal for a range of space-constrained industrial, commercial, or telecommunication applications where moisture and environmental contaminants are major concerns.

To meet a variety of design requirements, the rotary DIP switch models offer multiple termination types, including gull wing, angled gull wing, and J-hook gull wing as well as PC pin and crimped PC pin. The models also feature raised or flat actuator levels with arrow, cross, or slotted actuator styles. Carrying operating temperature ranges from -40 to 85°C, the micro rotary DIP switches hold 50 Vdc switching/non-switching rated voltages and 100 mA switching/non-switching rated currents.

The RDS-7229 models are available immediately with prices starting at $1.69 per unit at 500 pieces through distribution. Please contact CUI Devices for OEM pricing.

Cost: $1.69 per unit at 500 pieces through distribution

View details for the RDS-7229 series

ST’s FP-AI-VISION1 for Edge Computer Vision Applications

Posted on 4 March, 20214 March, 2021 by Saumitra Jagdale

Computer vision applications have tremendously increased since the progress in the field of machine learning and artificial intelligence. These applications come at the cost of high-end hardware or cloud support for advanced computations and processing. Hence, STMicroelectronics has announced a new AI software support along with the camera-module hardware, for building affordable applications. The system now supports Edge AI for operating computer-vision applications on the device itself, thus replacing the expensive cloud support for similar high-end applications.

ST’s FP-AI-VISION1 is a STM32Cube function pack that comes with various code instances. This can run computer-vision applications in the environment supporting convolutional neural networks for deep learning on STM32H747. The function pack is flexibly compatible with all STM32 MCUs. Hence, the developers need not start from scratch if they want to switch their STM-based hardware. The firmware also gives an option for using various neural networks with almost every dataset according to the choice of users. Although, the dataset needs to be manipulated so that it is compatible with the neural network.

Features of ST’s FP-AI-VISION1

Complete software support on ROM to develop computer vision applications on STM32 microcontroller
Image pre-processing library, STM32_Image for initial stages of the applications
Neural Network library optimized for STM32 (STM32_AI_Runtime) generated by means of the X-CUBE-AI Expansion Package for STM32CubeMX
Food recognition and Person presence detection application based on Convolutional Neural Network
USB webcam application for computer vision enabling the STM32H747I-DISCO board to capture video when connected to a host’
It comes with libraries which allow the function such as testing, debugging and validation of the embedded application
It supports features like camera frame capture to allow image dataset collection, which is a precise way of extracting frames from a video.
Sample implementations are available for the STM32H747I-DISCO Discovery board connected to the B-CAMS-OMV camera module bundle

The B-CAMS-OMV camera bundle is compatible with FP-AI-VISION1 firmware. It provides the hardware with optimized training and deployment features. The bundle also comes with ST’s MB1379 5-Mpixel OV5640 color camera module fitted to an adapter card. It can work with all STM32 Discovery and Evaluation boards with a ZIF connector. The software support of Waveshare and OpenMV connectors allows the users to connect multiple third-party infrared and visible-spectrum cameras to address a wider range of computer-vision applications

ST’s FP-AI-VISION1 firmware supports:

“various frame-buffer processing functions, camera drivers, and software for image capture, pre-processing, and neural-network inference. Several neural-network models are available, including a floating-point-based model and a quantized model generated by X-CUBE-AI, ST’s optimized C-code generator for artificial neural networks.”

The function pack is available for download on the STM website. The B-CAMS-OMV camera-module bundle is available for $56 on the website and other distributors. For more information visit the press release. Images and technical specifications have been taken from the product pages.

Automatic pill dispenser For The Elderly and Disabled persons.

Posted on 4 March, 20214 March, 2021 by Tope Oluyemi

Many people usually forget when it’s time to take their pills, this has led to the invention of pill dispensers. You can find two types of these dispensers, which are: non-electric, and automatic ones. Most of them are featuring a reminder mechanism, and both types usually need someone to organize the pills daily or weekly in different compartments. This makes a patient over-dependent on a caregiver to help with his or her drug, making the patient a dependent person. This has led a team of engineers to create a smart pill dispenser called SmartPill, which doesn’t need a handmade pill organization. It is accessible to disabled people and has the capacity to help patients be more independent.

