I have this attached time series signal (its actually from an electrostatic sensor, everytime someone walks or moves, I can see that in the signal). For the machine learning part, I would like to get only the signal when someone is walking or something, so when the signal passes a certain threshold for example. How do I get to do that in C language?
I have made a pressure sensor that when graphing Conductance vs Pressure (disregard the actual values in graph), has the following behaviors: First pressed it has one trendline Afterwards when decreasing pressure it shows hysteresis New trendline when increasing pressure. All new cycles of increase/decrease pressure follow (more or less) the lines of 2 and 3. Usually we fit the data with a linear equation. But was wondering if it made sense to use ML to fit the data, feeding …
I want to create a device to map caves in 3D in a cheap and fast way. For this I am planning to use two 2D lidar sensors and a digital compass. Since there is no GPS available in caves, I need an algorithm to predict/calculate the current geo-location of the device in the map that I am creating by walking around and measuring. This graphic represents the two lidars (red) mounted on the device, one vertical and one horizontal: …
I am currently diving deeper into understanding more about anomaly detection in regards to vehicle's data generated by sensors. It seems like there is no proper book or article that goes deeper into this topic. Hoping to get help on where and how I could find anything regarding this topic. Maybe some of you had to deal with something like this and have acquired some knowledge on where to find some sources. would really appreciate some help
I'm working in the prediction of time-to-failure of vehicles. The available data are the vehicle characteristics, such as make, type of vehicle (truck, car, etc.), year of manufacture, weight, region where is employed and lots more. Also the fuel consumption data is available, with the odometer updates and fuel load. But the richest source of data I have are the work orders, these are the maintenance task performed on every vehicle. Some of its important attributes are date, failure description …
When working with a 3D laser sensor (LiDAR), the volumetric point density versus distance $\rho_r$ can be theoretically worked out taking into account the physical properties of the laser (number of layers, TOF, etc.). On the other hand, it can also be computed $\hat \rho_r$ from the available training data, yielding into an estimation of this quantity. When this density vs. distance is wanted to be included in a classification problem (in the preprocessing stage as parameter) what value should …
Background: Problem type: Multi class classification The dataset contains around 1,000 samples (simulated dataset of sensor signals), where each sample is 2D i.e (1000 * 1000 * 8). Additionally, I have a few real world data, which is of shape (100 * 1000 * 8) I split my data into training and validation from the simulated data set and use the real world data as test set. I performed cross validation with 5 folds + data augmentation techniques since I …
I have several expert persons performing the same specific action (for example, squat or leap forward) multiple times. Say 5 persons do 100 squats each. They have an accelerometer attached to the same body parts. I record the accelerometer readings and get 100*5 = 500 data samples. They do it for multiple different actions (squat, push up, leap forward, etc). The way they record the action is as follows: Start recording (push button) Do the action Stop recording (push button) …
I am using four different sensor models (four different devices) to gather some biometric data(eg. heart rate) for the same user, for the same time period. In an ideal scenario, all the devices are properly positioned, so for the time being we are not concerned about null values and bad readings. The data follows this format: timestamp: XXXXXXXXXX //unix epoch timestamp duration: XXX //duration in seconds value: XX state: <state> //string that characterizes the state of the user based on …
I am writing my data science master thesis on "Decision making with sensor fusion and machine learning in unmanned systems" What are the sub topics similar to "Collusion avoidance", "Navigation" and "Object Detection"? Are there publications about using big data in robotic systems, apart from these three?
Let's assume I have readings coming from sensors. For every sensor, I have the following information: all the data it reads its location Now, given an arbitrary sensor at an arbitrary location between these sensors, we want to predict the stream of data coming from this sensor. We would like to generate a synthetic stream of data, using the data coming from the other sensors and knowing their locations relative to our target sensor. How would I go about solving …