#emmc module

[eric2yyrrr]

https://www.futureelectronics.com/p/semiconductors--memory--storage--embedded-storage/emmc04g-w627-x03u-kingston-1111540

Memory card, what is ram, Ram digital, data storage, emmc module

4 GB 11.5 x 13 x 1.0 Surface Mount v5.0 eMMC Flash Memory - FBGA-153, I TEMP

[dvd2llips]

https://www.futureelectronics.com/p/semiconductors--memory--storage--embedded-storage/emmc04g-wt32-01g10-kingston-6179835

eMMC components, NAND Flash Memory, eMMC modules, storage capacity

EMMC 5.1 INTERFACE,153-BALL FBGA,3.3V,-25C-+85C

[bliiot-jerry]

ARMxy SBC BL310 ARM Embedded Computer in Industrial Boiler Monitoring and control

Case Details

Boiler monitoring requires real-time acquisition of parameters such as temperature, pressure, and flow, execution of control logic, and data transmission to the cloud for analysis. The BL310 series, powered by the NXP i.MX6ULL Cortex-A7 processor (up to 800MHz), combined with rich I/O interfaces, flexible communication modules, and industrial-grade design, perfectly meets the complex requirements of boiler monitoring.

Industrial Environment Adaptability

Wide Temperature Range: Operates reliably from -40°C to 85°C, suitable for high- or low-temperature boiler room environments.

Electromagnetic Compatibility: Passes GB/T 9254 Class A and CISPR 32 Class A tests, ensuring resistance to electromagnetic interference and stable operation in industrial settings.

Protection and Installation: IP30 protection rating, aluminum alloy + stainless steel casing, and DIN35 rail mounting, ideal for space-constrained control cabinets.

1.2 Data Acquisition and Control

Versatile I/O Interfaces: Supports RS485, RS232, CAN, DI/DO, relays, AI/AO, RTD, and thermocouple signals through X-series and Y-series I/O boards, meeting the needs for temperature, pressure, and flow data acquisition in boilers.

Precise Control: Supports PWM output and pulse counting for valve control and flow monitoring.

Flexible Expansion: Built-in Mini PCIe interface supports WiFi/4G modules for wireless data transmission.

1.3 Communication and Cloud Integration

Industrial Protocol Support: Equipped with BLIoTLink software, supporting protocols like Modbus, BACnet, IEC104, MQTT, and OPC UA, enabling seamless integration with PLCs, SCADA systems, or cloud platforms (e.g., AWS IoT Core, Thingsboard).

Remote Management: BLRAT remote access tool facilitates device maintenance, reducing on-site operational costs.

Rapid Development: Integrated Node-Red enables quick development of IoT applications, simplifying data visualization and logic control.

Typical Application Scenarios in Boiler Monitoring

The BL310 can implement the following core functions in boiler monitoring:

Temperature Monitoring: Uses Y51/Y53 (PT100/PT1000) or Y58 (thermocouple) boards to measure boiler water and flue gas temperatures.

Pressure Monitoring: Acquires pressure sensor data via Y31/Y33 (4-20mA or 0-10V).

Flow Monitoring: Monitors fuel or water flow using Y95/Y96 (pulse counting).

Status Control and Alarms: Implements switching control and alarm outputs via Y01/Y02 (DI/DO) or Y24 (relay) boards.

Remote Monitoring: Transmits data to cloud platforms via 4G/WiFi modules and MQTT protocol for real-time boiler status monitoring.

Data Analysis: Leverages cloud platforms for historical data storage, trend analysis, and energy optimization.

Recommended Configuration

To meet boiler monitoring requirements, the following BL310 configuration is recommended:

Model: BL310L-SOM314-X4-Y51-Y24

Host: BL310L (with 4G module for remote monitoring).

SOM Module: SOM314 (512MB DDR3L, 8GB eMMC, -40~85°C, suitable for high storage and wide temperature needs).

X Board: X4 (2x RS485 + 2x CAN, for connecting to PLCs or other devices).

Y Board 1: Y51 (2-channel 3-wire PT100 for precise temperature monitoring).

Y Board 2: Y24 (4-channel relay output for controlling alarms or valves).

Software Configuration:

Pre-installed BLIoTLink and BLRAT, supporting Modbus-to-MQTT conversion and integration with Thingsboard or AWS IoT Core.

Node-Red for developing data dashboards to display real-time temperature, pressure, and other parameters.

Implementation Suggestions

4.1 Hardware Installation

Install the BL310 in the boiler control cabinet using DIN35 rail mounting, ensuring proper grounding via the 1-pin GND terminal.

Use a 24VDC power supply (supports 9-36V wide voltage range) with reverse polarity and overcurrent protection for enhanced safety.

Configure WiFi/4G antennas as needed to ensure stable signal transmission.

4.2 Software Development

Use Node-Red to quickly develop data acquisition, alarm logic, and visualization dashboards.

