Qeexo AutoML 1.11.3
December 16, 2020
New Features
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Added new machine learning models: Polynomial Support Vector Machine (POLYSVM), RBF Support Vector Machine (RBFSVM), Gaussian Naive Bayes (GNB), One Class Random Forest (ORF).
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Added optional step to collect and upload Test Data datasets to measure trained model performance against the uploaded datasets.
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Added PCA plot and F1 score to model details.
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Added time limit and trial threshold to Hyperparameter Tuning Optimizer.
Bug Fixes & Improvements
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Removed Chrome extension dependency by adding a Qeexo AutoML native app.
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STWIN now supports both digital and analog microphones.
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Added proxy server option for Qeexo AutoML installers to bypass network restrictions.
Known Issues
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Multiple CSV files upload with different sensor configurations is not supported and may lead to unexpected issues. Each upload should only contain data with the same sensor configurations.
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Uploading a large amount of data through multiple CSV files at once may fail when server traffic overloads.
Additional notes
- SensorTile.Box now supports DFU mode, without the need for ST-LINK/v2.
Previous Releases
Qeexo AutoML 1.10.3
September 24, 2020
New Features
• Added new hardware platform: Arduino Nano 33 IoT, featuring an Arm Cortex-M0+ MCU.
• Renesas RA6M3 ML Sensor Module now supports live classification over Bluetooth.
• Bronze users who are using the Arduino Nano 33 BLE Sense can now create custom applications by downloading machine learning models as statically-linked libraries.
Bug Fixes & Improvements
• Fixed training failures of the CNN model under certain specific configurations.
• Fixed re-recording so that original data will be properly overwritten.
Known Issues
• Selecting data from the 1 Hz low-power accelerometer sensor from the STWINKT1 hardware sometimes causes training failures.
• Certain USB Type-C hubs cause connection issues in macOS.
Tips
• Use an USB A-to-C adapter rather than a hub to minimize connection issues if using macOS.
• If hardware is not behaving as expected, try disconnecting and reconnecting the USB cable and retry.
Qeexo AutoML 1.9.1
September 5, 2020
New Features
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Added new hardware platform: RA6M3 ML Sensor Module from Renesas. Currently, accelerometer, gyroscope, humidity, light, and temperature sensors are supported.
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Improved Qeexo AutoML sign up process.
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Added DFU mode support for ST SensorTile.box, which means that the ST-LINK/v2 device programmer and adapter are no longer required to flash firmware on this hardware platformthe SensorTile.box.
Bug Fixes & Improvements
- Fixed latency calculation failures.
- Added various UI-related fixes/improvements.
Known Issues
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Specific configurations of multiple sensors when selected together with the microphone, sometimes causes training failures of the CNN model.
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Selecting data from the 1Hz Accelerometer lLow-power accelerometer sensor from STWINKT1 hardware sometimes causes training failures.
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Sometimes, the original data would still exists after re-recording.
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Class label names cannot contain special characters such as <, >, |, :, “, and \ .
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Certain USB Type-C hubs cause connection issues in macOS. We recommend using an USB A-to-C adapter rather than a hub.
Qeexo AutoML 1.8.2
August 10, 2020
New Features
- Added new hardware platform: STWINKT1 from STMicroelectronics.
- Added new ML algorithms: one class SVM, linear SVM, RNN, CRNN.
- Added Live Classification Analysis to finetune trained models.
- Added quantization options to reduce model size.
Bug Fixes & Improvements
- Fixed a flashing issue on the Arduino Nano 33 BLE Sense.
- Fixed a training issue that occurs when only one axis (x, y, or z) is selected for accelerometer and/or gyroscope.
- Added data-check-related improvements.
- Added various UI-related fixes/improvements.
Known Issues
- When different data types (Continuous/Event) are grouped together during training, unexpected results such as training failure may occur. We recommend not mixing different data types within a Group.
- Specific configurations of multiple sensors when selected together with the microphone, sometimes causes training failures of the CNN model.
- Class label names cannot contain special characters such as <, >, |, :, “, and \ .
- Certain USB Type-C hubs cause connection issues in macOS. We recommend using an USB A-to-C adapter rather than a hub.