SmartPill system features two subsystems:

Smartpill Dispenser: A physical device that organizes, stores, and dispenses the pills. It also acts as the main interface for the patient.
Smartpill Web App: This is a web user interface that will enable the doctor or caregiver for the patient to configure and track patient intakes.

The web application features a nice friendly user interface, which enables the doctor to configure up to 4 different pills for each Smartpill Dispenser, and will select up to 3 intakes per day for each pill. This feature enables the doctor to change the dosage remotely whenever it is needed.

SmartPill system is designed taking into consideration the circumstances of some patients, like patients with senile dementia, Alzheimer or visual or audible impairments. Due to these health limitations, the dispenser is fitted with a large-format screen that will enable the patient to fill the pills in a comfortable way. It also features audible and light feedback that will remind the patient to take the pills at the right time. The patient does not have to worry about the placement of different pills in their position, the device Features a smart mechanism that distributes the pills inside the pill dispenser and dispenses the required dose at the right time. SmartPill Dispenser enables a wireless Internet connection to the AWS Cloud via the WiFi connection of the Microchip AVR-IoT board so that it can be remotely monitored and configured through the web application. The app also shows when the pills have been taken and will notify the caregiver if the pill is not taken.

The AVR-IoT is connected via AWS IoT Core by MQTT protocol. This enables bidirectional connectivity between the Cloud ecosystem and the hardware itself. This communication sends the dispensing message to the Dispenser, and also the confirmation events from the dispenser to the cloud. The SmartPill Web App helps to enhance the experience that entails configuring an electronic device and controlling someone’s else medication. The core of the device is an Arduino Mega that controls the servo motors that dispense the pills, and also the Nextion touch screen, the Real-Time Clock (RTC), the sound effects, and the AVR-IoT communications board. The AVR-IoT is connected to the Arduino Mega via a Serial UART connection, and the Servo motors are controlled by PWM. The RTC is connected to the Arduino Mega by I2C, and IR barrier Sensor for detecting the pills is connected to the analog pins of the Arduino board.

The dispensing system work features various sections with different parts and functionality. The image above breaks down the various parts and they are explained below:

Base: The base supports the rest of the dispenser. It features two holes which enables the motion sensor functional. It also contains a flap which brings the pills into the cup.
Slider: The top of the slider supports the pill containers. You can find a slope underneath it, in which the pills travels all the way down to the flap. On the bottom of the slider, there is a gap designed to put all the electronics in it.
Side parts: The side parts are used to protect the hardware and pills from humidity, temperatures, dirt and dust.
Nextion NX8048T070: This is the screen interface between the dispenser and the user.
Top parts: The top part is used to protect the hardware and pills from humidity, temperatures, dirt and dust, and it also separates the drum from the pill containers
Parallax Servo 900-0005: It is a 360º electric motor which places the drum in the right position
Drum: The drum is a connection tube between the outside and the pill containers. It rotates in order to get to the right container.
Back door: The back door helps to get in and out the pill containers with the slider.
Speakers: Used for communicating to the user all the information needed.
Ultrasonic Sensor – HC SR04 : This device helps detect when the cup has been moved away.
Pill containers: The container houses the pills and, they dispense them at the right moment with the right quantity. We should note that as a prototype, the pill dispenser is designed with 4 containers. However, it can be adapted to a larger capacity.

You can get more information about the project here.

OSD32MP1-RED – A Full Featured Development Platform

Posted on 4 March, 2021 by mixos

The OSD32MP1-RED is a full-featured Reference, Evaluation, and Development platform for the STM32MP1 based SiP, the OSD32MP15x. It provides access to a number of standard communication interfaces like WiFi and Bluetooth, 1Gb Ethernet, and CAN. It supports HDMI or DSI displays and has a connector for a camera. The OSD32MP1-RED expands easily by providing connectors that are compatible with Raspberry Pi, MikroElektronika mikroBUS™ Click, and STMicroelectronics Motor Control Header.