Configure BLIoTLink to collect Modbus RTU/TCP data and transmit it to the cloud via MQTT.

Set up BLRAT for remote access, enabling maintenance personnel to monitor device status in real time.

4.3 Testing and Validation

Conduct high/low-temperature and electromagnetic compatibility tests before deployment to ensure reliability in boiler room environments.

Refer to technical support’s Node-Red and BLIoTLink development examples for rapid function validation.

Test 4G/WiFi connection stability to ensure uninterrupted data transmission.

Precautions

I/O Board Selection: Note that Y63 (4x RS485/RS232) occupies two Y slots, preventing the addition of a second Y board.

Storage Needs: For large historical data storage, choose SOM314 (8GB eMMC) over SOM310 (256MB Nand).

Network Security: Configure MQTT authentication and encryption to prevent data breaches.

Custom Development: For special requirements (e.g., vibration monitoring), contact Beilai Technology for customization (see Technical Support section).

Conclusion

The BL310 ARM embedded computer, with its high-performance processor, versatile I/O interfaces, industrial-grade reliability, and flexible software ecosystem, provides an efficient and intelligent solution for boiler monitoring. By enabling real-time data acquisition, remote monitoring, and cloud platform integration, the BL310 significantly enhances the automation and operational efficiency of boiler systems, contributing to the advancement of industrial IoT and smart manufacturing. Whether for new boiler monitoring systems or upgrades to existing setups, the BL310 is a reliable and trusted choice.

[bliiot]

The BL370 series is powered by the industrial-grade Rockchip RK3562/RK3562J processor, featuring a multi-core heterogeneous architecture with a quad-core ARM Cortex-A53 and a single-core ARM Cortex-M0, clocked at up to 1.8GHz/2.0GHz. It offers a robust solution with 4GB LPDDR4X RAM and 32GB eMMC storage, along with a rich set of I/O interfaces. The built-in 1 TOPS NPU supports deep learning capabilities, making it ideal for AI-driven applications.

Key Features:

High Reliability and Cost-Effectiveness: The BL370 series is widely used in industrial control, edge computing, AIoT, artificial intelligence, communication management, AGV robots, machine vision, robotics, industrial IoT gateways, energy storage systems, automation control, and rail transportation.

Versatile Connectivity:

Data Acquisition and Control: Supports communication, PWM output, pulse counting, and more.

Video Processing: Capable of 1080P@60fps H.264 encoding and 4K@30fps H.265 decoding.

Wireless Communication: Built-in Mini PCIe interface supports Bluetooth, WiFi, 4G, and 5G modules.

Software and Development Support:

Operating Systems: Linux-5.10.198, Linux-RT-5.10.198, Ubuntu 20.04, Debian 11 (planned), Android 13 (planned).

Development Tools: Docker containers, Node-RED, and Qt-5.15.2 for GUI development.

Industrial Software:

Robust Design for Harsh Environments:

The BL370 series has undergone professional electrical performance design and high/low-temperature testing, ensuring stable operation in extreme conditions with temperatures ranging from -40°C to 85°C and resistance to electromagnetic interference. Its DIN35 rail mounting makes it suitable for various industrial applications.

Typical Application Areas:

Industrial Control

Energy Storage Systems (EMS/BMS)

AIoT and Artificial Intelligence

Smart Manufacturing

Communication Management

AGV Robots

Machine Vision

Edge Computing

Motion Control

Robotics

Rail Transportation

Smart Devices

The BL370 series combines high performance, reliability, and versatility, making it an ideal solution for demanding industrial and IoT applications.

[forlinx]

NXP Unveils FRDM i.MX 93 Development Board to Accelerate Modern Industrial and Edge Intelligence Advancements

Recently, NXP Semiconductors introduced the FRDM i.MX 93 development board, the first development board in the FRDM series based on MPU. It is designed with a focus on low cost and compactness, featuring the NXP i.MX 93 series application processor. It aims to provide users with an efficient and reliable solution for developing modern industrial control and edge intelligence applications.

One of the key highlights of the FRDM i.MX 93 development board is its onboard IW612 module, which utilizes NXP’s Tri-Radio solution, integrating Wi-Fi 6, Bluetooth 5.4, and 802.15.4 triple wireless communication technologies. It not only enhances the stability and speed of wireless communication but also provides developers with a wider range of connectivity options to meet the needs of various application scenarios.

In addition to its powerful wireless communication capabilities, the FRDM i.MX 93 development board is equipped with a rich set of hardware resources. HDMI display interface supports high-definition video output, greatly facilitating the development of multimedia applications. LPDDR4/LPDDR4X memory and eMMC storage ensure fast data processing and storage, further improving development efficiency. The board also features a power management integrated circuit (PMIC) and an EXPIO interface compatible with Raspberry Pi pin definitions, to meet the needs of developers in different application scenarios.

