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Album: Public (Denise SANFILIPPO)

by Denise SANFILIPPO
DeniseSANFILIPPO_17-1718877646504.png
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Figure 1: Pedometer - Configuration tab
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Figure 3: Pedometer - Regression Tool
Figure 4: FSM - Configuration tab
Figure 4: FSM - Configuration tab
Figure 5: FSM - Testing tab
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Figure 5: FSM - Testing tab
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Figure 6: FSM - Debug tab
Figure 7: MLC - Data patterns
Figure 8: MLC - ARFF generation
Figure 9: MLC - Decision tree generation
Figure 10: MLC - UCF generation
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Figure 11: MLC - Datalog analysis
Figure 12: MLC - Decision tree output viewer
Figure 13: MLC - Data injection
Figure 16: Wearable applications and asset tracking examples
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Figure 14: Sensor Evaluation tab - Quick Setup
Figure 15: Sensor Evaluation - Features Demo
Figure 18: Setup used for this hands-on
Figure 2: Pedometer - Debug tab
Figure 2: Pedometer - Debug tab
Figure 2: Pedometer - Debug tab
Figure 1: An industrial robotic arm for a pick and place use case
Figure 2: Hardware setup: STWIN.box and STEVAL-MKI245KA
Figure 3: STWIN.box DFU mode is enabled
Figure 4: Loading of STWIN.box firmware
Figure 5: HSDPython_SDK installation
Figure 6: High Speed Datalog GUI – Connect button
Figure 7: High Speed Datalog GUI – Enabling ISM330BX
Figure 8: High Speed Datalog GUI – Setting ODR and FS and Start/Stop logging
Figure 9: STWIN.box and ISM330BX mounted on a robotic arm for anomaly detection
Figure 10: MLC configuration in ISM330BX and STWIN.box
Figure 11: Robotic arm – Detection of stationary state
Figure 12: Robotic arm – Detection of pick and place movement
Figure 13: Robotic arm – when the anomaly is detected, the robotic arm stops moving
Figure 6: High Speed Datalog GUI – Connect button
Figure 9: STWIN.box and ISM330BX mounted on a robotic arm for anomaly detection
Figure 1: The datalog we have previously acquired using High Speed Datalog is converted to a compatible format to be imported in MEMS-Studio
Figure 2: The collected data is imported in MEMS-Studio
Figure 3: The collected data is labeled and divided into three files, one per class (stationary, pick and place movement and detected anomaly)
Figure 4: In the MLC tool of MEMS-Studio, the three files generated can be imported with their label
Figure 5: In the MLC tool of MEMS-Studio, the manual configuration of the machine learning core parameters like inputs, window features and so on is performed
Figure 6: In the MLC tool of MEMS-Studio, the decision tree can be generated and the user can also visualize its info
Figure 7: In the MLC tool of MEMS-Studio, the .ucf file can be generated
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Figure 18: Setup used for this hands-on
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Added ‎2024-06-20 6:53 AM
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Figure 18: Setup used for this hands-on

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