we need to measure the temperature of the battery of the electric car we make and reflect the voltage on the screen, as well as show the speed of the vehicle and the percentage of the battery on the screen.it needs to do this with arduino mega and transfer the data there to the computer and back it up to the SD card.It would be great if you could help me write the code for this.
I want the codes to be written and to be informed about it.I want you to help me finish it in 1.5 months and help me make it.
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I am trying to quantify change in water quality around storm drains before and after a rain event by quantifying imagery I have acquired. What I am thinking would be similar to an NDVI but for areas of dirty and clean water. I have been looking around for options on what to use to do this but haven't found anything successful. The end goal would be a quantification of a few images and then creating the equivalent of a dNDVI for the change over the days. Does anybody have recommendations for this?
I have tried change detection and compute change but have come up empty thus far
Can anyone advise how to get this project started if what I want is
some gadgets/app/program to scan maybe weather.com continuously
when there is a categorical change say from dry to rain three hours later
send an alert to my phone
you should use service worker for this purpose when your data update or meet certain limit you will get notified
https://developers.google.com/web/ilt/pwa/introduction-to-push-notifications
I am working on a side project and was looking to see if there are any API's that given a vehicle's year, make, and model, it would show sort of a timeline for that vehicle's maintenance. For example, how often to change oil, when to change transmission fluid, etc.
Does anyone know of any API's that have this sort of information? I was going to end up trying to create a basic timeline that works with every car, but I thought it would be better to be more accurate with the chosen vehicle model.
I wanted to learn to predict future events like......being able to predict number of plane crashes in 2018 using past two decades of plane crash data.....or.....predict how many tee-shirts with justin beibers face on it will be sold by 2018 depending upon fan base from previuos data..........or how many iphones 8's and samsungs s9's will be sold if they decide to launch on the same exact date....predicting somewhat accurate whole sale market.....stuff like that....please suggest a book...i really love head first series....is head first data analysis right for me? ....I dont lnow if i can ask questions other than programming here or not.....but here i am.....By the way does big data have anything to do with this?
it all falls in the category of data science (which is big data and data analysis). What you need for predictions and such stuff is some machine learning approach to data you have or can access about stuff you want to predict.
I'd recommend this, newest series of articles: https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12
Apart from really nice intro, you'll find lots of resources for further learning there.
Also I highly recommend deeplearning.ai and machine learning course from Stanford you can find on Coursera.
Cheers!
I think most of the scenarios that you have asked are a case of Supervised Learning which is a type of machine learning, wherein you have previous data to train your machine learning model with the input and output values and once you have trained a model you feed new input values and it gives you the output which is the prediction.
I would highly recommend the following Machine Learning course by Andrew NG which on Coursera which covers all the basics of ML including Supervised and Unsupervised Learning.
https://www.coursera.org/learn/machine-learning
As for the books the following link from Analytics Vidya is a great place to start with, you can go through the books as they can give you some good basics of statistics and data sciences.
https://www.analyticsvidhya.com/blog/2015/10/read-books-for-beginners-machine-learning-artificial-intelligence/
As for the differences between Data Science, Data Analytics and Big Data. Data science and data analytics are similar in the sense that they both try to find patterns in data and based on those pattern you derive some insights.
Big data on the other hand is basically Data of huge size which is distributed across multiple machines, so you can store and compute large amount of data simultaneously and in parallel.
So you may ask how is big data and machine learning related? well the answer lies in the training of machine learning model, since the accuracy of prediction is to a certain extent depends on the amount of data you train it on. So more the training data better the predictions and in terms of quantity big data way ahead of others, hence the relation.
For a personal project I thought about a Pi Car that drives forward in a loop between the living room and kitchen and is able to steer itself between hallways and avoid collisions.
I was able to create this PoC using Behavioral Cloning. I manually drove the RC car along a black line on the floor while it recorded images.
I then ran the images through a ConvNet and used the model to predict the left and right motor controls. It worked, but left a lot to be desired.
Now I would like to repeat this PoC without manual training. A sonar sensor / LiDAR would work to avoid collision, but I am hoping to learn more about CV
The approach I have in mind is:
1) have the car in continuous forward motion while it records images
2) feed the images into a ConvNet to learn features like how close an object is
3) feed the output of the ConvNet into an RNN
4) the RNN will guide a reinforcement policy
5) the policy is simple: anything blocking your forward motion should be avoided
This is loosely based on this work at Samsung and UC. My thinking is the features learned from the CNN will be used by the RNN in a time series to learn how close an object is.
Think of the car moving closer and closer to the couch. The features of the couch will change; thus hopefully inferring depth. Blocked forward motion would mean objects are getting closer.
One of the issues at now is how can I reflect being blocked by objects in the policy outside of a simulator?
In a ROS simulator it would be easy, since Gazebo gives you x,y coordinates and I can set a rate of change as good forward motion vs. being blocked.
But how can I do this on a physical robot that has no localization?
Also I am not enrolled in any classes and have been following free online material for the last year for all of this. Any critique, feedback and discussion is highly needed!