Moreover, the FRDM i.MX 93 development board supports GoPoint for i.MX Applications Processors. This feature helps developers quickly understand and apply the powerful functions of the i.MX processor through comprehensive demonstrations for various purposes, thereby accelerating product time-to-market. It serves as an ideal platform for beginners to learn and practice embedded development and is also a capable assistant for professional developers in prototyping and product development.

Click the link below to visit the NXP official website and learn more about the product information of the FRDM i.MX 93 development board.

[siliconsignalsblog]

Persistent Storage in Zephyr: Saving Data to Files

Introduction

In this series of blog posts introducing The Zephyr Project RTOS, we have primarily concentrated on Zephyr internals and infrastructure. Recall that Zephyr wants to be a leading RTOS for devices with limited resources that are connected. To guarantee a platform that is secure, dependable, and vendor-neutral, Zephyr incorporates open-source and security best practices.

I'll demonstrate how to make an application in Zephyr to store data on a microSD card in this blog post. Even though the majority of embedded systems today can upload sensor data via the internet, the connection might be erratic. There are two benefits to having a microSD card. It can be expanded, to start. A micro-SD card of one size can be changed for a larger one, but onboard RAM and flash storage are fixed. Second, a desktop computer can be used to access data from a microSD card.

Hardware

This blog post will use the Nordic nRF52840 development kit (https://www.nordicsemi.com/Products/Development-hardware/nrf52840-dk). We will connect the nRF52840 development kit to the SparkFun microSD Transflash Breakout board (https://www.sparkfun.com/products/544). Any microSD card from a reputable vendor will suffice.

The following diagram shows the connections between the SparkFun microSD module and the nRF52840 development kit:

Embedded Software

We will go over the pertinent parts of the embedded software that interface with the SD card in this section. First, we can use West to get Zephyr v3.5 by running the following command: $> west init -m <a href="https://github.com/zephyrproject-rtos/zephyr">https://github.com/zephyrproject-rtos/zephyr</a> --mr v3.5.0 zephyrproject$> cd zephyrproject$> west update

Second, we can clone the repository that contains our test application: $> git clone https://github.com/mabembedded/zephyr-sd-spi.git

Third, we need to make sure that the exFAT scheme—which is Windows' default—is used to format our SD card. Lastly, we can open a terminal interface and use the USB connection to connect the nRF52840 development kit to our PC. We can build and flash the application by executing the following commands: $> cd zephyr-sd-spi$> cmake –preset build$> west build && west flash

We should see the following output in the terminal interface: *** Booting Zephyr OS build zephyr-v3.5.0 ***[00:00:00.402,770] <inf> sd: Maximum SD clock is under 25MHz, using clock of 24000000Hz[00:00:00.414,215] <inf> main: Block count 384503808Sector size 512Memory Size(MB) 187746Disk mounted.Listing dir /SD: ...[DIR ] System Volume Information[FILE] test_data.txt (size = 13)Successfully mounted SD cardmain - successfully created file

If we plug in the SD card to our PC and open it up in File Explorer, we should see “test_data.txt” with the string “hello world!” on the first line, as seen below:

Kconfig

The following relevant Kconfig options are enabled in the “prj.conf” file, with a description of each:

CONFIG_DISK_ACCESS: This option allows for the disk access subsystem.

CONFIG_FILE_SYSTEM: This option allows for the filesystem subsystem.

CONFIG_FAT_FILESYSTEM_ELM: This option instructs Zephyr to use the “ELM” FAT FS implementation, found on http://elm-chan.org/.

CONFIG_FS_FATFS_MOUNT_MKFS: This option instructs Zephyr to create a disk with a FAT filesystem if none is found.

CONFIG_FS_FATFS_EXFAT: This option enables the exFAT partition scheme.

CONFIG_DISK_DRIVER_SDMMC: This option enables the SD/EMMC driver.

CONFIG_SPI: This option enables the SPI subsystem.

CONFIG_GPIO: This option enables the GPIO subsystem.

Devicetree Overlay

Additionally, there are two reasons why we must create a Devicetree overlay. The nRF52840 development kit's pins for the SPI connection to the SparkFun Transflash breakout board must first be updated. Secondly, we need to tell the application firmware that an SD card is plugged in. As indicated below, we must first add a new entry to the pinctrl block in order to update the SPI pins: &pinctrl {        custom_spi: custom_spi {                group1 {                        psels = <NRF_PSEL(SPIM_SCK, 0, 26)>,                                <NRF_PSEL(SPIM_MOSI, 0, 27)>,                                <NRF_PSEL(SPIM_MISO, 1, 8)>;                };        };};

Then, we need to update the SPI block in the overlay with our custom pinctrl (and also add the GPIO for the CS line): &spi1 {        status = "okay";        pinctrl-0 = <&custom_spi>;        pinctrl-1 = <&custom_spi>;        pinctrl-names = "default", "sleep";        cs-gpios = <&gpio0 2 GPIO_ACTIVE_LOW>;...

