Home/Self-Learn/Task-Finland.md

@@ -22,7 +22,7 @@ You can check the datasheet of the used Sensor PZEM-016.
 * **Power factor**
 * **Alarm level**
 
-In the figure you can see a sample of the data visualised as a graph. In the graph you can see one of the captured quantities ![Raw-DataSample](../images/Sample1.png)
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 The IoT device sends this information cyclically to a topic on the MQTT broker. To understand the system architecture, please refer to the schematic diagram below.
 
@@ -32,7 +32,7 @@ The IoT device sends this information cyclically to a topic on the MQTT broker.
 As you can see, we will operate the coffee machine through the power box. The power box automatically connects to our WiFi router. We have created an MQTT broker on a server laptop. The data is published on a topic there. If you have no experience with MQTT communication, please read the following article: https://www.hivemq.com/blog/how-to-get-started-with-mqtt/
 
 We receive characteristic energy profiles for the different product possibilities of the Senseo. What we aim to achieve at the end of the course is the determination of the product based on this data and a trained AI model.
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+In the figure you can see a sample of the data visualised as a graph. In the graph you can see one of the captured quantities
 ![CurrentCurves](../images/4_Current_Curves.jpg)
 
 One possibility would be to manually label these energy data with the different product categories and use this information to train a model. As you can imagine, this is very labor-intensive and error-prone. Therefore, we want to "hack" our coffee machine and use low-cost sensors to automatically obtain additional information from the machine. This will mainly be your task in the hands-on course. To achieve our goals within a week, you should come up with a strategy in advance on how the system architecture could look.