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Copy file name to clipboardExpand all lines: content/hardware/01.mkr/01.boards/mkr-wifi-1010/tutorials/mkr-battery-app-note/mkr-battery-app-note.md
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@@ -124,7 +124,7 @@ We will go through the lines needed to create a Sketch to read the battery value
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**4.** We will now create a variable to store the maximum source voltage `max_Source_voltage` as well as the upper (`batteryFullVoltage`) and lower (`batteryEmptyVoltage`) values for the battery. We will also define the battery capacity as `batteryCapacity` so as to determine the charging current. Since we are using a 750 mAh battery in this example, we will set the value to `0.750`.
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```arduino
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int max_Source_voltage;
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float max_Source_voltage;
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float batteryFullVoltage = 4.2;
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float batteryEmptyVoltage = 3.3;
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**13.** In order to convert `rawADC` into a voltage reading (`voltADC`) we will divide `rawADC` by 4095 and then multiply it by the analog reference voltage (3.3V).
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```arduino
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voltADC = rawADC * (3.3/4095.0);
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voltADC = rawADC * 3.3 / 4096.0;
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```
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**14.** The `voltADC` variable gives us the voltage sensed directly on the PB09 pin. This voltage is passed through the voltage divider, so it is a fraction of the actual battery voltage. We can then calculate the equivilanet battery voltage as follows.
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**14.** The `voltADC` variable gives us the voltage sensed directly on the PB09 pin. This voltage is passed through the voltage divider, so it is a fraction of the actual battery voltage. We can then calculate the equivalent battery voltage as follows.
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```arduino
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voltBat = voltADC * (max_Source_voltage/3.3);
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voltBat = max_Source_voltage * rawADC / 4096.0;
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```
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**15.** We can approximate the battery voltage to be proportional to the capacity level. Since the `map()` function does not work with float variables, we will manually map the values.
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int R1 = 330000; // resistor between battery terminal and SAMD pin PB09
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int R2 = 1000000; // resistor between SAMD pin PB09 and ground
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int max_Source_voltage; // upper source voltage for the battery
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float max_Source_voltage; // upper source voltage for the battery
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// define voltage at which battery is full/empty
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float batteryFullVoltage = 4.2; //upper voltage limit for battery
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{
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rawADC = analogRead(ADC_BATTERY); //the value obtained directly at the PB09 input pin
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voltADC = rawADC * (3.3/4095.0); //convert ADC value to the voltage read at the pin
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voltBat = voltADC * (max_Source_voltage/3.3); //we cannot use map since it requires int inputs/outputs
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voltADC = rawADC * 3.3 / 4096.0; //convert ADC value to the voltage read at the pin
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voltBat = max_Source_voltage * rawADC / 4096.0; //we cannot use map since it requires int inputs/outputs
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int new_batt = (voltBat - batteryEmptyVoltage) * (100) / (batteryFullVoltage - batteryEmptyVoltage); //custom float friendly map function
Copy file name to clipboardExpand all lines: content/hardware/02.uno/boards/uno-q/datasheet/datasheet.md
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@@ -254,6 +254,120 @@ JMISC handles both domains: 1.8 V MPU lines sit alongside 3.3 V MCU signals (e.g
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MPU GPIO signals operate in the application processor's low-voltage domain (1.8 V). Ensure any connection to the microcontroller is level-compatible with its I/O voltage rail (3.3 V). For example, use a level shifter or an open-drain configuration with a pull-up to the microcontroller's I/O rail.