The onboard eMMC comes preloaded with an Opensource Linux Distribution allowing you to begin development straight out of the box.

OSD32MP1-RED Features

Full Featured Reference, Evaluation, Development Platform
OSD32MP157C-512M-BAA
- STM32MP157C Dual Arm® Cortex® A7 + M4
- 512MB DDR3 Memory
- STPMIC1A Power Management
- EEPROM
- Oscillator
- Passives
Connectivity
- WiFi/Bluetooth
- 10/100/1000 Ethernet
- CAN FD
- USB Host
- USB-C OTG
- UART
- JTAG (STLink)
Memory
- µSD Card
- eMMC
Display
- HDMI
- DSI
Camera
Expansion
- Raspberry Pi Compatible Header
- MikroElektronika Click Header
- Motor Control Header
4 Layer Design

Block

With its communication interfaces, peripherals, and expansion capabilities, the OSD32MP1-RED is a perfect platform for quickly developing IOT, high-end HMI, or real time control applications.

more information: https://octavosystems.com/octavo_products/osd32mp1-red/

Fast Sense brings 8x performance to mobile robots with its revolutionary Edge Computational AI platform

Posted on 4 March, 2021 by mixos

Fast Sense Studio, a leading developer of mobile robotics solutions, today announced that its Fast Sense AI Edge Computer has been launched at the Embedded world 2021 DIGITAL.

Fast Sense AI Edge Computer is a powerful on-board computer bringing scalable Edge AI capabilities to mobile robotics.

It is a board-level circuit that integrates the power of six computers in a single module. Six independent AI inference engines (Intel GPU, 2x Myriad X, 3x Coral) operate in one compact device enabling the running of six different neural nets simultaneously without performance degradation, while receiving data from several sources in real time.

Fast Sense AI Edge Computer delivers massive artificial intelligence capabilities and has eight times more computational power compared to existing products in the market.

Thanks to six independent Edge AI engines, Fast Sense AI Edge Computer has sufficient hardware resources to run different AI algorithms and control a robot that needs to process data from different sensors like video cameras and react to changes in its environment in real-time.

The Fast Sense AI Edge Computer makes it possible to run a robot with three video cameras pointing in different directions where each video stream has to be processed with three different neural nets: object detection, semantic segmentation and depth estimation. It consists of a single board COM

Express module with Intel CPU, a set of edge AI accelerators to inference several neural nets on-board in real time and has numerous hardware interfaces to robotic sensors and actuators. The platform is shipped as a ready-to-use device with software examples of running ROS (Robot Operating System) algorithms with integrated neural nets meaningful for robotic applications, running in isolated Docker containers.

The Fast Sense AI Edge Computer can be integrated with multiple robotics platforms, e.g. drones and robots with wheels for a great number of use cases. This universal reasoning system can be applied across different classes of physical agents such as delivery robots, autonomous machines, facilities inspection, logistics robots (AGV) and disinfection drones.

“We believe in the future where reliable physical agents become relevant for routine tasks that most of us perform today. Reliability is what we are working on today, creating the environment for robots to process a large amount of data on board, learning from this and making independent, safe, and reliable decisions,” said Yuri Rumyancev, Co-founder of СТО Fast Sense Studio. “Bringing together hardware and robotics engineers, software developers and data scientists, we have developed a prototype computer for mobile robotics with six independent edge AI accelerators from Intel and Google. It is fully tested and already integrated into our wheeled robot. The launch of the Fast Sense AI Edge Computer indicates a new benchmark for end-to-end robotic systems to help people achieve a new level of productivity.”

Fast Sense X Robotics AI Platform is being demonstrated at the Embedded World 2021 from 1 to 5 March, 2021.

In order to request a Demo, please visit the Fast Sense Studio website.

