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If you’re designing any kind of computing
application with the Jetson Nano,
you seriously can’t do without
a heatsink if you want to
avoid overheating problems.
-
Fully compatible with NVIDIA Jetson
Nano Module
-
Essential for high-performance
computing: Keeps module temperatures
low to avoid overheating and
throttling
-
Effective thermal management: Full
Aluminum Alloy Heatsink
-
Safe for use: Spring mounting
interface keeps excessive pressure
off the Jetson Nano module
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Combine Aluminum
Heatsink for Jetson Nano
Module with the NVIDIA Jetson
Nano Module and Jetson
Mate Cluster Mini, and you’ll get a
thoroughly well-cooled GPU cluster
solution!
Run intensive workloads like data analytics
and even machine learning on the edge
without any worry of overheating and
performance throttling issues.
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Jetson
Xavier NX Module
Aluminum Heatsink
$19.90
-
Fully compatible
with NVIDIA Jetson
Xavier NX Module
-
Essential for
high-performance
computing: Keeps
module temperatures
low to avoid
overheating and
throttling
-
Effective thermal
management: Full
Aluminum heatsink
with fan
-
Easy to install:
Mounting bracket
& screws
included
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-
Rich interfaces for industrial
applications
-
Industrial-grade
isolated DI/DO terminal
-
Industrial-grade
isolated RS-485
interface
-
Diverse network connectivity
options
-
Dual-band 2.4GHz/5GHz Wi-Fi
-
Gigabit Ethernet
-
4G/LTE communication
-
Designed for reliability with
built-in uninterruptible power
supply (UPS) with supercap
-
Integrated thermal management
with full Aluminum heatsink
enclosure
-
Convenient form factor with 35mm
DIN and wall mount
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PIzza is a micro server based on the
Raspberry Pi Compute Module 4, designed for
makers and commercial applications alike.
With diverse connectivity options and
expandable storage, it can be used for
various networking or multimedia
applications.
-
Diverse network connectivity options
-
Dual-band 2.4GHz/5GHz WiFi
-
Dual Gigabit Ethernet ports
-
4G/LTE communication via
mini PCIe (other modules
supported)
-
Expandable Storage: M.2 NVME Socket
for SSD Expansion
-
Multimedia Ready: HDMI Output at up
to 4Kp60
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Integrated Thermal Management:
Aluminum Heatsink with ABS Body
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Azure
Certified Wio Terminal
CO2 Checker $102.80
-
A cost-effective CO2
environmental
measurement device
that can be
connected to the
Internet and cloud.
-
Easy to detect
temperature,
humidity, and CO2
concentration and
display the
measurement on the
screen.
Also display
the WBGT (wet bulb
globe temperature).
-
Analyze data
and
enable notifications
by sending values to
Azure IoT.
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People
Counting System built with Wio
Terminal and Ultrasonic Sensor
$41.20
Normal ultrasonic sensors can easily
measure changes in distance to
obstacles, but what about complex
real-world tasks like people counting?
Well, with TinyML running right on Wio
Terminal, we can train a machine
learning model on distance patterns to
recognize when people are moving in or
out of a room!
-
Powered by TinyML, train machine
learning model on distance patterns
-
Gather the data through Edge
Impulse
-
Use continuous inference example to
make sure not missing any important
data.
-
Store the room occupancy data in the
cloud and visualize it on PC
-
Connect to Azure IoT Central, watch
the detailed progress feedback on
the Serial Terminal
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Smart
Garden IoT Monitor built with Wio
Terminal $46.70
With the Wio Terminal Smart Garden Kit,
you can collect and measure soil
moisture, light, temperature, and
humidity for plants on the Wio
Terminal LCD and visualize
data!
Conveniently use a suite of
plug-and-play Grove sensor modules, and
display the information neatly on the
Wio Terminal’s built-in display. With
onboard Bluetooth or WiFi, you can even
add cloud capabilities to your smart
garden system and view the collected
data remotely!
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To help beginners get started with Raspberry
Pi Pico quickly, we are releasing the
Starter Guide of Raspberry Pi Pico Based on
MicroPython. Through four chapters and a
total of 16 lessons of study, this course
can help you get started with Raspberry Pi
Pico easily. With its help, you can learn
MicroPython from scratch, and build some
interesting projects.
It is not required to have knowledge of
MicroPython programming or electronics to
begin. The course will take you through
learning this knowledge step by step, and
quickly put it into practice in each
project.
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It’s GIVEAWAY time! In late May, we have
released the RP2040 Microcontrollers
including a module and a dev board based
on Raspberry
Pi RP2040 chip. Currently,
we’re manufacturing the first batch of
the series with the expected shipping
date in early July.
If you want the chance to win a free Wio
RP2040 Module or a Wio RP2040 Dev Board,
simply do the following two easy steps
(we have 5 of each to give away):
1) Follow
us on
LinkedIn for
future chances to win if you do not win
this time.
2) Check out more
details about the
products, and
then fill
in an online application
form with your
project idea to win them away.
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Starting
Machine Learning with
Wio RP2040 board
Train and deploy a simple
neural network for
classifying paper-rock
gestures with just a single
light sensor. Let’s see how
to get Wio RP2040 mini Dev
Board started with Machine
learning at the very edge!
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Like many of us during Covid-19, Benjamin
Cabé was baking sourdough bread but he was
at home in France. He wondered how he might
tell if his dough was done proofing. He
began working on an artificial nose that
uses physical sensors to detect gas but also
uses machine learning to identify the smell.
His project is on the cover of Make: Volume
77. In this conversation, Make team and
Benjamin talk about how his nose works, how
it knows what it does and the limits of what
it knows.
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by Antonio
Ruiz
Antonio Ruiz developed a solar-powered
sensing device, able to determine and track
key environmental parameters in
real-time.
The LCD screen on the Wio Terminal, not only
allowed a display of the results but it
could also be used to show in a map the
position where the different measurements
had been taken. To power this project,
Antonio Ruiz employed a solar panel and a
rechargeable lithium-ion battery, that could
be connected with the Wio Terminal.
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Age Gender Recognition
model
training/inference/evaluation
scripts+reference model
are published in
Seeed_Python_MachineLearning
Final accuracy of
pre-trained
MobileFaceNet model
(alpha = 2.0, total
params: 798,982, model
size: 3.1 Mb) is 92% for
gender and 78% for age.
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