The following needs to be added in the “spi1” node to inform the application of the existence of the SD card: ...        sdhc0: sdhc@0 {                compatible = "zephyr,sdhc-spi-slot";                reg = <0>;                status = "okay";                label = "SDHC_0";                mmc {                        compatible = "zephyr,sdmmc-disk";                        status = "okay";                };                spi-max-frequency = <24000000>;        };};

Application Source

With the Devicetree Overlay and Kconfig installed, we can go over the implementation step-by-step. To make sure our program can accurately read the files on the SD card, I made two helper functions. The prototype for the first function, "lsdir," is as follows:staticintlsdir(constchar *path);

This function prints all of the directories and files contained in a given path when it receives it as input. The second function, "mount_sd_card," makes use of "lsdir." The following tasks are carried out by this function:

Initializes the underlying disk via “disk_access_init.”

Retrieves the number of sectors via “disk_access_ioctl” with “DISK_IOCTL_GET_SECTOR_COUNT” as a parameter.

Retrieves the sector size via “disk_access_ioctl” with “DISK_IOCTL_GET_SECTOR_SIZE” as a parameter.

Prints the total space of the SD card using the information retrieved above.

Mounts the SD card. If the SD card was successfully mounted, the function lists the files and directories at the root of the SD card. If not, the function tries to mount again.

In "main," "mount_sd_card" is used as the first function. It initializes the "fs_file_t" data structure, which is displayed below, upon success. Every subsequent file operation will make use of the data structure.structfs_file_t data_filp;fs_file_t_init(&data_filp);

The "fs_unlink" function is then used to remove "test_data.txt" from the SD card's root, if it exists. The following line creates a new file named "test_data.txt" and opens it for writing: fs_open(&data_filp, "/SD:/test_data.txt", FS_O_WRITE | FS_O_CREATE);

Finally, the following lines are used to write “hello world!” to the file that was created: sprintf(file_data_buffer, "hello world!\n");ret = fs_write(&data_filp, file_data_buffer, strlen(file_data_buffer));fs_close(&data_filp);

Summary

In this blog post, we demonstrated how to mount a microSD card, write data to it, and create a new file on the microSD card using a Zephyr application. Devices in the field that need to periodically write data to off-board memory can benefit from the lessons learned from such an application, particularly in situations where Internet access may be intermittent. We will continue our journey of writing a custom BLE application that runs on Zephyr in the upcoming blog post!

If you're looking to enhance your embedded systems with advanced storage capabilities like microSD integration or custom BLE applications, Silicon Signals is here to help. Our team specializes in hardware design, software development, and integration of cutting-edge solutions using Zephyr and other RTOS platforms.

👉 Contact Us Today to explore how we can elevate your projects with tailored embedded systems solutions!

[howzitsa]

Lenovo IP1i Gen 7 Intel Celeron N4020 Celeron® Notebook Roam wherever life takes you while connecting and exploring with the remarkably thin and lightweight IdeaPad 1i Gen 7 (15″ Intel) laptop. It boots up in seconds with Flip to Start, which only requires you to open the lid to power up and is driven by up to Intel® Celeron® processors that let you multitask with ease. Maximized experiences & unlimited uses The IdeaPad 1i Gen 7 (15" Intel) is exactly what you need in an everyday use laptop. Watch shows on an expansive up to 15.6" FHD display with a razor-thin frame. Listen to rich and clear audio from two Dolby Audio™ speakers. And with a battery that lasts all day and charges super-fast, you can work from anywhere while enjoying clear video calls with Smart Noise Cancelling. Key Specifications Processor: Intel Celeron N4020 (2C / 2T, 1.1 / 2.8GHz, 4MB) Memory: 8GB SO-DIMM DDR4-2400 Storage: 256GB SSD M.2 2242 PCIe 3.0x4 NVMe Screen size: 15.6" FHD (1920x1080) TN 220nits Anti-glare Operating system: Windows 11 Home Single Language PERFORMANCE Processor: Intel Celeron N4020 (2C / 2T, 1.1 / 2.8GHz, 4MB) Graphics: Integrated Intel UHD Graphics 600 Chipset: Intel SoC Platform Memory: 8GB SO-DIMM DDR4-2400 Memory Slots: One DDR4 SO-DIMM slot Max Memory: Up to 8GB (8GB SO-DIMM) DDR4-2400 offering (8GB module need to be purchased separately in order to upgrade) Storage: 256GB SSD M.2 2242 PCIe 3.0x4 NVMe Storage Support: One drive, up to 256GB M.2 2242 SSD or 512GB M.2 2280 SSD (512GB SSD need to be purchased separately in order to upgrade) Storage Slot: Non-eMMC models: one M.2 slot One M.2 2280 PCIe 2.0 slot Card Reader: SD Card Reader Optical: None Audio Chip: High Definition (HD) Audio AUDIO Stereo speakers, 1.5W x2, Dolby Audio Camera: HD 720p with Privacy Shutter Microphone: 2x, Array Battery: Integrated 42Wh Max Battery Life: Local video (1080p) playback@150nits: 11 hr Power Adapter: 45W Round Tip (3-pin) DESIGN Display: 15.6" FHD (1920x1080) TN 220nits Anti-glare Touchscreen: None Keyboard: Non-backlit, English Case Color: Cloud Grey Surface Treatment: IMR (In-Mold Decoration by Roller) Case Material: PC-ABS (Top), PC-ABS (Bottom) Dimensions (WxDxH): 360.2 x 236 x 17.9 mm (14.18 x 9.29 x 0.70 inches) Weight: Starting at 1.54 kg (3.4 lbs) SOFTWARE Operating System: Windows 11 Home Single Language, English CONNECTIVITY Ethernet: No Onboard Ethernet WLAN + Bluetooth Wi-Fi 6, 11ax 2x2 + BT5.1 Standard Ports 1x USB 2.0 1x USB 3.2 Gen 1 1x USB-C 3.2 Gen 1 (support data transfer only) 1x HDMI 1.4b 1x Card reader 1x Headphone / microphone combo jack (3.5mm) 1x Power connector SECURITY & PRIVACY Security Chip: Firmware TPM 2.0 Fingerprint Reader: None Other Security Camera privacy shutter CERTIFICATIONS Green Certifications ENERGY STAR 8.0 ErP Lot 3 RoHS compliant INSIDE OF THE BOX 1 x Notebook 1 x AC Adapter