6-Channel Capacitive Touch Sensor Module

The module described here is a 6-channel capacitive touch sensor. The project is based on CAP1206 IC which contains six (6) individual capacitive touch sensor inputs with programmable sensitivity for use in touch sensor applications. Each sensor input is calibrated to compensate for system parasitic capacitance and automatically recalibrated to compensate for gradual environmental changes. The CAP1206 features Multiple Pattern Touch recognition that allows the user to select a specific set of buttons to be touched simultaneously. If this pattern is detected, a status bit is set and an interrupt is generated. The CAP1206 has Active and Standby states, each with its own sensor input configuration controls. Power consumption in the Standby state is dependent on the number of sensor inputs enabled as well as averaging, sampling time, and cycle time. Deep Sleep is the lowest power state available, drawing 5μA (typical) of current. In this state, no sensor inputs are active, and communications will wake the device.

Refer to the datasheet of CAP1206 for more information on configuration and controlling the module using a microcontroller, Arduino, or Raspberry Pi.

Features

Operating Supply 3.3V to 5V DC
Onboard SMBUS/I2C Interface
Onboard Power LED
6 PCB Touch Pads
I2C ADDRESS 0x28
PCB dimensions: 83.82 x 24.13 mm

Schematic

Parts List

NO.	QNTY.	REF.	DESC	MANUFACTURER	SUPPLIER	SUPPLIER PART NO
1	1	CN1	5 PIN MALE HEADER 2.54MM PITCH	ADAM TECH	DIGIKEY	2057-PH1RA-05-UA-ND
2	1	C1	0.1uF/50V SMD SIZE 0805	MURATA/YAGEO
3	1	C2	10uF/16V SMD SIZE 1210 OR 1206	MURATA/YAGEO
4	1	D1	LED RED SMD SIZE 0805	OSRAM	DIGIKEY	475-1415-1-ND
5	3	R1,R2,R3	10K 5% SMD SIZE 0805	MURATA/YAGEO
6	1	R4	1K 5% SMD SIZE 0805	MURATA/YAGEO
7	6	TP1-TP6	PCB TOUCH
8	1	U1	CAP1206 14SOIC	MICROCHIP	DIGIKEY	CAP1206-1-SL-CT-ND

Connections

Gerber View

Photos

CAP1206 Datasheet

NVIDIA introduces low cost Jetson TX2 NX SO-DIMM module

Posted on 2 March, 2021 by mixos

NVIDIA® Jetson™ TX2 NX delivers the next step in AI performance for entry level embedded and edge products. It provides up to 2.5X the performance of Jetson Nano, and shares form-factor and pin compatibility with Jetson Nano and Jetson Xavier™ NX.

The compact, power efficient Jetson TX2 NX system-on-module (SOM) is perfect for creating mass-market AI products in the fields of manufacturing, logistics, retail, service, agriculture, smart city, and healthcare and life sciences. Intelligent machine OEMs can now scale their product offerings with pin-compatible Jetson modules, while leveraging cloud-native technologies to build, deploy, and manage the same software across all of them.

Pre-trained AI models from NVIDIA NGC™, together with the NVIDIA Transfer Learning Toolkit, provide a faster path to inference with optimized AI networks, while containerized deployments bring flexible and seamless updates.

NVIDIA JetPack™ SDK enables development of AI applications for Jetson TX2 NX with accelerated libraries supporting all major AI frameworks, as well as computer vision, graphics, multimedia, and more. Together with the latest NVIDIA tools for application development and optimization, JetPack ensures fast time to market and reduced development costs.

Technical Specifications

AI Performance 1.33 TFLOPs
GPU NVIDIA Pascal™ Architecture GPU with 256 CUDA cores
CPU Dual-core NVIDIA Denver 2 64-bit CPU and quad-core ARM A57 Complex
Memory 4GB 128-bit LPDDR4, 1600 MHz – 51.2 GBs
Storage 16GB eMMC 5.1 Flash Storage

Ease of development and speed of deployment—plus a unique combination of form-factor, performance, and power advantage—make Jetson TX2 NX the ideal mass-market AI product platform to get to market and continuously update over the lifetime of a product.