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</div>
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<divstyle="page-break-after: always;"></div>
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## Hardware Acceleration
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<pstyle="text-align: justify;">The UNO Q provides hardware acceleration for both 3D graphics and video encoding/decoding through the integrated Adreno 702 GPU running at 845 MHz.</p>
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### Graphics Acceleration
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<pstyle="text-align: justify;">The Adreno 702 GPU provides hardware-accelerated 3D graphics rendering through open-source Mesa drivers. Applications can access GPU acceleration via standard graphics APIs, including OpenGL, OpenGL ES, Vulkan, and OpenCL.</p>
<pstyle="text-align: justify;">The Adreno 702 GPU features unified memory architecture, sharing system RAM with the CPU for data transfer. It supports 64-bit memory addressing and provides direct rendering capabilities for optimal graphics performance.</p>
<pstyle="text-align: justify;">The Mesa graphics stack provides support for standard OpenGL extensions and features. Applications using OpenGL, OpenGL ES, or Vulkan will automatically use hardware acceleration without additional configuration. Standard graphics utilities such as <code>mesa-utils</code> and <code>vulkan-tools</code> work out of the box on the UNO Q.</p>
<strong>Note:</strong> The OpenGL and Vulkan drivers are available through the <strong>freedreno (OpenGL/OpenGL ES)</strong> and <strong>turnip (Vulkan)</strong> open-source Mesa drivers, providing transparency and community support. While the Adreno 702 hardware supports Vulkan 1.1, the current driver implementation provides Vulkan 1.0.318. <strong>There are no UNO Q-specific OpenGL or Vulkan examples. However, standard Mesa utilities and examples from the Mesa project can be used as references.</strong>
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</div>
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### Video Acceleration
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<pstyle="text-align: justify;">The Adreno 702 GPU includes dedicated hardware video encoders and decoders accessible through the <code>V4L2 (Video4Linux2)</code> API via <code>/dev/video0</code> and <code>/dev/video1</code> devices. Hardware acceleration is available for the following video codecs:</p>
<pstyle="text-align: justify;">The hardware video encoder and decoder offload compression and decompression tasks from the CPU to dedicated hardware, enabling efficient real-time video processing. This reduces system power consumption and allows the CPU to focus on application logic. Hardware acceleration is available for resolutions up to 1920×1080 (Full HD), including common formats such as 720p (1280×720).</p>
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#### GStreamer Integration
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<pstyle="text-align: justify;">The recommended approach for accessing hardware video acceleration is through <strong>GStreamer</strong>, which provides a high-level pipeline interface to the V4L2 devices. The following GStreamer elements provide hardware-accelerated video processing:</p>
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For H.264 decoding, the following pipeline can be used:
<strong>Developer Access:</strong> The V4L2 video devices are accessible through standard Linux APIs, allowing direct integration into C/C++ applications using libv4l2 or through higher-level frameworks like GStreamer, FFmpeg, or OpenCV with V4L2 backend support.
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</div>
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### OpenCL Support
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<pstyle="text-align: justify;">OpenCL 2.0 support is available through the Mesa implementation, allowing general-purpose GPU (GPGPU) computing for parallel processing tasks, scientific computing, and compute-intensive operations. The Adreno 702's OpenCL capabilities allow offloading compute-intensive workloads from the CPU to the GPU for improved performance.</p>
1. Install Arduino App Lab [1], launch it, and connect UNO Q, use a **USB-C data** cable for PC-hosted mode, or simply power the board for SBC mode.
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2. The board will automatically check for updates. If there are any updates available, you will be prompted to install them. Once the update is finished, the Arduino App Lab[1] will need to be restarted.
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3. During the first setup, you will be asked to provide a name and password for the device (default is `arduino` / `arduino`). You will also be asked to provide Wi-Fi® credentials for your local network.
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3. During the first setup, you will be asked to provide a name and password for the device. You will also be asked to provide Wi-Fi® credentials for your local network.
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4. To test the board, navigate to an example App in the **"Examples"** section of the Arduino App Lab[1], and click on the "Run" button in the top right corner. You can also create a new App in the **"Apps"** section.
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5. The status of the App can be monitored in the console tab of the App.
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@@ -727,6 +841,7 @@ Lors de l’ installation et de l’ exploitation de ce dispositif, la distance
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