[govindhtech]

AdLink I-Pi SMARC 1200 Plus DevKit With MediaTek Genio 1200

I-Pi SMARC 1200

AdLink I-Pi SMARC 1200 Plus DevKit, an octa-core MediaTek Genio 1200 processor-powered AI and graphics-focused solution. The octa-core Arm Cortex-A78 and Cortex-A55 CPUs in the Genio 1200 are paired with a 5-core Arm Mali-G57 GPU for cutting-edge 3D graphics and an embedded NPU that can handle up to 5 TOPS of edge AI data.

The on-device AI processing capabilities of the I-Pi SMARC 1200 include computer vision and deep learning neural network acceleration. Moreover, up to three MIPI camera inputs and several 4K displays are supported by the SMARC. For a variety of next-generation AI focused developments, including smart homes, industrial IoT, 3K multimedia apps, and human-machine interfaces, this is the go-to option. Product developers can explore new avenues and expedite proof of concept creation before production with the dev kit, which lowers costs and shortens time to market.

Pi SMARC 1200

MediaTek Genio 1200 platform

The Octa-core MediaTek MT8395 powering the LEC-MTK-I1200 module (Cortex-A78 x4 + A55 x4 in the arm)

The Smart Mobility Architecture, or SMARC Form Factor

SMARC 2.1 specification: a very small and power-efficient design appropriate for edge computing and embedded applications.

Due to its small size (82 x 80 mm), it is perfect for designs that need to save space, like industrial IoT systems, robotics, smart home appliances, and AI cameras.

All-encompassing Connectivity

Support for Bluetooth 5.2 and Wi-Fi 6 allows for fast wireless data transfer.

Support for optional 5G: Enables cellular high-speed connectivity for Internet of Things applications that need dependable network access.

Gigabit Ethernet: Quick connectivity for edge devices via wired networks.

Display and Graphics

Equipped with a Mali-G57 GPU, capable of encoding and decoding 4K60 HDR video.

There are several display interfaces available for connecting high-definition monitors and displays, including as HDMI, eDP, and MIPI DSI.

A Variety of I/O Interfaces

Peripheral connectivity via PCIe, USB 3.1, and I2C enables expansion with extra parts including cameras, sensors, and storage devices.

AI cameras can be connected via the MIPI CSI camera interface, which is helpful for robotics and smart surveillance applications.

AI & Machine Learning on the Edge

For real-time decision-making, the AI Processing Unit reduces dependency on cloud computing by enabling effective AI inferencing at the edge.

Perfect for jobs involving natural language processing, computer vision, and machine learning.

Stored Information and Memory

Supports high-speed memory operations with LPDDR4x RAM.

Sffers external SD card ports, UFS, and eMMC as storage options.

Low Power Need

It is appropriate for battery-powered IoT devices such as smart cameras, portable robotics, and IoT sensors because it was designed with energy efficiency in mind.

Uses in Industry

Suitable for industries where AI and IoT solutions are essential, such as smart manufacturing, smart cities, healthcare, and retail.

Creation and Personalization

Offers compatibility for Linux, Android, and other embedded operating systems together with a thriving software ecosystem.

Gives developers the ability to design unique apps and solutions for AI-powered Internet of Things gadgets.

Read more on Govindhtech.com

[sdizdar]

Mixtile Edge 2 Kit– AI based bee detection and tracking

Here I describe usage of Mixtile Edge 2 Kit in agriculture, bee detection, which can be essential for health and survival of bees.