Samples of Jetson TX2 NX modules are available now for $200 on Arrow or 140 EUR on Silicon Highway Direct. Further details may be found on the Jetson product page. [via www.cnx-software.com]

Introducing tCam-Mini – An IR thermal camera board with ESP32 module

Posted on 2 March, 20212 March, 2021 by mixos

tCam-Mini is a small wireless streaming thermal imaging camera designed by Dan Julio to make it easy to get and use radiometric data from a Flir Lepton 3.5 sensor. Radiometric data includes the temperature of every pixel captured by the Lepton allowing all kinds of interesting thermographic analysis. Of course that data can also be turned into the beautiful false-color images everyone associates with thermal imaging.

tCam-Mini comes with a capable desktop application, running on Linux, Mac OS X and Windows, making it easy-to-use, right out of the box. Custom applications running on any platform can easily communicate with tCam-Mini via a socket interface. Commands and data are transferred as easy-to-parse json strings.

Capabilities

The tCam-Mini camera exposes the full capabilities of the Lepton.
The camera can operate in either Radiometric/TLinear (each pixel contains temperature data) or AGC modes (no temperature data in each pixel but better images).
Simple json-based command set with communication of a TCP/IP Socket. Makes interfacing with the camera very simple from a custom application.
AP or STA (client) Wifi modes (static or DHCP-served IPV4 address).
Single image or streaming data modes.
Control over sensor emissivity, gain and spotmeter location.

The companion desktop application makes it easy to use the camera and analyze the data from it.

Display images or streams with multiple palettes.
Save and load images or streams in files preserving the radiometric data for use later or by other applications. Two file formats: image and video (described in the github repository).
Export images as jpg, png or tiff files.
Copy current image to computer’s clipboard.
Histogram display and analysis of pixel populations.
Spotmeter and up to four additional markers showing temperature at various points in an image.
Graphing function to plot spotmeter and marker data over time.
Graph baseline mode to allow comparing temperatures to a reference point in the scene (for example to compare a temperature to a blackbody constant).
Export graph data in a text file for analysis by other programs.
Print graph (or create a PDF on computers that can print to PDF).

Hardware Overview

Espressif ESP32-WROVER-E Module (ESP32-D0WD0V3, 8 MB PSRAM, 8 MB Flash) with built-in antenna
Flir Lepton 3.5 (160×120 pixel radiometric LWIR camera with shutter)
CP2102N-A02 USB to UART bridge with ESP32 boot loader control
Multi-voltage power supply (3.3V, 3.0V, 2.8V, 1.2V)
Dual color (Red/Green) status LED
Factory Wifi Reset button

Open Source

The hardware and firmware design files are available in Dan’s Github repository. The firmware is designed to be compiled using Espressif IDF tools and new code can be loaded via the built-in USB Serial port. Precompiled binary files are also provided for easy upgrades.

Development is ongoing with new features and additional software support planned including a python library and web server.

more information: https://groupgets.com/campaigns/919-tcam-mini

Capacitech Cable-Based Capacitor

Posted on 2 March, 2021 by mixos

Capacitech Cable-Based Capacitor (CBC) offers a 1.6V_DC rated voltage, 1.25A current rating, and 0.3mA maximum leakage current. The CBC is a flexible, wire-shaped supercapacitor that offers space and aesthetic advantages through the ability to be used as part of a product or system’s wiring infrastructure. This supercapacitor’s thin and flexible design allows it to be built inside of DC power cords, rather than be limited to a printed circuit board. Capacitech Cable-Based Capacitor is ideal for emergency lighting, automotive systems, energy harvesting, and renewable energy systems.

Features

Flexible and wire-shape form factor
Use as part of wiring infrastructure
Cable-based backup power
Cable-based boost power
Size, space, aesthetic advantages
Electronics miniaturization

Applications

Peak power complement
Emergency lighting
Renewable energy systems
Automotive systems
IoT, automation, and security devices
Backup system
Energy harvesting
Advanced and smart metering

more information: https://www.capacitechenergy.com