Story

Mixtile is professional IoT hardware solution provider specialized in Linux and Android-based embedded systems.Mixtile Edge 2 Kit is high-performance ARM single board computer. It comes in variants of 2GB of LPDDR4 DRAM and 16GB eMMC Flash storage, or 4GB of LPDDR4 DRAM and 32GB eMMC Flash storage. This single board computer comes with preinstalled Android 11, and it runs Ubuntu Linux operating system in Android container. It comes with large connectivity options (Bluetooth, 4G/5G Cellular, GPS, and Lora, Zigbee and Z-Wave). For those, you will need module, but it comes with default onboard Wi-Fi connectivity, Gigabit Ethernet Port (RJ45) and Serial Port (RS-485). Because it comes with RS-485 port, which is industrial standard, and it comes within a strong metal case, it seems to me that it can be really used in industrial projects. I used official Raspberry Pi 5 power supply in order to power up my Mixtile Edge 2 Kit.So, an idea came to me why not to use it in agriculture, bee detection, which can be essential for health and survival of bees.This project will cover setting up Mixtile Edge 2 Kit, and custom photo dataset form video in order to train custom YOLOv5 bee detection model. YOLOv5 models must be trained on labelled data in order to learn classes of objects in that data.I gathered data from video and trained model on my PC.To train a model, I used python and typed in command line:

python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights best.pt

My training results are summarized in the following table:

Training results

From this table you can see that images are divided into 4 detection classes:

Bee

Drone

Pollenbee

Queen

Example for each class is summarized in a table below:

Bee classes

1. Getting started

First, I will write about software part of the project, and later on steps of starting the recognition.

1.1 What is YOLOv5?

If you have been in the field of machine learning and deep learning for some time now, there is a high chance that you have already heard about YOLO. YOLO is short for You Only Look Once. It is a family of single-stage deep learning-based object detectors. It was written using Python language, and the framework used is PyTorch.

To ease control, I connected usb mouse to the one of three Mixtile Edge 2 Kit USB3 port. I used Ubuntu Linux for this project. Ubuntu on container is installed in Android system of Mixtile Edge 2 Kit by default. When you boot Mixtile Edge 2 Kit, you get Android OS. Since I wanted to access Edge 2 Kit remotely, and get easier control, I installed droidVNC server from this link:

It is an Android VNC server using Android 5+ APIs. It does not require root access.

I started the VNC server, connected with VNC Viewer and I got the following Android 11 screen:

Android 11

After that, I installed SimpleSSHD from this link:

SimpleSSHD is a SSH server Android app, based on Dropbear.It allows user access (user ssh) or full root access (by setting the login shell to /system/xbin/su) (if root is allowed).

After I installed SSH server, I connected to it via putty SSH terminal. Username and Password are root/root.

Com.hubware.ubuntu is ubuntu on a container and we are connected to it immidiately.

Now we are going to install required software.

First, you will need to upgrade Ubuntu by typing in the command: apt-get upgrade.

Second, I installed python by typing: apt-get install python.

You will also need pip, the package installer for Python.

2. Installing the YOLOv5 Environment

To start off we first clone the YOLOv5 repository and install dependencies. This will set up our programming environment to be ready to running object detection training and inference commands.

Install git: apt-get install git

Clone YOLOv5 repository:

git clone https://github.com/ultralytics/yolov5

Move to YOLOv5 folder:

cd yolov5

Install dependencies:

pip install -r requirements.txt

Wait some time to download and install all requirement packages, I waited 25 minutes, because there are a lot of python packages to install besides YOLOv5. YOLOv5 needs numpy package, scipy, OpenCV, etc.

The putty connection and installation process looks like below:

I transferred my model best.pt to the yolov5 installation folder via SCP, with MobaXterm.

You can simply download my model immidiate by typing:

wget https://github.com/sdizdarevic/beedetectionyolov5/raw/main/best.pt

Also, download original video by typing:

wget https://sdizdarevic.typepad.com/cr/bees-orig.mp4

Now, the final step is detection, and we are interested in the “result” content video.

python3 detect.py --weights best.pt --source bees-orig.mp4

The process of detection looks like below:

In the last lines from last picture we can see the detected number of bees at any point in time.

The summarized short steps to follow are below:

git clone https://github.com/ultralytics/yolov5

cd yolov5

pip install -r requirements.txt

wget https://github.com/sdizdarevic/beedetectionyolov5/raw/main/best.pt

wget https://sdizdarevic.typepad.com/cr/bees-orig.mp4

python3 detect.py --weights best.pt --source

Demonstrated videos are on urls with detection finished completely on Mixtile Edge 2 Kit. Output video is in folder runs/detect/exp2.

Original video:

Result video:

Last, but not less important: If you want to safely turn off your Mixtile Edge 2 Kit, I recommend you to install Shutdown (no Root) application: https://play.google.com/store/apps/details?id=com.samiadom.Shutdown&hl=en.

3.Conclusion:

After testing I found out that the Mixtile Edge 2 Kit is designed with wide range of applications, from industrial applications, IOT devices, smart home automation, to more than capable AI and edge detection. It is low powered device, with a lot of built-in connectivity options.

I would like to thank amazing Mixtile people for creating this amazing peace of hardware and especially for sending me the Mixtile Edge 2 Kit. Also, Mixtile nurtures the open source values and software, and I believe more people and companies will be involved in making projects with this board.

All in all, I recommend this board for implementing types of projects I described here.

[mcttvietnam]

Module CPU SMARC v2.1 SoM Axiomtek SCM187 với i.MX 8M Mini Quad Core SoC

[bliiot-jerry]

ARMxy SBC Embedded Controller BL340 in sewage Treatment System Monitoring

Case Details

Introduction

Real-time monitoring of wastewater treatment systems is critical for ensuring water quality compliance, optimizing process flows, and reducing operational costs. The ARMxy BL340 series embedded controller, powered by the Allwinner T507-H quad-core ARM Cortex-A53 processor, offers high performance, low power consumption, and flexible I/O configurations, making it ideal for industrial Internet of Things (IoT) applications in wastewater treatment monitoring. This article explores the design and application of the BL340 in wastewater treatment systems, analyzing its technical advantages and practical outcomes.

System Design

Hardware Architecture

The BL340 series adopts a modular design, with core hardware components including:

Processor: Allwinner T507-H, quad-core Cortex-A53, up to 1.4 GHz, paired with 8/16 GB eMMC storage and 1/2 GB DDR4 memory, meeting data processing and storage requirements.

Sensor Interfaces: Supports various sensors via Y-series I/O boards, such as pH (Y33/Y34, 0-5/10V analog input), dissolved oxygen (Y31, 4-20mA input), turbidity (Y36, ±5V/±10V differential input), and temperature (Y51/Y52, PT100/PT1000 RTD).

Communication Modules: Includes 3×10/100M Ethernet ports, 2×USB 2.0, Mini PCIe (4G/WiFi/Bluetooth), and a NANO SIM slot for remote data transmission.

Power and Installation: Supports 9-36 VDC wide voltage input with reverse polarity and overcurrent protection, designed for DIN35 rail mounting, suitable for harsh wastewater treatment environments.

Environmental Adaptability: Certified with IP30 protection and -40~85°C wide temperature testing, ensuring reliability in humid, high-temperature, or vibrating conditions.

Software Architecture

The BL340 supports multiple operating systems and development tools, with a software architecture comprising:

Operating Systems: Linux-4.9.170, Ubuntu 20.04, or Android 10, with Docker container support for rapid deployment.

Protocol Conversion: Pre-installed BLloTLink software supports protocols like Modbus, MQTT, and OPC UA, compatible with cloud platforms such as AWS IoT Core and ThingsBoard.

Data Processing: Utilizes Node-Red and Qt-5.12.5 for data acquisition, processing, and visualization, supporting real-time water quality parameter analysis.

Remote Access: BLRAT tool enables remote maintenance and configuration, enhancing operational efficiency.

Functionality and Applications

Real-Time Water Quality Monitoring

The BL340 collects critical wastewater treatment parameters (e.g., pH, dissolved oxygen, turbidity, temperature, and conductivity) via Y-series I/O boards. For instance, the Y31 module connects to 4-20mA dissolved oxygen sensors, and the Y51 module supports PT100 temperature sensors. Data is sampled via ADC, processed by the BL340, and used to generate real-time water quality reports.

Remote Monitoring and Alarming

The BL340 uploads data to cloud platforms via 4G or WiFi modules, enabling remote monitoring through web interfaces or mobile applications. When water quality parameters exceed thresholds (e.g., pH <6 or >9), the system sends alerts via MQTT and can control valves or pumps using the Y24 relay module to automatically adjust processes.

Data Storage and Analysis

The BL340 supports local SD card storage and cloud backups, archiving historical water quality data. Node-Red facilitates trend analysis, such as correlating dissolved oxygen levels with aeration energy consumption, to optimize wastewater treatment processes.

Typical Application Case

In a municipal wastewater treatment plant, the BL340B (equipped with 3×Ethernet ports and 2×Y-board slots) was deployed to monitor a biological reaction tank. The system configuration included:

Hardware: BL340B-SOM341-X23-Y31-Y51, featuring 4×RS485, 4×DI/DO, 4×4-20mA inputs (dissolved oxygen, turbidity), and 2×PT100 (temperature).

Functionality: Real-time water quality data collection, uploaded to the ThingsBoard platform via 4G, with automated aeration pump control.

Results: The system operated stably, reduced manual inspections, improved effluent compliance, and lowered energy consumption by approximately 15%.

Technical Advantages

High Performance and Low Power: The quad-core Cortex-A53 processor with a 1.4 GHz clock speed ensures efficient data processing, while the wide-voltage power design minimizes energy use.

Flexible I/O Configuration: Supports various X/Y-series I/O boards, accommodating diverse sensor and control requirements.

Robust Communication: Multiple Ethernet ports and 4G/WiFi modules support complex network environments, with BLloTLink enabling seamless integration with mainstream cloud platforms.

Industrial-Grade Reliability: Certified through electromagnetic compatibility (EMC) and environmental adaptability tests (-40~85°C, IP30, vibration resistance), suitable for harsh wastewater treatment conditions.

Ease of Development: Node-Red and Qt tools simplify application development, with BLRAT supporting remote debugging, reducing deployment time.

Challenges and Solutions

Sensor Drift: Regular calibration or software compensation algorithms (e.g., Kalman filtering) enhance data accuracy.

Network Stability: 4G redundancy and local caching ensure reliable data transmission.

Data Security: MQTT over TLS and device authentication safeguard data transfers.

Conclusion

The ARMxy BL340 series embedded controller demonstrates significant advantages in wastewater treatment system monitoring due to its high performance, flexibility, and industrial-grade reliability. Its modular design and robust communication capabilities meet diverse monitoring needs, enabling wastewater treatment plants to achieve intelligent and efficient operations. As industrial IoT technologies advance, the BL340 will play an increasingly vital role in water treatment applications.

[bliiot]

ARMxy RK3588 Industrial Controller Modbus RTU to Modbus TCP for Protocol Bridging

BL450 Series ARM Embedded Computer is an industrial-grade ARM controller with flexible I/O configuration, based on the Rockchip RK3588J/RK3588 processor, featuring a quad-core ARM Cortex-A76 + quad-core ARM Cortex-A55 + triple-core ARM Cortex-M0 architecture, witha clock speed of up to 2.0G/2.4GHz. It is equipped with 32GB/64GB/128GB eMMC and 4GB/8GB/16GB LPDDR4X RAM and ROM configurations. Supporting a rich set of I/O interfaces, it also integrates a 6TOPS NPU, enabling deep learning capabilities. The BL450 series iswidely used in industrial control, edge computing, AIoT, artificial intelligence, communication management, AGV robots, machine vision inspection, robotics, industrial IoT gateways, energy storage systems, automation control, and transportation infrastructure.

BL450 Series ARM Embedded Computer offers 1 to 3 optional RJ-45 network ports, including two 10/100/1000M ports and one 10/100M adaptive port, along with 2×USB 3.1, one optional HDMI 2.1, and optional X-series and Y-series I/O boards for communication, PWM output, pulse counting, and other data acquisition and control functions. It supports 8K@30fps H.264 video encoding and 8K@60fps H.265 video decoding. Built-in Mini PCIe interface allows support for Bluetooth, WiFi, 4G, and 5G communication modules.

BL450 Series supports multiple operating systems, including Linux-5.10.209, Linux-RT-5.10.209, Ubuntu 20.04, Debian 11, and Android 13. It is also compatible with Docker containers, Node-Red, and Qt-5.15.10 for graphical development. The BLIoTLink industrial protocol conversion software enables fast industrial data acquisition and conversion, facilitating seamless integration with mainstream IoT cloud platforms and industrial SCADA software. Additionally, the BLRAT remote access tool provides remote access and maintenance, while Node-Red allows for rapid IoT application development.

Designed with professional electrical performance and high/low-temperature testing, the BL450 series operates reliably under harsh electromagnetic interference and extreme temperatures ranging from -40°C to 85°C. With DIN35 rail mounting, it is suitable for various industrial applications.

[forlinx]

High-Performance and Low-Power Consumption Industrial-Grade SoM

🎉Amazing Product! The Forlinx SoM iMX8M Plus System-on-Module is Here!🎉

💥Powered by the robust NXP i.MX 8M Plus processor with 4 A53 cores and an M7 core, running at up to 1.6GHz for exceptional performance!

💡A variety of storage configurations are available, including 1GB/2GB/4GB LPDDR4 memory and 16GB eMMC storage to meet your diverse needs.

🌟Rich high-speed communication interfaces such as 2 USB 3.0, 1 PCIe 3.0, 2 SDIO 3.0, 2 CAN-FD, etc., easily handling 5G networks, high-definition videos and more challenges!

🎬4K picture quality and high-fidelity voice. The HDMI interface supports 4K display output, along with LVDS and MIPI-DSI display interfaces, and the audio technology is newly upgraded!

🤖Advanced multimedia technology with 3D/2D graphics acceleration, machine learning and vision functions. Built-in NPU (AI computing power up to 2.3TOPS) and image signal processor (ISP).

💪The supporting OKMX8MP-C development board helps you evaluate quickly. The base board has super computing and multimedia capabilities with a wide range of interfaces.

📄Comprehensive download resources are available and accessories are optional. Enjoy high-quality technical support upon purchase!

Come and experience this innovative product and embark on a new chapter of smart technology! 🔗